WO2024038156A1 - A system and method for determining a quantitative post-treatment symptom reduction index based on a quantified pressure pattern - Google Patents

A system and method for determining a quantitative post-treatment symptom reduction index based on a quantified pressure pattern Download PDF

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Publication number
WO2024038156A1
WO2024038156A1 PCT/EP2023/072720 EP2023072720W WO2024038156A1 WO 2024038156 A1 WO2024038156 A1 WO 2024038156A1 EP 2023072720 W EP2023072720 W EP 2023072720W WO 2024038156 A1 WO2024038156 A1 WO 2024038156A1
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WIPO (PCT)
Prior art keywords
pressure
pattern
treatment
vessel
pressure pattern
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PCT/EP2023/072720
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French (fr)
Inventor
Jeroen SONCK
Carlos COLLET
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Sonck Jeroen
Collet Carlos
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Publication of WO2024038156A1 publication Critical patent/WO2024038156A1/en

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/02007—Evaluating blood vessel condition, e.g. elasticity, compliance
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/021—Measuring pressure in heart or blood vessels
    • A61B5/0215—Measuring pressure in heart or blood vessels by means inserted into the body
    • A61B5/02158—Measuring pressure in heart or blood vessels by means inserted into the body provided with two or more sensor elements
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor

Definitions

  • the invention is related to the field of data processing systems and computer implemented methods for data analysis with respect to symptoms of ischemia, such as for example caused by the potential presence of a stenosis, stricture, lesion, ... in a vessel, and more particularly in a blood vessel, such as for example a coronary artery.
  • EP3827742 It is known from for example EP3827742 that for appropriate treatment decisions on ischemic cardiopathy such as cardiac angina, it is important to evaluate whether myocardial ischemia exists, whereby intracoronary blood flow becomes inadequate. As described in EP3827742 treatment such as stent placement in a lesioned blood vessel without myocardial ischemia is not only meritless, but also likely to lead to a poor prognosis due to in-stent restenosis, increased haemorrhagic events resulting from internal use of an antiplatelet agent, or the like.
  • ischemia diagnosis is for example conducted during cardiac catheterization, by means of a method that supports a diagnostic or treatment decision by calculating a diagnostic support index generally known as fractional flow reserve or FFR, or alternatively a non-hyperaemic pressure ratio such as for example instantaneous wave-free ratio or instant flow reserve or iFR, typically based on measurement data from for example a coronary insertion guide wire equipped with a pressure sensor and known as a pressure wire.
  • a diagnostic support index generally known as fractional flow reserve or FFR
  • a non-hyperaemic pressure ratio such as for example instantaneous wave-free ratio or instant flow reserve or iFR
  • the FFR described above which is an index found with the microvascular resistance of the patient reduced by medication, or in other words under hyperaemic conditions, is defined as a pressure ratio Pd/Pa between pressure in a proximal stenotic segment upstream of a stenotic lesion in a coronary artery, also referred to as proximal pressure or Pa, and pressure in a distal stenotic segment on the downstream side, also referred to as distal pressure or Pd.
  • proximal pressure or Pa pressure in a proximal pressure or Pa
  • distal pressure or Pd pressure in a distal stenotic segment on the downstream side
  • the iFR is another index calculated based on an average value of all pressure ratios Pd/Pa measured without administering a medicine to the patient, for example during the mesodiastolic to telediastolic period in which the intravascular resistance value at rest is considered to be generally low, and this index supports a diagnostic decision in which it is determined that the patient has myocardial ischemia, forexample, when the iFR is lower than 0.89.
  • EP3806725 It is known from EP3806725 that the use of such prior art pressure-based data and/or ratios such as FFR or iFR or similar hyperaemic or non-hyperaemic pressure ratios, have limitations with respect to the evaluation of symptoms of myocardial ischemia, and more specifically angina pectoris. It is for example disclosed that there is a large number of patients with symptoms of myocardial ischemia, and more specifically angina pectoris, for example assessed by means of Anginal scores based on patient's symptoms, which have a normal FFR, or in other words an FFR which does not support a diagnostic decision to determine that the patient suffers from myocardial ischemia, or more specifically angina pectoris. In EP3806725 it is proposed to make use of additional flow-based information in order to support an improved evaluation.
  • FFR or iFR or similar hyperaemic or non-hyperaemic pressure ratios have limitations with respect to the evaluation of symptoms of myocardial ischemia, and more specifically angina pector
  • EP3355771 a system for evaluating a vessel of a patient based on intravascular pressure measurements and identifying an available treatment type, such as for example by identifying a stent deployment location within the vessel, in function of a desired pressure value, such as for example a desired pressure ratio of distal pressure measurements relative to proximal pressure measurements.
  • a desired pressure value such as for example a desired pressure ratio of distal pressure measurements relative to proximal pressure measurements.
  • Such systems typically require a multitude of inputs, a multitude of complex interactions with a user interface from an operator and provide output from an operator in a form that requires detailed and extensive analysis.
  • such a desired pressure ratio might not be a reliable indicator for reliably evaluating the effect of the treatment on the symptoms of myocardial ischemia, and more specifically angina pectoris.
  • a data processing system comprising:
  • a pressure pattern module configured to quantify data of a pressure pattern along at least a section of a vessel of a subject with symptoms indicative of ischaemia, wherein the data of the pressure pattern comprises a pressure data series comprising a plurality of pressure data entries associated with intermittent pressure measurements along at least a longitudinal section of the vessel;
  • a pattern processing module configured to determine a quantitative post-treatment symptom reduction index indicative of a predicted post-treatment ischaemia symptom reduction based on the quantified pressure pattern provided by the pressure pattern module.
  • the quantitative post-treatment symptom reduction index differs from the quantified pressure pattern, such as for example PPG as explained in further detail below.
  • the quantified pressure pattern provides a numerical value characteristic for the pattern of the pressure data series. Although this quantified pressure pattern can provide an indication whether a particular treatment will result in a higher chance for an improvement for one pressure pattern versus another pressure pattern, this typically relates to an improvement with respect to for example a desired overall pressure parameter such as for example FFR.
  • Such a quantified pressure pattern further is not providing a numerical value indicating the amount of predicted post-treatment ischaemia symptom reduction, at best it can provide an indication whether the treatment will result in a high or low improvement with respect to a particular pressure parameter, it does not provide an actual numerical amount of how much the pressure parameter will improve, it also does not provide a numerical amount of how much post-treatment symptoms will be reduced with respect to pre-treatment symptoms.
  • the quantified post-treatment symptom reduction index comprises and/or consists of a numerical value, indicative and/or equal to the amount of predicted posttreatment ischaemia symptom reduction.
  • such a numerical value of the quantitative post-treatment symptom reduction index could for example be a percentage or absolute value indicating the amount of predicted post-treatment ischaemia symptom reduction. According to some embodiments, such a numerical value could for example be represented in any suitable form, such as for example as in numerical form, in a suitable graphic representation, ... .
  • post-treatment SAQ or post-treatment SAQ angina frequency refers to measurements of SAQ or SAQ angina frequency from a subject after the treatment to relieve the subject from the symptoms of ischaemia, such as angina pectoris.
  • the treatment is PCI
  • the pattern processing module is configured to determine the quantitative post-treatment symptom reduction index indicative of:
  • said indicator of the chance is a percentage value.
  • the pressure pattern module is configured to quantify the data of the pressure pattern along at least a section of a coronary artery of a subject with symptoms indicative of myocardial ischaemia;
  • the pattern processing module is configured to determine the quantitative post-treatment symptom reduction index indicative of a predicted post-treatment myocardial ischaemia symptom reduction based on the quantified pressure pattern provided by the pressure pattern module.
  • a system wherein the treatment is a percutaneous coronary intervention or PCI.
  • the predicted posttreatment myocardial ischaemia symptom reduction is a predicted post-PCI myocardial ischaemia symptom reduction.
  • the pressure pattern module is configured to quantify the data of the pressure pattern along at least a section of a coronary artery of a subject with angina pectoris;
  • the pattern processing module is configured to determine the quantitative post-treatment symptom reduction index indicative of a predicted post-treatment angina pectoris reduction based on the quantified pressure pattern provided by the pressure pattern module.
  • the treatment is a percutaneous coronary intervention or PCI.
  • the predicted posttreatment angina pectoris reduction is a predicted post-PCI angina pectoris reduction.
  • the data of the pressure pattern comprises a pressure data series comprising a plurality of pressure data entries associated with intermittent pressure measurements along at least a longitudinal section of the vessel.
  • Such data of such a pressure pattern is readily available and is obtainable by means of existing diagnostic equipment and procedures, such as for example known from WO2020/212459A1 or from Collet C, Sonck J et al. Measurement of Hyperaemic Pullback Pressure Gradients to Characterize Patterns of Coronary Atherosclerosis. Journal of the American College of Cardiology 2019;74:1772-1784. Such data of a such a pressure pattern is typically referred to as a pullback curve.
  • the pressure data series comprises one or more of the following:
  • the system processes a pressure data series of a subject, which is readily available, for example as data stored from prior measurements, or during a procedure for determining such a pressure data series, which is readily available, for example from a system for generating, outputting and/or storing such a pressure data series resulting from measurements by means of an intracoronary pullback operation, or any other suitable way of obtaining or deriving such pressure data series.
  • the data series with intermittent pressure measurements along the section of the vessel also allows to evaluate, in addition to the overall pressure change of this section, the specific localised changes of the pressure along this section of the vessel, or in other words the pattern of the pressure pattern along this section of the vessel.
  • the pressure data entry is associated with one or more of the following:
  • the system processes a pressure data series of a subject, which is readily available, for example as data stored from prior measurements, or during a procedure for determining such a pressure data series, which is readily available, for example from a system for making intra-coronary pressure measurements, or alternatively deriving such pressure values from other measurements related to properties of the vessel and/or the flow or pressure along the vessel.
  • the quantified pressure pattern provided by the pressure pattern module is determined by the pressure pattern module by means of:
  • the pressure pattern is quantified in a simple, efficient, reliable, and reproducible way. This allows an operator of the data processing system to review and interpret such a quantified pressure pattern, which forms the basis forthe predicted post-treatment ischaemia symptom reduction in an efficient and reliable way, thereby increasing the reliability and reducing any uncertainty and increasing confidence for an operator when evaluating the results of the data processing system.
  • the predetermined subsection of the pressure pattern is in the range of 2% up to and including 80%, preferably in the range of 5% up to and including 50% of the overall pressure pattern, preferably in the range of 10% up to and including 40%, for example in the range of 15% up to and including 30%, for example 20%.
  • the quantified pressure pattern provided by the pressure pattern module is determined by the pressure pattern module by means of: - a functional outcome index or PPG index based on the formula: maximum pressure change
  • - threshold exceeding portion is defined as said portion of the overall pressure pattern in which said rate of change is equal to or larger than said predetermined threshold.
  • said pressure pattern comprises a data series representing a fractional flow reserve pullback curve comprising pressure data entries associated with FFR values associated with a ratio of:
  • the quantified pressure pattern provided by the pressure pattern module is determined by the pressure pattern module by means of:
  • maximum A FFR is defined as the maximum difference between FFR values associated with the start and the end of said subsection of the pressure pattern
  • a FFR is defined as the difference between FFR values associated with the start and the end of said overall pressure pattern
  • - threshold exceeding portion is defined as said portion of the overall pressure pattern in which said rate of change in FFR is equal to or larger than said threshold.
  • - the threshold exceeding portion is determined as the ratio of: - respectively the length or duration of the part of the pressure pattern in which said rate of change is equal to or larger than said predetermined threshold, with respect to
  • said subsection of the pressure pattern is determined as a subsection of the pressure pattern with respectively a predetermined length or duration.
  • said overall pressure pattern is determined as a pullback pressure pattern along at least a section of the vessel, preferably between the ostium and the distal end of the vessel.
  • the predetermined length could for example be 20mm, and/or the predetermined duration could for example be 2s.
  • a coronary vessel from a patient under hyperaemic conditions.
  • pressure values that represent an FFR pullback curve during a pullback operation. Determining such an FFR pullback curve is done by determining FFR values from measurements of the movable pressure sensor, also referred to as distal pressure or Pd, with respect to a stationary pressure sensor, also referred to as proximal or aortic pressure Pa, during the pullback time period.
  • the stationary pressure sensor is positioned at the ostium of the vessel and that the movable pressure sensor, during the pullback time period is moved between a more distal part of the vessel, for example the most distal part of the vessel, or a part of the vessel distal of a suspected stenosis, stricture or lesion, and the ostium of the vessel.
  • FFR values of an FFR pullback curve are typically determined as the ratio of Pd/Pa, wherein Pd and Pa could for example be determined from the measured pressure values after any suitable form of pre-processing such as for example by means of a moving mean or average function which is configured to filter out the rhythmic and/or periodical component of the heartbeat cycle.
  • FFR could for example be defined as the ratio of mean or average distal coronary pressure, which is the pressure measured by the movable pressure sensor, and the mean or average aortic pressure, which for example is the pressure measured by the stationary pressure sensor, measured during, preferably maximal, hyperaemia that is preferably achieved through administration of a potent vasodilator such as for example adenosine, ATP or papaverine either by IV infusion or by intracoronary bolus injection.
  • a potent vasodilator such as for example adenosine, ATP or papaverine either by IV infusion or by intracoronary bolus injection.
  • the FOI can be referred to as a Pullback Pressure Gradient or PPG or PPG index.
  • PPG index quantifies the pressure pattern in a reliable and comprehensible way for an operator of the system.
  • the PPG index provides for a quantitative, reproducible value, in a range of 0 up to and including 1, which is representative of the pressure pattern along the section of the vessel, which is not subject to for example undesired intra-operator variations related to visual assessment of the pattern.
  • system further comprises:
  • pre-treatment symptom module configured to quantify data of pre-treatment symptoms indicative of ischaemia
  • pre-treatment overall pressure gradient module configured to quantify data associated with the overall pressure gradient between a pressure data entries associated with both end points of said at least a longitudinal section of the vessel
  • a vessel type determination module configured to determine an index associated with the vessel type.
  • the pattern processing module is further configured to determine the quantitative post-treatment symptom reduction index indicative of a predicted post-treatment ischaemia symptom reduction based on:
  • a SAQ or SAQ score is the outcome of a measurement by means of a validated questionnaire comprising three domains: physical limitation, angina frequency, and quality of life.
  • the SAQ has a scale from 0 to 100, with higher values indicating better health status. In the angina frequency domain, an SAQ score of 100 indicates freedom from angina.
  • a SAQ score is also representative of a SAQangina frequency, which relates to the frequency at which the subject experiences angina symptoms.
  • SAQ angina frequency scale of 0-30 points indicates daily angina, 31-60 points indicate weekly angina, 61-99 points indicate monthly angina, and 100 points indicate no angina.
  • pre-treatment SAQ or pretreatment SAQangina frequency this refers to measurements of SAQor SAQangina frequency from a subject before the treatment to relieve the subject from the symptoms of ischaemia, such as angina pectoris.
  • the treatment is PCI, there could be made reference to for example pre-PCI SAQ or pre-PCI SAQ angina frequency.
  • the quantified pre-treatment symptoms indicative of ischaemia comprises or consists of one or more of the following: - Pre-treatment Seattle Angina Questionnaire score or SAQ;
  • the vessel type index comprises or consists of one or more of the following:
  • LAD index indicating whether the vessel is a left anterior descending artery or not
  • a vessel type index representative of a specific coronary artery vessel or branch such as for example one or more of the following:
  • LAD is the largest vessel of the heart, and such an embodiment thus also allows for a more reliable and standardized approach.
  • the quantified overall pressure gradient comprises or consists of one or more of the following:
  • the pattern processing module is further configured to determine the quantitative post-treatment symptom reduction index indicative of a predicted post-treatment ischaemia symptom reduction based on the quantified pressure pattern provided by the pressure pattern module and one or more of the following:
  • the pattern processing module is further configured to determine the quantitative post-treatment symptom reduction index by means of a model based on the quantified pressure pattern provided by the pressure pattern module and one or more of the following:
  • the model comprises or consists of a regression model comprising one or more additional quantitative parameters as further independent variables.
  • the model comprises or consists of a binomial regression model with the following equation:
  • the model comprises or consists of the following equation:
  • the data processing system comprises or is coupled to a display configured to output the quantitative post-treatment symptom reduction index determined by the pattern processing module.
  • the pressure pattern module quantifying the data of a pressure pattern along at least a section of a vessel of a subject with symptoms indicative of ischaemia
  • the pattern processing module determining a quantitative post-treatment symptom reduction index indicative of a predicted post-treatment ischaemia symptom reduction based on:
  • a computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method of the second aspect.
  • a computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out the method of the second aspect.
  • Figure 1 schematically shows an embodiment of a data processing system for determining a quantitative post-treatment symptom reduction index
  • Figure 2 schematically shows an embodiment of a pressure data series comprising data entries of absolute pressure values received by a data processing system similar as shown in Figures 1;
  • Figure 3 schematically shows an embodiment of a quantified pressure pattern derived from the pressure data series of Figure 2;
  • Figure 4 schematically shows an embodiment of a regression model for use by the embodiment of the data processing system of Figure 1;
  • FIG. 1 schematically shows an exemplary embodiment of a data processing system 100.
  • the data processing system 100 could comprise any suitable general purpose computing system and/or any suitable application specific computing system, more particularly any suitable computing system part of any suitable medical analysis and/or monitoring equipment, ... .
  • the data processing system 100 comprises a pressure pattern module 120 and a pattern processing module 130.
  • the pressure pattern module 120 operates to quantify data of a pressure pattern 112 along at least a section of a vessel 10 of a subject 2 with symptoms indicative of ischaemia.
  • the pattern processing module 130 operates to determine a quantitative post-treatment symptom reduction index 132, which is indicative of a predicted post-treatment ischaemia symptom reduction based. As shown, according to the embodiment, the pattern processing module 130 determines this quantitative posttreatment symptom reduction index 132 based on the quantified pressure pattern 122 provided by the pressure pattern module 120.
  • the vessel 10 of the subject 2 is a coronary artery 10.
  • the system 100 might also be useful for any other suitable vessels 10, such as for example any other blood vessel, such as arteries, vanes, etc., vessels from the lymphatic system, ... .
  • the pressure pattern module 120 thus operates to quantify the data of the pressure pattern 112 along at least a section 12 of a coronary artery 10 of a subject 2 with symptoms indicative of myocardial ischaemia.
  • the pattern processing module 130 thus determines a quantitative post-treatment symptom reduction index 132 indicative of a predicted post-treatment myocardial ischaemia symptom reduction.
