WO2017174764A1 - Mathematical model and score for predicting the presence of paroxysmal atrial fibrillation - Google Patents

Mathematical model and score for predicting the presence of paroxysmal atrial fibrillation Download PDF

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WO2017174764A1
WO2017174764A1 PCT/EP2017/058337 EP2017058337W WO2017174764A1 WO 2017174764 A1 WO2017174764 A1 WO 2017174764A1 EP 2017058337 W EP2017058337 W EP 2017058337W WO 2017174764 A1 WO2017174764 A1 WO 2017174764A1
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age
score
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Stefan Kallenberger
Hugo A. Katus
Constanze Schmidt
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Universitaet Heidelberg
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/08—Clinical applications
    • A61B8/0883—Clinical applications for diagnosis of the heart
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/02—Measuring pulse or heart rate
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/06—Measuring blood flow
    • A61B8/065—Measuring blood flow to determine blood output from the heart
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/48—Diagnostic techniques
    • A61B8/488—Diagnostic techniques involving Doppler signals
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/52—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/5215—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving processing of medical diagnostic data
    • A61B8/5223—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving processing of medical diagnostic data for extracting a diagnostic or physiological parameter from medical diagnostic data
    • 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
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • 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
    • 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
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders

Definitions

  • the present invention refers to a method for predicting paroxysmal atrial fibrillation in a subject. Furthermore, the present invention refers to a system allowing the prediction of paroxysmal atrial fibrillation.
  • Atrial fibrillation is the most frequent rhythm disorder, and its prevalence is expected to further increase due to demographic transition [1].
  • AF is firstly diagnosed after a stroke or a transient ischemic event. For this reason, early diagnosis of AF episodes is essential.
  • paroxysmal AF pAF
  • cAF chronic AF
  • Further optimization of easy im- plementable non-invasive methods for pAF detection represents an important task for transla- tional electrophysiological research, as recently declared in the EHRA roadmap to improve the quality of atrial fibrillation management [4] .
  • ECG surface electrocardiogram
  • Holter ECG monitoring is used to detect pAF [6] .
  • intra-cardiac ECG measured with cardiac device electrodes or catheter electrodes during ablation procedures is used for AF detection.
  • Risk stratification tools were established for the prevention of stroke, transient ischemic attacks or other thromboembolic complications.
  • the CHADS2 and CHA2DS2-VASC scores are part of the common clinical practice for guiding prophylactic anticoagulation therapy [6] . It can be expected that, within the context of the evolving area of systems medicine, further predictive models will be developed, which integrate clinical parameters from different diagnostic techniques, to predict the individual risk for the development of pathologies and can be used to optimize personalized therapies.
  • Implantable cardiac monitors were developed, which can be implanted in subjects who once suffered from a stroke of unknown cause. Said cardiac monitors allow continuously recording the heart rhythm for the following three years. Abnormalities are registered within certain time intervals. When the monitor is read out, abnormalities can be diagnosed by the doctor in charge. Thus, in the case of atrial fibrillation the patient can be treated accordingly in order to prevent any further strokes caused by atrial fibrillation. In particular, for pAF patients such a monitor would be useful since only by continuous observation these randomly occurring fibrillations can be detected. However, also the usage of such an implantable monitor would be associated with a surgical intervention besides high costs for the device.
  • one object of the present invention is to provide a method for easily and reliably predicting paroxysmal atrial fibrillation in a subject.
  • Another object of the present invention is to provide a system for predicting paroxysmal atrial fibrillation in a subject.
  • a method for predicting paroxysmal atrial fibrillation comprising
  • LA left atrium size
  • An advantage coming along with the method according to the present invention is that for the prediction of paroxysmal atrial fibrillation no time-consuming diagnostic procedures or surgical intervention are required. Rather, only by collecting easy obtainable data said prediction can be made.
  • a method is provided for classification between sinus rhythm (SR) and pAF.
  • SR sinus rhythm
  • subject any human or animal subject is meant.
  • the subject is a human.
  • the data required for the method according to the present invention is clinical data which comprises basic physiologic (age and smoker), cardiologic and medical history parameters (heart frequency, sleep apnea, hyperlipidemia, type 2 diabetes mellitus, catheter ablation). Additionally, medication data (intake of beta blocker) and echocardiographic parameters (left ventricular end- diastolic diameter (LV ESD), aortic root diameter, left atrial diameter (LA) and tissue Doppler imaging velocity during atrial contraction (TDI, A') are comprised.
  • basic physiologic age and smoker
  • cardiologic and medical history parameters heart frequency, sleep apnea, hyperlipidemia, type 2 diabetes mellitus, catheter ablation.
  • medication data intake of beta blocker
  • echocardiographic parameters left ventricular end- diastolic diameter (LV ESD), aortic root diameter, left atrial diameter (LA) and tissue Doppler imaging velocity during atrial contraction (TDI, A') are comprised.
  • the third most significant parameter is TDI, A' and the fourth most significant parameter for a reliable prediction is aortic root diameter.
  • TDI, A' and/or aortic root diameter should be included.
  • the subject has hyperlipidemia and/or type II diabetes mellitus.
  • At least one or any combination of two or more of cathe- ter ablation, left ventricular injection fraction, heart rate, sleep apnea, intake of beta blocker and smoker can be determined.
  • the performance of the method according to the invention can be increased in order to classify between pAF and SR more reliably.
  • the method disclosed herein comprises the step of determining at least one of the following sets of parameters:
  • Data concerning physiological or medication parameters can easily be obtained from a subject, for example, by personal interrogation. The same applies to medical history parameters.
  • echocardiographic examinations can be performed on commercially available ultrasound systems (Vivid S5, Vivid i, Vivid 7 and Vivid E9 GE Healthcare Vingmed, Trondheim, Norway and ie33, Philips, Eindhoven, the Netherlands) according to the guidelines of the American Society of Echocardiography [10].
  • the echocardiography images include parasternal, apical and subxiphoidal views using 1.5 to 4.0 MHz phase-array transducers.
  • all the examinations are performed with 2D echocardiography for anatomic imaging and Doppler echocardiography for assessment of velocities.
  • LA size is determined as the maximal distance between the posterior aortic root wall and the posterior left atrial wall at the end of systole.
  • Aortic root diameter and LV end- systolic diameters are preferably obtained in the parasternal long axis view.
  • Tissue Doppler images (TDI) velocities are preferably measured in the apical four chamber view.
  • images are digitally stored in a Picture Archiving and Communication System (PACS) and analyzed at clinical workstations.
  • PACS Picture Archiving and Communication System
  • hyperlipidemia or diabetes can easily be determined by performing in vitro assays.
  • hyperlipidemia the lipids within a blood sample are measured.
  • glucose concentration within the blood is measured.
  • assays are standard in vitro methods, which are known to a person skilled in the art. In order to perform these assays a blood sample from the patient is required.
  • parameters "smoker”, “sleep apnea” "hy- perlipidemia”, “type II diabetes mellitus”, “catheter ablation” or “intake of beta blocker” will have a value of 1, or otherwise will have a value of 0 in absence of the property.
  • desired parameters their combination allows deriving a test score based on the measured parameters. Beside the determination of the desired parameters and the derivation of the test score based on the measured parameters a threshold score is provided. The threshold score represents a value allowing to conclude whether the result of the method actually indicates an increased probability for the presence of paroxysmal atrial fibrillation in the examined subject or not.
  • the result can be interpreted as the presence of an increased probability for paroxysmal atrial fibrillation in the examined subject.
  • presence of pAF can be assumed, further (electrophysiological) investigations can be conducted to verify the presence of pAF.
  • the method disclosed herein allows predicting the presence of pAF by using the data for parameters that are obtained anyway when the subject to be investigated is undergoing an echocardiographic examination.
  • a prediction for the presence of pAF can be made, which usually is difficult to diagnose.
  • patients can be identified for which it might be necessary to conduct, for example, a long term observation of the cardiac functions. This, in the end, can lead to the verification of the presence of pAF, allowing an application of the required treatment. Only when being aware of an increased risk for pAF the subject can be treated accordingly, and the risk for suffering from (another) stroke can be decreased or even eliminated.
  • the determined parameters can be combined either in a logistic or in a linear function.
  • Ao,root is the diameter of the aortic root of the subject
  • LA is the size of the left atrium of the subject
  • LV left ventricular end-systolic diameter of the subject
  • TDI,A' is the tissue Doppler imaging velocity during atrial contraction of the subject
  • HF is the heart frequency of the subject
  • sleep apnea indicates whether the subject has sleep apnea
  • hyperlipidemia indicates whether the subject has hyperlipidemia
  • type II diabetes indicates whether the subject has type II diabetes
  • ⁇ blocker indicates whether the subject is on ⁇ blockers
  • catheter ablation indicates whether the subject had a catheter ablation.
  • the score variant indices r represent the parameter subsets to be determined.
  • weights are either 0 for not included variables or have values obtained by logistic regression based on a training set of patient data. To obtain weights and offsets, maximum likelihood estimation algorithms can be applied.
  • the offsets and the weights depend on the amount and the kind of parameters that are supposed to be determined.
  • the corresponding values for the logistic coefficient offsets and the weights for the different parameters depending on the actual set of parameters can be found in table 1.
  • age is the age of the subject.
  • LA is the size of the left atrium of the subject.
  • the offset ⁇ , ⁇ equals -8.797, ⁇ , ⁇ equals 0.12329, and 9 ag e,r equals 0.03436.
  • the indicated offsets and weighting coefficients in Formula I can be varied by a maximum of 10%.
  • the offsets and weighting coefficients vary by a maximum of 5%, 4% or 3%. More preferably, the offsets and weighting coefficients vary by a maximum of 2% or, most preferably, they vary by a maximum of 1 % .
  • the presence of paroxysmal atrial fibrillation is predicted when the logistic test score equals or exceeds a threshold score at a value of about 0.1506.
  • age is the age of the subject
  • Ao,root is the diameter of the aortic root of the subject
  • LA is the size of the left atrium of the subject
  • TDI, A' is the tissue Doppler imaging velocity during atrial contraction of the subject.
  • the indicated offsets and weighting coefficients in Formula II can be varied by a maxi- mum of 10%.
  • the offsets and weighting coefficients vary by a maximum of 5%, 4% or 3% . More preferably, the offsets and weighting coefficients vary by a maximum of 2% or, most preferably, they vary by a maximum of 1 % .
  • the threshold score provided is about 0.1324. This means presence of paroxysmal atrial fibrillation is predicted when the logistic test score equals or exceeds said threshold score.
  • threshold score depends on the parameter set which is determined. This means that depending on the parameters that are actually determined in addition to the basic four parameters the threshold score for the linear model varies.
  • threshold scores S r thd were determined from receiver operating characteristic (ROC) curves. ROC curves indicate sensitivity versus false positive rate, equivalent to 1 - specificity, and are obtained by applying variable model thresholds on patient datasets. ROC curves were calculated after applying 100-fold crossvalidation of prediction models on a patient dataset. Threshold scores for testing with a sensitivity of 80% based on different parameter combinations can be found in table 1. To claim an easy implementable decision aid for initializing diagnostic testing for pAF the reduced logistic model can be transformed to a li model.
  • Table 1 contains only the coefficients of logarithmic model scores, values for p ⁇ (J r ) , and differences p g9 (J, ) - P i (J r ) . cordingly, linear score weights are obtained by inserting Formula III in Formula IV, which results in
  • age is the age of the subject
  • Ao,root is the diameter of the aortic root of the subject
  • LA is the size of the left atrium of the subject
  • LV left ventricular end-systolic diameter of the subject
  • TDI,A' is the tissue Doppler imaging velocity during atrial contraction of the subject
  • HF is the heart frequency of the subject
  • sleep apnea indicates whether the subject has sleep apnea
  • hyperlipidemia indicates whether the subject has hyperlipidemia
  • type II diabetes indicates whether the subject has type II diabetes
  • ⁇ blocker indicates whether the subject is on ⁇ blockers
  • catheter ablation indicates whether the subject had a catheter ablation.
  • threshold values L r thd are obtained, according to Formulas I to IV, by inserting threshold scores for logarithmic model scores S , d into
  • age is the age of the subject
  • LA is the size of the left atrium of the subject.
  • the indicated offsets and weighting coefficients in Formula VII can be varied by a maximum of 10%.
  • the offsets and weighting coefficients vary by a maximum of 5%, 4% or 3%. More preferably, the offsets and weighting coefficients vary by a maximum of 2% or, most preferably, they vary by a maximum of 1%.
