Machine-learning based diagnosis of heart failure with preserved ejection fraction: how much do we agree with the guidelines?

Sergio Sanchez-Martinez 1 Nicolas Duchateau 2 Tamas Erdei 3 Gabor Kunszt 4 Anna Degiovanni 5 Erberto Carluccio 6 Alan Fraser 3 Gemma Piella 1 Bart Bijnens 1
1 Universitat Pompeu Fabra [Barcelona]
2 ASCLEPIOS - Analysis and Simulation of Biomedical Images
CRISAM - Inria Sophia Antipolis - Méditerranée
3 WHRI - Wales Heart Research Institute [Cardiff]
4 Oslo University Hospital, Department of cardiology
5 University of Eastern Piedmont, Department of cardiology, Novara
6 UNIPG - Università degli Studi di Perugia
Abstract : Purpose: Current diagnosis of heart failure with preserved ejection fraction (HFPEF) is suboptimal since it oversimplifies abnormalities by only considering "simple" key markers of disease. We investigate whether a comprehensive analysis of multiple myocardial velocity profiles, acquired during a stress echocardiography protocol, can identify characteristic patterns of cardiac (dys-)function and aid in better staging of HFPEF patients. Methods: Velocity profiles were extracted at the basal septum and lateral wall of the left ventricle from rest and exercise tissue Doppler acquisitions. The population consisted of 33 healthy subjects (67±4 years) and 72 HFPEF (72±6 years, diagnosed with the current guidelines). Each cardiac phase was identified and used to temporally align the velocity profiles. An unsupervised machine learning algorithm (multiple kernel learning) was used to fuse the heterogeneous input data and to reduce their complexity. Agglomerative hierarchical clustering was performed on this set to identify different groupings within the population and position each subject with regard to all the others according to their similarity. Results: The identified groups substantially differed for the parameters classically used for diagnosis (E/e’ ratio and 6MWT: p<0.03; NT-proBNP: p=0.052) and correlated with the clinical diagnosis (sens=88.9%, spec=54.6%). The probability of membership to each group revealed a healthy and a HFPEF region (sens=92.9%, spec=87.5%) and a transition zone where most of the "conflictive" diagnosis cases were lying (figure). Conclusion: Our method reduced the complexity of myocardial motion descriptors towards a representation where patients can be staged into clinically-relevant regions. The transition zone suggested "conflictive" cases where so called healthy subjects showed hypertensive remodeling, which may evolve into HFPEF and need closer follow up.
Type de document :
Communication dans un congrès
EuroEcho-Imaging, Dec 2016, Leipzig, Germany. 2016, European Heart Journal: Cardiovascular Imaging, Abstracts from the EuroEcho-Imaging congress
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https://hal.inria.fr/hal-01352285
Contributeur : Nicolas Duchateau <>
Soumis le : dimanche 7 août 2016 - 17:00:51
Dernière modification le : jeudi 17 janvier 2019 - 13:48:04

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  • HAL Id : hal-01352285, version 1

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Sergio Sanchez-Martinez, Nicolas Duchateau, Tamas Erdei, Gabor Kunszt, Anna Degiovanni, et al.. Machine-learning based diagnosis of heart failure with preserved ejection fraction: how much do we agree with the guidelines?. EuroEcho-Imaging, Dec 2016, Leipzig, Germany. 2016, European Heart Journal: Cardiovascular Imaging, Abstracts from the EuroEcho-Imaging congress. 〈hal-01352285〉

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