WO2011046729A1 - Pattern recognition system for classifying the functional status of patients with pulmonary hypertension, including pulmonary arterial and pulmonary vascular hypertension - Google Patents
Pattern recognition system for classifying the functional status of patients with pulmonary hypertension, including pulmonary arterial and pulmonary vascular hypertension Download PDFInfo
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- WO2011046729A1 WO2011046729A1 PCT/US2010/050197 US2010050197W WO2011046729A1 WO 2011046729 A1 WO2011046729 A1 WO 2011046729A1 US 2010050197 W US2010050197 W US 2010050197W WO 2011046729 A1 WO2011046729 A1 WO 2011046729A1
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
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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
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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
- A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
- A61B5/083—Measuring rate of metabolism by using breath test, e.g. measuring rate of oxygen consumption
- A61B5/0836—Measuring rate of CO2 production
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/48—Other medical applications
- A61B5/4884—Other medical applications inducing physiological or psychological stress, e.g. applications for stress testing
-
- 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
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
-
- 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
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Z—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR
- G16Z99/00—Subject matter not provided for in other main groups of this subclass
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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
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
- A61B5/083—Measuring rate of metabolism by using breath test, e.g. measuring rate of oxygen consumption
- A61B5/0833—Measuring rate of oxygen consumption
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H15/00—ICT specially adapted for medical reports, e.g. generation or transmission thereof
Definitions
- the present invention relates generally to the field, including pulmonary arterial and pulmonary
- the present method provides a more sensitive, physiologic, and easier to use method than currently available classification systems.
- the present invention provides feedback during long-term follow-up and treatment in patients with PH.
- the non-specific nature of symptoms associated with PH means that the diagnosis cannot be made on symptoms alone.
- a series of investigations is required to make an initial diagnosis, to refine that diagnosis in terms of clinical class of pulmonary hypertension, and to evaluate the degree of functional and hemodynamic impairment.
- NYHA New York Heart Association
- WHO World Health Organization
- the major shortcoming of the NYHA/ HO system is that it relies on subjective observations by the patient and interpretation of those observations by the physician.
- the 6-minute walk test while simple and convenient, has many limitations including issues relating to
- the present advance to a large extent, obviates the problems discussed in the foregoing for the NYHA/WHO Classification system, for peak VO 2 testing for functional classification, and for the 6-minute hall walk for therapy tracking.
- this multiparametric score is obtained by either exercising the patient to a maximal value, or by utilization of gas exchange
- the present invention involves the use of exercise- related data in a method of pattern recognition for diagnosing the presence of Pulmonary Hypertension and classifying the functional status of patients with chronic PH using a multiparametric index (MPI PH ) .
- MPI PH multiparametric index
- the present invention provides a single
- MPI PH multiparametric score that can be used to quantify the degree of severity of a patient with PH by combining certain Feature Extraction Steps, for example, steps 1-8, explained below, with an additional term, A-35mmHg, as will be explained.
- the value for MPI PH may be expressed as follows:
- MPIp H (A-35mmHg) +G* 1+H*W2+I*W3+J*W4 +K*W5+L*W6
- A-L are individual ranking parameters derived from exercise data and Wl - W6 are weighting factors.
- H represents an increase in pulmonary blood flow or improved matching of ventilation to perfusion, it has been determined that a relatively larger value for H (delay) is indicative of lower severity; thus, a positive value of H reduces the negative total MPI PH value.
- the values for 1- 6 in the above equation are statistical weighting factors that may or may not equal 1 (a value of 1, of course will not alter the total value of MPI PH ) .
- a value of 1 was used for all the weighting factors in conjunction with Figure 7 and Figure 8.
- individual weighting factors can be determined to fine tune the computation of MPI PH .
- cardiopulmonary exercise gas exchange measurements is obtained 1) at rest, 2) during physical exercise testing performed in accordance with a standardized workload protocol as the forcing function to elicit physiologic changes resulting from the workload, and 3) during a short recovery period following exercise termination.
- Physiologic changes are measured using a
- CPX cardiopulmonary exercise testing system
- cardiopulmonary exercise gas exchange analysis is made for each test data set.