  • the presence of paroxysmal atrial fibrillation is predicted when the linear test score equals or exceeds a threshold score at a value of about 53.10.
  • transforming S 4 (Formula II), according to Formulas III to V, to a linear function in order to obtain the linear test score L 4 it reads
  • age is the age of the subject
  • Ao,root is the diameter of the aortic root of the subject
  • LA is the size of the left atrium of the subject.
  • TDI, A' is the tissue Doppler imaging velocity during atrial contraction of the subject.
  • the indicated offsets and weighting coefficients in Formula VII can be varied by a maximum of 10%.
  • the offsets and weighting coefficients vary by a maximum of 5%, 4% or 3%. More preferably, the offsets and weighting coefficients vary by a maximum of 2% or, most preferably, they vary by a maximum of 1%.
  • the threshold score derived by applying Formula VI on the threshold for S 4 is about 54.88. This means presence of paroxysmal atrial fibrillation is predicted when the linear test score equals or exceeds said threshold score.
  • threshold score depends on the parameter set which is determined. This means that depending on the parameters that are actually determined in addition to the basic four parameters the threshold score for the linear model varies.
  • threshold scores L r tM for the different parameter combinations in linear scores L r can be obtained by inserting logarithmic model thresholds S r thd from Table 1 in Formula VI.
  • presence of paroxysmal atrial fibrillation in a subject to be examined can be predicted by using either a logistic or a linear function model.
  • a system for predicting paroxysmal atrial fibrillation comprising
  • an ultrasound apparatus for determining at least the parameters tissue Doppler imaging velocity during atrial contraction, left atrium size, and aortic root diameter
  • an output device wherein the analyzing device is embodied for analyzing the information provided by the ultrasound apparatus and the input device, and the output device is embodied for outputting the result derived from the analysis by indicating whether an increased probability for paroxysmal atrial fibrillation is present.
  • the present invention provides a system that can easily be incorporated within clinical routine examinations.
  • the components required are usually present in clinics anyway and therefore it merely requires the integration of the desired algorithms within the analyzing device to obtain the scores as disclosed herein.
  • This simple integration brings along the highly useful support in making the decision whether additional treatments like, for example, Holier ECG, should be applied to a subject or not. Due to the ability of the system to predict an increased probability for pAF patients can be identified that might suffer from pAF, which usually stays unobserved.
  • the system does not require any specific examination of a subject but works with data obtained by routine examinations.
  • the doctor in charge can be provided with this information during performing the routine examination with the difference that when a decision has to be made whether the subject needs further treatment, support is provided by the system as disclosed herein.
  • Figure 1 is a schematic depiction of a system for predicting the presence of pAF.
  • An ultrasound apparatus 1 is used to obtain echocardiography data by examining the subject to be investigated. These data are transferred to an analyzing device 3.
  • the analyzing device 3 is embodied for analyzing the information provided by the ultrasound apparatus and the input device 2.
  • the input device 2 is used for entering additional data for parameters to be determined. Such data can, for example, be the age of the subject to be investigated or whether the subject is a smoker or suffers of hyperlipidemia or diabetes mellitus. Further data that can be entered via the input device are data relating to the medication like the intake of beta blockers etc.
  • As input device any kind of computer like a computer, laptop, a tablet or a cellphone can be used.
  • the analyzing device 3 can, for example, be any kind of computer suitable for carrying out the required analysis.
  • the data can then be transferred from the analyzing device to an output device 4 which allows the presentation of the obtained result in any suitable way.
  • the information provid- ed by the output device should in some way provide the information whether the examined subject has an increased probability for the presence of pAF or not.
  • connection between the different components can be via wire or can be wireless. How the data can be transferred between the different components is known to a person skilled in the art.
  • the ultrasound apparatus can be any device known by persons skilled in the art suitable for determining echocardiographic parameters in particular tissue Doppler imaging velocity during atrial contraction, left atrium size and aortic root diameter.
  • tissue Doppler imaging velocity during atrial contraction is a device known by persons skilled in the art suitable for determining echocardiographic parameters in particular tissue Doppler imaging velocity during atrial contraction, left atrium size and aortic root diameter.
  • Examples of such commercially available ultrasound systems are Vivid S5, Vivid i, Vivid 7 and Vivid E9 GE Healthcare Ving- med, Trondheim, Norway and ie33, Philips, Eindhoven, the Netherlands.
  • the input device can be any device known to a person skilled in the art suitable for entering or feeding data into the system. Examples for such input devices are any kind of keyboards optionally integrated within a tablet, a laptop or a cellphone.
  • the input device can further comprise a storing device for storing the entered data. Such a combined device could be a computer.
  • the analyzing device can be any kind of device known to a person skilled in the art suitable for analyzing the entered data in order to derive the desired scores like test scores, threshold scores, offsets, weight coefficients etc. Furthermore, such an analyzing device should be able to combine the obtained data in a way that the test scores are obtained by the Formulas for a logistic (Formula I) or linear model (Formula V), as described above.
  • the analyzing device should be embodied for evaluating the information provided by the ultrasound apparatus and the input device. Thus, the information obtained with either the ultrasound apparatus or the input device come together within the analyzing device in order to obtain the test score, which derives from the data obtained by both the ultrasound apparatus and the input device.
  • the output device is any kind of device suitable for displaying the result derived from the analysis of the analyzing device. Thereby the output device should be embodied in order to indicate whether an increased probability for pAF is present or not.
  • Output devices are any kind of displays like, for example, on a computer, on a tablet or on a cellphone or on any kind of screen.
  • the different components of the system can be connected in any way embodied for transferring the data to be transferred as known to a person skilled in the art. I.e. the components can be connected via wire or wireless or obtain the data from any storage system that can be used for these devices.
  • system disclosed herein is embodied to perform the method for predicting pAF as disclosed herein.
  • LA is the size of the left atrium of the subject.
  • age is the age of the subject.
  • weights for the parameters LA and age are 0.12329 and 0.03436, respectively. Since the other parameters are not included within this set their values can be considered as being 0.
  • the following is the logistic model equation for the method according to the present invention comprising the determination of the four parameters tissue Doppler imaging velocity to an atrial contraction, left atrium size, age and aortic root diameter.
  • TDI,A' is the tissue Doppler imaging velocity during atrial contraction of the subject
  • LA is the size of the left atrium of the subject
  • age is the age of the subject.
  • Ao,root is the diameter of the aortic root of the subject.
  • the weights for the parameters TDI,A', LA, age and Ao,root are -0.1382, 0.08260, 0.04363 and 0.0802, respectively. Since the other parameters are not included within this set their values can be considered as being 0.
  • age is the age of the subject
  • Ao,root is the diameter of the aortic root of the subject
  • LA is the size of the left atrium of the subject
  • LV left ventricular end-systolic diameter of the subject
  • TDI,A' is the tissue Doppler imaging velocity during atrial contraction of the subject
  • HF is the heart frequency of the subject
  • sleep apnea indicates whether the subject has sleep apnea
  • hyperlipidemia indicates whether the subject has hyperlipidemia
  • type II diabetes indicates whether the subject has type II diabetes
  • ⁇ blocker indicates whether the subject is on ⁇ blockers
  • catheter ablation indicates whether the subject had a catheter ablation.
  • TDI tissue Doppler imaging velocity during atrial contraction of the subject
  • LA is the size of the left atrium of the subject
  • age is the age of the subject.
  • Ao,root is the diameter of the aortic root of the subject.
  • weights for the parameters LA and age are 2.587 and 0.7211, respectively. Since the other parameters are not included within this set their values can be considered as being 0.
  • the offset is -95.19.
  • TDI tissue Doppler imaging velocity during atrial contraction of the subject
  • LA is the size of the left atrium of the subject
  • age is the age of the subject.
  • Ao,root is the diameter of the aortic root of the subject.
  • the weights for the parameters TDI, A', LA, age and Ao,root are -2.770, 1.656, 0.8746 and 1.608, respectively. Since the other parameters are not included within this set their values can be considered as being 0.
  • the offset is -95.89. In this case at 80% sensitivity a threshold score of L 4 > 54.88 has to be attained for classification as pAF.
  • age is the age of the subject
  • Ao,root is the diameter of the aortic root of the subject
  • LA is the size of the left atrium of the subject
  • LV left ventricular end-systolic diameter of the subject
  • TDI,A' is the tissue Doppler imaging velocity during atrial contraction of the subject
  • HF is the heart frequency of the subject
  • sleep apnea indicates whether the subject has sleep apnea
  • hyperlipidemia indicates whether the subject has hyperlipidemia
  • type II diabetes indicates whether the subject has type II diabetes
  • ⁇ blocker indicates whether the subject is on ⁇ blockers
  • catheter ablation indicates whether the subject had a catheter ablation.
  • Vaziri SM Larson MG, Benjamin EJ, Levy D. Echocardiographic predictors of nonrheumatic atrial fibrillation. The Framingham Heart Study. Circulation. 1994;89: 724-730.

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Abstract

The present invention refers to a method for predicting paroxysmal atrial fibrillation in a subject. Furthermore, the present invention refers to a system allowing the prediction of paroxysmal atrial fibrillation.

Description

MATHEMATICAL MODEL AND SCORE FOR PREDICTING THE PRESENCE OF PAROXYSMAL ATRIAL FIBRILLATION
The present invention refers to a method for predicting paroxysmal atrial fibrillation in a subject. Furthermore, the present invention refers to a system allowing the prediction of paroxysmal atrial fibrillation.
Atrial fibrillation (AF) is the most frequent rhythm disorder, and its prevalence is expected to further increase due to demographic transition [1]. In some cases, AF is firstly diagnosed after a stroke or a transient ischemic event. For this reason, early diagnosis of AF episodes is essential. In particular, paroxysmal AF (pAF) often remains unobserved, in contrast to chronic AF (cAF), and is a frequent cause of cryptogenic ischemic stroke [2-5]. Further optimization of easy im- plementable non-invasive methods for pAF detection represents an important task for transla- tional electrophysiological research, as recently declared in the EHRA roadmap to improve the quality of atrial fibrillation management [4] .
Traditionally, surface electrocardiogram (ECG) is the basic method for AF diagnosis. Holter ECG monitoring is used to detect pAF [6] . In addition, intra-cardiac ECG measured with cardiac device electrodes or catheter electrodes during ablation procedures is used for AF detection. Risk stratification tools were established for the prevention of stroke, transient ischemic attacks or other thromboembolic complications. In particular, the CHADS2 and CHA2DS2-VASC scores are part of the common clinical practice for guiding prophylactic anticoagulation therapy [6] . It can be expected that, within the context of the evolving area of systems medicine, further predictive models will be developed, which integrate clinical parameters from different diagnostic techniques, to predict the individual risk for the development of pathologies and can be used to optimize personalized therapies.
Previous studies have analyzed the pathophysiological involvement of echocardiographic parameters that reflect hemodynamic alterations in the development of AF, in order to improve the risk assessment of individual patients for developing AF. Patients with non-rheumatic atrial fibrillation showed left atrial (LA) enlargement, increased left ventricular (LV) wall thickness, and reduced end-diastolic to end-systolic fractional shortening of the LV [7]. It was shown that at a higher age, echocardiographic measures of the diastolic function are significantly associated with an increased risk of AF [8] . Left ventricular dysfunction and LA size were shown to be pre- dictive for thromboembolic events in patients with non- valvular AF [9] . Implantable cardiac monitors were developed, which can be implanted in subjects who once suffered from a stroke of unknown cause. Said cardiac monitors allow continuously recording the heart rhythm for the following three years. Abnormalities are registered within certain time intervals. When the monitor is read out, abnormalities can be diagnosed by the doctor in charge. Thus, in the case of atrial fibrillation the patient can be treated accordingly in order to prevent any further strokes caused by atrial fibrillation. In particular, for pAF patients such a monitor would be useful since only by continuous observation these randomly occurring fibrillations can be detected. However, also the usage of such an implantable monitor would be associated with a surgical intervention besides high costs for the device.
This clearly demonstrates that there is a need for providing a method in order to predict pAF. While cAF can easily be detected, pAF often remains unobserved.
Therefore, one object of the present invention is to provide a method for easily and reliably predicting paroxysmal atrial fibrillation in a subject.
Another object of the present invention is to provide a system for predicting paroxysmal atrial fibrillation in a subject.
The objects are solved by a method and a system according to the independent claims. Preferred embodiments of the invention are defined in the corresponding subclaims.