  • the treatment under evaluation by the system is for example a percutaneous coronary intervention or PCI. It is clear that alternative embodiments are available for any other suitable treatment of myocardial ischaemia symptoms, such as for example coronary artery bypass grafting or CABG, treatment by medication, or any other suitable treatment or combination of treatment by medication, nondrug therapy, surgery, non-surgical treatment, ... .
  • the treatment under evaluation by the system 100 is PCI
  • the predicted post-treatment myocardial ischaemia symptom reduction 132 as determined by the pattern processing module 130 thus is a suitable quantitative parameter or index indicative of a predicted postPCI myocardial ischaemia symptom reduction 132.
  • the pressure pattern module 120 thus operates to quantify the data of the pressure pattern 112 along at least a section 12 of a coronary artery 10 of a subject 2 with angina pectoris, which is a specific and reliable symptom indicative of myocardial ischaemia.
  • the pattern processing module 130 thus determines a quantitative post-treatment symptom reduction index 132 indicative of a predicted post-treatment angina pectoris reduction, preferably a predicted post-PCI angina pectoris reduction.
  • the data of the pressure pattern 112 provided to the pressure pattern module 120 comprises a pressure data series 114 comprising a plurality of pressure data entries 116 associated with intermittent pressure measurements along at least a longitudinal section 12 of the vessel 10. It is clear that the pressure data series 114 comprises preferably a sufficient amount of pressure data entries 116 to reliably discern and assess any patterns in the pressure data entries 116 along the longitudinal section 12 of the vessel 10.
  • the longitudinal section 12 of the vessel corresponds to a section corresponding to or substantially corresponding to the section between the ostium and the distal end of the vessel 10, however it is clear that alternative embodiments are possible in which a pressure data series 114 associated with any suitable longitudinal section 12 of the vessel 10 is used, preferably a longitudinal section 12 of the vessel 10 potentially comprising diseased subsections, such as for example schematically illustrated by means of a suspected lesion or stricture 14 in the vessel 10 of the subject 2.
  • the data processing system 100 is receiving a pressure data series 114 with pressure data entries 116 resulting from pressure measurements that are associated with a potential blockage or restriction of the flow in the vessel 10, such as for example the blood flow in a coronary vessel 10, or according to alternative embodiments the flow of any other fluid in any other suitable vessel.
  • the pressure data series 116 is associated with measurements performed during what is referred to as a pullback operation. At the start of the pullback operation, for example at a time referred to as tl, a pressure sensor 22 is positioned, downstream or distal of a suspected stenosis 14 or stricture in the vessel 10.
  • the pressure measured by the pressure sensor 22 is typically also referred to as the distal pressure or Pd.
  • the pullback operation is then started, by moving the pressure sensor 22, for example by means of a guidewire 28, upstream along the section 12 of the vessel 10, thereby proceeding to the state schematically illustrated in Figure 1, and until the suspected stenosis 14 has been transgressed and the pullback operation is ended at the downstream end of the section 12 of the vessel 10.
  • a pressure data series 114 comprising a time-based pressure data series.
  • the pressure data series 114 comprises a series of intermittent pressure data entries 116 of values associated with pressure measurements from a sensor 22 moved longitudinally along at least the longitudinal section 12 of the vessel 10.
  • the pressure measurements of the movable sensor 22 for the pressure data entries 116 of the pressure data series 114 are for example taken at regular intermittent time intervals, when the sensor 22 is moved along the section 12 of the vessel 10.
  • the pressure data series 114 comprises a position-based pressure data series
  • the data entries 116 correspond to pressure measurements of a sensor 12 at intermittent locations along the section 12 of the vessel 10.
  • the pressure data series 114 further also comprises pressure data entries 116 with additional values associated with pressure measurements from a stationary sensor 24.
  • the pressure data entries 116 of the pressure data series 114 could be associated with one or more absolute pressure values, for example corresponding to the pressure measurements of one or more intracoronary pressure sensors 22, 24.
  • a measurement system 30 configured to receive data entries 116 and generate a dataset 32 comprising pressure values measured during a pullback time operation, which is for example performed during a pullback time period.
  • the pullback time period corresponds to the time period during which a set of pressure values along the length of the vessel 10 is measured by means of a movement of a pressure sensor 22 along a vessel 10.
  • the pressure sensor 22 is moved along the vessel 10 in the direction indicated with the arrow, which is typically in a direction against the direction of the flow of fluid in the vessel 10, such as for example in a direction against the direction of the blood flow in a coronary artery, however it is clear that alternative embodiments are possible in which the direction of movement has a different relationship with direction of the flow of the fluid in the vessel.
  • the pressure sensors 22, 24 are for example part of a catheter 20, which, after being inserted into the vessel 10, is able to determine the pressure of the fluid flow in the vessel at the location of the respective pressure sensor 22, 24.
  • the catheter 20 for example comprises a sheath 21 and a guidewire 28.
  • the guidewire 28 comprises a tip to which a pressure sensor 22 is mounted for measuring the pressure of the fluid in the vessel 10, in this embodiment the pressure in the blood.
  • the tip of the guidewire 28 with pressure sensor 22 is positioned at a predetermined distance from the sheath 21 of the catheter 20, for example at a most distal end of the vessel 10, such as for example the most distal end of a coronary artery, or a location distal of a suspected stenosis. Subsequently this distance is reduced during the pullback operation by pulling the guidewire 28 in a direction indicated with the arrow, so that the tip of the guidewire 28, moves closer to the tip of the sheath 21 of the catheter 20.
  • Such a movement of the guidewire 28 and the pressure sensor 22 mounted at its tip is caused by an operation that can be referred to as pulling the guidewire 28 through the sheath 21 of the catheter 20, as is well known to a person skilled in the art.
  • the pressure sensor 22 is configured to move along a predetermined section 12 or part 12 of the vessel 10. This part 12 of the vessel 10, which is associated with the pullback operation, and thus also with the pressure pattern 112 generated by means of such a pullback operation, is determined by the position of the pressure sensor 22 at the start of the pullback operation, and by the position of the pressure sensor 22 at the end of the pullback operation.
  • a position-based pressure data series 114 is generated by measurement system in data indicative for the position along the section 12 of the vessel 10 is stored or derivable from the pressure data series 114 for the pressure pattern 112 as provided to the data processing system 100.
  • the data processing system 30 enables measurements of pressure patterns along the length of the vessel that are associated with a potential blockage or restriction of the flow in the vessel, such as for example the blood flow in a coronary vessel, or the flow of any other fluid in any other suitable vessel.
  • the pullback operation may start in such a way that the pressure sensor 22 is positioned, downstream or distal of a suspected stenosis 14 or stricture in the vessel 10.
  • the pressure measured by the pressure sensor 22 is typically also referred to as the distal pressure or Pd.
  • the pullback operation after being started is continued by moving the pressure sensor 22 along the direction indicated by the arrow in Figure 1, for example by means of the guidewire 28, upstream along the vessel 10, and subsequently further continued until the suspected stenosis 14 has been transgressed and the pullback operation is ended, or the tip of the sheath 21 of the catheter, or the ostium of the vessel 10 is reached.
  • the pressure sensor 24 remains stationary during the pullback operation and is configured to measure the pressure of the fluid flow upstream of the section 12 of the vessel 10 along which the pressure sensor 24 moves during the pullback operation. Or in other words, this pressure sensor 24 is configured to measure the pressure in the vessel 10 upstream of or proximal to a suspected stenosis 14 or stricture in the vessel 10.
  • the pressure measured by the pressure sensor 24 is typically also referred to as the proximal pressure or aortic pressure Pa.
  • the pressure measurements of both sensors 22, 24 generate pressure data entries 116, which are for example stored in a dataset 32 of the measurement system 30 and subsequently provided as a pressure data series 114 to the data processing system 100, the pressure data series being representative of a pressure pattern 112 along the section 12 of the vessel 10 of the pullback operation.
  • the pressure data entries 116 could for example be absolute pressure measurements, such as for example resulting from a measurement of sensor 22, also referred to as Pd as mentioned above, of 33 mm Hg and for example a further absolute pressure measurement of sensor 24, also referred to as Pa as mentioned above, of 69 mm Hg.
  • the pressure data series 114 is provided by the measurement system 30 to the pressure data module 110 of the data processing system, could thus comprise a series of pressure data entries 126 representing values of such a absolute pressure measurements.
  • Alternative embodiments are possible however in which the pressure data series 114 is provided to the data processing 100 in any other suitable way, for example directly from the sensors, or in which the measurement system 30 is integrated into or part of the data processing system, or in which the pressure data series 114 is determined, generated or provided from any other suitable storage system or invasive or non-invasive measurement system for generating such a pressure data series 114 associated with a section 12 of a vessel 10.
  • pressure data entries 116 with absolute pressure values there could be provided a time-based or location-based pressure data series 114 to the pressure data module 110, or alternatively the pressure data module 110 could determine from pressure data entries 116 with absolute pressure values, a pressure pattern 112 comprising based on a pressure data series 114 with a series of pressure data entries of relative pressure values, such as for example series of pressure ratio values.
  • a pressure data entry could for example correspond to a pressure ratio value, typically referred to as Pd/Pa, which is associated with a ratio of the pressure measurement Pd from the sensor 22 moved longitudinally along the longitudinal section 12 of the vessel 10, and the pressure measurement from the stationary pressure sensor 24.
  • the data processing system 100 comprises a pressure data module 110 configured to perform any such operations as necessary or desirable on the received pressure data series 114 and the series of pressure data entries 116, for converting the pressure data entries 116 and/or calculating additional pressure data entries 116 to a pressure data series referred to as the pressure pattern, which is suitable for processing by the pressure pattern module 120.
  • the pressure data module 110 could for example be configured to receive a pressure data series 114, or convert a received pressure data series 114, such that it comprises a series of data entries 116 representing a Fractional Flow Reserve or FFR value, or alternatively a non-hyperaemic pressure ratio such as for example an instantaneous wave-free ratio value, an Instant Flow Reserve or iFR value.
  • the series of data entries 116 could for example comprise values representing similar hyperaemic or non-hyperaemic pressure ratios.
  • Determining such an FFR pullback curve is for example done by determining FFR values from measurements of the movable pressure sensor 22, also referred to as distal pressure or Pd, with respect to a stationary pressure sensor 24, also referred to as proximal or aortic pressure Pa, over the distance travelled or over the time period of the pullback operation.
  • the stationary pressure sensor 24 is for example positioned at the ostium of the vessel and that the movable pressure sensor 22, during the pullback operation is moved along a longitudinal section 12 of the vessel 10, for example between a more distal part of the vessel 10, for example the most distal part of the vessel 10, or a part of the vessel 10 distal of a suspected stenosis 14, stricture or lesion, and the ostium of the vessel 10.
  • the pressure data entries 116 of the pressure data series 114 derived from these pressure values are referred to as FFR values of a FFR pullback curve and are typically determined as the ratio of Pd/Pa under such hyperaemic conditions.
  • the FFR values result from pressure measurements performed under maximal, hyperaemia.
  • maximal, hyperaemia could preferably achieved through administration of a potent vasodilator such as for example adenosine, ATP or papaverine either by IV infusion or by intracoronary (IC) bolus injection.
  • a potent vasodilator such as for example adenosine, ATP or papaverine either by IV infusion or by intracoronary (IC) bolus injection.
  • IC intracoronary
  • the pressure data module 110 of the data processing system 100 could for example operate in such a way that it subjects the pressure data series 114 provided by the measurement system 30 to any suitable form of pre-processing such as for example by means of a moving mean or average function.
  • the pre-processing of the pressure data module 110 could for example be configured to filter out the rhythmic and/or periodical component of the heartbeat cycle, or to filter out any noise or disturbances in the measurement signal.
  • the pressure pattern 112 derived from the pressure data series 114 by the pressure data module 110 and provided to the pressure pattern module 120 could according to a preferred embodiment comprise or consist of a pressure data series 114 of or a pressure data series 114 for determining an FFR pullback curve, which has been subjected to such a suitable form of pre-processing by the pressure data module 110.
  • the FFR value of the FFR pullback curve at that point in time would also be equal to 0,48.
  • a ratio is determined from measurements performed while the subject was not being subjected to hyperaemic conditions, in which by means of a suitable form of pre-processing, which for example determines and selects the most suitable point in time during a heartbeat cycle for the selection the measurement value for determining a ratio, which is referred to as a non-hyperaemic pressure ratio, such as for example the instantaneous wave-free ratio or the Instant Flow Reserve value or the iFR.
  • a suitable form of pre-processing which for example determines and selects the most suitable point in time during a heartbeat cycle for the selection the measurement value for determining a ratio, which is referred to as a non-hyperaemic pressure ratio, such as for example the instantaneous wave-free ratio or the Instant Flow Reserve value or the iFR.
  • any suitable absolute, relative and/or ratio of pressure values are measured or derived for providing suitable pressure data entries 116 for a suitable series of pressure data 114 for a pressure pattern 112 associated with a section 12 of a vessel 10. It is clear that still further alternative embodiments are possible in which such pressure ratio values are any other suitable hyperaemic pressure ratio values or non-hyperaemic pressure ratio values.
  • the pressure pattern 112 provided to the pressure pattern module 120 could alternatively or additionally comprise pressure data entries comprising a rate of change of absolute pressure values, a rate of change of relative pressure values, a rate of change of pressure ratios, a rate of change of Fractional Flow Reserve values, a rate of change of non-hyperaemic pressure ratio values, a rate of change of an instantaneous wave-free ratio values or Instant Flow Reserve values or iFR values, ... .
  • the time-reference comprises an indication of the time during the pullback time period during which a pullback operation is performed with pressure measurements along the section 12 of the vessel 10.
  • this could for example be graphically represented as a graph of the pressure data series 114 representative of a pressure pattern 112.
  • the x-axis for example provides an indication of the number of seconds that have elapsed since a starting point for the data series 114 generated during the pullback operation during the pullback time period, or in other words the overall pressure pattern 112.
  • the pressure data entries 116 of the data series 114 could for example be represented as data points on the y-axis along the time-reference of the x-axis.
  • the start and end of the pullback operation and which could for example be indicated by means of suitable markers, or other suitable inputs to indicate, receive and/or store the start and end time of the pullback operation.
  • the pressure data series 114 is for example time-referenced to for example the starting time of the pullback operation, or in any other suitable way.
  • the time-reference for the time-referenced pressure data of the dataset 32 could also be determined and/or derived from an indication of a measurement frequency and/or a measurement interval of the pressure data entries 116 of the pressure data series 114.
  • the pressure data entries 116 correspond to measurements with a measurement frequency of 500Hz or any other suitable measurement frequency
  • the time-reference of the pressure data series 114 which comprises a set of consecutive intermittent measurements at this measurement frequency can be derived from that measurement frequency.
  • each pressure data entry 116 in the pressure data series 114 is then for example spaced 2ms apart, or any other suitable time period derived from the measurement frequency.
  • a location-based data series 114 could be provided with a suitable location-based reference for the data entries 116 of the data series 114, such that the pressure data entries 116 can be referenced to their location along the section 12 of the vessel 10 of the pressure pattern 112.
  • this refers to the gradient or slope between for example two pressure measurements or ratios and their corresponding pressure data points 116 of the pressure data series 114, with respect to the time-reference or locationreference, such as for example a rate of change per second, or any other suitable time reference, or per mm or any other suitable location reference of the pressure data points 116 of the pressure data series 114.
  • Figure 2 schematically shows an exemplary embodiment of a pressure data series 114 comprising data entries 116 of absolute pressure values received by a data processing system 100 similar as shown in Figures 1.
  • Figure 3 schematically shows an exemplary embodiment of a quantified pressure pattern 122 derived from the pressure data series 114 of Figure 2.
  • the data processing system 100 has calculated a quantified pressure pattern 122 by means of the pressure pattern module 120 in the form of a suitable Functional Outcome Index, or FOI, which can for example be referred to as an embodiment of a Pullback Pressure Gradient or PPG or PPG index.
  • FOI Functional Outcome Index
  • Such a FOI or PPG index 122 quantifies the pressure pattern 112 in a reliable and comprehensible way and preferably provides a quantitative, reproducible numerical value, for example in a range of 0 up to and including 1, which is representative of the pressure pattern 112 along the section 12 of the vessel 10. It is clear that such an embodiment is advantageous as such a quantification of the pressure pattern 112 is not subject to for example undesired intra-operator variations or bias resulting from visual assessment of the pressure pattern 112.
  • the quantified pressure pattern 112 is for example calculated by the pressure pattern module 120 in such a way, that the pressure pattern module 120 outputs a numerical value of the FOI, such this numerical value quantifies the pattern of the pressure pattern 112 determined from the pressure data series 114. It is clear that when referring to the pressure pattern 112, this refers to the specific shape and/or evolution of the pressure pattern 112 along the section 12 of the vessel 10, and more particularly the shape and/or evolution of the pressure pattern 112 at a multitude of intermediate points in between the endpoints along of the section 12 of the vessel 10.
  • the quantified pressure pattern 112 which is calculated by the pressure pattern module 120 as a FOI, such as for example a PPG Index, and is a numerical value, which is preferably representative of at least one of the following functional patterns of coronary artery disease: the functional pattern of a focal coronary artery disease when the value is higher than 0.7; the functional pattern of a diffuse coronary artery disease when the value is lower than 0.4; and/or functional pattern of a mixed coronary artery disease when the value is between 0.4 and 0.7.
  • FOI such as for example a PPG Index
  • such an embodiment of the quantified pressure pattern 112 quantifies the pressure pattern 112 on a continuous scale, of which the values are not limited to the three general categories of pressure patterns above, but provide a unique or more unique identifier for specific shape of the pressure pattern 112.
  • the FOI is for example calculated to be 0.86 based on a formula described below and the exemplary pressure pattern 112 derived from the pressure data series 114 shown in Figures 2 and 3, based on pressure data entries 116 derived from measurements from a measurement system 30 during a pullback operation along a section 12 of a coronary artery vessel 10.
  • a pressure pattern 112 corresponding to a pullback operation during a time period starting at tl and ending at t3.
  • This pressure pattern 112, associated with the overall section 12 of the vessel 10, will also be referred to as the overall pressure pattern 40, and is associated with the time period between tl and t3, or alternatively is associated with the length of the section 12 of the vessel
  • a subsection of the pressure pattern 42 which is determined as a subsection 42 of the overall pressure pattern 40 with respectively a predetermined length or duration.
  • This predetermined length or duration could for example be a predetermined absolute value, such as for example 20mm or 2 s, or any suitable alternative value, or a relative value, such as for example respectively 20% of the time period associated with the overall pressure pattern 40, or the length of the section 12 of the vessel 10. It is clear that alternative embodiments are possible with suitable values for determining the subsection 42 of the overall pressure pattern 40, or in other words the subsection 42 of the pressure pattern 112. 7.
