Maja Cikes1, Sergio Sanchez-Martinez2, Brian Claggett3, Nicolas Duchateau4, Gemma Piella2, Constantine Butakoff2, Anne Catherine Pouleur5, Dorit Knappe6, Tor Biering-Sørensen3,7, Valentina Kutyifa8, Arthur Moss8, Kenneth Stein9, Scott D Solomon3, Bart Bijnens2,10. 1. Department of Cardiovascular Diseases, University of Zagreb School of Medicine, and University Hospital Center Zagreb, Zagreb, Croatia. 2. Department of Information and Communication Technologies, University Pompeu Fabra, Barcelona, Spain. 3. Brigham and Women's Hospital, Boston, MA, USA. 4. Creatis, CNRS UMR5220, INSERM U1206, Université Lyon 1, France. 5. Division of Cardiology, Cliniques Saint-Luc UCL, Brussels, Belgium. 6. University Heart Center Hamburg, Hamburg, Germany. 7. Herlev & Gentofte Hospital - Copenhagen University, Copenhagen, Denmark. 8. University of Rochester, Rochester, NY, USA. 9. Boston Scientific, Minneapolis, MN, USA. 10. ICREA, Barcelona, Spain.
Abstract
AIMS: We tested the hypothesis that a machine learning (ML) algorithm utilizing both complex echocardiographic data and clinical parameters could be used to phenogroup a heart failure (HF) cohort and identify patients with beneficial response to cardiac resynchronization therapy (CRT). METHODS AND RESULTS: We studied 1106 HF patients from the Multicenter Automatic Defibrillator Implantation Trial with Cardiac Resynchronization Therapy (MADIT-CRT) (left ventricular ejection fraction ≤ 30%, QRS ≥ 130 ms, New York Heart Association class ≤ II) randomized to CRT with a defibrillator (CRT-D, n = 677) or an implantable cardioverter defibrillator (ICD, n = 429). An unsupervised ML algorithm (Multiple Kernel Learning and K-means clustering) was used to categorize subjects by similarities in clinical parameters, and left ventricular volume and deformation traces at baseline into mutually exclusive groups. The treatment effect of CRT-D on the primary outcome (all-cause death or HF event) and on volume response was compared among these groups. Our analysis identified four phenogroups, significantly different in the majority of baseline clinical characteristics, biomarker values, measures of left and right ventricular structure and function and the primary outcome occurrence. Two phenogroups included a higher proportion of known clinical characteristics predictive of CRT response, and were associated with a substantially better treatment effect of CRT-D on the primary outcome [hazard ratio (HR) 0.35; 95% confidence interval (CI) 0.19-0.64; P = 0.0005 and HR 0.36; 95% CI 0.19-0.68; P = 0.001] than observed in the other groups (interaction P = 0.02). CONCLUSIONS: Our results serve as a proof-of-concept that, by integrating clinical parameters and full heart cycle imaging data, unsupervised ML can provide a clinically meaningful classification of a phenotypically heterogeneous HF cohort and might aid in optimizing the rate of responders to specific therapies.
RCT Entities:
AIMS: We tested the hypothesis that a machine learning (ML) algorithm utilizing both complex echocardiographic data and clinical parameters could be used to phenogroup a heart failure (HF) cohort and identify patients with beneficial response to cardiac resynchronization therapy (CRT). METHODS AND RESULTS: We studied 1106 HF patients from the Multicenter Automatic Defibrillator Implantation Trial with Cardiac Resynchronization Therapy (MADIT-CRT) (left ventricular ejection fraction ≤ 30%, QRS ≥ 130 ms, New York Heart Association class ≤ II) randomized to CRT with a defibrillator (CRT-D, n = 677) or an implantable cardioverter defibrillator (ICD, n = 429). An unsupervised ML algorithm (Multiple Kernel Learning and K-means clustering) was used to categorize subjects by similarities in clinical parameters, and left ventricular volume and deformation traces at baseline into mutually exclusive groups. The treatment effect of CRT-D on the primary outcome (all-cause death or HF event) and on volume response was compared among these groups. Our analysis identified four phenogroups, significantly different in the majority of baseline clinical characteristics, biomarker values, measures of left and right ventricular structure and function and the primary outcome occurrence. Two phenogroups included a higher proportion of known clinical characteristics predictive of CRT response, and were associated with a substantially better treatment effect of CRT-D on the primary outcome [hazard ratio (HR) 0.35; 95% confidence interval (CI) 0.19-0.64; P = 0.0005 and HR 0.36; 95% CI 0.19-0.68; P = 0.001] than observed in the other groups (interaction P = 0.02). CONCLUSIONS: Our results serve as a proof-of-concept that, by integrating clinical parameters and full heart cycle imaging data, unsupervised ML can provide a clinically meaningful classification of a phenotypically heterogeneous HF cohort and might aid in optimizing the rate of responders to specific therapies.
Authors: Bettina Zippel-Schultz; Carsten Schultz; Dirk Müller-Wieland; Andrew B Remppis; Martin Stockburger; Christian Perings; Thomas M Helms Journal: Herzschrittmacherther Elektrophysiol Date: 2021-01-15
Authors: Albert K Feeny; Mina K Chung; Anant Madabhushi; Zachi I Attia; Maja Cikes; Marjan Firouznia; Paul A Friedman; Matthew M Kalscheur; Suraj Kapa; Sanjiv M Narayan; Peter A Noseworthy; Rod S Passman; Marco V Perez; Nicholas S Peters; Jonathan P Piccini; Khaldoun G Tarakji; Suma A Thomas; Natalia A Trayanova; Mintu P Turakhia; Paul J Wang Journal: Circ Arrhythm Electrophysiol Date: 2020-07-06
Authors: Albert K Feeny; John Rickard; Divyang Patel; Saleem Toro; Kevin M Trulock; Carolyn J Park; Michael A LaBarbera; Niraj Varma; Mark J Niebauer; Sunil Sinha; Eiran Z Gorodeski; Richard A Grimm; Xinge Ji; John Barnard; Anant Madabhushi; David D Spragg; Mina K Chung Journal: Circ Arrhythm Electrophysiol Date: 2019-06-20
Authors: Albert K Feeny; John Rickard; Kevin M Trulock; Divyang Patel; Saleem Toro; Laurie Ann Moennich; Niraj Varma; Mark J Niebauer; Eiran Z Gorodeski; Richard A Grimm; John Barnard; Anant Madabhushi; Mina K Chung Journal: Circ Arrhythm Electrophysiol Date: 2020-06-14
Authors: Rakesh K Mishra; Geoffrey H Tison; Qizhi Fang; Rebecca Scherzer; Mary A Whooley; Nelson B Schiller Journal: J Am Soc Echocardiogr Date: 2020-01-14 Impact factor: 5.251