Importance of Systematic Right Ventricular Assessment in Cardiac Resynchronization Therapy Candidates: A Machine Learning Approach

dc.contributor.authorGalli, Elena
dc.contributor.authorLe Rolle, Virginie
dc.contributor.authorSmiseth, Otto A.
dc.contributor.authorDuchenne, Jurgen
dc.contributor.authorAalen, John M.
dc.contributor.authorLarsen, Camilla K.
dc.contributor.authorSade, Elif A.
dc.contributor.authorHubert, Arnaud
dc.contributor.authorAnilkumar, Smitha
dc.contributor.authorPenicka, Martin
dc.contributor.authorLinde, Cecilia
dc.contributor.authorLeclercq, Christophe
dc.contributor.authorHernandez, Alfredo
dc.contributor.authorVoigt, Jens-Uwe
dc.contributor.authorDonal, Erwan
dc.contributor.pubmedID33422667en_US
dc.date.accessioned2022-06-17T08:59:59Z
dc.date.available2022-06-17T08:59:59Z
dc.date.issued2021
dc.description.abstractBackground: Despite all having systolic heart failure and broad QRS intervals, patients screened for cardiac resynchronization therapy (CRT) are highly heterogeneous, and it remains extremely challenging to predict the impact of CRT devices on left ventricular function and outcomes. The aim of this study was to evaluate the relative impact of clinical, electrocardiographic, and echocardiographic data on the left ventricular remodeling and prognosis of CRT candidates by the application of machine learning approaches. Methods: One hundred ninety-three patients with systolic heart failure receiving CRT according to current recommendations were prospectively included in this multicenter study. A combination of the Boruta algorithm and random forest methods was used to identify features predicting both CRT volumetric response and prognosis. Model performance was tested using the area under the receiver operating characteristic curve. The k-medoid method was also applied to identify clusters of phenotypically similar patients. Results: From 28 clinical, electrocardiographic, and echocardiographic variables, 16 features were predictive of CRT response, and 11 features were predictive of prognosis. Among the predictors of CRT response, eight variables (50%) pertained to right ventricular size or function. Tricuspid annular plane systolic excursion was the main feature associated with prognosis. The selected features were associated with particularly good prediction of both CRT response (area under the curve, 0.81; 95% CI, 0.74-0.87) and outcomes (area under the curve, 0.84; 95% CI, 0.75-0.93). An unsupervised machine learning approach allowed the identification of two phenogroups of patients who differed significantly in clinical variables and parameters of biventricular size and right ventricular function. The two phenogroups had significantly different prognosis (hazard ratio, 4.70; 95% CI, 2.1-10.0; P < .0001; log-rank P < .0001). Conclusions: Machine learning can reliably identify clinical and echocardiographic features associated with 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. (J Am Soc Echocardiogr 2021;34:494-502.)en_US
dc.identifier.endpage502en_US
dc.identifier.issn0894-7317en_US
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85100615915en_US
dc.identifier.startpage494en_US
dc.identifier.urihttps://hal.archives-ouvertes.fr/hal-03128136/file/Galli%20et%20al%20-%202021%20-%20Importance%20of%20systematic%20right%20ventricular%20assessment%20in%20cardiac%20resynchronization.pdf
dc.identifier.urihttp://hdl.handle.net/11727/7059
dc.identifier.volume34en_US
dc.identifier.wos000752444300002en_US
dc.language.isoengen_US
dc.relation.isversionof10.1016/j.echo.2020.12.025en_US
dc.relation.journalJOURNAL OF THE AMERICAN SOCIETY OF ECHOCARDIOGRAPHYen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergien_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCardiac resynchronization therapyen_US
dc.subjectHeart failureen_US
dc.subjectMachine learningen_US
dc.subjectRight ventricleen_US
dc.titleImportance of Systematic Right Ventricular Assessment in Cardiac Resynchronization Therapy Candidates: A Machine Learning Approachen_US
dc.typearticleen_US

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