- Figure 1 is a schematic drawing that illustrates the functional components of a CPX testing system usable with the present invention
- Figure 2 is a schematic drawing that illustrates one form of exercise protocol that is used to place a volume load on the cardiopulmonary system
- Figure 3 illustrates an organization of the measured data once it is acquired from the cardiopulmonary exercise gas exchange analyzer
- Figure 4 illustrates a normal ETCO 2 response to exercise .
- Figure 5 illustrates the calculation of the areas over the resting ETCO 2 baseline during exercise (Area 0) and the area under the resting ETC0 2 baseline during exercise (Area U) ;
- Figure 6 illustrates features extracted from the ETC0 2 vs. time plot
- Figure 7 illustrates an analysis flow chart to compute MPI PH
- Figure 9 illustrates a description scheme employed by the present invention for displaying the resultant
- Figure 10 illustrates a trend plot of test time- sequential MPI PH values.
- PAP Pulmonary Artery Pressure
- the MPI PH was computed for each of the four patient tests represented in the Yasunobu study.
- the feature extraction method performed on the ETC0 2 vs. time plot is depicted in Figure 6.
- measurements A through F represent the following
- ETCO 2 represents the matching of ventilation to pulmonary perfusion and typically will not disclose any type of significantly elevated PVR, until during an exercise stimulus. As PH worsens, an elevated Pulmonary Vascular Resistance (PVR) will exist even at rest.
- Measurement H represents the "delay time" in sympathetic and neurohormonal induced Measurements A and B, depending on the degree/severity of pulmonary vasoconstriction, represents the severity of PH.
- Measurement G represents a transient, normal
- the slope, measurement I, of the ETC0 2 drop following exercise onset reflects the rate of increased PVR.
- Measurement E represents the degree of mismatching and expiration of CO2 (partial pressure) just at the end of exercise while measurement F reflects the degree of attenuation in PVR due to the sympathetic exercise stimulus being withdrawn.
- the potential increase in ETCO2 at end exercise as compared to the lowest ETC0 2 value at point "D" or the nadir, represents an increase in
- the present invention includes a pattern recognition system consisting of a) a cardiopulmonary exercise gas exchange analyzer that gathers the observations to be classified or described, b) a feature extraction mechanism that computes numeric information from the observations, and c) a
- the data measured during exercise quantifies how an individual is able to function in the physical world in terms of the physiologic changes that the individual experiences when engaged in the
- the physiologic changes are measured using a
- CPX cardiopulmonary exercise testing system
- the data gathering aspect of the invention involves known techniques and analyses, and the calculations for formulating predictive assessments are readily available in the scientific literature (see the bibliography in References).
- the present invention enables an observer to gain new and valuable insight into the present condition and condition trends in patients.
- cardiopulmonary exercise gas exchange analysis is made for each test data set.
- the performance of such a test is well understood by individuals skilled in the art, and no further explanation of this is believed necessary.
- FIG. 1 illustrates typical equipment whereby a cardiopulmonary exercise test (CPX) may be conducted and the results displayed in accordance with the method of the present invention.
- the system is seen to include a data processing device, here shown as a personal computer of PC 12, which comprises a video display terminal 14 with associated mouse 16, report printer 17 and a keyboard 18.
- the system further has a floppy disc handler 20 with associated floppy disc 22.
- the floppy-disc handler 20 input/output interfaces comprise read/write devices for reading prerecorded information stored, deleting, adding or changing recorded information, on a machine-readable medium, i.e., a floppy disc, and for providing signals which can be considered as data or operands to be manipulated in accordance with a software program loaded into the RAM or ROM memory (not shown) included in the computing module 12.
- the equipment used in the exercise protocol can be a simple stair step of a known height.
- a CPX testing system 34 interfaces with the subject 30 during operation of the exercise test.
- the physiological variables may be selected from heart rate (HR) , ventilation (VE) , rate of oxygen uptake or consumption (V0 2 ) and carbon dioxide production (VC0 2 ) end tidal C0 2 (ETC0 2 ) or other variables derived from these basic measurements.
- Physiological data collected is fed into the computing module 12 via a conductor 31, or other communication device.
- the workload protocol is illustrated in Figure 2 and is organized in to a rest phase 50, and exercise phase 52, and a recovery phase 54.
