CRT Candidates - A Machine Learning Approach

Machine learning can reliably identify clinical and echocardiographic features associated with Cardiac Resynchronization Therapy (CRT) response and prognosis. The evaluation of both right ventricular size and functional parameters has pivotal importance for the risk stratification of CRT candidates and should be systematically performed in patients undergoing CRT.

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The application of machine learning (ML) methods to a set of common clinical, electrocardiographic, and echocardiographic data emphasizes the importance of a multiparametric approach for both the identification of CRT response and the prediction of prognosis after CRT. Our results underscore the importance of right ventricular function on both CRT response and outcome and the pivotal role of the global assessment of heart function in patients undergoing CRT. Despite these interesting results, additional studies on a broader population will be necessary to fully validate and understand the clinical application of ML to CRT. Although there remains much to understand, as demonstrated in our study, it is likely that the application of ML-derived algorithms will allow the stratification of CRT candidates to guide patients toward specific therapeutic approaches and additional focused clinical trials.

Importance of Systematic Right Ventricular Assessment in Cardiac Resynchronization Therapy Candidates: A Machine Learning Approach - PubMed (nih.gov)

J Am Soc Echocardiogr. 2021 May;34(5):494-502
Elena Galli, Virginie Le Rolle, Otto A Smiseth, Jurgen Duchenne, John M Aalen, Camilla K Larsen, Elif A Sade, Arnaud Hubert, Smitha Anilkumar, Martin Penicka , Cecilia Linde, Christophe Leclercq, Alfredo Hernandez, Jens-Uwe Voigt, Erwan Donal