  • the predetermined subsection 42 of the pressure pattern 112 could for example be in the range of 2% up to and including 80%, preferably in the range of 5% up to and including 50% of the overall pressure pattern 40, preferably in the range of 10% up to and including 40%, for example in the range of 15% up to and including 30%.
  • the threshold exceeding portion is defined as the ratio of respectively the length or duration of the part indicated with reference 64, with respect to respectively the length or duration of the overall pressure pattern 40.
  • the part 64 refers to a part 64 of the overall pressure pattern 40 in which a rate of change 60 of the pressure pattern 112 is equal to or larger than a predetermined threshold 62. It is clear that this rate of change 60 of the pressure pattern 112, refers to the rate of change of the pressure pattern 112 in function of time during a pullback operation along the section 12 of the vessel, or in function of position along the section 12 of the vessel 10.
  • such a rate of change could for example be symbolised by means of d(Pd/PA)/dt, dFFR/dt, ... , A(Pd/Pa)/At, A(FFR)/At, ..., d(Pd/PA)/ds, dFFR/ds, ... , A(Pd/Pa)/As, A(FFR)/As, ... and could thus be referred to as a differential, gradient, gradient of instantaneous Pd/Pa, FFR, ... data entries per unit time or displacement.
  • the pressure pattern 112 comprises a data series representing a fractional flow reserve (FFR) pullback curve comprising pressure data entries associated with FFR values, which could also be referred to as instantaneous FFR values.
  • FFR values are for example associated with a ratio of a pressure measurement Pd from the sensor 22 moved longitudinally along the longitudinal section 12 of the vessel 10; and a pressure measurement Pa from a stationary pressure sensor 24, or in other words Pd/Pa as shown on the left y-axis of Figure 3, whereby these values represent FFR values 50 when these are related to measurements performed under ischaemic conditions.
  • the pressure pattern 112 or in other words the overall pressure pattern 40 refers to the shape of the curve with FFR values associated with section 12 of the vessel 10, or in other words the shape of this curve between tl and t3 in Figure 3.
  • the pressure pattern module will for example determine the quantified pressure pattern 122 by means of the following formula for the functional outcome index FOl, which can also be referred to as the PPG Index:
  • maximum A FFR (subsection of the pressure pattern) is defined as the maximum difference between FFR values 50 associated with the start and the end of the subsection 42 of the pressure pattern 112, or in other words the subsection 42 of the overall pressure pattern 40 of which the difference between FFR values 50 at the start and end of this subsection 42 is maximum;
  • a FFR (overall pressure pattern) is defined as the difference between FFR values 50 associated with the start and the end of said overall pressure pattern 40;
  • - threshold exceeding portion is defined as the portion 64 of the overall pressure pattern 40 in which the rate of change 60 in FFR 50 is equal to or larger than the predetermined threshold 62.
  • the predetermined threshold 62 is set to a suitable value, such as for example a rate of change in the range of at least 0.0005 per second, or per mm, or per l/100th of the length or duration of the overall pullback pressure pattern 40, preferably in the range of at least 0.0010, for example 0.0015, or any other suitable threshold that is indicative of a relevant rate of change in the pressure pattern 112 that is representative of a particular pattern of stenosis, stricture, lesion, ... in the vessel 10.
  • a suitable value such as for example a rate of change in the range of at least 0.0005 per second, or per mm, or per l/100th of the length or duration of the overall pullback pressure pattern 40, preferably in the range of at least 0.0010, for example 0.0015, or any other suitable threshold that is indicative of a relevant rate of change in the pressure pattern 112 that is representative of a particular pattern of stenosis, stricture, lesion, ... in the vessel 10.
  • the threshold 62 for the rate of change could for example be defined as a rate of change of the pressure pattern 112 in the range of at least 0.05% of a maximum value of the pressure pattern with respect to a unit time or unit distance, preferably in the range of at least 0.10%, for example equal to 0.15%, wherein the unit time is for example one second or l/100th of the time period associated with the overall pullback pressure pattern 40, or wherein the unit displacement is for example one mm or l/100th of the length of the section 12 of the vessel 10 associated with the overall pullback pressure pattern 40.
  • the threshold for the rate of change of the FFR pressure pattern 112 could for example be 0.15%/mm or 0.0015/mm, or 0.15%/s or 0.0015/s, ... It is clear that still further alternative embodiments are possible.
  • the quantified pressure pattern 122 could be determined by the pressure pattern module 123 by means of a FOI based on the formula following formula: wherein: maximum pressure change (subsection of the pressure pattern) is defined the maximum pressure change 26, 56 associated with said subsection of the pressure pattern 42; pressure change (overall pressure pattern) is defined as said pressure change associated with the overall pressure pattern (40); and
  • - threshold exceeding portion is defined as said portion (64) of the overall pressure pattern (40) in which said rate of change (60) is equal to or larger than said predetermined threshold (62).
  • the maximum pressure change (subsection of the pressure pattern) could for example be similar as described above, the maximum pressure change 56 for a suitable subsection 42 of the overall pattern 40, but for example in which the Pd/Pa values of the pressure pattern 112 were not measured under ischaemic conditions, or where any other suitable ratio of pressure values is made use of.
  • the maximum pressure change (subsection of the pressure pattern) , could for example be embodied as the maximum change in Pd for a suitable subsection 42 of an overall pressure pattern 112, which is then for example embodied as the pressure data series 114 represented as Pd.
  • the quantified pressure pattern 122 provided by the pressure pattern module 120 is determined by the pressure pattern module 120 by means of the combination of the following two components: First a contribution of a maximum pressure change 56 associated with a predetermined subsection 42 of the pressure pattern 112 with respect to a pressure change associated with the overall pressure pattern 40. And secondly a portion 64 of the overall pressure pattern 40 in which the rate of change 60 is equal to or larger than a predetermined threshold 62.
  • the pressure pattern 112 of Figure 3 in the form of a ratio of the Pd/Pa is derived as ratio of the pressure data entries 116 the two pressure data series 114 of Pd and Pa as shown in Figure 2.
  • the Pd/Pa or FFR values of the pressure pattern of Figure 3, which could for example be referred to as an FFR pullback curve, are typically determined as the ratio of Pd/Pa, wherein Pd and Pa could for example be determined from the measured pressure values by means of the measurement system 30, preferably after being subjected to any suitable form of pre-processing such as for example by means of a moving mean or average function which is configured to filter out the rhythmic and/or periodical component of the heartbeat cycle.
  • This pre-processing could for example be performed by the measurement system 30 or the pressure data module 110 of the data processing system.
  • the pressure measurements of the pressure sensor 22 are provided as pressure data entries 116 of a pressure data series 114 to the pressure data module 110, which processes it by calculated from a pressure data series 114 represented as a moving mean pressure data series 22m, which is for exampled calculated from the pressure data series 114 labelled based on the measurements of the pressure sensor 22.
  • this pre-processing of the pressure data module 110 is configured to reduce the variation in the pressure signal caused by the different phases of a heartbeat cycle.
  • similarly also the pressure data series 114 with pressure data entries 116 from measurements of the pressure sensor 24 are processed by the pressure data module 110 such that a moving mean pressure data series 24m.
  • any other suitable type of pre-processing of the pressure data series by the pressure data module 110 and/or the measurement system 30 is possible, such as for example making use of one or more of the following signal processing operations a moving or rolling mean, average, variation, minimum, maximum, ... .
  • no pre-processing of the pressure data series 114 could be desired.
  • This pre-processing of the pressure measurements could for example be performed by the data processing system 30.
  • the quantified pressure pattern 122 which is for example an embodiment of the FOI, or any other suitable embodiment of a quantitative parameter derived from the pressure pattern 112, a quantitative parameter which is not only determined by the endpoints of the pressure pattern, but also by the intermediate values between these endpoints, or in other words the specific shape of the pressure pattern 122, is determined by the pressure pattern module 120 and provided to the pattern processing module 130.
  • the pressure pattern module 120 for example provides a FOI, such as for example a PPG Index calculated from a specific pressure pattern 120 to the pattern processing module 130.
  • the pattern processing module 130 determines a quantitative posttreatment symptom reduction index 132 based on this quantified pressure pattern 122.
  • the pattern processing module 120 could optionally make use of further input parameters for determining the quantitative post-treatment symptom reduction index 132, it is clear that according to a particular embodiment the pattern processing module 130 only makes use of the quantified pressure pattern 122 for determining the quantitative post-treatment symptom reduction index 132.
  • the pattern processing module 130 makes use of a suitable model 134, such as for example a regression model, for calculating the quantitative post-treatment symptom reduction index 132 from the quantified pressure pattern 122.
  • This model 134 can for example be determined from data related to prior treatments of subjects with symptoms indicative of ischaemia or similar medical research or experiments. This data could for example comprise data related to the pressure pattern 112 of subjects and data of these subjects related to the quantitative post-treatment symptom reduction index 132.
  • a suitable model 134 such as for example a suitable regression model or any other suitable statistical modelling or other suitable modelling was derived, which
  • 3 are parameters, such as for example suitable constants for the regression equation.
  • any suitable regression model such as for example a binomial regression model, a linear regression model, etc.
  • any other suitable model for example resulting from processing the data by means of machine learning, a neural network, any suitable form of artificial intelligence computer implemented methods, etc. are also possible.
  • the data processing system 100 could for example also optionally comprise a pre-treatment symptom module 140, a pre-treatment overall pressure gradient module 150, and/or a vessel type determination module 160.
  • the pre-treatment symptom module 140 is configured to quantify data 144 of pretreatment symptoms indicative of ischaemia, such as for example a pre-treatment Pretreatment SAQ or Pre-treatment SAQ angina frequency.
  • pre-treatment Pretreatment SAQ or Pre-treatment SAQ angina frequency For patients with coronary artery disease, the Seattle Angina Questionnaire or SAQ provides for an embodiment of the most commonly used quantitative measurement of patients' symptoms of angina and the extent to which these angina symptoms affect the functioning and quality of life of these patients.
  • a SAQ or SAQ score such as for example a SAQ-7 score, is the outcome of a measurement by means of a validated questionnaire comprising three domains: physical limitation, angina frequency, and quality of life.
  • the SAQ-7 has a scale from 0 to 100, with higher values indicating better health status.
  • an SAQ score of 100 indicates freedom from angina.
  • a SAQ score is also representative of a SAQ angina frequency, which relates to the frequency at which the subject experiences angina symptoms.
  • SAQ angina frequency scale of 0-30 points indicates daily angina, 31-60 points indicate weekly angina, 61- 99 points indicate monthly angina, and 100 points indicate no angina).
  • pretreatment SAQ or pre-treatment SAQ angina frequency this refers to measurements of SAQ or SAQ angina frequency from a subject before the treatment to relieve the subject from the symptoms of ischaemia, such as angina pectoris.
  • the treatment is PCI
  • the pre-treatment symptom module 140 provides this pre-PCI SAQ 142 or any other suitable quantified pre-treatment symptoms indicative of ischaemia 142 to the pattern processing module 130 for determining the quantitative post-treatment symptom reduction index 132.
  • the data 144 provided to the optional pre-treatment symptom module 140 could for example be retrieved by the data processing system from a suitable database storing the pre-treatment SAQ score, or any data from which the pretreatment SAQ score could be derived, however, there are other alternative embodiments possible in which for example the pre-treatment SAQ score is provided as a manual input by an operator of the data processing system 100, etc. .
  • the data processing system 100 further comprises an optional vessel type determination module 160 configured to determine an index 162 associated with the vessel type 164.
  • this index 162 could for example comprise a LAD index 164 indicating whether the vessel is a left anterior descending artery or not.
  • LAD index 164 preferably is quantified as 1 of the vessel is a left anterior descending artery and 0 if the vessel is not a left anterior descending artery. It is however clear that further alternative embodiments are possible in which the type of vessel is quantified by means of a suitable index 162.
  • the vessel type of the vessel 10 could for example be derived manually or automatically by means of a suitable measurement system 166, such as for example a suitable medical imaging device configured to provide suitable images from which the vessel type can be determined.
  • the medical image data, or any other suitable or derived medical data 164 could then be provided to the vessel type determination module 160 as an input from which the vessel type index 162 can be determined.
  • the LAD index 162 or any other suitable vessel type parameter is provided to the data processing system 100 by an operator as a manual input, etc. .
  • the vessel type index is for example representative of a specific coronary artery vessel or branch, such as for example one or more of the following: Left coronary artery; Left anterior descending artery; Left circumflex artery; Posterior descending artery; Ramus or intermediate artery; Right coronary artery; Right marginal artery; Posterior descending artery;
  • the data processing system further also comprises a pre-treatment overall pressure gradient module 150.
  • the overall pressure gradient module quantifies data associated with the overall pressure gradient 118 between pressure data entries associated with both end points of said at least a longitudinal section 12 of the vessel 10.
  • the pre-treatment overall pressure gradient module 150 provides a quantified pre-treatment overall pressure gradient 152 to the pattern processing module 130 for determining the quantitative post-treatment symptom reduction index 132.
  • the overall pressure gradient 118 could for example be derived from the pressure data series 114 by the pressure data module 110, however it is clear that alternative embodiments are possible in which this overall pressure gradient 118 is provided for example in the form of data outputted by the measurement system 30, or as data provided by means of manual input to the data processing system 100, etc. . According to the embodiment described with respect to Figures 2 and 3, the overall pressure gradient 118 could for example be derived from the pressure data entries 116 of the pressure data series 114 or the pressure pattern 112 at tl and/or t3.
  • the pressure gradient 118 and the derived quantified pre-treatment overall pressure gradient 152 could for example correspond to the Pd/Pa value or the FFR value at tl, such as shown in Figure 3, which corresponds to the overall FFR value associated with the section 12 of the vessel 10, however, it is clear that other alternative embodiments are possible in which other suitable values with respect to overall pressure gradient of the section 12 of the vessel 10 are being extractable.
  • the quantified pre-treatment overall pressure gradient 152 could for example be referred to as a quantified pre-PCI overall pressure gradient 152, or according to the embodiment described above pre-PCI FFR 152 associated with the section 12 of the vessel 10.
  • Such a quantified overall pressure gradient 152 thus for example comprises or consists of a Fractional Flow Reserve or FFR value of the longitudinal section 12 of the vessel 10.
  • a similar hyperaemic pressure ratio value or an instantaneous wave-free ratio or Instant Flow Reserve or iFR value or a similar non- hyperaemic pressure ratio, ... of the longitudinal section 12 of the vessel 10.
  • any other suitable ratio such as for example a Pd/Pa ratio, in which the pressure ratio value associated with a ratio of:
  • the pattern processing module 130 is operated to determine the quantitative post-treatment symptom reduction index 132 indicative of a predicted post-treatment ischaemia symptom reduction based on the quantified pressure pattern 122 provided by the pressure pattern module 120 and one or more of the following: the vessel type index 162; the quantified pre-treatment symptoms indicative of ischaemia 142; and/or the quantified overall pressure gradient 152.
  • this could for example be implemented by means of a suitable model 134, such as a regression model, comprising one or more of these additional quantitative parameters 162, 142, 152 as further independent variables.
  • a binomial regression model 134 was for example derived from similar historical medical data and/or experimental data related to PCI treatment of subjects with angina pectoris, with the following equation:
  • o, pi, P2, P3 are parameters, such as for example suitable constants for the binomial regression equation.
  • the probability of being angina free can be determined for each patient before the treatment, such as for example a PCI treatment.
  • a PCI treatment such as for example a PCI treatment.
  • An advantageous aspect of this particular embodiment is that all input data is available before such a PCI treatment, for example before a treatment making use of vessel instrumentation with stents or balloons.
  • the pattern processing module 130 operates to determine the quantitative post-treatment symptom reduction index 132 indicative of the chance of obtaining a predetermined post-treatment SAQ equal to one hundred it is clear that alternative embodiments are possible.
  • the quantitative post-treatment symptom reduction index 132 indicative of the chance of obtaining a predetermined post-treatment SAQ equal to, larger than or equal to, lower than or equal to, ... any other suitable predetermined value for a predetermined post-treatment SAQ.
  • an indicator of the chance of obtaining a predetermined a predetermined post-treatment SAQ angina frequency wherein, for example the predetermined post-treatment SAQangina frequency is equal to zero or 0Hz or in other words the absence of the occurrence of the symptoms, or equal to, lower than or equal to, larger than or equal to, ... any other suitable predetermined value.
  • the quantitative post-treatment symptom reduction index 132 indicative of Post-treatment SAQor Post-treatment SAQangina frequency value itself is calculated by the pattern processing module 130 as a suitable quantitative posttreatment symptom reduction index 132.
  • the indicator of the chance is preferably a percentage value.
  • the quantitative post-treatment symptom reduction index 132 could be provided as a suitable output by the data processing system 100, which could for example be displayed to an operator on a suitable display 170, or alternatively provided for further processing or storage by the data processing system 100 itself or any other suitable data processing system.
  • any suitable visual element is used to display the quantitative post-treatment symptom reduction index 132, such as for example a graph, a colour coded output element, etc. .
  • the pressure data series 114 from which the pressure pattern 112 is derived and/or the pressure pattern 112 itself preferably comprises a multitude of pressure data entries 116 in between the endpoints, preferably more than 10, more than 100, more than 100 of such intermediate pressure data entries 116.
  • any suitable sensor measurements and/or data from which such pressure data entries 126 could be derived are also possible, such as for example pressure values, flow values, measurement of structural dimensions of the vessel and/or its lumen, etc. for example by means of any suitable sensor for measuring this, such as ultrasonic sensors, medical imaging sensors, such as for example Computed Tomography, X-ray images, etc. , systems for extracting and/or simulating flow and/or pressure based on vessel models, etc. .
  • Figure 5 schematically shows a suitable computing system 200, 300 for implementing the data processing system 100 and/or executing the computer-implemented method described above.
  • Figure 5 thus shows a suitable computing system 200, 300 for hosting a data processing system or data acquisition system, comprising a processor configured to perform the computer-implemented method 100 or any of its components as described with reference to the above-mentioned embodiments.
  • Computing system 200 for example comprises and/or consists of as a suitable general-purpose or application specific computer or integrated processing circuit and comprise a bus 210, a processor 202, a local memory 204, one or more optional input interfaces 214, one or more optional output interfaces 216, a communication interface 212, a storage element interface and one or more storage elements 206.
  • Bus 210 may comprise one or more conductors that permit communication among the components of the computing system.
  • Processor 202 may include any type of conventional processor or microprocessor that interprets and executes programming instructions.