- the workload may also be quantified by requiring the patient to maintain a desired stepping cadence by the addition of an audible metronome that guides the frequency of the steps taken during the exercise phase.
- Step 1 - Detection An impetus for the use of statistical pattern recognition comes from new methods of analyzing cardiopulmonary data published in the
- Step 2 Delay time -
- the delay time (H) is
- the delay time is calculated by subtracting the time value for C from the starting time of the exercise phase.
- Step 3 First rise -
- the first rise, measurement G is calculated by subtracting the average resting value of
- ETCO2 (A) from inflection point B the maximum ETCO2 value greater than A and which occurs prior to reaching
- Step 4 Nadir - The smallest value of ETC0 2 occurring after point C is then determined as inflection point D.
- Step 5 - Slope - The next step is to compute the regression line through those data points for ETC0 2 from inflection point C to D.
- a is the intercept
- b is the slope.
- the a and b values are chosen so that the sum of squared deviations from the line is minimized.
- the best line is called the regression line, and the equation describing it is called the regression equation.
- Step 6 - Drop - The next step is to compute the maximum drop in ETCO 2 , J, by subtracting the inflection point D from inflection point C.
- Step 7 - Intra-exercise rebound The next step is to compute the value of the intra-exercise rebound. This step may yield a value of 0 in the case where ETC0 2 continues to drop until the end of the exercise phase.
- the value of K is computed by subtracting inflection point D from E.
- Step 8 Recovery rebound - The final step is to compute the value of the recovery rebound
- MPIpH Score In Figure 8, 4 patient tests representing different degrees of severity are presented. As can be seen, the patterns are similar to that illustrated in Figure 6. However, the values for A, as derived in Step 1 above, are shifted downward from the normal value for ETCO 2 at rest (estimated to be 35 mmHg based on previous studies to date) . In order to provide a single, multiparametric score that can be used to quantify the degree of severity of a patient with PH, the present invention combines the Feature Extraction Steps 1-8 with an additional term, A-
- MPI PH (A-35mmHg) +G*W1 +H*W2 +I*W3 +J*W4 + K*W5 +L*W6
- the values for A-L have been described previously in Feature Extraction, Steps 1-8.
- the basic objective in formulating the value for MPI PH in this way is to obtain a negative value for patients with PH, and to obtain a value the magnitude of which is larger with increasing severity of the disease.
- G represents an appropriate directional change in
- H represents an increase in pulmonary blood flow or improved matching of ventilation to perfusion, it has been determined that a relatively larger value for H (delay) is indicative of lower severity, thus a positive value of G reduces the negative total PI PH value.
- FIG. 7 A flowchart for computing MPI PH is depicted in Figure 7 in which the MPI PH score is computed for Figure 6. Using this same flowchart, the MPI PH values for Figure 8 are computed and displayed on the left side of Figure 8.
- Wl - 6 in the above equation are weighting factors that may or may not equal 1 (a value of 1 not altering the total value of MPI PH ) .
- cardiopulmonary exercise test variables such as
- the present invention provides further diagnostic information to confirm or rule out the presence of PH.
- FIG. 10 In order to provide a rapid assessment of the effect of any given therapy for PH over time, one example of a trend plot for MPI PH values over time is illustrated in the graph in Figure 10.
- the individual values of MPI PH for each test date are plotted serially.
- the Area O/Area U ratio can be plotted similarly in a time-sequential manner.
