Clinical InvestigationEchocardiography, Machine Learning, and Cardiac Resynchronization TherapyImportance of Systematic Right Ventricular Assessment in Cardiac Resynchronization Therapy Candidates: A Machine Learning Approach
Graphical abstract
Section snippets
Population
Two hundred nine patients with systolic HF undergoing CRT device implantation according to current guidelines1 at Oslo University Hospital (Norway), Leuven University Hospital (Belgium), Rennes University Hospital (France), Aalst OLV Hospital (Belgium), and Karolinska University Hospital (Sweden) between August 2015 and November 2017 were prospectively included in this observational, multicenter study. Sixteen patients were excluded from the final analysis because of study withdrawal (n = 4),
Results
The baseline characteristics of the population are depicted in Table 1.
The population had a mean age of 67 years. The majority of patients were men (70%), with a high prevalence (67%) of nonischemic dilated cardiomyopathy. One hundred sixty-eight patients (87%) had typical LBBB.
Discussion
In this prospective, multicentric study, we have shown the feasibility and validity of the application of both supervised and unsupervised ML approaches to a comprehensive pattern of preimplantation clinical, biochemical, electrocardiographic, and echocardiographic data obtained in everyday clinical practice in CRT candidates. Our analysis allowed the identification of groups of features that are significantly associated with CRT response and prognosis and the identification of two phenogroups
Conclusion
The application of 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 RV 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
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2022, JACC: Heart FailureCitation Excerpt :In addition to biventricular dyssynchrony, the role of right ventricular (RV) dysfunction in predicting outcomes after CRT is still debatable.33,34 A recent ML approach by Galli et al28 combined clinical, electrocardiographic, LV, and RV echocardiography parameters to predict CRT response and outcomes. Out of the 16 variables that were predictive of CRT response, 8 pertained to RV dynamics.
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2021, International Journal of CardiologyCitation Excerpt :Intriguingly, with multiple distinct algorithms we reached similar results showing that linear models performed better in prediction than nonlinear ones. Moreover, two recent studies reported construction of ML predictive models of CRT response incorporating more echocardiographic features, some particularly correlated to LV mechanical dyssynchrony, and achieved AUCs of over 0.8 [27,28]. These results highlighted the promising future of integrating the strength of ML techniques and features specifically correlated to LV dyssynchrony to further improve predictive performance.
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Drs. Galle and Le Rolle contributed equally to this work.
Dr. Aalen was supported by a grant from the Norwegian Health Association. Dr. Larsen was a recipient of a clinical research fellowship from the South-Eastern Norway Regional Health Authority. Dr. Voigt holds a research mandate from the Research Foundation Flanders (FKM1832917N). Dr. Le Rolle was supported by the French National Research Agency (ANR-16-CE19-0008-01, project MAESTRo).
Conflicts of interest: None.