  • Local memory 204 may include a random-access memory (RAM) or another type of dynamic storage device that stores information and instructions for execution by processor 202 and/or a read only memory (ROM) or another type of static storage device that stores static information and instructions for use by processor 202.
  • Input interface 214 may comprise one or more conventional mechanisms that permit an operator, sensors or other systems or devices to input information to the computing device 200, such as a keyboard 220, a mouse, a pen, voice recognition and/or biometric mechanisms, a data acquisition interface 230, a data acquisition system, etc.
  • Output interface 216 may comprise one or more conventional mechanisms that output information to the operator, such as a display 240, a printer, a speaker, etc.
  • Communication interface 212 may comprise one or more transceiver-like mechanisms such as for example one or more wired or wireless interfaces, such as for example Ethernet interfaces that enables computing system 200 to communicate with other devices and/or systems, for example mechanisms for communicating with one or more other computing systems 300.
  • the communication interface 212 of computing system 200 may be connected to such another computing system 300 by means of a local area network (LAN) or a wide area network (WAN), such as for example the internet.
  • LAN local area network
  • WAN wide area network
  • Storage element interface may comprise a storage interface such as for example a Serial Advanced Technology Attachment (SATA) interface or a Small Computer System Interface (SCSI), or any other suitable storage interface for connecting bus 210 to one or more storage elements 206, such as one or more local disks, for example hard disk drives, flash storage devices, etc., and control the reading and writing of data to and/or from these storage elements 206.
  • SATA Serial Advanced Technology Attachment
  • SCSI Small Computer System Interface
  • storage elements 206 such as one or more local disks, for example hard disk drives, flash storage devices, etc.
  • any other suitable computer-readable media such as a removable magnetic disk, optical storage media such as a CD or DVD, -ROM disk, solid state drives, flash memory cards, ... could be used.
  • the system according to the above-mentioned embodiments could be part of a suitable system running on a computing system 200 locally available to a user, such as a personal computer, laptop, a smart phone, a tablet computer, an electronic book reader, a portable computer, a desktop computer, etc. or on a remotely accessible computing system such as one or more servers available to a plurality of users.
  • the system may also be part of suitable servers, for example comprising web-based tools.
  • the system and the associated computer-implemented method can be implemented as programming instructions stored in the local memory 204 of the computing system 200 for execution by its processor 202.
  • these components could be stored on the storage element 206 or be accessible from another computing system 300 through the communication interface 212.
  • system and the associated computer-implemented method are provided as a computer program comprising software code adapted to perform this computer-implemented method when executed by a computing system.
  • system and the associated computer-implemented method could also be provided as a computer readable storage medium comprising computer-executable instructions which, when executed by a computing system, perform the computer-implemented method.

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Abstract

There is described a data processing system (100) comprising a pressure pattern module (120) configured to quantify data of a pressure pattern (112) along at least a section (12) of a vessel (10) of a subject (2) with symptoms indicative of ischaemia. The data processing system (100) further comprises a pattern processing module (130) configured to determine a quantitative post-treatment symptom reduction index (132) indicative of a predicted post-treatment ischaemia symptom reduction based on the quantified pressure pattern (122) provided by the pressure pattern module (120).

Description

A SYSTEM AND METHOD FOR DETERMINING A QUANTITATIVE POST-TREATMENT SYMPTOM REDUCTION INDEX BASED ON A QUANTIFIED PRESSURE PATTERN.
FIELD
The invention is related to the field of data processing systems and computer implemented methods for data analysis with respect to symptoms of ischemia, such as for example caused by the potential presence of a stenosis, stricture, lesion, ... in a vessel, and more particularly in a blood vessel, such as for example a coronary artery.
PRIOR ART
It is known from for example EP3827742 that for appropriate treatment decisions on ischemic cardiopathy such as cardiac angina, it is important to evaluate whether myocardial ischemia exists, whereby intracoronary blood flow becomes inadequate. As described in EP3827742 treatment such as stent placement in a lesioned blood vessel without myocardial ischemia is not only meritless, but also likely to lead to a poor prognosis due to in-stent restenosis, increased haemorrhagic events resulting from internal use of an antiplatelet agent, or the like.
As further known from EP3827742 prior art ischemia diagnosis, is for example conducted during cardiac catheterization, by means of a method that supports a diagnostic or treatment decision by calculating a diagnostic support index generally known as fractional flow reserve or FFR, or alternatively a non-hyperaemic pressure ratio such as for example instantaneous wave-free ratio or instant flow reserve or iFR, typically based on measurement data from for example a coronary insertion guide wire equipped with a pressure sensor and known as a pressure wire.
As described in EP3827742, the FFR described above, which is an index found with the microvascular resistance of the patient reduced by medication, or in other words under hyperaemic conditions, is defined as a pressure ratio Pd/Pa between pressure in a proximal stenotic segment upstream of a stenotic lesion in a coronary artery, also referred to as proximal pressure or Pa, and pressure in a distal stenotic segment on the downstream side, also referred to as distal pressure or Pd. When the FFR is lower than 0.80 or 0.75, this supports a diagnostic decision to determine that the patient suffers from myocardial ischemia, which supports a treatment decision for administering coronary stenting or other treatment.
As further described in EP3827742 the iFR is another index calculated based on an average value of all pressure ratios Pd/Pa measured without administering a medicine to the patient, for example during the mesodiastolic to telediastolic period in which the intravascular resistance value at rest is considered to be generally low, and this index supports a diagnostic decision in which it is determined that the patient has myocardial ischemia, forexample, when the iFR is lower than 0.89.
It is known from EP3806725 that the use of such prior art pressure-based data and/or ratios such as FFR or iFR or similar hyperaemic or non-hyperaemic pressure ratios, have limitations with respect to the evaluation of symptoms of myocardial ischemia, and more specifically angina pectoris. It is for example disclosed that there is a large number of patients with symptoms of myocardial ischemia, and more specifically angina pectoris, for example assessed by means of Anginal scores based on patient's symptoms, which have a normal FFR, or in other words an FFR which does not support a diagnostic decision to determine that the patient suffers from myocardial ischemia, or more specifically angina pectoris. In EP3806725 it is proposed to make use of additional flow-based information in order to support an improved evaluation.
This leads to additional and more complex measurements and calculations, which lead to the risk of a more complex, slower and cumbersome process for acquiring the measurements, a more complex, slower, less transparent and more difficult method for processing and evaluating these measurements and the derived parameters thereby increasing the risk of an untimely, inaccurate or unreliable diagnosis and/or corresponding treatment decision. This is especially relevant when an efficient diagnosis and treatment decision is desired such as for example in the context of an emergency treatment or in the context of providing assistive input to a medical team for making a decision on the next steps of a surgical intervention.
Further there is known from EP3355771 a system for evaluating a vessel of a patient based on intravascular pressure measurements and identifying an available treatment type, such as for example by identifying a stent deployment location within the vessel, in function of a desired pressure value, such as for example a desired pressure ratio of distal pressure measurements relative to proximal pressure measurements. Such systems typically require a multitude of inputs, a multitude of complex interactions with a user interface from an operator and provide output from an operator in a form that requires detailed and extensive analysis. Additionally, as already indicated above, such a desired pressure ratio, might not be a reliable indicator for reliably evaluating the effect of the treatment on the symptoms of myocardial ischemia, and more specifically angina pectoris.
Further, it is for example known that there is a large number of patients with symptoms of myocardial ischemia, and more specifically angina pectoris, for example assessed by means of Anginal scores based on patient's symptoms, which have an abnormal FFR, and based on this abnormal FFR undergo stent placement but remain symptomatic after the procedure.
It is thus clear that such prior art systems provide for a more complex, slower, less reliable, less transparent and more difficult method for processing, evaluating and displaying measurements and parameters thereby increasing the risk of an untimely, inaccurate or unreliable diagnosis and/or corresponding treatment decision. This is especially relevant when an efficient diagnosis and treatment decision is desired such as for example in the context of an emergency treatment or in the context of providing assistive input to a medical team for making a decision on the next steps of a surgical intervention.
It is clear that such similar as above, also in other applications where a subject suffers from symptoms of ischaemia associated with at least a section of a vessel, such as for example any other blood vessel, such as arteries, vanes, etc., vessels from the lymphatic system, etc.
There is thus a need for a simpler, faster, more accurate, more transparent, easier and more reliable system enabling and supporting a more reliable, timely and accurate diagnosis, treatment decision and assessment of a treatment outcome on symptoms of ischemia, myocardial ischemia, and more specifically angina pectoris. SUMMARY
According to a first aspect, there is provided a data processing system comprising:
- a pressure pattern module configured to quantify data of a pressure pattern along at least a section of a vessel of a subject with symptoms indicative of ischaemia, wherein the data of the pressure pattern comprises a pressure data series comprising a plurality of pressure data entries associated with intermittent pressure measurements along at least a longitudinal section of the vessel; and
- a pattern processing module configured to determine a quantitative post-treatment symptom reduction index indicative of a predicted post-treatment ischaemia symptom reduction based on the quantified pressure pattern provided by the pressure pattern module.
In this way a more simple and faster system is provided as only the pressure pattern along a vessel is required for determining the predicted post-treatment ischaemia symptom reduction. As soon as data of this pressure pattern along the vessel is available, the determination can be made instantly. There is no need for extensive or complex interaction of an operator for providing further inputs or manipulating a user interface. As will be described in further detail below, it has been found that, taking into account the pattern of the pressure measurements along the vessel, there can be made a more reliable prediction about posttreatment ischaemia symptom reduction, especially when considering the pressure pattern along coronary artery and the prediction of post-treatment Angina Pectoris reduction. In this way there is made available a more accurate and reliable tool which support a more reliable, timely and accurate diagnosis, treatment decision and assessment of a treatment outcome on symptoms of ischemia, myocardial ischemia, and more specifically angina pectoris.
It is clear that the quantitative post-treatment symptom reduction index differs from the quantified pressure pattern, such as for example PPG as explained in further detail below. The quantified pressure pattern provides a numerical value characteristic for the pattern of the pressure data series. Although this quantified pressure pattern can provide an indication whether a particular treatment will result in a higher chance for an improvement for one pressure pattern versus another pressure pattern, this typically relates to an improvement with respect to for example a desired overall pressure parameter such as for example FFR. Such a quantified pressure pattern further is not providing a numerical value indicating the amount of predicted post-treatment ischaemia symptom reduction, at best it can provide an indication whether the treatment will result in a high or low improvement with respect to a particular pressure parameter, it does not provide an actual numerical amount of how much the pressure parameter will improve, it also does not provide a numerical amount of how much post-treatment symptoms will be reduced with respect to pre-treatment symptoms. . In other words, the quantified post-treatment symptom reduction index comprises and/or consists of a numerical value, indicative and/or equal to the amount of predicted posttreatment ischaemia symptom reduction. It is clear that such a numerical value of the quantitative post-treatment symptom reduction index could for example be a percentage or absolute value indicating the amount of predicted post-treatment ischaemia symptom reduction. According to some embodiments, such a numerical value could for example be represented in any suitable form, such as for example as in numerical form, in a suitable graphic representation, ... .
When referring to post-treatment SAQ or post-treatment SAQ angina frequency, this refers to measurements of SAQ or SAQ angina frequency from a subject after the treatment to relieve the subject from the symptoms of ischaemia, such as angina pectoris. When the treatment is PCI, there could be made reference to for example post-PCI SAQ or post-PCI SAQ angina frequency.
According to an embodiment, there is provided a system wherein:
- the pattern processing module is configured to determine the quantitative post-treatment symptom reduction index indicative of:
- Post-treatment SAQ;
- Post-treatment SAQ angina frequency;
- an indicator of the chance of obtaining a predetermined post-treatment SAQ and/or a predetermined post-treatment SAQ angina frequency, wherein, optionally:
- the predetermined post-treatment SAQ is equal to one hundred;
- the predetermined post-treatment SAQ angina frequency is equal to zero; and/or
- said indicator of the chance is a percentage value. According to an embodiment, there is provided a system, wherein:
- the pressure pattern module is configured to quantify the data of the pressure pattern along at least a section of a coronary artery of a subject with symptoms indicative of myocardial ischaemia; and
- the pattern processing module is configured to determine the quantitative post-treatment symptom reduction index indicative of a predicted post-treatment myocardial ischaemia symptom reduction based on the quantified pressure pattern provided by the pressure pattern module.
In this way, there is made available a more accurate, reliable, and efficient tool is provided for timely and accurate diagnosis, treatment decision and assessment of a treatment outcome on symptoms of ischemia, myocardial ischemia, and more specifically angina pectoris.
According to an embodiment, there is provided a system, wherein the treatment is a percutaneous coronary intervention or PCI. In other words, wherein the predicted posttreatment myocardial ischaemia symptom reduction, is a predicted post-PCI myocardial ischaemia symptom reduction.
In this way, there is made available a more accurate, reliable, and efficient tool is provided for timely and accurate diagnosis, treatment decision and assessment of a PCI outcome on symptoms of myocardial ischemia, and more specifically angina pectoris. It is estimated that approximately 1.72% of the world's population is affected by myocardial ischemia and is assessed as one of the most important causes of death, disability, and human suffering globally, and an increasing number of cases are expected due to population aging.
According to an embodiment, there is provided a system, wherein:
- the pressure pattern module is configured to quantify the data of the pressure pattern along at least a section of a coronary artery of a subject with angina pectoris; and
- the pattern processing module is configured to determine the quantitative post-treatment symptom reduction index indicative of a predicted post-treatment angina pectoris reduction based on the quantified pressure pattern provided by the pressure pattern module. In this way, there is made available a more accurate, reliable, and efficient tool is provided for timely and accurate diagnosis, treatment decision and assessment of a treatment outcome on symptoms of angina pectoris. Myocardial ischaemia is one of the most frequent causes of death in the Western World, and angina pectoris, which is a central component of the burden of coronary artery disease, can in this way be more accurately and reliably studied, evaluated, and treated.
According to an embodiment, there is provided a system, wherein the treatment is a percutaneous coronary intervention or PCI. In other words, wherein the predicted posttreatment angina pectoris reduction, is a predicted post-PCI angina pectoris reduction.
According to an embodiment, there is provided a system, wherein the data of the pressure pattern comprises a pressure data series comprising a plurality of pressure data entries associated with intermittent pressure measurements along at least a longitudinal section of the vessel.
Such data of such a pressure pattern, is readily available and is obtainable by means of existing diagnostic equipment and procedures, such as for example known from WO2020/212459A1 or from Collet C, Sonck J et al. Measurement of Hyperaemic Pullback Pressure Gradients to Characterize Patterns of Coronary Atherosclerosis. Journal of the American College of Cardiology 2019;74:1772-1784. Such data of a such a pressure pattern is typically referred to as a pullback curve.
According to an embodiment, there is provided a system, wherein the pressure data series comprises one or more of the following:
- a time-based pressure data series;
- a position-based pressure data series;
- a data series with values associated pressure measurements from a sensor moved longitudinally along at least the longitudinal section of the vessel, and optionally additional values associated with pressure measurements from a stationary sensor. In this way the system processes a pressure data series of a subject, which is readily available, for example as data stored from prior measurements, or during a procedure for determining such a pressure data series, which is readily available, for example from a system for generating, outputting and/or storing such a pressure data series resulting from measurements by means of an intracoronary pullback operation, or any other suitable way of obtaining or deriving such pressure data series. The data series with intermittent pressure measurements along the section of the vessel also allows to evaluate, in addition to the overall pressure change of this section, the specific localised changes of the pressure along this section of the vessel, or in other words the pattern of the pressure pattern along this section of the vessel.
According to an embodiment there is provided a system, wherein the pressure data entry is associated with one or more of the following:
- an absolute pressure value;
- a relative pressure value;
- a pressure ratio value;
- a pressure ratio value associated with a ratio of:
- a pressure measurement from a sensor moved longitudinally along at least the longitudinal section of the vessel; and
- a pressure measurement from a stationary pressure sensor;
- a hyperaemic pressure ratio value;
- a Fractional Flow Reserve or FFR value;
- a non-hyperaemic pressure ratio value;
- an instantaneous wave-free ratio or Instant Flow Reserve or iFR value;
- a rate of change of absolute pressure values;
- a rate of change of relative pressure values;
- a rate of change of pressure ratio values;
- a rate of change of hyperaemic pressure ratio values;
- a rate of change of Fractional Flow Reserve values;
- a rate of change of non-hyperaemic pressure ratio values;
- a rate of change of instantaneous wave-free ratio or Instant Flow Reserve or iFR values. In this way the system processes a pressure data series of a subject, which is readily available, for example as data stored from prior measurements, or during a procedure for determining such a pressure data series, which is readily available, for example from a system for making intra-coronary pressure measurements, or alternatively deriving such pressure values from other measurements related to properties of the vessel and/or the flow or pressure along the vessel.
According to an embodiment, there is provided a system, wherein the quantified pressure pattern provided by the pressure pattern module is determined by the pressure pattern module by means of:
- a contribution of a maximum pressure change associated with a predetermined subsection of the pressure pattern with respect to a pressure change associated with the overall pressure pattern; and
- a portion of the overall pressure pattern in which the rate of change is equal to or larger than a predetermined threshold.
In this way the pressure pattern is quantified in a simple, efficient, reliable, and reproducible way. This allows an operator of the data processing system to review and interpret such a quantified pressure pattern, which forms the basis forthe predicted post-treatment ischaemia symptom reduction in an efficient and reliable way, thereby increasing the reliability and reducing any uncertainty and increasing confidence for an operator when evaluating the results of the data processing system.
According to an embodiment, there is provided a system, wherein the predetermined subsection of the pressure pattern is in the range of 2% up to and including 80%, preferably in the range of 5% up to and including 50% of the overall pressure pattern, preferably in the range of 10% up to and including 40%, for example in the range of 15% up to and including 30%, for example 20%.
According to an embodiment, there is provided a system, wherein the quantified pressure pattern provided by the pressure pattern module is determined by the pressure pattern module by means of: - a functional outcome index or PPG index based on the formula: maximum pressure change
- - - : - — +(l-threshold exceeding portion)
FOI = - , wherein:
2 maximum pressure change is defined said maximum pressure change associated with said subsection of the pressure pattern; pressure change is defined as said pressure change associated with the overall pressure pattern; and
- threshold exceeding portion is defined as said portion of the overall pressure pattern in which said rate of change is equal to or larger than said predetermined threshold.