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Priority Applications (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
EP10823812.2A EP2480130A4 (en) | 2009-09-25 | 2010-09-24 | Pattern recognition system for classifying the functional status of patients with pulmonary hypertension, including pulmonary arterial and pulmonary vascular hypertension |
AU2010307157A AU2010307157A1 (en) | 2009-09-25 | 2010-09-24 | Pattern recognition system for classifying the functional status of patients with pulmonary hypertension, including pulmonary arterial and pulmonary vascular hypertension |
CA2780384A CA2780384A1 (en) | 2009-09-25 | 2010-09-24 | Pattern recognition system for classifying the functional status of patients with pulmonary hypertension, including pulmonary arterial and pulmonary vascular hypertension |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US12/567,005 US8775093B2 (en) | 2007-09-17 | 2009-09-25 | Pattern recognition system for classifying the functional status of patients with pulmonary hypertension, including pulmonary arterial and pulmonary vascular hypertension |
US12/567,005 | 2009-09-25 |
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WO2011046729A1 true WO2011046729A1 (en) | 2011-04-21 |
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Application Number | Title | Priority Date | Filing Date |
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PCT/US2010/050197 WO2011046729A1 (en) | 2009-09-25 | 2010-09-24 | Pattern recognition system for classifying the functional status of patients with pulmonary hypertension, including pulmonary arterial and pulmonary vascular hypertension |
Country Status (4)
Country | Link |
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US (1) | US8775093B2 (en) |
EP (1) | EP2480130A4 (en) |
AU (1) | AU2010307157A1 (en) |
WO (1) | WO2011046729A1 (en) |
Families Citing this family (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9053562B1 (en) | 2010-06-24 | 2015-06-09 | Gregory S. Rabin | Two dimensional to three dimensional moving image converter |
WO2012142608A2 (en) | 2011-04-15 | 2012-10-18 | Vanderbilt University | Oral end tidal carbon dioxide probe for diagnosing pulmonary arterial hypertension |
US9992021B1 (en) | 2013-03-14 | 2018-06-05 | GoTenna, Inc. | System and method for private and point-to-point communication between computing devices |
US10010264B2 (en) | 2013-07-25 | 2018-07-03 | Shape Medical Systems, Inc. | Pattern recognition system for quantifying the likelihood of the contribution of multiple possible forms of chronic disease to patient reported dyspnea |
US11154214B2 (en) | 2017-04-14 | 2021-10-26 | Shape Medical Systems, Inc. | Gas exchange systems and methods for calculating ventilatory threshold and evaluating pulmonary arterial hypertension |
US11497439B2 (en) | 2019-03-05 | 2022-11-15 | Shape Medical Systems, Inc. | Pattern recognition system for classifying the functional status of patients with chronic heart, lung, and pulmonary vascular diseases |
US11324954B2 (en) | 2019-06-28 | 2022-05-10 | Covidien Lp | Achieving smooth breathing by modified bilateral phrenic nerve pacing |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4930519A (en) * | 1984-04-02 | 1990-06-05 | Medical Graphics Corporation | Method of graphing cardiopulmonary data |
US20030208106A1 (en) * | 2002-05-03 | 2003-11-06 | Cortex Biophysik Gmbh | Method of cardiac risk assessment |
US20040138716A1 (en) * | 2003-01-10 | 2004-07-15 | Steve Kon | System and method for detecting circadian states using an implantable medical device |
US20090076347A1 (en) | 2007-09-17 | 2009-03-19 | Shape Medical Systems, Inc. | Pattern Recognition System for Classifying the Functional Status of Patients with Chronic Disease |
-
2009
- 2009-09-25 US US12/567,005 patent/US8775093B2/en active Active
-
2010
- 2010-09-24 EP EP10823812.2A patent/EP2480130A4/en not_active Ceased
- 2010-09-24 WO PCT/US2010/050197 patent/WO2011046729A1/en active Application Filing
- 2010-09-24 AU AU2010307157A patent/AU2010307157A1/en not_active Abandoned
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4930519A (en) * | 1984-04-02 | 1990-06-05 | Medical Graphics Corporation | Method of graphing cardiopulmonary data |
US20030208106A1 (en) * | 2002-05-03 | 2003-11-06 | Cortex Biophysik Gmbh | Method of cardiac risk assessment |
US20040138716A1 (en) * | 2003-01-10 | 2004-07-15 | Steve Kon | System and method for detecting circadian states using an implantable medical device |
US20090076347A1 (en) | 2007-09-17 | 2009-03-19 | Shape Medical Systems, Inc. | Pattern Recognition System for Classifying the Functional Status of Patients with Chronic Disease |
Non-Patent Citations (1)
Title |
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See also references of EP2480130A4 |
Also Published As
Publication number | Publication date |
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AU2010307157A1 (en) | 2012-05-24 |
EP2480130A1 (en) | 2012-08-01 |
US8775093B2 (en) | 2014-07-08 |
US20100016750A1 (en) | 2010-01-21 |
EP2480130A4 (en) | 2015-03-25 |
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