Search

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., (© 2018 The Authors. European Journal of Heart Failure © 2018 European Society of Cardiology.)

Contemporary imaging modalities offer noninvasive quantification of myocardial deformation; however, they make gross assumptions about internal structure of the cardiac walls. Our aim is to study the possible impact of the trabeculations on the stroke volume, strain, and capacity of differently sized ventricles. The cardiac left ventricle is represented by an ellipsoid and the trabeculations by a tissue occupying a fixed volume. The ventricular contraction is modeled by scaling the ellipsoid whereupon the measurements of longitudinal strain, end-diastolic, end-systolic, and stroke volumes are derived and compared. When the trabeculated and nontrabeculated ventricles, having the same geometry and deformation pattern, contain the same amount of blood and contract with the same strain, we observed an increased stroke volume in our model of the trabeculated ventricle. When these ventricles contain and eject the same amount of blood, we observed a reduced strain in the trabeculated case. We identified that a trade-off between the strain and the amount of trabeculations could be reached with a 0.35- to 0.41-cm dense trabeculated layer, without blood filled recesses (for a ventricle with end-diastolic volume of about 150 mL). A trabeculated ventricle can work at lower strains compared to a nontrabeculated ventricle to produce the same stroke volume, which could be a possible explanation why athletes and pregnant women develop reversible signs of left ventricular noncompaction, since the trabeculations could help generating extra cardiac output. This knowledge might help to assess heart failure patients with dilated cardiomyopathies who often show signs of noncompaction., (Copyright © 2017 John Wiley & Sons, Ltd.)