According to the first aspect of the present invention a method is provided for predicting paroxysmal atrial fibrillation comprising
i) the step of determining at least the following parameters of a subject:
a) age, and
b) left atrium size (LA),
ii) deriving a test score based on the measured parameters;
iii) providing a threshold score; and
iv) comparing the test score with the threshold score, wherein a test score equal to or higher than the threshold score is predictive for the presence of paroxysmal atrial fibrillation. With this method it is possible to predict whether paroxysmal atrial fibrillation is present in a subject or not. Thus, this method allows to easily and reliably determine if any (medical) treatment should be applied to the patient in order to prevent any (further) stroke.
An advantage coming along with the method according to the present invention is that for the prediction of paroxysmal atrial fibrillation no time-consuming diagnostic procedures or surgical intervention are required. Rather, only by collecting easy obtainable data said prediction can be made. Hence, by analyzing pathophysiological aspects and echocardiographic parameters in AF a method is provided for classification between sinus rhythm (SR) and pAF. In the clinical practice this method contributes to the early detection of pAF in patients undergoing an echocardiographic investigation and therefore creates an additional diagnostic value of echocardiographic param- eters. The indication of a risk for pAF could suggest conducting further electrophysiological investigations to verify the presence of pAF.
As "subject" any human or animal subject is meant. Preferably the subject is a human.
The data required for the method according to the present invention is clinical data which comprises basic physiologic (age and smoker), cardiologic and medical history parameters (heart frequency, sleep apnea, hyperlipidemia, type 2 diabetes mellitus, catheter ablation). Additionally, medication data (intake of beta blocker) and echocardiographic parameters (left ventricular end- diastolic diameter (LV ESD), aortic root diameter, left atrial diameter (LA) and tissue Doppler imaging velocity during atrial contraction (TDI, A') are comprised.
Of these various parameters LA, and age represent the basic set of parameters that are de- termined in order to make a reliable prediction. The determination of any further parameters is optional, whereby any combination of parameters can be chosen.
The third most significant parameter is TDI, A' and the fourth most significant parameter for a reliable prediction is aortic root diameter. Thus, preferably in addition to LA and age, TDI, A' and/or aortic root diameter should be included.
Preferably, in addition to LA, and age and, optionally to TDI, A', and/or aortic root diameter, it is determined whether the subject has hyperlipidemia and/or type II diabetes mellitus.
These two additional parameters are easily obtainable by routine in vitro blood sample testings.
Additionally, as further parameter at least one or any combination of two or more of cathe- ter ablation, left ventricular injection fraction, heart rate, sleep apnea, intake of beta blocker and smoker can be determined. By adding further parameters to the set of parameters to be determined the performance of the method according to the invention can be increased in order to classify between pAF and SR more reliably.
In a preferred embodiment, the method disclosed herein comprises the step of determining at least one of the following sets of parameters:
A) Tissue Doppler imaging velocity during atrial contraction, left atrium size, age and aortic root diameter;
B) Tissue Doppler imaging velocity during atrial contracting, left atrium size, age, aortic root diameter and hyperlipidemia; C) Tissue Doppler imaging velocity during atrial contracting, left atrium size, age, aortic root diameter and diabetes mellitus;
D) Tissue Doppler imaging velocity during atrial contracting, left atrium size, age, aortic root diameter, hyperlipidemia and diabetes mellitus; and/or
E) Tissue Doppler imaging velocity during atrial contracting, left atrium size, age, aortic root diameter, hyperlipidemia, diabetes mellitus, catheter ablation, left ventricular end-diastolic diameter, heart rate, sleep apnea, intake of beta blocker and smoker.
Data concerning physiological or medication parameters can easily be obtained from a subject, for example, by personal interrogation. The same applies to medical history parameters.
For a person skilled in the art it is common knowledge how the echocardiographic parameters can be determined. For example, echocardiographic examinations can be performed on commercially available ultrasound systems (Vivid S5, Vivid i, Vivid 7 and Vivid E9 GE Healthcare Vingmed, Trondheim, Norway and ie33, Philips, Eindhoven, the Netherlands) according to the guidelines of the American Society of Echocardiography [10].
In a preferred embodiment, the echocardiography images include parasternal, apical and subxiphoidal views using 1.5 to 4.0 MHz phase-array transducers. Preferably, all the examinations are performed with 2D echocardiography for anatomic imaging and Doppler echocardiography for assessment of velocities.
Preferably, LA size is determined as the maximal distance between the posterior aortic root wall and the posterior left atrial wall at the end of systole. Aortic root diameter and LV end- systolic diameters (LV, ESD) are preferably obtained in the parasternal long axis view. Tissue Doppler images (TDI) velocities are preferably measured in the apical four chamber view. Preferably, images are digitally stored in a Picture Archiving and Communication System (PACS) and analyzed at clinical workstations.
The presence of hyperlipidemia or diabetes can easily be determined by performing in vitro assays. In terms of hyperlipidemia the lipids within a blood sample are measured. For testing the presence of type II diabetes mellitus, the glucose concentration within the blood is measured. These assays are standard in vitro methods, which are known to a person skilled in the art. In order to perform these assays a blood sample from the patient is required.
In terms of the binary parameters "smoker", "sleep apnea", "hyperlipidemia", "type II diabetes mellitus", "catheter ablation" and "intake of beta blocker" it has to be determined whether the subject to be examined actually does smoke, suffers from sleep apnea, suffers from hyperlipidemia, suffers from type II diabetes mellitus, has had the catheter ablation or takes beta blockers, respectively. Accordingly, if a property is present in a patient, parameters "smoker", "sleep apnea" "hy- perlipidemia", "type II diabetes mellitus", "catheter ablation" or "intake of beta blocker" will have a value of 1, or otherwise will have a value of 0 in absence of the property. Once the desired parameters have been determined, their combination allows deriving a test score based on the measured parameters. Beside the determination of the desired parameters and the derivation of the test score based on the measured parameters a threshold score is provided. The threshold score represents a value allowing to conclude whether the result of the method actually indicates an increased probability for the presence of paroxysmal atrial fibrillation in the examined subject or not. If the derived test score equals or exceeds the provided threshold score, the result can be interpreted as the presence of an increased probability for paroxysmal atrial fibrillation in the examined subject. Given the case that, according to the performed method as disclosed herein, presence of pAF can be assumed, further (electrophysiological) investigations can be conducted to verify the presence of pAF.
Thus, the method disclosed herein allows predicting the presence of pAF by using the data for parameters that are obtained anyway when the subject to be investigated is undergoing an echocardiographic examination. Hence, without any additional effort when performing these routine examinations, a prediction for the presence of pAF can be made, which usually is difficult to diagnose. By this, patients can be identified for which it might be necessary to conduct, for example, a long term observation of the cardiac functions. This, in the end, can lead to the verification of the presence of pAF, allowing an application of the required treatment. Only when being aware of an increased risk for pAF the subject can be treated accordingly, and the risk for suffering from (another) stroke can be decreased or even eliminated.
In order to derive the test score the determined parameters can be combined either in a logistic or in a linear function.
When combining the determined parameters in a logistic function, the formula for obtaining the test score Sr is
Figure imgf000008_0001
(Formula I)
with the score variants r, the offsets ΘΟ,Γ and the weights
. n . n . n . n . n
θ - \θΠ ag9Ρe,r ','θ ^Ao,rooi,r 5' U LT AA,r r ?' ^L VV,E F.SSDD,r r ? ^ "rTDI ,A 4 ' r r ?' ^ HHFF ,r r ?' ^ ^ sleep apnea, r '
Figure imgf000008_0002
yper p ema L,, rr '' ttyyppee IIII ddiiaabbeetteess,, rr '' ssmmookkeerr,, rr '' β [ blocker, r ablation, r and wherein
10 age is the age of the subject,
Ao,root is the diameter of the aortic root of the subject,
LA is the size of the left atrium of the subject,
LV, ESD is left ventricular end-systolic diameter of the subject,
TDI,A' is the tissue Doppler imaging velocity during atrial contraction of the subject,
HF is the heart frequency of the subject,
sleep apnea indicates whether the subject has sleep apnea,
hyperlipidemia indicates whether the subject has hyperlipidemia,
type II diabetes indicates whether the subject has type II diabetes,
smoker indicates whether the subject is a smoker,
β blocker indicates whether the subject is on β blockers, and
catheter ablation indicates whether the subject had a catheter ablation.
In Formula I and all other formulas disclosed herein the score variant indices r represent the parameter subsets to be determined. In said generalized formula for the logistic score, which contains the subsets of variables and offsets, weights are either 0 for not included variables or have values obtained by logistic regression based on a training set of patient data. To obtain weights and offsets, maximum likelihood estimation algorithms can be applied.
The offsets and the weights depend on the amount and the kind of parameters that are supposed to be determined. The corresponding values for the logistic coefficient offsets and the weights for the different parameters depending on the actual set of parameters can be found in table 1.
The formula for obtaining the logistic test score S2 with the two basic parameters age and
LA is
Figure imgf000009_0001
(Formula VIII)
wherein
age is the age of the subject, and
LA is the size of the left atrium of the subject.
In Formula I the offset ΘΟ,Γ equals -8.797, ΘΙΑ,Γ equals 0.12329, and 9age,r equals 0.03436. The indicated offsets and weighting coefficients in Formula I can be varied by a maximum of 10%. Preferably, the offsets and weighting coefficients vary by a maximum of 5%, 4% or 3%. More preferably, the offsets and weighting coefficients vary by a maximum of 2% or, most preferably, they vary by a maximum of 1 % . In the method of the invention the presence of paroxysmal atrial fibrillation is predicted when the logistic test score equals or exceeds a threshold score at a value of about 0.1506.
The formula for obtaining the logistic test score S4 with the four basic parameters TDI, A', LA, age and aortic root diameter is
Figure imgf000010_0001
(Formula II)
wherein
age is the age of the subject,
Ao,root is the diameter of the aortic root of the subject,
LA is the size of the left atrium of the subject,
TDI, A' is the tissue Doppler imaging velocity during atrial contraction of the subject.
In Formula II the offset ΘΟ,Γ equals -9.402, 9age,r equals 0.04363, ΘΑΟ,ΓΟΟΙ,Γ equals 0.08023, ΘΙΑ,Γ equals 0.08260 and ΘΊΌΙ,Α',Γ equals -0.1382.
The indicated offsets and weighting coefficients in Formula II can be varied by a maxi- mum of 10%. Preferably, the offsets and weighting coefficients vary by a maximum of 5%, 4% or 3% . More preferably, the offsets and weighting coefficients vary by a maximum of 2% or, most preferably, they vary by a maximum of 1 % .
When using Formula II, i.e., a logistic function with the four basic parameters age, aortic root diameter, left atrium size and tissue Doppler imaging velocity during atrial contraction the threshold score provided is about 0.1324. This means presence of paroxysmal atrial fibrillation is predicted when the logistic test score equals or exceeds said threshold score.
The threshold score depends on the parameter set which is determined. This means that depending on the parameters that are actually determined in addition to the basic four parameters the threshold score for the linear model varies. For all models Sr , threshold scores Sr thd were determined from receiver operating characteristic (ROC) curves. ROC curves indicate sensitivity versus false positive rate, equivalent to 1 - specificity, and are obtained by applying variable model thresholds on patient datasets. ROC curves were calculated after applying 100-fold crossvalidation of prediction models on a patient dataset. Threshold scores for testing with a sensitivity of 80% based on different parameter combinations can be found in table 1. To claim an easy implementable decision aid for initializing diagnostic testing for pAF the reduced logistic model can be transformed to a li model. Also, for the linear model the same principle applies, i.e., in case the derived test score equals or exceeds a provided threshold score, this dicts the presence of pAF is increased and further diagnostic procedures such as Holter ECG can be applied to validate or disprove the presenc pAF.
To derive linear scores the calibrated logistic model with two, three, four, five, six, seven, eight, nine, ten, eleven or twelve parameters ca transformed to linear scores. Logarithmic scores Sr can be transformed to linear scores r for the same subset of variables. Accordingly, li scores read
Figure imgf000011_0001
HF
+ eHF,r · jj— + ¾eep aPnea,r - sleeP aPnea + ¾yperiiPidemia,r - yperlipidemia + „ · type II diabet
+ ¾moker,r - smoker + ¾ blocker r · p blocker + < catheter ablation r · catheter ablation
(Formula III).