According to an embodiment, there is provided a system, wherein
- said pressure pattern comprises a data series representing a fractional flow reserve pullback curve comprising pressure data entries associated with FFR values associated with a ratio of:
- a pressure measurement from a sensor moved longitudinally along at least the longitudinal section of the vessel; and
- a pressure measurement from a stationary pressure sensor; and wherein the quantified pressure pattern provided by the pressure pattern module is determined by the pressure pattern module by means of:
- a functional outcome index or PPG index based on the formula: (threshold exceeding portion))
FOI =
Figure imgf000012_0001
2 wherein: maximum A FFR is defined as the maximum difference between FFR values associated with the start and the end of said subsection of the pressure pattern;
- A FFR is defined as the difference between FFR values associated with the start and the end of said overall pressure pattern; and
- threshold exceeding portion is defined as said portion of the overall pressure pattern in which said rate of change in FFR is equal to or larger than said threshold.
According to an embodiment, there is provided a system, wherein:
- the threshold exceeding portion is determined as the ratio of: - respectively the length or duration of the part of the pressure pattern in which said rate of change is equal to or larger than said predetermined threshold, with respect to
- respectively the length or duration of the overall pressure pattern.
According to an embodiment, there is provided a system, wherein:
- said subsection of the pressure pattern is determined as a subsection of the pressure pattern with respectively a predetermined length or duration.
According to an embodiment, there is provided a system, wherein:
- said overall pressure pattern is determined as a pullback pressure pattern along at least a section of the vessel, preferably between the ostium and the distal end of the vessel.
According to a particular embodiment, where the vessel is a coronary artery, the predetermined length could for example be 20mm, and/or the predetermined duration could for example be 2s.
According to particular embodiments it is advantageous to quantify the patterns of coronary artery functional disease in a coronary vessel from a patient under hyperaemic conditions. Under such conditions there can be generated pressure values that represent an FFR pullback curve during a pullback operation. Determining such an FFR pullback curve is done by determining FFR values from measurements of the movable pressure sensor, also referred to as distal pressure or Pd, with respect to a stationary pressure sensor, also referred to as proximal or aortic pressure Pa, during the pullback time period. It is clear that for example the stationary pressure sensor is positioned at the ostium of the vessel and that the movable pressure sensor, during the pullback time period is moved between a more distal part of the vessel, for example the most distal part of the vessel, or a part of the vessel distal of a suspected stenosis, stricture or lesion, and the ostium of the vessel. When such measurements are for example performed under hyperaemic conditions in a coronary artery, then these values that are determined based on these measurements during the pullback time period, during which the movable sensor is moved along the vessel, are referred to as FFR values of an FFR pullback curve, and are typically determined as the ratio of Pd/Pa, wherein Pd and Pa could for example be determined from the measured pressure values after any suitable form of pre-processing such as for example by means of a moving mean or average function which is configured to filter out the rhythmic and/or periodical component of the heartbeat cycle. It is thus clear that according to a preferred embodiment FFR could for example be defined as the ratio of mean or average distal coronary pressure, which is the pressure measured by the movable pressure sensor, and the mean or average aortic pressure, which for example is the pressure measured by the stationary pressure sensor, measured during, preferably maximal, hyperaemia that is preferably achieved through administration of a potent vasodilator such as for example adenosine, ATP or papaverine either by IV infusion or by intracoronary bolus injection. Under such conditions of hyperemia, by rendering myocardial microvascular resistance constant and minimal, the impact of disease in the epicardial conduit artery on myocardial blood flow is more advantageously separated out.
According to such embodiments, the FOI can be referred to as a Pullback Pressure Gradient or PPG or PPG index. Such a PPG index quantifies the pressure pattern in a reliable and comprehensible way for an operator of the system. In this way the PPG index provides for a quantitative, reproducible value, in a range of 0 up to and including 1, which is representative of the pressure pattern along the section of the vessel, which is not subject to for example undesired intra-operator variations related to visual assessment of the pattern.
According to an embodiment, there is provided a system, wherein the system further comprises:
- a pre-treatment symptom module configured to quantify data of pre-treatment symptoms indicative of ischaemia;
- a pre-treatment overall pressure gradient module configured to quantify data associated with the overall pressure gradient between a pressure data entries associated with both end points of said at least a longitudinal section of the vessel,
- a vessel type determination module configured to determine an index associated with the vessel type.
According to an embodiment, there is provided a system, wherein the pattern processing module is further configured to determine the quantitative post-treatment symptom reduction index indicative of a predicted post-treatment ischaemia symptom reduction based on:
- the quantified pressure pattern provided by the pressure pattern module;
- the vessel type index;
- the quantified pre-treatment symptoms indicative of ischaemia; and/or
- the quantified overall pressure gradient.
In this way, based on these additional input parameters, a more reliable, precise and robust model can be generated. These parameters are typically readily available, for example at a time when an operator needs to make a timely or final judgement about how to proceed with treatment or diagnosis, and the overall pressure gradient can typically be determined or derived from the pressure data series for the pressure pattern.
For patients with coronary artery disease, the Seattle Angina Questionnaire or SAQ provides for an embodiment of the most commonly used quantitative measurement of patients' symptoms of angina and the extent to which these angina symptoms affect the functioning and quality of life of these patients. A SAQ or SAQ score, such as for example a SAQ-7 score or any other suitable SAQ score, is the outcome of a measurement by means of a validated questionnaire comprising three domains: physical limitation, angina frequency, and quality of life. The SAQ has a scale from 0 to 100, with higher values indicating better health status. In the angina frequency domain, an SAQ score of 100 indicates freedom from angina. A SAQ score is also representative of a SAQangina frequency, which relates to the frequency at which the subject experiences angina symptoms. SAQ angina frequency scale of 0-30 points indicates daily angina, 31-60 points indicate weekly angina, 61-99 points indicate monthly angina, and 100 points indicate no angina. When referring to pre-treatment SAQ or pretreatment SAQangina frequency, this refers to measurements of SAQor SAQangina frequency from a subject before the treatment to relieve the subject from the symptoms of ischaemia, such as angina pectoris. When the treatment is PCI, there could be made reference to for example pre-PCI SAQ or pre-PCI SAQ angina frequency.
According to an embodiment, there is provided a system wherein:
- the quantified pre-treatment symptoms indicative of ischaemia comprises or consists of one or more of the following: - Pre-treatment Seattle Angina Questionnaire score or SAQ;
- Pre-treatment SAQ angina frequency,
According to an embodiment, there is provided a system wherein:
- the vessel type index comprises or consists of one or more of the following:
- a LAD index indicating whether the vessel is a left anterior descending artery or not;
- a vessel type index representative of a specific coronary artery vessel or branch, such as for example one or more of the following:
- Left coronary artery;
- Left anterior descending artery;
- Left circumflex artery;
- Posterior descending artery;
- Ramus or intermediate artery;
- Right coronary artery;
- Right marginal artery;
- Posterior descending artery.
In this way a more reliable prediction is obtained, for example taking into account whether the vessel type is LAD or not. The LAD is the largest vessel of the heart, and such an embodiment thus also allows for a more reliable and standardized approach.
According to an embodiment, there is provided a system wherein:
- the quantified overall pressure gradient comprises or consists of one or more of the following:
- - a pressure ratio value associated with a ratio of:
- a pressure measurement from a sensor at one end of said at least a longitudinal section of the vessel; and
- a pressure measurement from a pressure sensor at another end of said at least a longitudinal section of the vessel;
- a hyperaemic pressure ratio value of said at least a longitudinal section of the vessel;
- a Fractional Flow Reserve or FFR value of said at least a longitudinal section of the vessel; - a non-hyperaemic pressure ratio value of said at least a longitudinal section of the vessel;
- an instantaneous wave-free ratio value or Instant Flow Reserve value or i FR value of said at least a longitudinal section of the vessel.
According to an embodiment, there is provided a system, wherein the pattern processing module is further configured to determine the quantitative post-treatment symptom reduction index indicative of a predicted post-treatment ischaemia symptom reduction based on the quantified pressure pattern provided by the pressure pattern module and one or more of the following:
- the vessel type index;
- the quantified pre-treatment symptoms indicative of ischaemia; and/or
- the quantified overall pressure gradient.
According to an embodiment, there is provided a system, wherein the pattern processing module is further configured to determine the quantitative post-treatment symptom reduction index by means of a model based on the quantified pressure pattern provided by the pressure pattern module and one or more of the following:
- the vessel type index;
- the quantified pre-treatment symptoms indicative of ischaemia; and/or
- the quantified overall pressure gradient, wherein optionally the model comprises or consists of a regression model comprising one or more additional quantitative parameters as further independent variables.
According to an embodiment, there is provided a system, wherein the model comprises or consists of a binomial regression model with the following equation:
(quantitative post-treatment symptom reduction index) = po + pi. (quantified pressure pattern) + 2. (vessel type index)+ 3. (quantified overall pressure gradient), wherein o, pi, P2, P3 are parameters, wherein optionally po, pi, P2, P3 are constants.
According to an embodiment, there is provided a system, wherein the model comprises or consists of the following equation:
(probability of post-PCI SAQ=100) = |30 + pi. (PPG index) + 2.(LAD index)+ |33.(pre-PCI FFR), wherein optionally O=O.86 +/- 10%, pi=3.82 +/- 10%, P2=-O.35 +/- 10%, 3=- 5.36 +/-10%.
According to an embodiment, there is provided a system wherein the data processing system comprises or is coupled to a display configured to output the quantitative post-treatment symptom reduction index determined by the pattern processing module.
According to a second aspect there is provided a computer-implemented method for operating the data processing system according to the first aspect, wherein the method comprises the steps of:
- the pressure pattern module quantifying the data of a pressure pattern along at least a section of a vessel of a subject with symptoms indicative of ischaemia; and
- the pattern processing module determining a quantitative post-treatment symptom reduction index indicative of a predicted post-treatment ischaemia symptom reduction based on:
- the quantified pressure pattern provided by the pressure pattern module.
According to a third aspect, there is provided a computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method of the second aspect.
According to a fourth aspect, there is provided a computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out the method of the second aspect.
It is clear that further alternative embodiments and/or combinations are possible, in particular further embodiments of the second, third or fourth aspect similar to the embodiments of the first aspect. FIGURES
Exemplary embodiments will now be described with reference to the following drawings, in which:
Figure 1 schematically shows an embodiment of a data processing system for determining a quantitative post-treatment symptom reduction index;
Figure 2 schematically shows an embodiment of a pressure data series comprising data entries of absolute pressure values received by a data processing system similar as shown in Figures 1;
Figure 3 schematically shows an embodiment of a quantified pressure pattern derived from the pressure data series of Figure 2;
Figure 4 schematically shows an embodiment of a regression model for use by the embodiment of the data processing system of Figure 1;
DESCRIPTION
Figure 1 schematically shows an exemplary embodiment of a data processing system 100. The data processing system 100 could comprise any suitable general purpose computing system and/or any suitable application specific computing system, more particularly any suitable computing system part of any suitable medical analysis and/or monitoring equipment, ... . According to the embodiment shown the data processing system 100 comprises a pressure pattern module 120 and a pattern processing module 130. The pressure pattern module 120 operates to quantify data of a pressure pattern 112 along at least a section of a vessel 10 of a subject 2 with symptoms indicative of ischaemia. The pattern processing module 130 operates to determine a quantitative post-treatment symptom reduction index 132, which is indicative of a predicted post-treatment ischaemia symptom reduction based. As shown, according to the embodiment, the pattern processing module 130 determines this quantitative posttreatment symptom reduction index 132 based on the quantified pressure pattern 122 provided by the pressure pattern module 120.
According to the embodiment shown in Figure 1 the vessel 10 of the subject 2 is a coronary artery 10. However, it is clear that, according to alternative embodiments, the system 100 might also be useful for any other suitable vessels 10, such as for example any other blood vessel, such as arteries, vanes, etc., vessels from the lymphatic system, ... . According to the embodiment of Figure 1, the pressure pattern module 120 thus operates to quantify the data of the pressure pattern 112 along at least a section 12 of a coronary artery 10 of a subject 2 with symptoms indicative of myocardial ischaemia. According to this embodiment the pattern processing module 130 thus determines a quantitative post-treatment symptom reduction index 132 indicative of a predicted post-treatment myocardial ischaemia symptom reduction.
According to the embodiment shown in Figure 1, the treatment under evaluation by the system is for example a percutaneous coronary intervention or PCI. It is clear that alternative embodiments are available for any other suitable treatment of myocardial ischaemia symptoms, such as for example coronary artery bypass grafting or CABG, treatment by medication, or any other suitable treatment or combination of treatment by medication, nondrug therapy, surgery, non-surgical treatment, ... . According to the embodiment shown, where the treatment under evaluation by the system 100 is PCI the predicted post-treatment myocardial ischaemia symptom reduction 132 as determined by the pattern processing module 130, thus is a suitable quantitative parameter or index indicative of a predicted postPCI myocardial ischaemia symptom reduction 132.
More specifically according to the embodiment of Figure 1, the pressure pattern module 120 thus operates to quantify the data of the pressure pattern 112 along at least a section 12 of a coronary artery 10 of a subject 2 with angina pectoris, which is a specific and reliable symptom indicative of myocardial ischaemia. According to this embodiment the pattern processing module 130 thus determines a quantitative post-treatment symptom reduction index 132 indicative of a predicted post-treatment angina pectoris reduction, preferably a predicted post-PCI angina pectoris reduction. Angina pectoris is a central component of the burden of coronary artery disease and therefor and it is thus advantageous to improve the reliability and efficiency of an assessment of the effect of a treatment on angina pectoris and more specifically determining the effect of a PCI treatment on angina pectoris by means of a predicted post-PCI angina pectoris reduction. It is clear that alternative embodiments are possible related to other treatments than PCI or other symptoms of coronary artery disease than angina pectoris. According to the embodiment shown in Figure 1, and as described in further detail below, the data of the pressure pattern 112 provided to the pressure pattern module 120, comprises a pressure data series 114 comprising a plurality of pressure data entries 116 associated with intermittent pressure measurements along at least a longitudinal section 12 of the vessel 10. It is clear that the pressure data series 114 comprises preferably a sufficient amount of pressure data entries 116 to reliably discern and assess any patterns in the pressure data entries 116 along the longitudinal section 12 of the vessel 10.
According to a preferred embodiment the longitudinal section 12 of the vessel corresponds to a section corresponding to or substantially corresponding to the section between the ostium and the distal end of the vessel 10, however it is clear that alternative embodiments are possible in which a pressure data series 114 associated with any suitable longitudinal section 12 of the vessel 10 is used, preferably a longitudinal section 12 of the vessel 10 potentially comprising diseased subsections, such as for example schematically illustrated by means of a suspected lesion or stricture 14 in the vessel 10 of the subject 2. As shown, according to an embodiment in which the data processing system 100 is receiving a pressure data series 114 with pressure data entries 116 resulting from pressure measurements that are associated with a potential blockage or restriction of the flow in the vessel 10, such as for example the blood flow in a coronary vessel 10, or according to alternative embodiments the flow of any other fluid in any other suitable vessel. According to the embodiment shown, the pressure data series 116 is associated with measurements performed during what is referred to as a pullback operation. At the start of the pullback operation, for example at a time referred to as tl, a pressure sensor 22 is positioned, downstream or distal of a suspected stenosis 14 or stricture in the vessel 10. According to such an embodiment, the pressure measured by the pressure sensor 22 is typically also referred to as the distal pressure or Pd. According to the embodiment shown, the pullback operation is then started, by moving the pressure sensor 22, for example by means of a guidewire 28, upstream along the section 12 of the vessel 10, thereby proceeding to the state schematically illustrated in Figure 1, and until the suspected stenosis 14 has been transgressed and the pullback operation is ended at the downstream end of the section 12 of the vessel 10.
According to the embodiment shown in Figure 1, there is generated a pressure data series 114 comprising a time-based pressure data series. According to such an embodiment the pressure data series 114 comprises a series of intermittent pressure data entries 116 of values associated with pressure measurements from a sensor 22 moved longitudinally along at least the longitudinal section 12 of the vessel 10. In such a time-based pressure data series, the pressure measurements of the movable sensor 22 for the pressure data entries 116 of the pressure data series 114 are for example taken at regular intermittent time intervals, when the sensor 22 is moved along the section 12 of the vessel 10.
It is however clear that alternative embodiments are possible, in which for example the pressure data series 114 comprises a position-based pressure data series, the data entries 116 correspond to pressure measurements of a sensor 12 at intermittent locations along the section 12 of the vessel 10.
Preferably, according to the embodiment of Figure 1, the pressure data series 114 further also comprises pressure data entries 116 with additional values associated with pressure measurements from a stationary sensor 24.
According to such an embodiment, the pressure data entries 116 of the pressure data series 114 could be associated with one or more absolute pressure values, for example corresponding to the pressure measurements of one or more intracoronary pressure sensors 22, 24.
According to the embodiment of Figure 1, there is for example provided a measurement system 30 configured to receive data entries 116 and generate a dataset 32 comprising pressure values measured during a pullback time operation, which is for example performed during a pullback time period. According to such an embodiment, the pullback time period corresponds to the time period during which a set of pressure values along the length of the vessel 10 is measured by means of a movement of a pressure sensor 22 along a vessel 10. According to the embodiment shown in Figure 1 the pressure sensor 22 is moved along the vessel 10 in the direction indicated with the arrow, which is typically in a direction against the direction of the flow of fluid in the vessel 10, such as for example in a direction against the direction of the blood flow in a coronary artery, however it is clear that alternative embodiments are possible in which the direction of movement has a different relationship with direction of the flow of the fluid in the vessel.
According to the embodiment of Figure 1, the pressure sensors 22, 24 are for example part of a catheter 20, which, after being inserted into the vessel 10, is able to determine the pressure of the fluid flow in the vessel at the location of the respective pressure sensor 22, 24. According to such an embodiment the catheter 20 for example comprises a sheath 21 and a guidewire 28. The guidewire 28 comprises a tip to which a pressure sensor 22 is mounted for measuring the pressure of the fluid in the vessel 10, in this embodiment the pressure in the blood. As known to a person skilled in the art, at the start of a pullback operation for the generation of a data series 114 of a pressure pattern 112, the tip of the guidewire 28 with pressure sensor 22 is positioned at a predetermined distance from the sheath 21 of the catheter 20, for example at a most distal end of the vessel 10, such as for example the most distal end of a coronary artery, or a location distal of a suspected stenosis. Subsequently this distance is reduced during the pullback operation by pulling the guidewire 28 in a direction indicated with the arrow, so that the tip of the guidewire 28, moves closer to the tip of the sheath 21 of the catheter 20. Such a movement of the guidewire 28 and the pressure sensor 22 mounted at its tip is caused by an operation that can be referred to as pulling the guidewire 28 through the sheath 21 of the catheter 20, as is well known to a person skilled in the art. During such a pullback operation, as shown in Figures 1, it is clear that the pressure sensor 22 is configured to move along a predetermined section 12 or part 12 of the vessel 10. This part 12 of the vessel 10, which is associated with the pullback operation, and thus also with the pressure pattern 112 generated by means of such a pullback operation, is determined by the position of the pressure sensor 22 at the start of the pullback operation, and by the position of the pressure sensor 22 at the end of the pullback operation. It is clear that, according to the embodiment of Figure 1, in which a time-based pressure data series 114 is generated, there is no need for any precise positional reference, with respect to this start and end position of the section 12 of the vessel 10 along which the pressure sensor 22 travels during the pullback operation. According to such an embodiment, all that is required for the measurement system 30 to generate the pressure data series 114 of a pressure pattern 112 is an indication of the start time and the end time of the pullback operation, as schematically shown by means of the clock element in measurement system 30. It is clear that alternative embodiments are possible in which a position-based pressure data series 114 is generated by measurement system in data indicative for the position along the section 12 of the vessel 10 is stored or derivable from the pressure data series 114 for the pressure pattern 112 as provided to the data processing system 100.
