Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy

TY - JOUR

T1 - Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy

AU - Cikes, Maja

AU - Sanchez-Martinez, Sergio

AU - Claggett, Brian

AU - Duchateau, Nicolas

AU - Piella, Gemma

AU - Butakoff, Constantine

AU - Pouleur, Anne Catherine

AU - Knappe, Dorit

AU - Biering-Sørensen, Tor

AU - Kutyifa, Valentina

AU - Moss, Arthur

AU - Stein, Kenneth

AU - Solomon, Scott D

AU - Bijnens, Bart

N1 - © 2018 The Authors. European Journal of Heart Failure © 2018 European Society of Cardiology.

PY - 2019/1

Y1 - 2019/1

N2 - 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.

AB - 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.

KW - Aged

KW - Algorithms

KW - Cardiac Resynchronization Therapy/methods

KW - Echocardiography

KW - Female

KW - Follow-Up Studies

KW - Heart Failure/diagnosis

KW - Heart Ventricles/diagnostic imaging

KW - Humans

KW - Machine Learning

KW - Male

KW - Middle Aged

KW - Reproducibility of Results

KW - Retrospective Studies

KW - Stroke Volume/physiology

KW - Ventricular Function, Left/physiology

U2 - 10.1002/ejhf.1333

DO - 10.1002/ejhf.1333

M3 - SCORING: Journal article

C2 - 30328654

VL - 21

SP - 74

EP - 85

JO - EUR J HEART FAIL

JF - EUR J HEART FAIL

SN - 1388-9842

IS - 1

ER -