To simplify using linear scores, they can be transformed to a standardized interval by subtracting the 1% percentile value of J r ,
Figure imgf000011_0002
{J r ) , tiplying by 100, and dividing by the difference between the 99% percentile and the 1% percentiles, p99 (Jr )— Pi( r ) , which results in
Figure imgf000011_0003
(Formula IV)
A person skilled in the art would know that the coefficients of the linear scores are redundant once knowing the values of the logistic coeffi scores. Therefore, Table 1 contains only the coefficients of logarithmic model scores, values for p{ (J r ) , and differences pg9 (J, ) - Pi (Jr ) . cordingly, linear score weights are obtained by inserting Formula III in Formula IV, which results in
age Λ Ao. root _LA LV, ESP ΤΡΙ, Α'
LR - Y0,r + rage,r ' + ΨΑ + YLA,r h YLV,ESD,r h YTDI,A',r ', V rHF,r ' ~ y mm mm mm cm/s 1/
+ ^sieep apneas - sleep apnea + ^hyperi^demm^ - hyperlipidemia + ^type n diabetes r · type II diabetes + Smoker,,- ' smoker + ^ bIocker>r · β blocker + Catheter abiadon,,- ' catheter ablation
(Formula V)
with offsets φ() Γ and weights
Φ ~ Wage, Γ ΦΑΟ, root, r ^LA,r WLV ,ESD ,r ' ΦτΌΙ , A', r ^HF,r ' Asleep apnea, r '
^hyperlipidemia, r ' ^type II diabetes, r > ^smoker, blocker, r ' ^catheter ablation, r wherein
age is the age of the subject,
Ao,root is the diameter of the aortic root of the subject,
LA is the size of the left atrium of the subject,
LV, ESD is left ventricular end-systolic diameter of the subject,
TDI,A' is the tissue Doppler imaging velocity during atrial contraction of the subject, HF is the heart frequency of the subject,
sleep apnea indicates whether the subject has sleep apnea,
hyperlipidemia indicates whether the subject has hyperlipidemia,
type II diabetes indicates whether the subject has type II diabetes,
smoker indicates whether the subject is a smoker,
β blocker indicates whether the subject is on β blockers, and
catheter ablation indicates whether the subject had a catheter ablation.
To predict presence of pAF based on linear scores, as for logarithmic scores, score values have to equal or exceed certain threshold values Lr thd . These threshold values for linear scores are obtained, according to Formulas I to IV, by inserting threshold scores for logarithmic model scores S ,d into
Figure imgf000013_0001
(Formula VI)
When transforming S2 (Formula VIII), according to Formulas III to V, to a linear function in order to obtain the linear test score L2 it reads
Li = -95.19 + 2.587■— + 0.7211 ·^- mm y
wherein
age is the age of the subject,
LA is the size of the left atrium of the subject.
The indicated offsets and weighting coefficients in Formula VII can be varied by a maximum of 10%. Preferably, the offsets and weighting coefficients vary by a maximum of 5%, 4% or 3%. More preferably, the offsets and weighting coefficients vary by a maximum of 2% or, most preferably, they vary by a maximum of 1%.
In the method of the invention the presence of paroxysmal atrial fibrillation is predicted when the linear test score equals or exceeds a threshold score at a value of about 53.10. When transforming S4 (Formula II), according to Formulas III to V, to a linear function in order to obtain the linear test score L4 it reads
L4 = -95.89 + 0.8746 · ^ + 1.608 - + 1.656 - ^ - 2.770 ·
y mm mm cm/s
(Formula VII)
wherein
age is the age of the subject,
Ao,root is the diameter of the aortic root of the subject,
LA is the size of the left atrium of the subject, and
TDI, A' is the tissue Doppler imaging velocity during atrial contraction of the subject. The indicated offsets and weighting coefficients in Formula VII can be varied by a maximum of 10%. Preferably, the offsets and weighting coefficients vary by a maximum of 5%, 4% or 3%. More preferably, the offsets and weighting coefficients vary by a maximum of 2% or, most preferably, they vary by a maximum of 1%.
When using Formula VII, i.e. a linear function with the four basic parameters age, aortic root diameter, left atrium size and tissue Doppler imaging velocity during atrial contraction the threshold score derived by applying Formula VI on the threshold for S4 , again for testing with 80% sensitivity, is about 54.88. This means presence of paroxysmal atrial fibrillation is predicted when the linear test score equals or exceeds said threshold score.
The threshold score depends on the parameter set which is determined. This means that depending on the parameters that are actually determined in addition to the basic four parameters the threshold score for the linear model varies. For testing with 80% sensitivity, threshold scores Lr tM for the different parameter combinations in linear scores Lr can be obtained by inserting logarithmic model thresholds Sr thd from Table 1 in Formula VI.
By the method according to the present invention, presence of paroxysmal atrial fibrillation in a subject to be examined can be predicted by using either a logistic or a linear function model.
According to a further aspect of the present invention, a system is provided for predicting paroxysmal atrial fibrillation comprising
i) an ultrasound apparatus for determining at least the parameters tissue Doppler imaging velocity during atrial contraction, left atrium size, and aortic root diameter,
ii) an input device,
iii) an analyzing device, and
iv) an output device, wherein the analyzing device is embodied for analyzing the information provided by the ultrasound apparatus and the input device, and the output device is embodied for outputting the result derived from the analysis by indicating whether an increased probability for paroxysmal atrial fibrillation is present.
Thus, the present invention provides a system that can easily be incorporated within clinical routine examinations. The components required are usually present in clinics anyway and therefore it merely requires the integration of the desired algorithms within the analyzing device to obtain the scores as disclosed herein. This simple integration, however, brings along the highly useful support in making the decision whether additional treatments like, for example, Holier ECG, should be applied to a subject or not. Due to the ability of the system to predict an increased probability for pAF patients can be identified that might suffer from pAF, which usually stays unobserved.
For the system according to the present invention no special instruments or devices are required. Rather, the system works with devices present in clinics anyway.
Additionally, the system does not require any specific examination of a subject but works with data obtained by routine examinations. By automatically calculating the required scores and presenting the final result the doctor in charge can be provided with this information during performing the routine examination with the difference that when a decision has to be made whether the subject needs further treatment, support is provided by the system as disclosed herein.
The system according to the invention will now be explained by way of an example with reference to the attached drawing in which Figure 1 is a schematic depiction of a system for predicting the presence of pAF.
An ultrasound apparatus 1 is used to obtain echocardiography data by examining the subject to be investigated. These data are transferred to an analyzing device 3. The analyzing device 3 is embodied for analyzing the information provided by the ultrasound apparatus and the input device 2. The input device 2 is used for entering additional data for parameters to be determined. Such data can, for example, be the age of the subject to be investigated or whether the subject is a smoker or suffers of hyperlipidemia or diabetes mellitus. Further data that can be entered via the input device are data relating to the medication like the intake of beta blockers etc. As input device any kind of computer like a computer, laptop, a tablet or a cellphone can be used. The analyzing device 3 can, for example, be any kind of computer suitable for carrying out the required analysis. The data can then be transferred from the analyzing device to an output device 4 which allows the presentation of the obtained result in any suitable way. The information provid- ed by the output device should in some way provide the information whether the examined subject has an increased probability for the presence of pAF or not.
The connection between the different components can be via wire or can be wireless. How the data can be transferred between the different components is known to a person skilled in the art.
The ultrasound apparatus can be any device known by persons skilled in the art suitable for determining echocardiographic parameters in particular tissue Doppler imaging velocity during atrial contraction, left atrium size and aortic root diameter. Examples of such commercially available ultrasound systems are Vivid S5, Vivid i, Vivid 7 and Vivid E9 GE Healthcare Ving- med, Trondheim, Norway and ie33, Philips, Eindhoven, the Netherlands.
The input device can be any device known to a person skilled in the art suitable for entering or feeding data into the system. Examples for such input devices are any kind of keyboards optionally integrated within a tablet, a laptop or a cellphone. The input device can further comprise a storing device for storing the entered data. Such a combined device could be a computer.
The analyzing device can be any kind of device known to a person skilled in the art suitable for analyzing the entered data in order to derive the desired scores like test scores, threshold scores, offsets, weight coefficients etc. Furthermore, such an analyzing device should be able to combine the obtained data in a way that the test scores are obtained by the Formulas for a logistic (Formula I) or linear model (Formula V), as described above. The analyzing device should be embodied for evaluating the information provided by the ultrasound apparatus and the input device. Thus, the information obtained with either the ultrasound apparatus or the input device come together within the analyzing device in order to obtain the test score, which derives from the data obtained by both the ultrasound apparatus and the input device.
The output device is any kind of device suitable for displaying the result derived from the analysis of the analyzing device. Thereby the output device should be embodied in order to indicate whether an increased probability for pAF is present or not. Output devices are any kind of displays like, for example, on a computer, on a tablet or on a cellphone or on any kind of screen.
The different components of the system can be connected in any way embodied for transferring the data to be transferred as known to a person skilled in the art. I.e. the components can be connected via wire or wireless or obtain the data from any storage system that can be used for these devices.
In a preferred embodiment the system disclosed herein is embodied to perform the method for predicting pAF as disclosed herein. Examples
Logistic models
Logistic model for the basic set of two parameters
The following is the logistic model equation for the method according to the present invention comprising the determination of the two basic parameters left atrium size and age. In order to derive the test score S2 based on the measurement of these parameters S2 is
1
S.
f I A
exp 8.797 - 0.12329 0.03436 ·
V mm y J
wherein
LA is the size of the left atrium of the subject, and
age is the age of the subject.
In this Formula the weights for the parameters LA and age are 0.12329 and 0.03436, respectively. Since the other parameters are not included within this set their values can be considered as being 0. The offset is θο = -8.797.
In this case at 80% sensitivity a threshold score of S2 > 0.1506 has to be attained for classification as pAF.
Logistic model for a set of four parameters
The following is the logistic model equation for the method according to the present invention comprising the determination of the four parameters tissue Doppler imaging velocity to an atrial contraction, left atrium size, age and aortic root diameter. In order to derive the test score S4 based on the measurement of these parameters S4 is
Figure imgf000017_0001
wherein
TDI,A' is the tissue Doppler imaging velocity during atrial contraction of the subject, LA is the size of the left atrium of the subject,
age is the age of the subject, and
Ao,root is the diameter of the aortic root of the subject. In this Formula the weights for the parameters TDI,A', LA, age and Ao,root are -0.1382, 0.08260, 0.04363 and 0.0802, respectively. Since the other parameters are not included within this set their values can be considered as being 0. The offset is θο = -9.402.
In this case at 80% sensitivity a threshold score of S4≥ 0.1324 has to be attained for classi- fication as pAF.
Logistic model for the set with all twelve parameters
The following is the formula for the method according to the present invention comprising the determination of all twelve parameters. In order to derive the test score S12 based on the measurement of these parameters S12 is
1
10.63 - 0.03922. ^ - 0.1016 - A°'rOOt - 0.08772. ^
y mm mm
exp - 0.03890. I^ESD + 0.1834. TOI^ - 0.01783. ^
mm cm s 1/min
- 1.282 - sleep apnea - 0.4872 · hyperlipidemia + 0.02329 · type II diabetes
- 0.08833 - smoker- 0.6197 -P blocker - 2.498 · catheter ablation
wherein
age is the age of the subject,
Ao,root is the diameter of the aortic root of the subject,
LA is the size of the left atrium of the subject,
LV, ESD is left ventricular end-systolic diameter of the subject,
TDI,A' is the tissue Doppler imaging velocity during atrial contraction of the subject, HF is the heart frequency of the subject,
sleep apnea indicates whether the subject has sleep apnea,
hyperlipidemia indicates whether the subject has hyperlipidemia,
type II diabetes indicates whether the subject has type II diabetes,
smoker indicates whether the subject is a smoker,
β blocker indicates whether the subject is on β blockers, and
catheter ablation indicates whether the subject had a catheter ablation.
In this case at 80% sensitivity a threshold score of S12 > 0.1384 has to be attained for clas sification as pAF. Linear models
Linear model for the basic set of four parameters
The following is the linear formula for the method according to the present invention comprising the determination of the two basic parameters left atrium size and age. In order to derive the test score L2 based on the measurement of these parameters L2 is
L4 = -95.19 + 2.587 -— + 0.7211 - ^- mm y
wherein
TDI, A' is the tissue Doppler imaging velocity during atrial contraction of the subject,
LA is the size of the left atrium of the subject,
age is the age of the subject, and
Ao,root is the diameter of the aortic root of the subject.
In this Formula the weights for the parameters LA and age are 2.587 and 0.7211, respectively. Since the other parameters are not included within this set their values can be considered as being 0. The offset is
Figure imgf000019_0001
-95.19.
In this case at 80% sensitivity a threshold score of L4 > 53.10 has to be attained for classification as pAF.