According to the embodiment shown, shown, according to an embodiment in which the data processing system 30 enables measurements of pressure patterns along the length of the vessel that are associated with a potential blockage or restriction of the flow in the vessel, such as for example the blood flow in a coronary vessel, or the flow of any other fluid in any other suitable vessel. The pullback operation may start in such a way that the pressure sensor 22 is positioned, downstream or distal of a suspected stenosis 14 or stricture in the vessel 10. The pressure measured by the pressure sensor 22 is typically also referred to as the distal pressure or Pd. According to the embodiment shown, the pullback operation after being started is continued by moving the pressure sensor 22 along the direction indicated by the arrow in Figure 1, for example by means of the guidewire 28, upstream along the vessel 10, and subsequently further continued until the suspected stenosis 14 has been transgressed and the pullback operation is ended, or the tip of the sheath 21 of the catheter, or the ostium of the vessel 10 is reached.
According to the embodiment of Figure 1, the pressure sensor 24 remains stationary during the pullback operation and is configured to measure the pressure of the fluid flow upstream of the section 12 of the vessel 10 along which the pressure sensor 24 moves during the pullback operation. Or in other words, this pressure sensor 24 is configured to measure the pressure in the vessel 10 upstream of or proximal to a suspected stenosis 14 or stricture in the vessel 10. The pressure measured by the pressure sensor 24 is typically also referred to as the proximal pressure or aortic pressure Pa. According to the embodiment shown, the pressure measurements of both sensors 22, 24 generate pressure data entries 116, which are for example stored in a dataset 32 of the measurement system 30 and subsequently provided as a pressure data series 114 to the data processing system 100, the pressure data series being representative of a pressure pattern 112 along the section 12 of the vessel 10 of the pullback operation. According to the embodiment shown, the pressure data entries 116 could for example be absolute pressure measurements, such as for example resulting from a measurement of sensor 22, also referred to as Pd as mentioned above, of 33 mm Hg and for example a further absolute pressure measurement of sensor 24, also referred to as Pa as mentioned above, of 69 mm Hg.
According to the embodiment shown, the pressure data series 114 is provided by the measurement system 30 to the pressure data module 110 of the data processing system, could thus comprise a series of pressure data entries 126 representing values of such a absolute pressure measurements. Alternative embodiments are possible however in which the pressure data series 114 is provided to the data processing 100 in any other suitable way, for example directly from the sensors, or in which the measurement system 30 is integrated into or part of the data processing system, or in which the pressure data series 114 is determined, generated or provided from any other suitable storage system or invasive or non-invasive measurement system for generating such a pressure data series 114 associated with a section 12 of a vessel 10.
It is further clear that instead of, or in addition to, pressure data entries 116 with absolute pressure values, according to alternative embodiments, there could be provided a time-based or location-based pressure data series 114 to the pressure data module 110, or alternatively the pressure data module 110 could determine from pressure data entries 116 with absolute pressure values, a pressure pattern 112 comprising based on a pressure data series 114 with a series of pressure data entries of relative pressure values, such as for example series of pressure ratio values. According to a particular embodiment such a pressure data entry could for example correspond to a pressure ratio value, typically referred to as Pd/Pa, which is associated with a ratio of the pressure measurement Pd from the sensor 22 moved longitudinally along the longitudinal section 12 of the vessel 10, and the pressure measurement from the stationary pressure sensor 24.
According to a preferred embodiment the data processing system 100 comprises a pressure data module 110 configured to perform any such operations as necessary or desirable on the received pressure data series 114 and the series of pressure data entries 116, for converting the pressure data entries 116 and/or calculating additional pressure data entries 116 to a pressure data series referred to as the pressure pattern, which is suitable for processing by the pressure pattern module 120.
According to such embodiments, the pressure data module 110 could for example be configured to receive a pressure data series 114, or convert a received pressure data series 114, such that it comprises a series of data entries 116 representing a Fractional Flow Reserve or FFR value, or alternatively a non-hyperaemic pressure ratio such as for example an instantaneous wave-free ratio value, an Instant Flow Reserve or iFR value. According to alternative embodiment the series of data entries 116 could for example comprise values representing similar hyperaemic or non-hyperaemic pressure ratios.
It is clear that in the context of this description when referring to an FFR value in association with pressure data entries 116 of a pressure data series 114, this refers to a series of FFR values along the section 12 of the vessel 10, also referred to as an FFR curve or FFR pullback curve. Such an embodiment is especially useful for quantifying the patterns of coronary artery functional disease in a coronary vessel from a patient under hyperaemic conditions. Under such conditions there can be generated pressure values that represent an FFR pullback curve during a pullback operation. Determining such an FFR pullback curve is for example done by determining FFR values from measurements of the movable pressure sensor 22, also referred to as distal pressure or Pd, with respect to a stationary pressure sensor 24, also referred to as proximal or aortic pressure Pa, over the distance travelled or over the time period of the pullback operation. As referred to above, the stationary pressure sensor 24 is for example positioned at the ostium of the vessel and that the movable pressure sensor 22, during the pullback operation is moved along a longitudinal section 12 of the vessel 10, for example between a more distal part of the vessel 10, for example the most distal part of the vessel 10, or a part of the vessel 10 distal of a suspected stenosis 14, stricture or lesion, and the ostium of the vessel 10. When such measurements are for example performed under hyperaemic conditions in a coronary artery, then the pressure data entries 116 of the pressure data series 114 derived from these pressure values, are referred to as FFR values of a FFR pullback curve and are typically determined as the ratio of Pd/Pa under such hyperaemic conditions. Preferably, according such an embodiment the FFR values result from pressure measurements performed under maximal, hyperaemia. Such maximal, hyperaemia could preferably achieved through administration of a potent vasodilator such as for example adenosine, ATP or papaverine either by IV infusion or by intracoronary (IC) bolus injection. Such measurement conditions of hyperaemia are advantageous, as these conditions ensure that myocardial microvascular resistance is rendered constant and minimal, which allows to more advantageously separate out the impact on myocardial blood flow of disease, such as for example a lesion or stricture in the vessel, such as for example in an epicardial conduit artery.
According to a preferred embodiment, the pressure data module 110 of the data processing system 100 could for example operate in such a way that it subjects the pressure data series 114 provided by the measurement system 30 to any suitable form of pre-processing such as for example by means of a moving mean or average function. According to such advantageous embodiments, the pre-processing of the pressure data module 110 could for example be configured to filter out the rhythmic and/or periodical component of the heartbeat cycle, or to filter out any noise or disturbances in the measurement signal. According to such an embodiment, the pressure pattern 112 derived from the pressure data series 114 by the pressure data module 110 and provided to the pressure pattern module 120, could according to a preferred embodiment comprise or consist of a pressure data series 114 of or a pressure data series 114 for determining an FFR pullback curve, which has been subjected to such a suitable form of pre-processing by the pressure data module 110.
According to a particular example when the pressure Pd measured by sensor 22 is about 33mmHg and at that that same point in time during the pullback operation, the pressure Pa measured by sensor 24 is Pa=69mmHg, then the ratio of Pd/Pa = 33/69 = 0,48. When these measurements were performed under hyperaemic conditions and preferably after being submitted to any suitable form of pre-processing, then the FFR value of the FFR pullback curve at that point in time would also be equal to 0,48. It is clear that still further embodiments are possible in which for example such a ratio is determined from measurements performed while the subject was not being subjected to hyperaemic conditions, in which by means of a suitable form of pre-processing, which for example determines and selects the most suitable point in time during a heartbeat cycle for the selection the measurement value for determining a ratio, which is referred to as a non-hyperaemic pressure ratio, such as for example the instantaneous wave-free ratio or the Instant Flow Reserve value or the iFR. It is clear that still further embodiments are possible in which any suitable absolute, relative and/or ratio of pressure values are measured or derived for providing suitable pressure data entries 116 for a suitable series of pressure data 114 for a pressure pattern 112 associated with a section 12 of a vessel 10. It is clear that still further alternative embodiments are possible in which such pressure ratio values are any other suitable hyperaemic pressure ratio values or non-hyperaemic pressure ratio values.
According to still further embodiments, the pressure pattern 112 provided to the pressure pattern module 120 could alternatively or additionally comprise pressure data entries comprising a rate of change of absolute pressure values, a rate of change of relative pressure values, a rate of change of pressure ratios, a rate of change of Fractional Flow Reserve values, a rate of change of non-hyperaemic pressure ratio values, a rate of change of an instantaneous wave-free ratio values or Instant Flow Reserve values or iFR values, ... .
According to a particular embodiment, the time-reference comprises an indication of the time during the pullback time period during which a pullback operation is performed with pressure measurements along the section 12 of the vessel 10. As schematically shown in Figure 1, this could for example be graphically represented as a graph of the pressure data series 114 representative of a pressure pattern 112. As schematically shown, the x-axis for example provides an indication of the number of seconds that have elapsed since a starting point for the data series 114 generated during the pullback operation during the pullback time period, or in other words the overall pressure pattern 112. As further schematically shown, the pressure data entries 116 of the data series 114 could for example be represented as data points on the y-axis along the time-reference of the x-axis. The start and end of the pullback operation, and which could for example be indicated by means of suitable markers, or other suitable inputs to indicate, receive and/or store the start and end time of the pullback operation. It is however clear that alternative embodiments are possible in which the pressure data series 114 is for example time-referenced to for example the starting time of the pullback operation, or in any other suitable way. According to still further alternative embodiments, the time-reference for the time-referenced pressure data of the dataset 32 could also be determined and/or derived from an indication of a measurement frequency and/or a measurement interval of the pressure data entries 116 of the pressure data series 114. When for example it is known that the pressure data entries 116 correspond to measurements with a measurement frequency of 500Hz or any other suitable measurement frequency, then, the time-reference of the pressure data series 114 which comprises a set of consecutive intermittent measurements at this measurement frequency can be derived from that measurement frequency. In other words, each pressure data entry 116 in the pressure data series 114 is then for example spaced 2ms apart, or any other suitable time period derived from the measurement frequency. It is however clear that still further alternative embodiments are possible. It is further clear that similarly a location-based data series 114 could be provided with a suitable location-based reference for the data entries 116 of the data series 114, such that the pressure data entries 116 can be referenced to their location along the section 12 of the vessel 10 of the pressure pattern 112.
When referring to a rate of change above, it is clear that this refers to the gradient or slope between for example two pressure measurements or ratios and their corresponding pressure data points 116 of the pressure data series 114, with respect to the time-reference or locationreference, such as for example a rate of change per second, or any other suitable time reference, or per mm or any other suitable location reference of the pressure data points 116 of the pressure data series 114.
Figure 2 schematically shows an exemplary embodiment of a pressure data series 114 comprising data entries 116 of absolute pressure values received by a data processing system 100 similar as shown in Figures 1. Figure 3 schematically shows an exemplary embodiment of a quantified pressure pattern 122 derived from the pressure data series 114 of Figure 2. As shown in the example of Figure 3 As for example shown in the embodiments of Figure 3 the data processing system 100, has calculated a quantified pressure pattern 122 by means of the pressure pattern module 120 in the form of a suitable Functional Outcome Index, or FOI, which can for example be referred to as an embodiment of a Pullback Pressure Gradient or PPG or PPG index. Such a FOI or PPG index 122 quantifies the pressure pattern 112 in a reliable and comprehensible way and preferably provides a quantitative, reproducible numerical value, for example in a range of 0 up to and including 1, which is representative of the pressure pattern 112 along the section 12 of the vessel 10. It is clear that such an embodiment is advantageous as such a quantification of the pressure pattern 112 is not subject to for example undesired intra-operator variations or bias resulting from visual assessment of the pressure pattern 112.
According to the particular embodiment shown in Figure 3, the quantified pressure pattern 112, is for example calculated by the pressure pattern module 120 in such a way, that the pressure pattern module 120 outputs a numerical value of the FOI, such this numerical value quantifies the pattern of the pressure pattern 112 determined from the pressure data series 114. It is clear that when referring to the pressure pattern 112, this refers to the specific shape and/or evolution of the pressure pattern 112 along the section 12 of the vessel 10, and more particularly the shape and/or evolution of the pressure pattern 112 at a multitude of intermediate points in between the endpoints along of the section 12 of the vessel 10. According to the example shown, the quantified pressure pattern 112, which is calculated by the pressure pattern module 120 as a FOI, such as for example a PPG Index, and is a numerical value, which is preferably representative of at least one of the following functional patterns of coronary artery disease: the functional pattern of a focal coronary artery disease when the value is higher than 0.7; the functional pattern of a diffuse coronary artery disease when the value is lower than 0.4; and/or functional pattern of a mixed coronary artery disease when the value is between 0.4 and 0.7.
It is clear that, preferably, such an embodiment of the quantified pressure pattern 112 quantifies the pressure pattern 112 on a continuous scale, of which the values are not limited to the three general categories of pressure patterns above, but provide a unique or more unique identifier for specific shape of the pressure pattern 112. According to the embodiment shown in Figures 1 to 3, the FOI is for example calculated to be 0.86 based on a formula described below and the exemplary pressure pattern 112 derived from the pressure data series 114 shown in Figures 2 and 3, based on pressure data entries 116 derived from measurements from a measurement system 30 during a pullback operation along a section 12 of a coronary artery vessel 10. According to the embodiment shown in Figures 2 and 3, there is shown a pressure pattern 112 corresponding to a pullback operation during a time period starting at tl and ending at t3. This pressure pattern 112, associated with the overall section 12 of the vessel 10, will also be referred to as the overall pressure pattern 40, and is associated with the time period between tl and t3, or alternatively is associated with the length of the section 12 of the vessel As further shown in the embodiment of Figure 3, there is indicated a subsection of the pressure pattern 42, which is determined as a subsection 42 of the overall pressure pattern 40 with respectively a predetermined length or duration. This predetermined length or duration could for example be a predetermined absolute value, such as for example 20mm or 2 s, or any suitable alternative value, or a relative value, such as for example respectively 20% of the time period associated with the overall pressure pattern 40, or the length of the section 12 of the vessel 10. It is clear that alternative embodiments are possible with suitable values for determining the subsection 42 of the overall pressure pattern 40, or in other words the subsection 42 of the pressure pattern 112. 7. The predetermined subsection 42 of the pressure pattern 112 could for example be in the range of 2% up to and including 80%, preferably in the range of 5% up to and including 50% of the overall pressure pattern 40, preferably in the range of 10% up to and including 40%, for example in the range of 15% up to and including 30%.
As further indicated in the embodiment of Figure 3, there are provided indications for calculating a threshold exceeding portion. According to this embodiment, the threshold exceeding portion is defined as the ratio of respectively the length or duration of the part indicated with reference 64, with respect to respectively the length or duration of the overall pressure pattern 40. The part 64, refers to a part 64 of the overall pressure pattern 40 in which a rate of change 60 of the pressure pattern 112 is equal to or larger than a predetermined threshold 62. It is clear that this rate of change 60 of the pressure pattern 112, refers to the rate of change of the pressure pattern 112 in function of time during a pullback operation along the section 12 of the vessel, or in function of position along the section 12 of the vessel 10. According to this particular example, such a rate of change could for example be symbolised by means of d(Pd/PA)/dt, dFFR/dt, ... , A(Pd/Pa)/At, A(FFR)/At, ..., d(Pd/PA)/ds, dFFR/ds, ... , A(Pd/Pa)/As, A(FFR)/As, ... and could thus be referred to as a differential, gradient, gradient of instantaneous Pd/Pa, FFR, ... data entries per unit time or displacement. According to the embodiment shown in Figure 3, the pressure pattern 112 comprises a data series representing a fractional flow reserve (FFR) pullback curve comprising pressure data entries associated with FFR values, which could also be referred to as instantaneous FFR values. According to such an embodiment, the FFR values are for example associated with a ratio of a pressure measurement Pd from the sensor 22 moved longitudinally along the longitudinal section 12 of the vessel 10; and a pressure measurement Pa from a stationary pressure sensor 24, or in other words Pd/Pa as shown on the left y-axis of Figure 3, whereby these values represent FFR values 50 when these are related to measurements performed under ischaemic conditions. It is clear that the pressure pattern 112, or in other words the overall pressure pattern 40 refers to the shape of the curve with FFR values associated with section 12 of the vessel 10, or in other words the shape of this curve between tl and t3 in Figure 3. According to this embodiment, when this pressure pattern 112 is provided to the pressure pattern module 120, the pressure pattern module will for example determine the quantified pressure pattern 122 by means of the following formula for the functional outcome index FOl, which can also be referred to as the PPG Index:
FOl maximum A FFR (subsection of the pressure pattern)
+ (1 — (threshold exceeding portion))
A FFR (overall pressure pattern)
2 wherein: maximum A FFR (subsection of the pressure pattern) is defined as the maximum difference between FFR values 50 associated with the start and the end of the subsection 42 of the pressure pattern 112, or in other words the subsection 42 of the overall pressure pattern 40 of which the difference between FFR values 50 at the start and end of this subsection 42 is maximum;
- A FFR (overall pressure pattern) is defined as the difference between FFR values 50 associated with the start and the end of said overall pressure pattern 40; and
- threshold exceeding portion is defined as the portion 64 of the overall pressure pattern 40 in which the rate of change 60 in FFR 50 is equal to or larger than the predetermined threshold 62.