Linear model for a set of four parameters
The following is the linear formula for the method according to the present invention comprising the determination of the four parameters tissue Doppler imaging velocity to an atrial contraction, left atrium size, age and aortic root diameter. In order to derive the test score L based on the measurement of these parameters L4 is
L4 = -95.89 - 2.700 -≡^ + 1.656 - + 0.8746 + 1.608 - cm/s mm y mm
wherein
TDI, A' is the tissue Doppler imaging velocity during atrial contraction of the subject,
LA is the size of the left atrium of the subject,
age is the age of the subject, and
Ao,root is the diameter of the aortic root of the subject. In this Formula the weights for the parameters TDI, A', LA, age and Ao,root are -2.770, 1.656, 0.8746 and 1.608, respectively. Since the other parameters are not included within this set their values can be considered as being 0. The offset is
Figure imgf000020_0001
-95.89. In this case at 80% sensitivity a threshold score of L4 > 54.88 has to be attained for classification as pAF.
Linear model for the basic set of all twelve parameters
The following is the formula for the method according to the present invention comprising the determination of all twelve parameters. In order to derive the test score L12 based on the measurement of these parameters L12 is
L12 = -79.26 + 0.6135 + 1.589 - + 1.371 - mm mm
LV,ESD TDI, A' HF
- 0.6084 - ■2.868 - - 0.2789 - mm cm/s 1/min
+ 20.04 · sleep apnea + 7.620 · hyperlipidemia - 0.3643■ type II diabetes
+ 1.381■ smoker + 9.692 · β blocker + 39.06 · catheter ablation wherein
age is the age of the subject,
Ao,root is the diameter of the aortic root of the subject,
LA is the size of the left atrium of the subject,
LV, ESD is left ventricular end-systolic diameter of the subject,
TDI,A' is the tissue Doppler imaging velocity during atrial contraction of the subject,
HF is the heart frequency of the subject,
sleep apnea indicates whether the subject has sleep apnea,
hyperlipidemia indicates whether the subject has hyperlipidemia,
type II diabetes indicates whether the subject has type II diabetes,
smoker indicates whether the subject is a smoker,
β blocker indicates whether the subject is on β blockers, and
catheter ablation indicates whether the subject had a catheter ablation.
In this case at 80% sensitivity a threshold score of L12 > 58.33 has to be attained for classi- fication as pAF. Schmidt C, Kisselbach J, Schweizer PA, Katus HA, Thomas D. The pathology and treatment of cardiac arrhythmias: focus on atrial fibrillation. Vase Health Risk Manag. 2011;7: 193-202. doi: 10.2147/VHRM.S 10758
Christensen LM, Krieger DW, H0jberg S, Pedersen OD, Karlsen FM, Jacobsen MD, et al. Paroxysmal atrial fibrillation occurs often in cryptogenic ischaemic stroke. Final results from the SURPRISE study. Eur J Neurol. 2014;21: 884-889. doi: 10.1111/ene. l2400 Wohlfahrt J, Stahrenberg R, Weber-Kruger M, Groschel S, Wasser K, Edelmann F, et al. Clinical predictors to identify paroxysmal atrial fibrillation after ischaemic stroke. Eur J Neurol. 2014;21: 21-27. doi: 10.1111/ene.12198
Kirchhof P, Breithardt G, Bax J, Benninger G, Blomstrom-Lundqvist C, Boriani G, et al. A roadmap to improve the quality of atrial fibrillation management: proceedings from the fifth Atrial Fibrillation Network/European Heart Rhythm Association consensus conference. Europace. 2015; doi: 10.1093/europace/euv304
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Table 1. Coefficients, threshold values and scaling coefficients for logarithmic models Sr m- threshdex- coefficients for coefficients of logistic model variants Θ olds
for rescaling to sr Sr,thd linear score
thresholds at
sleep hyperli- type II catheter
r offset age Ao,root LA LV,ESD smoker β DTI, A' 80%
apnea pidemia diabetes blocker ablation
sensitivity
1 -10.626 0.03922 0.10159 0.08772 -0.0390 1.282 0.4872 -0.0233 0.0883 0, .6197 2.498 -0. .1834 0.01783 0.1384 6.394 -5. .558
2 -9.067 0.03806 0.09327 0.09142 -0.03743 1.225 0.3836 0.0282 0.1039 0, .5831 2.375 -0. .1846 0 0.1389 6.154 -5. .358
3 -12.366 0.03214 0.07657 0.11223 -0.02668 0.992 0.3035 0.0431 0.1730 0, .7837 1.985 0 0.02158 0.1483 6.038 -5. .145
4 -10.427 0.03019 0.06510 0.11706 -0.02438 0.898 0.1719 0.0986 0.1891 0, .7416 1.848 0 0 0.1484 5.598 -4. .894
5 -10.458 0.03819 0.10057 0.08651 -0.03852 1.227 0.4647 -0.0628 0.0786 0, .6617 0 -0. .1725 0.01691 0.1327 6.203 -5. .422
6 -9.009 0.03748 0.09321 0.08981 -0.03682 1.178 0.3675 -0.0151 0.0922 0, .6219 0 -0. .1757 0 0.1393 5.777 -5. .197
7 -12.190 0.03118 0.07672 0.11129 -0.02735 0.958 0.3039 0.0082 0.1641 0, .8161 0 0 0.02111 0.1547 5.875 -5. .064
8 -10.310 0.02944 0.06556 0.11591 -0.02477 0.869 0.1744 0.0630 0.1788 0, .7704 0 0 0 0.1515 5.479 -4. .826
9 -10.550 0.04176 0.09799 0.09438 -0.03605 1.390 0.5971 -0.0484 0.1096 0 2.592 -0. .1895 0.01724 0.1343 6.155 -5. .330
10 -9.052 0.04065 0.09012 0.09752 -0.03492 1.330 0.4918 0.0057 0.1242 0 2.456 -0. .1901 0 0.1344 5.967 -5. .268
11 -12.300 0.03517 0.06998 0.12207 -0.02304 1.137 0.4200 0.0249 0.1991 0 2.093 0 0.02077 0.1291 5.880 -4. .972
12 -10.455 0.03335 0.05969 0.12616 -0.02120 1.041 0.2914 0.0825 0.2125 0 1.943 0 0 0.1351 5.484 -4. .786
13 -10.362 0.04090 0.09697 0.09329 -0.03537 1.342 0.5796 -0.0892 0.1014 0 0 -0. .1794 0.01618 0.1361 5.884 -5. .173
14 -8.987 0.04022 0.09009 0.09604 -0.03405 1.290 0.4808 -0.0391 0.1137 0 0 -0. .1817 0 0.1397 5.689 -5. .150
15 -12.103 0.03421 0.07005 0.12131 -0.02350 1.106 0.4245 -0.0118 0.1906 0 0 0 0.02020 0.1275 5.562 -4. .875
16 -10.329 0.03265 0.06011 0.12512 -0.02140 1.015 0.2974 0.0452 0.2025 0 0 0 0 0.1338 5.234 -4. .705
17 -10.603 0.03865 0.09675 0.09122 -0.03691 1.219 0.5096 0.0182 0 0, .6383 2.439 -0. .1694 0.01629 0.1371 6.330 -5. .489
18 -9.162 0.03753 0.08928 0.09404 -0.03508 1.176 0.4167 0.0482 0 0, .6026 2.334 -0. .1715 0 0.1457 6.000 -5. .235
19 -12.189 0.03074 0.07222 0.11567 -0.02479 0.936 0.3440 0.0757 0 0, .8002 1.954 0 0.02025 0.1511 5.917 -5. .013
20 -10.353 0.02886 0.06184 0.11916 -0.02184 0.865 0.2195 0.1062 0 0, .7568 1.821 0 0 0.1511 5.543 -4. .791
21 -10.447 0.03768 0.09579 0.09020 -0.03672 1.170 0.4886 -0.0228 0 0, .6781 0 -0. .1596 0.01545 0.1361 6.150 -5. .382
22 -9.106 0.03697 0.08913 0.09274 -0.03473 1.134 0.4011 0.0050 0 0, .6394 0 -0. .1632 0 0.1441 5.749 -5. .158
23 -12.022 0.02987 0.07228 0.11487 -0.02557 0.905 0.3430 0.0410 0 0, .8305 0 0 0.01979 0.1396 5.700 -4. .961
24 -10.244 0..02818 0.06219 0..11824 -0.02243 0, .837 0.2210 0.0712 0 0, .7839 0 0 0 0..1561 5.383 -4.743
25 -10.510 0. .04111 0.09332 0. .09791 -0.03383 1. .325 0.6211 0.0011 0 0 2.536 -0.1756 0.01563 0. .1355 6.056 -5.263
26 -9.137 0. .04005 0.08639 0. .10021 -0.03235 1. .280 0.5265 0.0342 0 0 2.418 -0.1770 0 0. .1359 5.956 -5.253
27 -12.113 0. .03368 0.06606 0. .12543 -0.02073 1. .075 0.4607 0.0664 0 0 2.063 0 0.01938 0. .1396 5.637 -4.826
28 -10.380 0. .03194 0.05690 0. .12823 -0.01831 1. .004 0.3388 0.0995 0 0 1.918 0 0 0. .1403 5.419 -4.722
29 -10.334 0. .04029 0.09233 0. .09702 -0.03335 1. .281 0.6043 -0.0405 0 0 0 -0.1664 0.01466 0. .1361 5.809 -5.127
30 -9.073 0. .03962 0.08624 0. .09903 -0.03175 1. .243 0.5153 -0.0099 0 0 0 -0.1692 0 0. .1362 5.652 -5.127
31 -11.925 0. .03280 0.06600 0. .12483 -0.02133 1. .047 0.4632 0.0303 0 0 0 0 0.01882 0. .1345 5.450 -4.737
32 -10.262 0. .03129 0.05718 0. .12746 -0.01873 0, .980 0.3434 0.0632 0 0 0 0 0 0. .1435 5.219 -4.653
33 -10.217 0. .03661 0.09976 0. .08363 -0.03775 1. .237 0.4441 0 0.0737 0, .6589 2.437 -0.1789 0.01701 0. .1407 6.198 -5.414
34 -8.763 0. .03572 0.09189 0. .08751 -0.03607 1. .195 0.3555 0 0.0889 0, .6222 2.317 -0.1804 0 0. .1420 5.988 -5.236
35 -12.018 0. .03032 0.07531 0. .10866 -0.02563 0, .973 0.2840 0 0.1599 0, .8126 1.935 0 0.02088 0. .1551 5.820 -4.994
36 -10.183 0. .02872 0.06420 0. .11384 -0.02320 0, .897 0.1669 0 0.1768 0, .7713 1.796 0 0 0. .1480 5.418 -4.759
37 -10.045 0. .03554 0.09889 0. .08232 -0.03760 1. .176 0.4161 0 0.0640 0, .7011 0 -0.1684 0.01608 0. .1337 6.006 -5.274
38 -8.694 0. .03503 0.09195 0. .08578 -0.03573 1. .141 0.3329 0 0.0775 0, .6613 0 -0.1717 0 0. .1417 5.609 -5.083
39 -11.835 0. .02930 0.07554 0. .10758 -0.02642 0, .933 0.2783 0 0.1509 0, .8448 0 0 0.02038 0. .1525 5.692 -4.939
40 -10.058 0. .02789 0.06474 0. .11253 -0.02374 0, .860 0.1634 0 0.1666 0, .7999 0 0 0 0. .1529 5.330 -4.717
41 -10.118 0. .03920 0.09592 0. .09051 -0.03480 1. .345 0.5564 0 0.0960 0 2.540 -0.1854 0.01635 0. .1373 5.911 -5.132
42 -8.733 0. .03839 0.08858 0. .09382 -0.03347 1. .302 0.4668 0 0.1102 0 2.406 -0.1863 0 0. .1395 5.726 -5.101
43 -11.940 0. .03338 0.06858 0. .11867 -0.02189 1. .118 0.4020 0 0.1865 0 2.049 0 0.02004 0. .1315 5.658 -4.812
44 -10.202 0. .03192 0.05868 0. .12309 -0.01991 1. .041 0.2883 0 0.2005 0 1.897 0 0 0. .1388 5.303 -4.659
45 -9.924 0. .03830 0.09503 0. .08929 -0.03434 1. .290 0.5331 0 0.0879 0 0 -0.1755 0.01528 0. .1395 5.631 -4.981
46 -8.655 0. .03785 0.08864 0. .09218 -0.03286 1. .253 0.4491 0 0.0999 0 0 -0.1780 0 0. .1402 5.510 -4.995
47 -11.733 0. .03236 0.06872 0. .11773 -0.02247 1. .080 0.4001 0 0.1779 0 0 0 0.01944 0. .1320 5.355 -4.718
48 -10.067 0. .03113 0.05917 0. .12188 -0.02026 1. .007 0.2881 0 0.1905 0 0 0 0 0. .1387 5.065 -4.577
49 -10.231 0. .03633 0.09488 0. .08755 -0.03575 1. .187 0.4753 0 0 0, .6729 2.375 -0.1654 0.01552 0. .1396 6.097 -5.335
50 -8.882 0. .03541 0.08784 0. .09053 -0.03387 1. .153 0.3932 0 0 0, .6387 2.274 -0.1675 0 0. .1451 5.857 -5.116
51 -11.874 0. .02918 0.07095 0. .11256 -0.02384 0, .926 0.3312 0 0 0, .8269 1.900 0 0.01956 0. .1451 5.734 -4.902
52 -10.133 0. .02756 0.06097 0. .11633 -0.02091 0, .865 0.2173 0 0 0, .7858 1.768 0 0 0. .1524 5.410 -4.699
53 -10.066 0. .03529 0.09403 0. .08638 -0.03579 1. .131 0.4484 0 0 0, .7126 0 -0.1558 0.01470 0. .1368 5.941 -5.232
54 -8.811 0. .03473 0.08776 0. .08903 -0.03377 1. .103 0.3710 0 0 0, .6754 0 -0.1594 0 0. .1466 5.562 -5.018
55 -11.698 0.02825 0.07106 0..11157 -0.02473 0, .889 0.3240 0 0 0.8566 0 0 0..01910 0..1436 5, .556 -4..854
56 -10.013 0.02679 0.06136 0. .11521 -0.02163 0, .831 0.2127 0 0 0.8124 0 0 0 0. .1579 5, .252 -4. .655
57 -10.121 0.03886 0.09127 0. .09445 -0.03257 1. .294 0.5899 0 0 0 2.479 -0. .1719 0, .01484 0. .1395 5 .866 -5. .102
58 -8.845 0.03802 0.08479 0. .09691 -0.03102 1. .259 0.5071 0 0 0 2.364 -0. .1734 0 0. .1372 5, .723 -5. .084
59 -11.792 0.03217 0.06466 0. .12251 -0.01966 1. .068 0.4507 0 0 0 2.013 0 0. .01869 0. .1443 5, .416 -4. .682
60 -10.154 0.03071 0.05590 0. .12560 -0.01723 1. .007 0.3401 0 0 0 1.870 0 0 0. .1462 5, .259 -4. .593
61 -9.935 0.03797 0.09038 0. .09338 -0.03231 1. .244 0.5669 0 0 0 0 -0. .1630 0, .01387 0. .1370 5 .609 -4. .977
62 -8.765 0.03747 0.08470 0. .09551 -0.03066 1. .214 0.4890 0 0 0 0 -0. .1657 0 0. .1387 5, .461 -4. .979
63 -11.593 0.03123 0.06465 0. .12169 -0.02037 1. .032 0.4468 0 0 0 0 0 0. .01812 0. .1362 5, .288 -4. .596
64 -10.024 0.02997 0.05622 0. .12461 -0.01778 0, .976 0.3383 0 0 0 0 0 0 0. .1466 5, .026 -4. .525
65 -10.475 0.04043 0.09859 0. .08828 -0.03685 1. .218 0 0. .0902 0.0941 0.7412 2.430 -0. ,1723 0. .01610 0. .1266 6, .375 -5. .369
66 -9.079 0.03915 0.09214 0. .09130 -0.03590 1. .175 0 0. .1154 0.1053 0.6862 2.331 -0. ,1759 0 0. .1316 6, .021 -5. .106
67 -12.228 0.03329 0.07579 0. .11203 -0.02587 0, .953 0 0. .1124 0.1745 0.8477 1.967 0 0. .02037 0. .1425 5, .912 -5. .053
68 -10.416 0.03092 0.06534 0. .11664 -0.02399 0, .876 0 0. .1374 0.1886 0.7823 1.846 0 0 0. .1406 5, .604 -4. .828
69 -10.325 0.03952 0.09799 0. .08695 -0.03651 1. .169 0 0. .0450 0.0828 0.7758 0 -0. ,1631 0. .01527 0. .1292 6, .136 -5. .269
70 -9.023 0.03859 0.09226 0. .08966 -0.03532 1. .132 0 0. .0684 0.0929 0.7191 0 -0. 1680 0 0. .1376 5, .620 -5. .021
71 -12.055 0.03236 0.07600 0. .11106 -0.02650 0, .919 0 0. .0771 0.1649 0.8797 0 0 0. .01990 0. .1469 5, .776 -4. .990
72 -10.299 0.03019 0.06582 0. .11545 -0.02436 0, .847 0 0. .1022 0.1781 0.8113 0 0 0 0. .1465 5, .429 -4. .754
73 -10.375 0.04399 0.09312 0. .09734 -0.03256 1. .336 0 0. .0929 0.1238 0 2.520 -0. .1766 0, .01493 0. .1286 6 .032 -5. .051
74 -9.089 0.04277 0.08770 0. .09928 -0.03209 1. .289 0 0. .1187 0.1321 0 2.407 -0. .1795 0 0. .1219 5, .839 -5. .051
75 -12.122 0.03719 0.06798 0. .12335 -0.02138 1. .097 0 0. .1224 0.2049 0 2.078 0 0. .01905 0. .1372 5, .656 -4. .832
76 -10.447 0.03494 0.05943 0. .12654 -0.02012 1. .016 0 0. .1492 0.2141 0 1.947 0 0 0. .1398 5, .443 -4. .719
77 -10.205 0.04328 0.09265 0. .09599 -0.03190 1. .292 0 0. .0473 0.1135 0 0 -0. .1683 0, .01394 0. .1223 5 .661 -4. .931
78 -9.026 0.04238 0.08792 0. .09767 -0.03125 1. .250 0 0. .0712 0.1203 0 0 -0. .1722 0 0. .1260 5, .482 -4. .940
79 -11.927 0.03631 0.06813 0. .12251 -0.02174 1. .066 0 0. .0860 0.1953 0 0 0 0. .01844 0. .1373 5, .531 -4. .737
80 -10.322 0.03430 0.05988 0. .12544 -0.02026 0, .989 0 0. .1129 0.2038 0 0 0 0 0. .1401 5, .276 -4. .637
81 -10.442 0.04005 0.09363 0. .09163 -0.03464 1. .155 0 0. .1317 0 0.7632 2.372 -0. .1588 0, .01449 0. .1264 6 .269 -5. .282
82 -9.171 0.03885 0.08793 0. .09389 -0.03337 1. .124 0 0. .1400 0 0.7122 2.287 -0. .1627 0 0. .1374 5, .980 -5. .086
83 -12.033 0.03216 0.07120 0. .11542 -0.02384 0, .894 0 0. .1516 0 0.8712 1.932 0 0. .01888 0. .1546 5, .929 -5. .009
84 -10.340 0.02989 0.06197 0. .11870 -0.02138 0, .838 0 0. .1549 0 0.8075 1.818 0 0 0. .1477 5, .547 -4. .727
85 -10.303 0.03919 0.09306 0. .09051 -0.03448 1. .111 0 0. .0856 0 0.7958 0 -0. .1504 0, .01374 0. .1284 5 .999 -5. .188
86 -9.118 0.03831 0.08796 0..09254 -0.03305 1..085 0 0.0933 0 0.7432 0 -0..1554 0 0..1433 5..658 -5..031
87 -11.871 0.03132 0.07133 0. .11460 -0.02459 0, .863 0 0.1160 0 0.9006 0 0 0. .01843 0. .1577 5. .764 -4. .948
88 -10.232 0.02923 0.06233 0. .11775 -0.02194 0, .810 0 0.1200 0 0.8344 0 0 0 0. .1538 5. .418 -4. .687
89 -10.322 0.04357 0.08834 0. .10075 -0.03002 1. .271 0 0.1442 0 0 2.463 -0. .1633 0, .01325 0. .1265 5, .944 -4. .977
90 -9.172 0.04245 0.08371 0. .10203 -0.02926 1. .236 0 0.1540 0 0 2.366 -0. .1664 0 0. .1245 5. .761 -4. .925
91 -11.918 0.03606 0.06370 0. .12678 -0.01887 1. .034 0 0.1712 0 0 2.044 0 0. .01747 0. .1471 5. .546 -4. .681
92 -10.375 0.03394 0.05635 0. .12881 -0.01711 0, .975 0 0.1770 0 0 1.921 0 0 0. .1478 5. .418 -4. .614
93 -10.163 0.04290 0.08787 0. .09962 -0.02957 1. .231 0 0.0981 0 0 0 -0. .1558 0, .01233 0. .1274 5, .663 -4. .874
94 -9.111 0.04207 0.08382 0. .10072 -0.02870 1. .201 0 0.1071 0 0 0 -0. .1597 0 0. .1300 5. .447 -4. .819
95 -11.734 0.03525 0.06374 0. .12612 -0.01939 1. .006 0 0.1348 0 0 0 0 0. .01689 0. .1435 5. .341 -4. .592
96 -10.259 0.03336 0.05667 0. .12798 -0.01747 0, .950 0 0.1413 0 0 0 0 0 0. .1407 5. .235 -4. .544
97 -10.151 0.03830 0.09698 0. .08485 -0.03527 1. .204 0 0 0.0810 0.7707 2.361 -0. .1689 0, .01551 0. .1312 6, .215 -5. .227
98 -8.825 0.03719 0.09072 0. .08800 -0.03420 1. .168 0 0 0.0920 0.7184 2.262 -0. .1723 0 0. .1333 5. .874 -5. .028
99 -11.930 0.03174 0.07451 0. .10896 -0.02458 0, .954 0 0 0.1629 0.8736 1.907 0 0. .01980 0. .1469 5. .771 -4. .933
100 -10.193 0.02963 0.06435 0. .11371 -0.02266 0, .885 0 0 0.1772 0.8115 1.787 0 0 0. .1478 5. .415 -4. .700
101 -9.991 0.03727 0.09651 0. .08338 -0.03524 1. .147 0 0 0.0698 0.8040 0 -0. .1600 0, .01469 0. .1339 5, .948 -5. .131
102 -8.754 0.03649 0.09095 0. .08621 -0.03396 1. .117 0 0 0.0798 0.7501 0 -0. .1647 0 0. .1394 5. .488 -4. .899
103 -11.751 0.03072 0.07480 0. .10784 -0.02536 0, .913 0 0 0.1532 0.9040 0 0 0. .01933 0. .1500 5. .650 -4. .874
104 -10.069 0.02880 0.06490 0. .11240 -0.02320 0, .849 0 0 0.1668 0.8388 0 0 0 0. .1538 5. .284 -4. .659
105 -10.053 0.04204 0.09140 0. .09427 -0.03080 1. .328 0 0 0.1118 0 2.454 -0. .1736 0, .01433 0. .1244 5, .836 -4. .899
106 -8.838 0.04102 0.08616 0. .09636 -0.03020 1. .288 0 0 0.1200 0 2.342 -0. .1762 0 0. .1246 5, .684 -4. .904
107 -11.833 0.03582 0.06660 0. .12067 -0.01987 1. .104 0 0 0.1940 0 2.019 0 0, .01847 0. .1409 5, .476 -4. .673
108 -10.233 0.03385 0.05831 0. .12403 -0.01855 1. .032 0 0 0.2036 0 1.889 0 0 0. .1438 5, .264 -4. .603
109 -9.870 0.04121 0.09103 0. .09273 -0.03044 1. .275 0 0 0.1015 0 0 -0. .1656 0, .01334 0. .1270 5, .461 -4. .781
110 -8.759 0.04047 0.08648 0. .09454 -0.02968 1. .241 0 0 0.1084 0 0 -0. .1692 0 0. .1313 5, .300 -4. .784
111 -11.630 0.03484 0.06682 0. .11964 -0.02039 1. .066 0 0 0.1844 0 0 0 0, .01786 0. .1430 5, .347 -4. .577
112 -10.099 0.03310 0.05884 0. .12276 -0.01886 0, .998 0 0 0.1933 0 0 0 0 0. .1416 5, .122 -4. .525
113 -10.156 0.03822 0.09202 0. .08875 -0.03300 1. .151 0 0 0 0.7922 2.295 -0. .1554 0, .01390 0. .1335 6, .127 -5. .143
114 -8.951 0.03714 0.08653 0. .09109 -0.03176 1. .123 0 0 0 0.7445 2.214 -0. .1589 0 0. .1373 5. .858 -4. .966
115 -11.772 0.03094 0.06995 0. .11295 -0.02258 0, .903 0 0 0 0.8974 1.865 0 0. .01826 0. .1598 5. .801 -4. .897
116 -10.151 0.02884 0.06106 0. .11630 -0.02024 0, .849 0 0 0 0.8376 1.756 0 0 0. .1529 5. .392 -4. .645
117 -10.005 0..03723 0.091540..08743 -0.03315 1.099 0 0 0 0.8229 0 -0..1474 0,.01317 0..1302 5,.817 -5..026
118 -8.879 0. .03644 0.08663 0. .08952 -0.03176 1.076 0 0 0 0.7736 0 -0. .1520 0 0. .1448 5. .464 -4. .902
119 -11.603 0. .03000 0.07012 0. .11194 -0.02347 0.866 0 0 0 0.9249 0 0 0. .01783 0. .1625 5. .574 -4. .840
120 -10.031 0. .02807 0.06146 0. .11516 -0.02095 0.816 0 0 0 0.8624 0 0 0 0. .1568 5. .331 -4. .609
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279 -12.393 0.03574 0.07164 0. .09818 0 0. .914 0. .3133 -0.0073 0 0, .7519 0 0 0, .01871 0. .1475 5, .767 -4. .999
280 -10.641 0.03347 0.06187 0. .10301 0 0. .846 0. .2012 0.0301 0 0, .7187 0 0 0 0. .1529 5, .317 -4. .765
281 -11.268 0.04721 0.09061 0. .08000 0 1. .325 0. .5457 -0.0690 0 0 2.554 -0. .1537 0, .01482 0. .1368 5, .862 -5. .237