According to this particular embodiment the predetermined threshold 62 is set to a suitable value, such as for example a rate of change in the range of at least 0.0005 per second, or per mm, or per l/100th of the length or duration of the overall pullback pressure pattern 40, preferably in the range of at least 0.0010, for example 0.0015, or any other suitable threshold that is indicative of a relevant rate of change in the pressure pattern 112 that is representative of a particular pattern of stenosis, stricture, lesion, ... in the vessel 10. According to some embodiments the threshold 62 for the rate of change could for example be defined as a rate of change of the pressure pattern 112 in the range of at least 0.05% of a maximum value of the pressure pattern with respect to a unit time or unit distance, preferably in the range of at least 0.10%, for example equal to 0.15%, wherein the unit time is for example one second or l/100th of the time period associated with the overall pullback pressure pattern 40, or wherein the unit displacement is for example one mm or l/100th of the length of the section 12 of the vessel 10 associated with the overall pullback pressure pattern 40. For the FFR pressure pattern 112 shown in Figure 3, where, the maximum value for FFR is 1, so the threshold for the rate of change of the FFR pressure pattern 112, or in other words for example dFFR/dt or dFFR/ds, could for example be 0.15%/mm or 0.0015/mm, or 0.15%/s or 0.0015/s, ... It is clear that still further alternative embodiments are possible.
It is clear that, according to alternative embodiments, instead of a FOI based on a pressure pattern 112 making use of an FFR pressure data series 114 and gradients thereof, there could be made use of alternative embodiments for the pressure pattern, and a FOI could be determined based on any suitable FOI based on a similar processing of any of the above mentioned embodiments for the pressure data entries 116 and/or the pressure data series, 114. According to such alternative embodiments the quantified pressure pattern 122 could be determined by the pressure pattern module 123 by means of a FOI based on the formula following formula:
Figure imgf000033_0001
wherein: maximum pressure change (subsection of the pressure pattern) is defined the maximum pressure change 26, 56 associated with said subsection of the pressure pattern 42; pressure change (overall pressure pattern) is defined as said pressure change associated with the overall pressure pattern (40); and
- threshold exceeding portion is defined as said portion (64) of the overall pressure pattern (40) in which said rate of change (60) is equal to or larger than said predetermined threshold (62).
According to such embodiments, as shown in Figures 3, the maximum pressure change (subsection of the pressure pattern) could for example be similar as described above, the maximum pressure change 56 for a suitable subsection 42 of the overall pattern 40, but for example in which the Pd/Pa values of the pressure pattern 112 were not measured under ischaemic conditions, or where any other suitable ratio of pressure values is made use of. According to still a further alternative embodiment, such as for example shown in Figure 2, the maximum pressure change (subsection of the pressure pattern) , could for example be embodied as the maximum change in Pd for a suitable subsection 42 of an overall pressure pattern 112, which is then for example embodied as the pressure data series 114 represented as Pd.
Still further alternative embodiments are possible, in which preferably the quantified pressure pattern 122 provided by the pressure pattern module 120 is determined by the pressure pattern module 120 by means of the combination of the following two components: First a contribution of a maximum pressure change 56 associated with a predetermined subsection 42 of the pressure pattern 112 with respect to a pressure change associated with the overall pressure pattern 40. And secondly a portion 64 of the overall pressure pattern 40 in which the rate of change 60 is equal to or larger than a predetermined threshold 62.
It is clear that, according to the embodiment shown in Figures 2 and 3, the pressure pattern 112 of Figure 3, in the form of a ratio of the Pd/Pa is derived as ratio of the pressure data entries 116 the two pressure data series 114 of Pd and Pa as shown in Figure 2. As shown, according to this embodiment the Pd/Pa or FFR values of the pressure pattern of Figure 3, which could for example be referred to as an FFR pullback curve, are typically determined as the ratio of Pd/Pa, wherein Pd and Pa could for example be determined from the measured pressure values by means of the measurement system 30, preferably after being subjected to any suitable form of pre-processing such as for example by means of a moving mean or average function which is configured to filter out the rhythmic and/or periodical component of the heartbeat cycle. This pre-processing could for example be performed by the measurement system 30 or the pressure data module 110 of the data processing system. According to the embodiment of Figure 2, the pressure measurements of the pressure sensor 22 are provided as pressure data entries 116 of a pressure data series 114 to the pressure data module 110, which processes it by calculated from a pressure data series 114 represented as a moving mean pressure data series 22m, which is for exampled calculated from the pressure data series 114 labelled based on the measurements of the pressure sensor 22. Preferably this pre-processing of the pressure data module 110 is configured to reduce the variation in the pressure signal caused by the different phases of a heartbeat cycle. As further shown, according to the embodiment of Figure 2, similarly also the pressure data series 114 with pressure data entries 116 from measurements of the pressure sensor 24 are processed by the pressure data module 110 such that a moving mean pressure data series 24m. It is clear that alternative embodiments are possible in which any other suitable type of pre-processing of the pressure data series by the pressure data module 110 and/or the measurement system 30 is possible, such as for example making use of one or more of the following signal processing operations a moving or rolling mean, average, variation, minimum, maximum, ... . According to further alternative embodiments, no pre-processing of the pressure data series 114 could be desired. This pre-processing of the pressure measurements, according to the embodiment shown in Figure 4, could for example be performed by the data processing system 30.
As shown in Figure 1, the quantified pressure pattern 122, which is for example an embodiment of the FOI, or any other suitable embodiment of a quantitative parameter derived from the pressure pattern 112, a quantitative parameter which is not only determined by the endpoints of the pressure pattern, but also by the intermediate values between these endpoints, or in other words the specific shape of the pressure pattern 122, is determined by the pressure pattern module 120 and provided to the pattern processing module 130. According to an exemplary embodiment, the pressure pattern module 120 for example provides a FOI, such as for example a PPG Index calculated from a specific pressure pattern 120 to the pattern processing module 130. As already mentioned above, the pattern processing module 130, according to an embodiment, then determines a quantitative posttreatment symptom reduction index 132 based on this quantified pressure pattern 122. Although, as will be described in further detail below, according to the embodiment of Figure 1, the pattern processing module 120 could optionally make use of further input parameters for determining the quantitative post-treatment symptom reduction index 132, it is clear that according to a particular embodiment the pattern processing module 130 only makes use of the quantified pressure pattern 122 for determining the quantitative post-treatment symptom reduction index 132.
According to the embodiment shown, the pattern processing module 130, makes use of a suitable model 134, such as for example a regression model, for calculating the quantitative post-treatment symptom reduction index 132 from the quantified pressure pattern 122. This model 134, can for example be determined from data related to prior treatments of subjects with symptoms indicative of ischaemia or similar medical research or experiments. This data could for example comprise data related to the pressure pattern 112 of subjects and data of these subjects related to the quantitative post-treatment symptom reduction index 132. According to an embodiment shown in Figure 4, there was processed data from a group of subjects which comprised, data related to the pressure pattern 112 of these subjects in the form of a quantified pressure pattern 122 embodied as a PPG Index similar as described above. Additionally, as shown in Figure 4, for this group of subjects, there was also processed data related to the Seattle Angina Questionnaire of 7 items or SAQ-7 after treatment, which is an embodiment which quantifies the angina pectoris symptom level on a numerical scale from 0 - 100. From this data there was derived, the chance that the SAQ-7 Score would be 100 after treatment, which is representative of a symptom level of being free from angina pectoris, and is thus a particular embodiment of a quantitative post-treatment symptom reduction index 132 indicative of a predicted post-treatment angina pectoris reduction. As shown, in the embodiment of Figure 4, the grey area schematically represents the area with data points of the subjects of such prior treatments or medical experiments. The x-axis shows the PPG-index 122 and the y axis shows the chance of a post-treatment SAQ=100, wherein the treatment was a percutaneous coronary intervention or PCI, or in other words the chance or probability of a post-PCI SAQ equal to 100. As shown, based on this data a suitable model 134, such as for example a suitable regression model or any other suitable statistical modelling or other suitable modelling was derived, which In statistical modelling, regression analysis is a set of statistical processes for estimates the relationships between, the quantitative post-treatment symptom reduction index 132, such as for example the chance or probability of post-PCI SAQ=100, which is a dependent variable, also referred to as the 'outcome' or 'response' variable, or a 'label' in machine learning and one or more independent variables, often also referred to as 'predictors', 'covariates', 'explanatory variables' or 'features', which in this embodiment is the PPG Index which is an embodiment of a quantified pressure pattern 122. According to such an embodiment, the model 134 could for example be embodied as a regression model 134 where: quantitative post-treatment symptom reduction index 132 = f(quantified pressure pattern 122, P), wherein f is a function of the regression model, such as for example a linear function, a binomial function, etc. and |3 are parameters, such as for example suitable constants for the regression equation.
There could be made use of any suitable regression model, such as for example a binomial regression model, a linear regression model, etc. However, it is clear that any other suitable model, for example resulting from processing the data by means of machine learning, a neural network, any suitable form of artificial intelligence computer implemented methods, etc. are also possible.
According to the embodiment shown in Figure 1, however it is clear that in addition to the pattern processing module 130 could be provided with additional input parameters, in addition to the quantified pressure pattern 122 for determining the post-treatment myocardial ischaemia symptom reduction index 132.
According to the embodiment shown the data processing system 100 could for example also optionally comprise a pre-treatment symptom module 140, a pre-treatment overall pressure gradient module 150, and/or a vessel type determination module 160.
As shown, the pre-treatment symptom module 140 is configured to quantify data 144 of pretreatment symptoms indicative of ischaemia, such as for example a pre-treatment Pretreatment SAQ or Pre-treatment SAQ angina frequency. For patients with coronary artery disease, the Seattle Angina Questionnaire or SAQ provides for an embodiment of the most commonly used quantitative measurement of patients' symptoms of angina and the extent to which these angina symptoms affect the functioning and quality of life of these patients. A SAQ or SAQ score, such as for example a SAQ-7 score, is the outcome of a measurement by means of a validated questionnaire comprising three domains: physical limitation, angina frequency, and quality of life. The SAQ-7 has a scale from 0 to 100, with higher values indicating better health status. In the angina frequency domain, an SAQ score of 100 indicates freedom from angina. A SAQ score is also representative of a SAQ angina frequency, which relates to the frequency at which the subject experiences angina symptoms. SAQ angina frequency scale of 0-30 points indicates daily angina, 31-60 points indicate weekly angina, 61- 99 points indicate monthly angina, and 100 points indicate no angina). When referring to pretreatment SAQ or pre-treatment SAQ angina frequency, this refers to measurements of SAQ or SAQ angina frequency from a subject before the treatment to relieve the subject from the symptoms of ischaemia, such as angina pectoris. When the treatment is PCI, there could be made reference to for example pre-PCI SAQ or pre-PCI SAQ angina frequency. It is clear that the pre-treatment symptom module 140 provides this pre-PCI SAQ 142 or any other suitable quantified pre-treatment symptoms indicative of ischaemia 142 to the pattern processing module 130 for determining the quantitative post-treatment symptom reduction index 132. According to the embodiment shown, the data 144 provided to the optional pre-treatment symptom module 140 could for example be retrieved by the data processing system from a suitable database storing the pre-treatment SAQ score, or any data from which the pretreatment SAQ score could be derived, however, there are other alternative embodiments possible in which for example the pre-treatment SAQ score is provided as a manual input by an operator of the data processing system 100, etc. .
According to the embodiment shown in Figure 1, the data processing system 100 further comprises an optional vessel type determination module 160 configured to determine an index 162 associated with the vessel type 164. According to an embodiment, this index 162 could for example comprise a LAD index 164 indicating whether the vessel is a left anterior descending artery or not. Such a LAD index 164 preferably is quantified as 1 of the vessel is a left anterior descending artery and 0 if the vessel is not a left anterior descending artery. It is however clear that further alternative embodiments are possible in which the type of vessel is quantified by means of a suitable index 162. According to the embodiment shown, the vessel type of the vessel 10 could for example be derived manually or automatically by means of a suitable measurement system 166, such as for example a suitable medical imaging device configured to provide suitable images from which the vessel type can be determined. The medical image data, or any other suitable or derived medical data 164 could then be provided to the vessel type determination module 160 as an input from which the vessel type index 162 can be determined. It is clear that alternative embodiments are possible, in which for example the LAD index 162 or any other suitable vessel type parameter is provided to the data processing system 100 by an operator as a manual input, etc. . According to such alternative embodiments, the vessel type index is for example representative of a specific coronary artery vessel or branch, such as for example one or more of the following: Left coronary artery; Left anterior descending artery; Left circumflex artery; Posterior descending artery; Ramus or intermediate artery; Right coronary artery; Right marginal artery; Posterior descending artery;
According to the embodiment shown in Figure 1, the data processing system further also comprises a pre-treatment overall pressure gradient module 150. The overall pressure gradient module quantifies data associated with the overall pressure gradient 118 between pressure data entries associated with both end points of said at least a longitudinal section 12 of the vessel 10. As shown, in this way the pre-treatment overall pressure gradient module 150 provides a quantified pre-treatment overall pressure gradient 152 to the pattern processing module 130 for determining the quantitative post-treatment symptom reduction index 132. According to the embodiment shown, the overall pressure gradient 118 could for example be derived from the pressure data series 114 by the pressure data module 110, however it is clear that alternative embodiments are possible in which this overall pressure gradient 118 is provided for example in the form of data outputted by the measurement system 30, or as data provided by means of manual input to the data processing system 100, etc. . According to the embodiment described with respect to Figures 2 and 3, the overall pressure gradient 118 could for example be derived from the pressure data entries 116 of the pressure data series 114 or the pressure pattern 112 at tl and/or t3. According to a particular embodiment, the pressure gradient 118 and the derived quantified pre-treatment overall pressure gradient 152 could for example correspond to the Pd/Pa value or the FFR value at tl, such as shown in Figure 3, which corresponds to the overall FFR value associated with the section 12 of the vessel 10, however, it is clear that other alternative embodiments are possible in which other suitable values with respect to overall pressure gradient of the section 12 of the vessel 10 are being extractable. When the treatment is a PCI treatment, the quantified pre-treatment overall pressure gradient 152, could for example be referred to as a quantified pre-PCI overall pressure gradient 152, or according to the embodiment described above pre-PCI FFR 152 associated with the section 12 of the vessel 10. Such a quantified overall pressure gradient 152, thus for example comprises or consists of a Fractional Flow Reserve or FFR value of the longitudinal section 12 of the vessel 10. However, alternative embodiments are possible, such as for example a similar hyperaemic pressure ratio value, or an instantaneous wave-free ratio or Instant Flow Reserve or iFR value or a similar non- hyperaemic pressure ratio, ... of the longitudinal section 12 of the vessel 10. According to still further embodiments, any other suitable ratio, such as for example a Pd/Pa ratio, in which the pressure ratio value associated with a ratio of:
- a pressure measurement from a sensor 22 at one end of the longitudinal section of the vessel 10 and
- a pressure measurement from a pressure sensor (24) at another end of the longitudinal section 12 of the vessel 10. It is clear that such specific ratios, FFR values, etc. associated with both ends of the section 12 of the vessel 10 or the overall vessel 10 are representative of a pressure gradient, as such a ratio is determined at one end, with as reference value for the gradient, the ratio at the other end, which will have a value of 1.
According to the embodiment shown in Figure 1, the pattern processing module 130 is operated to determine the quantitative post-treatment symptom reduction index 132 indicative of a predicted post-treatment ischaemia symptom reduction based on the quantified pressure pattern 122 provided by the pressure pattern module 120 and one or more of the following: the vessel type index 162; the quantified pre-treatment symptoms indicative of ischaemia 142; and/or the quantified overall pressure gradient 152. According to an embodiment shown in Figure 1, this could for example be implemented by means of a suitable model 134, such as a regression model, comprising one or more of these additional quantitative parameters 162, 142, 152 as further independent variables.
According to a particular embodiment, a binomial regression model 134 was for example derived from similar historical medical data and/or experimental data related to PCI treatment of subjects with angina pectoris, with the following equation:
(quantitative post-treatment symptom reduction index 132) = po + pi. (quantified pressure pattern 122) + 2. (vessel type index 162)+ 3. (quantified overall pressure gradient 152)
Wherein o, pi, P2, P3 are parameters, such as for example suitable constants for the binomial regression equation.
According to a specific embodiment the model 134 was for example determined as: (probability of post-PCI SAQ=100 132) = po + pi.(PPG index 122) + P2.(LAD index 162)+ P3.(pre-PCI FFR 152). It is however clear that alternative embodiments are possible in which any other suitable combination of specific additional parameters as described above, in addition to the PPG index 122 are being used in the model 134, such as for example pre-PCI SAQ, etc.
According to an exemplary embodiment, the model, was for example determined as comprising the following parameters, based on specific historical and/or experimental data: P0=0.86, pi=3.82, P2=-0.35, P3=-5.36. It is however clear that alternative embodiments comprising alternative values for the parameters are possible, such as for example a variation of these values with +/- 10% or +-5%, or any other suitable values for these parameters derived from suitable historical medical data and/or experimental data.
Based on the formula above, the probability of being angina free can be determined for each patient before the treatment, such as for example a PCI treatment. An advantageous aspect of this particular embodiment is that all input data is available before such a PCI treatment, for example before a treatment making use of vessel instrumentation with stents or balloons.
To assess the performance of the embodiment of the model 134, and taking into account the bimodal distribution of the dependent variables in this binomial regression model, in which there are two states, namely: persistent angina after PCI treatment is absent, or in other words Post-PCI SAQ=100, and persistent angina after PCI treatment is still present, or in other words Post-PCI SAQ <100, the patients related to the historical and/or experimental data were classified into two groups: patients with more than 50% of probability of Post-PCI SAQ=100 and patients with less than 50% of probability of Post-PCI SAQ=100. The "Actual" vs. "Predictive" for the chance of Post-PCI SAQ=100, or in other words the chance for post- treatment residual angina still being present was evaluated using a 2x2 matrix, from which it was shown that this exemplary model 134 provided an accuracy of 72% (95% confidence interval or Cl 60% to 82%) with a sensitivity of 83%, specificity of 60%, positive predictive value of 68%, and negative predictive value of 78%. This example thus clearly shows that, making use of PPG-index or any other suitable quantified pre-treatment pressure pattern 122, a patient specific model 134 can be created to predict the likelihood of being angina free after PCI. This provides the potential to reduce the risk of execution of treatments in which the likelihood of symptom reduction and/or the expected level of symptom reduction is suboptimal.