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283 -12.432 0.03814 0.06599 0. .11103 0 1. .073 0. .4274 0.0282 0 0 2.106 0 0, .01870 0. .1326 5, .722 -4. .840
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285 -11.055 0.04634 0.08962 0. .07904 0 1. .282 0. .5286 -0.1068 0 0 0 -0. .1456 0, .01374 0. .1374 5, .654 -5. .120
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287 -12.233 0.03737 0.06583 0. .10985 0 1. .044 0. .4276 -0.0082 0 0 0 0 0, .01804 0. .1310 5, .502 -4. .751
288 -10.578 0.03538 0.05713 0. .11391 0 0. .977 0. .3165 0.0300 0 0 0 0 0 0. .1485 5, .191 -4. .634
289 -10.994 0.04299 0.09731 0. .06433 0 1. .224 0. .3730 0 0. .0059 0. .5639 2.481 -0. ,1552 0, .01603 0. .1394 6, .016 -5. .377
290 -9.553 0.04185 0.08960 0. .06823 0 1. .181 0. .2936 0 0. .0237 0. .5365 2.348 -0. ,1574 0 0. .1403 5. .851 -5. .130
291 -12.377 0.03545 0.07563 0. .09168 0 0. .966 0. .2498 0 0. .1008 0. .7442 2.002 0 0. .02009 0. .1529 5. .898 -5. .036
292 -10.539 0.03340 0.06456 0. .09773 0 0. .893 0. .1407 0 0. .1223 0. .7122 1.851 0 0 0. .1528 5. .345 -4. .748
293 -10.788 0.04194 0.09632 0. .06285 0 1. .164 0. .3441 0 -0.0021 0, .6033 0 -0. .1457 0, .01495 0. .1427 5, .832 -5. .256
294 -9.467 0.04115 0.08966 0. .06652 0 1. .128 0. .2711 0 0.0142 0. .5736 0 -0. ,1495 0 0. .1425 5. .533 -5. .027
295 -12.178 0.03455 0.07565 0. .08994 0 0. .924 0. .2416 0 0.0905 0. .7730 0 0 0. .01946 0. .1434 5. .630 -4. .961
296 -10.418 0.03271 0.06509 0. .09595 0 0. .856 0. .1355 0 0.1113 0. .7384 0 0 0 0. .1494 5. .202 -4. .686
297 -10.827 0.04463 0.09372 0. .07159 0 1. .321 0. .4720 0 0.0291 0 2.564 -0. .1634 0, .01559 0. .1406 5, .715 -5. .123
298 -9.443 0.04360 0.08650 0. .07494 0 1. .277 0. .3912 0 0.0455 0 2.421 -0. .1649 0 0. .1376 5, .591 -4. .961
299 -12.239 0.03740 0.06916 0. .10326 0 1. .101 0. .3627 0 0.1331 0 2.095 0 0, .01947 0. .1377 5, .514 -4. .705
300 -10.490 0.03557 0.05920 0. .10853 0 1. .027 0. .2557 0 0.1512 0 1.934 0 0 0. .1446 5, .161 -4. .579
301 -10.597 0.04368 0.09273 0. .07040 0 1. .267 0. .4486 0 0.0231 0 0 -0. .1546 0, .01441 0. .1341 5, .485 -4. .979
302 -9.343 0.04299 0.08656 0. .07350 0 1. .230 0. .3741 0 0.0375 0 0 -0. .1576 0 0. .1422 5, .314 -4. .861
303 -12.019 0..03647 0, .06915 0.10178 0 1.061 0..3583 0 0.1236 0 0 0 0, .01876 0..1374 5.259 -4, .616
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305 -10.989 0. .04303 0, .09186 0.06915 0 1.182 0. .4005 0 0 0.5750 2.423 -0. ,1423 0, .01440 0. .1399 6 .001 -5, .341
306 -9.673 0. .04181 0, .08510 0.07248 0 1.146 0. .3302 0 0 0.5507 2.311 -0. ,1451 0 0. .1422 5, .812 -5, .126
307 -12.219 0. .03459 0, .07049 0.09666 0 0.927 0. .2956 0 0 0.7541 1.969 0 0. .01861 0. .1501 5, .803 -4, .954
308 -10.490 0. .03238 0, .06072 0.10182 0 0.866 0. .1925 0 0 0.7252 1.829 0 0 0. .1578 5, .352 -4, .712
309 -10.796 0. .04204 0, .09097 0.06766 0 1.126 0. .3725 0 0 0.6117 0 -0. ,1336 0, .01342 0. .1393 5 .781 -5, .230
310 -9.588 0. .04114 0, .08508 0.07080 0 1.097 0. .3075 0 0 0.5853 0 -0. ,1376 0 0. .1483 5, .591 -5, .056
311 -12.030 0. .03380 0, .07044 0.09491 0 0.888 0. .2858 0 0 0.7804 0 0 0. .01801 0. .1504 5, .593 -4, .890
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313 -10.810 0. .04455 0, .08854 0.07656 0 1.279 0. .5030 0 0 0 2.505 -0. ,1506 0, .01395 0. .1406 5 .632 -5, .050
314 -9.552 0. .04345 0, .08226 0.07938 0 1.242 0. .4313 0 0 0 2.385 -0. ,1527 0 0. .1396 5, .571 -4, .960
315 -12.073 0. .03631 0, .06457 0.10846 0 1.058 0. .4118 0 0 0 2.060 0 0. .01801 0. .1325 5, .509 -4, .694
316 -10.434 0. .03436 0, .05586 0.11291 0 0.999 0. .3103 0 0 0 1.911 0 0 0. .1502 5, .244 -4, .580
317 -10.592 0. .04364 0, .08764 0.07535 0 1.228 0. .4798 0 0 0 0 -0. ,1426 0, .01288 0. .1408 5 .413 -4, .934
318 -9.453 0. .04285 0, .08224 0.07797 0 1.198 0. .4135 0 0 0 0 -0. ,1459 0 0. .1422 5, .328 -4, .866
319 -11.865 0. .03549 0, .06447 0.10698 0 1.022 0. .4052 0 0 0 0 0 0. .01733 0. .1345 5, .294 -4, .605
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321 -11.277 0. .04678 0, .09688 0.06929 0 1.235 0 -07 0.0326 0.6309 2.459 -0. ,1512 0, .01546 0. .1365 6 .267 -5, .397
322 -9.884 0. .04531 0, .09019 0.07236 0 1.186 0 0.0282 0.0445 0.5841 2.344 -0. ,1546 0 0. .1352 6, .036 -5, .172
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325 -11.089 0. .04585 0, .09613 0.06788 0 1.186 0 -0.0424 0.0232 0.6638 0 -0. ,1429 0, .01447 0. .1389 5 .971 -5, .289
326 -9.807 0. .04471 0, .09029 0.07083 0 1.145 0 -0.0153 0.0342 0.6165 0 -0. ,1476 0 0. .1394 5, .738 -5, .107
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329 -11.073 0. .04906 0, .09202 0.07878 0 1.338 0 0.0137 0.0640 0 2.530 -0. ,1583 0, .01458 0. .1381 5 .875 -5, .048
330 -9.780 0. .04769 0, .08632 0.08076 0 1.286 0 0.0418 0.0726 0 2.406 -0. 1609 0 0. .1243 5, .804 -4, .972
331 -12.435 0. .04102 0, .06864 0.10824 0 1.094 0 0.0772 0.1556 0 2.115 0 0. .01868 0. .1267 5, .640 -4, .876
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333 -10.865 0. .04826 0, .09138 0.07759 0 1.295 0 -0.0277 0.0560 0 0 -0. .1509 0, .01348 0. .1389 5 .536 -4, .942
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335 -12.226 0.04020 0.06864 0.10698 0 1.063 0 0.0408 0.1459 0 0 0 0.01798 0. .1284 5.432 -4.784
336 -10.622 0.03798 0.06037 0.11064 0 0.986 0 0.0708 0.1563 0 0 0 0 0. .1385 5.223 -4.652
337 -11.219 0.04652 0.09149 0.07376 0 1.175 0 0.0462 0 0.6519 2.402 -0. ,1382 0.01369 0. .1397 6.208 -5.341
338 -9.970 0.04511 0.08570 0.07638 0 1.138 0 0.0603 0 0.6103 2.307 -0. ,1420 0 0. .1376 6.018 -5.161
339 -12.403 0.03748 0.07079 0.09985 0 0.907 0 0.1015 0 0.7930 1.993 0 0.01809 0. .1558 5.882 -4.982
340 -10.718 0.03477 0.06166 0.10427 0 0.848 0 0.1122 0 0.7414 1.870 0 0 0. .1499 5.541 -4.827
341 -11.048 0.04567 0.09083 0.07242 0 1.131 0 0.0026 0 0.6826 0 -0. 1307 0.01280 0. .1401 5.861 -5.245
342 -9.899 0.04456 0.08575 0.07494 0 1.100 0 0.0160 0 0.6405 0 -0. ,1354 0 0. .1437 5.756 -5.097
343 -12.229 0.03678 0.07076 0.09836 0 0.876 0 0.0645 0 0.8190 0 0 0.01751 0. .1583 5.653 -4.913
344 -10.615 0.03427 0.06202 0.10280 0 0.821 0 0.0762 0 0.7658 0 0 0 0. .1546 5.407 -4.771
345 -10.990 0.04867 0.08684 0.08351 0 1.276 0 0.0699 0 0 2.475 -0. ,1456 0.01277 0. .1389 5.849 -4.993
346 -9.847 0.04739 0.08202 0.08513 0 1.237 0 0.0835 0 0 2.371 -0. 1487 0 0. .1255 5.755 -4.913
347 -12.204 0.03986 0.06375 0.11329 0 1.035 0 0.1310 0 0 2.082 0 0.01699 0. .1454 5.680 -4.813
348 -10.658 0.03743 0.05635 0.11633 0 0.974 0 0.1418 0 0 1.952 0 0 0. .1457 5.400 -4.675
349 -10.798 0.04793 0.08628 0.08238 0 1.237 0 0.0270 0 0 0 -0. 1390 0.01176 0. .1355 5.523 -4.883
350 -9.764 0.04693 0.08215 0.08392 0 1.204 0 0.0397 0 0 0 -0. ,1427 0 0. .1278 5.431 -4.838
351 -12.011 0.03915 0.06370 0.11209 0 1.007 0 0.0940 0 0 0 0 0.01631 0. .1412 5.428 -4.728
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353 -10.883 0.04413 0.09541 0.06582 0 1.200 0 0 0.0201 0.6626 2.404 -0. ,1485 0.01481 0. .1412 6.047 -5.248
354 -9.565 0.04285 0.08904 0.06909 0 1.162 0 0 0.0324 0.6187 2.293 -0. ,1519 0 0. .1370 5.856 -5.020
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356 -10.537 0.03407 0.06469 0.09787 0 0.884 0 0 0.1250 0.7471 1.841 0 0 0. .1474 5.311 -4.704
357 -10.694 0.04312 0.09476 0.06420 0 1.144 0 0 0.0101 0.6932 0 -0. ,1403 0.01384 0. .1388 5.774 -5.144
358 -9.481 0.04213 0.08921 0.06730 0 1.112 0 0 0.0216 0.6487 0 -0. ,1449 0 0. .1425 5.524 -4.956
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362 -9.461 0.04544 0.08496 0.07809 0 1.271 0 0 0.0626 0 2.356 -0. .1587 0 0. .1241 5.632 -4.822
363 -12.103 0.03925 0.06726 0.10612 0 1.093 0 0 0.1474 0 2.061 0 0.01810 0. .1311 5.454 -4.741
364 -10.493 0.03705 0.05884 0.11012 0 1.022 0 0 0.1590 0 1.922 0 0 0. .1429 5.180 -4.605
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393 -11.160 0.04572 0.09506 0.07623 0 0 0. .5053 -0.0657 0.0333 0 2.559 -0. .1638 0, .01617 0. .1266 5, .859 -5. .205
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993 -8.265 0.04141 0 0.08526 0 0 0.0368 0.6097 2.304 -0. .1290 0.01217 0. .1324 5.250 -4.719
994 -7.337 0.04020 0 0.08765 0 0 0.0487 0.5831 2.225 -0. .1321 0 0. .1407 5.066 -4.544
995 -10.040 0.03524 0 0.10563 0 0 0.1164 0.7475 1.920 0 0.01660 0. .1366 5.454 -4.664
996 -8.783 0.03306 0 0.10929 0 0 0.1320 0.7147 1.816 0 0 0. .1509 5.123 -4.578
997 -8.125 0.04037 0 0.08389 0 0 0.0245 0.6442 0 -0. .1210 0.01139 0. .1381 5.018 -4.627
998 -7.260 0.03939 0 0.08620 0 0 0.0359 0.6156 0 -0. .1248 0 0. .1462 4.854 -4.471
999 -9.859 0.03437 0 0.10406 0 0 0.1042 0.7741 0 0 0.01605 0. .1400 5.265 -4.600
1000 -8.653 0.03235 0 0.10770 0 0 0.1199 0.7388 0 0 0 0. .1508 4.966 -4.523
1001 -8.219 0.04379 0 0.09396 0 0 0.0681 0 2.381 -0. .1374 0.01158 0. .1224 4.971 -4.444
1002 -7.339 0.04270 0 0.09569 0 0 0.0777 0 2.293 -0. .1398 0 0. .1273 4.943 -4.444
1003 -10.108 0.03785 0 0.11784 0 0 0.1527 0 2.008 0 0.01592 0. .1321 5.145 -4.379
1004 -8.901 0.03589 0 0.12055 0 0 0.1648 0 1.896 0 0 0. .1436 4.938 -4.383