It is clear that, although, according to embodiments described above, the pattern processing module 130 operates to determine the quantitative post-treatment symptom reduction index 132 indicative of the chance of obtaining a predetermined post-treatment SAQ equal to one hundred it is clear that alternative embodiments are possible. For example, the quantitative post-treatment symptom reduction index 132 indicative of the chance of obtaining a predetermined post-treatment SAQ equal to, larger than or equal to, lower than or equal to, ... any other suitable predetermined value for a predetermined post-treatment SAQ. Or alternatively, an indicator of the chance of obtaining a predetermined a predetermined post-treatment SAQ angina frequency, wherein, for example the predetermined post-treatment SAQangina frequency is equal to zero or 0Hz or in other words the absence of the occurrence of the symptoms, or equal to, lower than or equal to, larger than or equal to, ... any other suitable predetermined value. It is clear that still further embodiments are possible in which for example, the quantitative post-treatment symptom reduction index 132 indicative of Post-treatment SAQor Post-treatment SAQangina frequency value itself is calculated by the pattern processing module 130 as a suitable quantitative posttreatment symptom reduction index 132. It is clear that according to such embodiments, in which a chance or probability is determined, the indicator of the chance is preferably a percentage value.
Preferably, as for example shown in Figure 1, the quantitative post-treatment symptom reduction index 132 could be provided as a suitable output by the data processing system 100, which could for example be displayed to an operator on a suitable display 170, or alternatively provided for further processing or storage by the data processing system 100 itself or any other suitable data processing system. According to a particular embodiment this display 170 could forexample display what is shown in Figures 2 and 3 to an operator, in which forexample in addition to the FOI 122, which is for example a PPG index 122, there is also displayed the quantitative post-treatment symptom reduction index 132, which is for example the %chance of post-PCI SAQ=100, labelled as SAQ100 132. It is however clear that numerous alternative embodiments are possible in which instead of or in addition to a numerical value, any suitable visual element is used to display the quantitative post-treatment symptom reduction index 132, such as for example a graph, a colour coded output element, etc. .
It is clear that the pressure data series 114 from which the pressure pattern 112 is derived and/or the pressure pattern 112 itself preferably comprises a multitude of pressure data entries 116 in between the endpoints, preferably more than 10, more than 100, more than 100 of such intermediate pressure data entries 116.
It is however clear that alternative embodiments are possible for generating and providing a pressure data series 114 with suitable pressure data entries 116 to the data processing system. It is clear that any suitable sensor measurements and/or data from which such pressure data entries 126 could be derived are also possible, such as for example pressure values, flow values, measurement of structural dimensions of the vessel and/or its lumen, etc. for example by means of any suitable sensor for measuring this, such as ultrasonic sensors, medical imaging sensors, such as for example Computed Tomography, X-ray images, etc. , systems for extracting and/or simulating flow and/or pressure based on vessel models, etc. .
It is clear that in the context of this description reference is made to a millimetre of mercury, which is a manometric unit of pressure currently defined as 133.322387415 pascals, and which is denoted mmHg or mm Hg, and which is a unit routinely used in medicine and many other scientific fields.
Figure 5 schematically shows a suitable computing system 200, 300 for implementing the data processing system 100 and/or executing the computer-implemented method described above. Figure 5 thus shows a suitable computing system 200, 300 for hosting a data processing system or data acquisition system, comprising a processor configured to perform the computer-implemented method 100 or any of its components as described with reference to the above-mentioned embodiments. Computing system 200 for example comprises and/or consists of as a suitable general-purpose or application specific computer or integrated processing circuit and comprise a bus 210, a processor 202, a local memory 204, one or more optional input interfaces 214, one or more optional output interfaces 216, a communication interface 212, a storage element interface and one or more storage elements 206. Bus 210 may comprise one or more conductors that permit communication among the components of the computing system. Processor 202 may include any type of conventional processor or microprocessor that interprets and executes programming instructions. Local memory 204 may include a random-access memory (RAM) or another type of dynamic storage device that stores information and instructions for execution by processor 202 and/or a read only memory (ROM) or another type of static storage device that stores static information and instructions for use by processor 202. Input interface 214 may comprise one or more conventional mechanisms that permit an operator, sensors or other systems or devices to input information to the computing device 200, such as a keyboard 220, a mouse, a pen, voice recognition and/or biometric mechanisms, a data acquisition interface 230, a data acquisition system, etc. Output interface 216 may comprise one or more conventional mechanisms that output information to the operator, such as a display 240, a printer, a speaker, etc. Communication interface 212 may comprise one or more transceiver-like mechanisms such as for example one or more wired or wireless interfaces, such as for example Ethernet interfaces that enables computing system 200 to communicate with other devices and/or systems, for example mechanisms for communicating with one or more other computing systems 300. The communication interface 212 of computing system 200 may be connected to such another computing system 300 by means of a local area network (LAN) or a wide area network (WAN), such as for example the internet. Storage element interface may comprise a storage interface such as for example a Serial Advanced Technology Attachment (SATA) interface or a Small Computer System Interface (SCSI), or any other suitable storage interface for connecting bus 210 to one or more storage elements 206, such as one or more local disks, for example hard disk drives, flash storage devices, etc., and control the reading and writing of data to and/or from these storage elements 206. Although the storage elements 206 above is described as a local disk, in general any other suitable computer-readable media such as a removable magnetic disk, optical storage media such as a CD or DVD, -ROM disk, solid state drives, flash memory cards, ... could be used.
The system according to the above-mentioned embodiments could be part of a suitable system running on a computing system 200 locally available to a user, such as a personal computer, laptop, a smart phone, a tablet computer, an electronic book reader, a portable computer, a desktop computer, etc. or on a remotely accessible computing system such as one or more servers available to a plurality of users. Alternatively, the system may also be part of suitable servers, for example comprising web-based tools. It is clear that, the system and the associated computer-implemented method, can be implemented as programming instructions stored in the local memory 204 of the computing system 200 for execution by its processor 202. Alternatively, these components could be stored on the storage element 206 or be accessible from another computing system 300 through the communication interface 212. In general, in this way the system and the associated computer-implemented method are provided as a computer program comprising software code adapted to perform this computer-implemented method when executed by a computing system. Alternatively, the system and the associated computer-implemented method could also be provided as a computer readable storage medium comprising computer-executable instructions which, when executed by a computing system, perform the computer-implemented method.
In the context of this description quantitative defines a value relating to a quantity that can be expressed in numbers or amounts, preferably as a numerical value representing a measured and/or calculated quantity or value. This is opposed to qualitative, which does not provide an indication of a quantity.
The present invention has been described with respect to particular embodiments and with reference to certain drawings, but the invention is not limited thereto but only by the claims. Any reference signs in the claims shall not be construed as limiting the scope. The drawings described are only schematic and are non-limiting. In the drawings, the size of some of the elements may be exaggerated and not drawn on scale for illustrative purposes. Where the term "comprising" is used in the present description and claims, it does not exclude other elements or steps. Where an indefinite or definite article is used when referring to a singular noun e.g. "a" or "an", "the", this includes a plural of that noun unless something else is specifically stated. Furthermore, the terms first, second, third and the like in the description and in the claims, are used for distinguishing between similar elements and not necessarily for describing a sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances and that the embodiments of the invention described herein are capable of operation in other sequences than described or illustrated herein. The following terms or definitions are provided solely to aid in the understanding of the invention. Unless specifically defined herein, all terms used herein have the same meaning as they would to one skilled in the art of the present invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art, e.g. in molecular biology, interventional cardiology fluid physics, biochemistry, and/or computational biology.

Claims

1. A data processing system (100) comprising:
- a pressure pattern module (120) configured to quantify data of a pressure pattern (112) along at least a section (12) of a vessel (10) of a subject (2) with symptoms indicative of ischaemia, wherein the data of the pressure pattern (112) comprises a pressure data series (114) comprising a plurality of pressure data entries (116) associated with intermittent pressure measurements along at least a longitudinal section (12) of the vessel (10); and
- a pattern processing module (130) configured to determine a quantitative post-treatment symptom reduction index (132) indicative of a predicted post-treatment ischaemia symptom reduction based on the quantified pressure pattern (122) provided by the pressure pattern module (120).
2. A system according to claim 1, wherein the pattern processing module (130) is configured to determine the quantitative post-treatment symptom reduction index (132) indicative of:
- Post-treatment SAQ;
- Post-treatment SAQ angina frequency; and/or
- an indicator of the chance of obtaining a predetermined post-treatment SAQ and/or a predetermined post-treatment SAQ angina frequency, wherein, optionally:
- the predetermined post-treatment SAQ is equal to one hundred;
- the predetermined post-treatment SAQ angina frequency is equal to zero; and/or
- said indicator of the chance is a percentage value.
3. A system according to claim 1 or 2, wherein:
- the pressure pattern module (120) is configured to quantify the data of the pressure pattern (112) along at least a section (12) of a coronary artery (10) of a subject (2) with symptoms indicative of myocardial ischaemia; and
- the pattern processing module (130) is configured to determine the quantitative posttreatment symptom reduction index (132) indicative of a predicted post-treatment myocardial ischaemia symptom reduction based on the quantified pressure pattern (122) provided by the pressure pattern module (120), and optionally wherein the treatment is a percutaneous coronary intervention or PCI.
4. A system according to claim any of the preceding claims, wherein:
- the pressure pattern module (120) is configured to quantify the data of the pressure pattern (112) along at least a section (12) of a coronary artery (10) of a subject (2) with angina pectoris; and
- the pattern processing module (130) is configured to determine the quantitative posttreatment symptom reduction index (132) indicative of a predicted post-treatment angina pectoris reduction based on the quantified pressure pattern (122) provided by the pressure pattern module (120).
5. A system according to claim 4, wherein the pressure data series (114) comprises one or more of the following:
- a time-based pressure data series;
- a position-based pressure data series;
- a data series with values associated pressure measurements from a sensor (22) moved longitudinally along at least the longitudinal section (12) of the vessel (10), and optionally additional values associated with pressure measurements from a stationary sensor (24), and/or wherein the pressure data entry (116) is associated with one or more of the following:
- an absolute pressure value;
- a relative pressure value;
- a pressure ratio value;
- a pressure ratio value associated with a ratio of:
- a pressure measurement from a sensor (22) moved longitudinally along at least the longitudinal section (12) of the vessel (10); and
- a pressure measurement from a stationary pressure sensor (24);
- a hyperaemic pressure ratio value;
- a Fractional Flow Reserve or FFR value;
- a non-hyperaemic pressure ratio value;
- an instantaneous wave-free ratio or Instant Flow Reserve or iFR value;
- a rate of change of absolute pressure values; - a rate of change of relative pressure values;
- a rate of change of pressure ratio values;
- a rate of change of hyperaemic pressure ratio values;
- a rate of change of Fractional Flow Reserve values;
- a rate of change of non-hyperaemic pressure ratio values;
- a rate of change of instantaneous wave-free ratio or Instant Flow Reserve or iFR values.
6. A system according to claim 4 or 5, wherein the quantified pressure pattern (122) provided by the pressure pattern module (120) is determined by the pressure pattern module (120) by means of:
- a contribution of a maximum pressure change (56) associated with a predetermined subsection (42) of the pressure pattern (112) with respect to a pressure change associated with the overall pressure pattern (40); and
- a portion (64) of the overall pressure pattern (40) in which the rate of change (60) is equal to or larger than a predetermined threshold (62).
7. A system according to claim 6, wherein the predetermined subsection (42) of the pressure pattern (112) is in the range of 2% up to and including 80%, preferably in the range of 5% up to and including 50% of the overall pressure pattern (40), preferably in the range of 10% up to and including 40%, for example in the range of 15% up to and including 30%, for example 20%.
8. A system according to claim 6 or 7, wherein the quantified pressure pattern (122) provided by the pressure pattern module (120) is determined by the pressure pattern module (120) by means of:
- a functional outcome index (FOI) or PPG index based on the formula:
Figure imgf000049_0001
wherein: maximum pressure change (subsection of the pressure pattern) is defined said maximum pressure change (56) associated with said subsection of the pressure pattern (42); pressure change (overall pressure pattern) is defined as said pressure change associated with the overall pressure pattern (40); and
- threshold exceeding portion is defined as said portion (64) of the overall pressure pattern (40) in which said rate of change (60) is equal to or larger than said predetermined threshold (62).
9. The system according to claim 8, wherein:
- said pressure pattern comprises a data series representing a fractional flow reserve (FFR) pullback curve comprising pressure data entries associated with FFR values associated with a ratio of:
- a pressure measurement from a sensor (22) moved longitudinally along at least the longitudinal section (12) of the vessel (10); and
- a pressure measurement from a stationary pressure sensor (24); and wherein the quantified pressure pattern (122) provided by the pressure pattern module (120) is determined by the pressure pattern module (120) by means of:
- a functional outcome index (FOl) or PPG index based on the formula:
FOl maximum A FFR (subsection of the pressure pattern)
+ (1 — (threshold exceeding portion))
A FFR (overall pressure pattern)
2 wherein: maximum A FFR (subsection of the pressure pattern) is defined as the maximum difference between FFR values (50) associated with the start and the end of said subsection of the pressure pattern (42);
- A FFR (overall pressure pattern) is defined as the difference between FFR values (50) associated with the start and the end of said overall pressure pattern (40); and
- threshold exceeding portion is defined as said portion (64) of the overall pressure pattern (40) in which said rate of change (60) in FFR (50) is equal to or larger than said threshold (62).
10. A system according to claim 8 or 9, wherein:
- the threshold exceeding portion (64) is determined as the ratio of:
- respectively the length or duration of the part of the pressure pattern in which said rate of change (60) is equal to or larger than said predetermined threshold (62), with respect to
- respectively the length or duration of the overall pressure pattern (40), and/or wherein:
- said subsection of the pressure pattern (42) is determined as a subsection of the pressure pattern (40) with respectively a predetermined length or duration, and/or wherein:
- said overall pressure pattern (40) is determined as a pullback pressure pattern along at least a section (12) of the vessel (10), preferably between the ostium and the distal end of the vessel (10).
11. A system according to any one of the preceding claims, wherein the system further comprises:
- a pre-treatment symptom module (140) configured to quantify data (144) of pre-treatment symptoms indicative of ischaemia (144);
- a vessel type determination module (160) configured to determine an index (162) associated with the vessel type (164); and/or
- a pre-treatment overall pressure gradient module (150) configured to quantify data associated with the overall pressure gradient (118) between pressure data entries associated with both end points of said at least a longitudinal section (12) of the vessel (10), and/or wherein the pattern processing module (130) is further configured to determine the quantitative post-treatment symptom reduction index (132) indicative of a predicted posttreatment ischaemia symptom reduction based on the quantified pressure pattern (122) provided by the pressure pattern module (120) and one or more of the following:
- the vessel type index (162);
- the quantified pre-treatment symptoms indicative of ischaemia (142); and/or
- the quantified overall pressure gradient (152).
12. A system according to claim 11, wherein:
- the quantified pre-treatment symptoms indicative of ischaemia (142) comprises or consists of one or more of the following:
- Pre-treatment SAQ; - Pre-treatement SAQ angina frequency, and/or wherein:
- the vessel type index comprises or consists of one or more of the following:
- a LAD index indicating whether the vessel is a left anterior descending artery or not;
- a vessel type index representative of a specific coronary artery vessel or branch, such as for example one or more of the following:
- Left coronary artery;
- Left anterior descending artery;
- Left circumflex artery;
- Posterior descending artery;
- Ramus or intermediate artery;
- Right coronary artery;
- Right marginal artery;
- Posterior descending artery, and/or wherein
- the quantified overall pressure gradient (152) comprises or consists of one or more of the following:
- - a pressure ratio value associated with a ratio of:
- a pressure measurement from a sensor (22) at one end of said at least a longitudinal section of the vessel (10); and
- a pressure measurement from a pressure sensor (24) at another end of said at least a longitudinal section (12) of the vessel (10);
- a hyperaemic pressure ratio value of said at least a longitudinal section of the vessel;
- a Fractional Flow Reserve or FFR value of said at least a longitudinal section (12) of the vessel (10);
- a non-hyperaemic pressure ratio value of said at least a longitudinal section of the vessel;
- an instantaneous wave-free ratio value or Instant Flow Reserve value or i FR value of said at least a longitudinal section (12) of the vessel (10).
13. A system according to any one of the preceding claims, wherein the pattern processing module (130) is further configured to determine the quantitative post-treatment symptom reduction index (132) indicative of a predicted post-treatment ischaemia symptom reduction based on the quantified pressure pattern (122) provided by the pressure pattern module (120) and one or more of the following:
- the vessel type index (162);
- the quantified pre-treatment symptoms indicative of ischaemia (142); and/or
- the quantified overall pressure gradient (152).
14. A system according to claim 13, wherein the pattern processing module (130) is further configured to determine the quantitative post-treatment symptom reduction index (132) by means of a model (134) based on the quantified pressure pattern (122) provided by the pressure pattern module (120) and one or more of the following:
- the vessel type index (162);
- the quantified pre-treatment symptoms indicative of ischaemia (142); and/or
- the quantified overall pressure gradient (152), wherein optionally the model (134) comprises or consists of a regression model comprising one or more additional quantitative parameters as further independent variables.
15. A system according to claim 14, wherein the model (134) comprises or consists of a binomial regression model (134) with the following equation:
(quantitative post-treatment symptom reduction index (132)) = po + pi. (quantified pressure pattern (122)) + 2. (vessel type index (162))+ 3. (quantified overall pressure gradient (152)), wherein o, pi, P2, P3 are parameters, wherein optionally po, pi, P2, P3 are constants.
16. A system according to claim 15, wherein the model (134) comprises or consists of the following equation:
(probability of post-PCI SAQ=100 (132)) = po + pi. (PPG index (122)) + P2.(LAD index (162))+ P3.(pre-PGI FFR (152)), wherein optionally PO=O.86 +/- 10%, pi=3.82 +/- 10%, P2=-O.35 +/- 10%, P3=- 5.36 +/-10%.
17. A system according to any one of the preceding claims, wherein the data processing system (100) comprises or is coupled to a display (170) configured to output the quantitative posttreatment symptom reduction index (132) determined by the pattern processing module 130. 18. A computer-implemented method for operating the data processing system (100) according to any of the preceding claims, wherein the method comprises the steps of:
- the pressure pattern module (120) quantifying the data of a pressure pattern (112) along at least a section (12) of a vessel (10) of a subject (2) with symptoms indicative of ischaemia; and
- the pattern processing module (130) determining a quantitative post-treatment symptom reduction index (132) indicative of a predicted post-treatment ischaemia symptom reduction based on:
- the quantified pressure pattern (122) provided by the pressure pattern module (120).
19. A computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method according to claim 18; and or a computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out the method according to claim 18.
PCT/EP2023/072720 2022-08-17 2023-08-17 A system and method for determining a quantitative post-treatment symptom reduction index based on a quantified pressure pattern WO2024038156A1 (en)

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