1005 -8.054 0.04288 0 0.09287 0 0 0.0573 0 0 -0. .1301 0.01068 0. .1299 4.687 -4.366
1006 -7.247 0.04200 0 0.09448 0 0 0.0661 0 0 -0. .1332 0 0. .1346 4.730 -4.345
1007 -9.909 0.03699 0 0.11653 0 0 0.1413 0 0 0 0.01528 0. .1328 4.952 -4.295
1008 -8.762 0.03521 0 0.11918 0 0 0.1533 0 0 0 0 0. .1461 4.771 -4.304
1009 -8.355 0.04122 0 0.08890 0 0 0 0.6384 2.245 -0. .1182 0.01065 0. .1391 5.242 -4.685
1010 -7.523 0.04007 0 0.09079 0 0 0 0.6152 2.181 -0. .1215 0 0. .1509 5.135 -4.569
1011 -10.004 0.03444 0 0.10959 0 0 0 0.7763 1.880 0 0.01524 0. .1451 5.452 -4.661
1012 -8.817 0.03219 0 0.11257 0 0 0 0.7460 1.785 0 0 0. .1581 5.067 -4.564
1013 -8.225 0.04028 0 0.08752 0 0 0 0.6689 0 -0. .1108 0.00995 0. .1392 4.979 -4.606
1014 -7.451 0.03932 0 0.08934 0 0 0 0.6440 0 -0. .1147 0 0. .1590 4.906 -4.512
1015 -9.837 0.03369 0 0.10800 0 0 0 0.7995 0 0 0.01473 0. .1501 5.256 -4.602
1016 -8.698 0.03159 0 0.11098 0 0 0 0 0 0.7670 0 0 0 0.1618 4.999 -4.510
1017 -8.287 0.04355 0 0.09820 0 0 0 0 0 0 2.320 -0.1269 0.01003 0.1382 4.963 -4.470
1018 -7.507 0.04252 0 0.09949 0 0 0 0 0 0 2.249 -0.1295 0 0.1355 4.918 -4.415
1019 -10.053 0.03693 0 0.12256 0 0 0 0 0 0 1.965 0 0.01452 0.1437 5.102 -4.360
1020 -8.923 0.03495 0 0.12466 0 0 0 0 0 0 1.863 0 0 0.1533 4.848 -4.330
1021 -8.133 0.04271 0 0.09707 0 0 0 0 0 0 0 -0.1203 0.00920 0.1387 4.731 -4.399
1022 -7.422 0.04187 0 0.09827 0 0 0 0 0 0 0 -0.1233 0 0.1421 4.733 -4.335
1023 -9.870 0.03618 0 0.12121 0 0 0 0 0 0 0 0 0.01392 0.1412 4.926 -4.281
1024 -8.797 0.03436 0 0.12329 0 0 0 0 0 0 0 0 0 0.1506 4.765 -4.260
Logistic model offsets θ0 r and coefficients Θ to 9catheter aUation r , according to Formula I are listed in columns 2 to 13. p99(Jr)-pi(f(Jr)), difference tween 99% and 1% percentiles of logarithmic model values for training dataset; pi(Jr), 1% percentile of logarithmic model values for training datas

Claims

Claims
Method for predicting paroxysmal atrial fibrillation comprising
i) the step of determining at least the following parameters of a subject:
a) age, and
b) left atrium size (LA),
ii) deriving a test score based on the measured parameters;
iii) providing a threshold score; and
iv) comparing the test score with the threshold score, wherein a test score equal to or higher than the threshold score is predictive for the presence of paroxysmal atrial fibrillation.
Method according to claim 1, wherein as further parameters:
(a) tissue Doppler imaging velocity during atrial contraction (TDI), and/or aortic root diameter are determined; and/or
(b) at least one of hyperlipidemia and diabetes mellitus is determined; and/or
(c) at least one or any combination of two or more of catheter ablation, left ventricular end-diastolic diameter, heart rate, sleep apnea, intake of beta blocker, and smoker is/are determined.
Method according to any one of claim 1 or 2, wherein the test score is derived by combining the determined parameters in a logistic function.
Method according to any one of claims 1 to 3, wherein the formula for obtaining the logistic test score Sr is
22
Figure imgf000058_0001
(Formula I)
with the score variants r, the offsets ΘΟ,Γ and the weights θγ — age r ? @Ao o,,rrooooti,,rr > y ^ ~ L L·AΆ,,rr > y ^ ~ L nV' ,,E^SD,,rr > y ^ ~ T I DII ,,AΆ ,rr > y ^ ~ fHHFF ,,rr >> t/ sleep apnea, r > hyperlipidemia, r ' ttyyppee IIII ddiiabetes, r > smoker, r ' β blocker, r > catheter ablation, r J
and wherein
age is the age of the subject,
Ao,root is the diameter of the aortic root of the subject,
LA is the size of the left atrium of the subject,
LV, ESD is left ventricular end-systolic diameter of the subject,
TDI,A' is the tissue Doppler imaging velocity during atrial contraction of the subject,
HF is the heart frequency of the subject,
sleep apnea indicates whether the subject has sleep apnea,
hyperlipidemia indicates whether the subject has hyperlipidemia,
type II diabetes indicates whether the subject has type II diabetes,
smoker indicates whether the subject is a smoker,
β blocker indicates whether the subject is on β blockers, and
catheter ablation indicates whether the subject had a catheter ablation.
Method according to any of claims 1 to 4, wherein the formula for obtaining the logistic test score S2 is
Figure imgf000059_0001
wherein
age is the age of the subject and
LA is the size of the left atrium of the subject, and wherein the offsets and weighting coefficients can be varied by a maximum of 10%.
Method according to claim 5, wherein the probability for the presence of paroxysmal atrial fibrillation is increased when the logistic test score equals or exceeds a threshold score at a value of about 0.1506.
Method according to any of claims 1 to 6, wherein the formula for obtaining the logistic test score S4 is
1
, (Formula II)
Figure imgf000059_0002
wherein
age is the age of the subject,
Ao,root is the diameter of the aortic root of the subject, LA is the size of the left atrium of the subject, and
TDI, A' is the tissue Doppler imaging velocity during atrial contraction of the subject, and wherein the offsets and weighting coefficients can be varied by a maximum of 10%.
Method according to claim 7, wherein the probability for the presence of paroxysmal atrial fibrillation is increased when the logistic test score equals or exceeds a threshold score at a value of about 0.1324.
Method according to claim 1 or 2, wherein the test score is derived by combining the determined parameters in a linear function.
Method according to claim 1 or 2, wherein the formula for obtaining the linear test score Lr is
25
age Ao, root , LA LV, ESD , TDI, Α' ±
Lr - Ψθ, Γ + <Page,r + Ψλο,τοοΙ,τ + ΨΐΑ,τ + V>LV,ESD,r + ΨΤΟΙ ,Α' r Γ~ + <PHF
y mm mm mm ' cm/s
+ ^sieep apnea,, · sleep apnea + ^hyperlipidemia,r · hyperlipidemia + ^type n diabetes · type II diabetes
+ ^smoker,r · smoker + blockei. r · β blocker + ^catheter ablatlon,r · catheter ablation
(Formula V)
with the variables r, the offsets φο.Γ and the weights
Φν ~
Figure imgf000061_0001
' ΦΐΙΈ ,r ' Asleep apnea, r > ^hyperlipidemia, r ' ^type II diabetes, r ' ^smoker, r > Φ§ blockers ^catheter ablation.r ) and wherein
age is the age of the subject,
Ao,root is the diameter of the aortic root of the subject,
LA is the size of the left atrium of the subject,
LV, ESD is left ventricular end-systolic diameter of the subject,
TDI,A' is the tissue Doppler imaging velocity during atrial contraction of the subject, HF is the heart frequency of the subject,
sleep apnea indicates whether the subject has sleep apnea,
hyperlipidemia indicates whether the subject has hyperlipidemia,
type II diabetes indicates whether the subject has type II diabetes,
smoker indicates whether the subject is a smoker,
β blocker indicates whether the subject is on β blockers, and
catheter ablation indicates whether the subject had a catheter ablation. 11. Method according to claims 1 or 2, wherein the formula for obtaining the linear test score L2 is
L2 = -95.19 + 2.587 -— + 0.7211 - ^
mm y
wherein
age is the age of the subject,
LA is the size of the left atrium of the subject, and wherein the offsets and weighting coefficients can be varied by a maximum of 10%.
12. Method according to claim 11, wherein the probability for the presence of paroxysmal atrial fibrillation is increased when the linear test score equals or exceeds a threshold score at a value of about 53.10.
13. Method according to claim 1 or 2, wherein the formula for obtaining the linear test score L4 is
L4 = -95.89 - 2.700 ·≡^ + 1.656 - ^- + 0.8746 + 1.608 - ,
cm/s mm y mm
(Formula VII)
wherein
age is the age of the subject,
Ao,root is the diameter of the aortic root of the subject,
LA is the size of the left atrium of the subject, and
TDI, A' is the tissue Doppler imaging velocity during atrial contraction of the subject, and wherein the offsets and weighting coefficients can be varied by a maximum of 10%.
14. Method according to claim 13, wherein the probability for the presence of paroxysmal atrial fibrillation is increased when the linear test score equals or exceeds a threshold score at a value of about 54.88.
15. System for predicting paroxysmal atrial fibrillation comprising
i) an ultrasound apparatus for determining at least the parameters tissue Doppler imaging velocity during atrial contraction, left atrium size, and aortic root diameter, ii) an input device,
iii) an analyzing device, and
iv) an output device,
wherein the analyzing device is embodied for analyzing the information provided by the ultrasound apparatus and the input device and the output device is embodied for outputting the result derived from the analysis by indicating whether an increased probability for paroxysmal atrial fibrillation is present wherein the system is preferably embodied to perform a method according to claims 1 to 14.
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