Journal Article

Redefining β-blocker response in heart failure patients with sinus rhythm and atrial fibrillation: a machine learning cluster analysis

Mobley, Alastair R, Coats, Andrew J S, Slater, Karin, Flather, Marcus D, Duan, Jinming, Bunting, Karina V, Bruce, Emma-Jane, Tica, Otilia, Barsky, Andrey D, Williams, John A, Gill, Simrat K, Aziz, Furqan, Pendleton, Samantha, Chernbumroong, Saisakul, Cardoso, Victor Roth, Karwath, Andreas, Wang, Xiaoxia, Gkoutos, Georgios V, Kotecha, Dipak

2021

Overview

Mortality remains unacceptably high in patients with heart failure and reduced left ventricular ejection fraction (LVEF) despite advances in therapeutics. We hypothesised that a novel artificial intelligence approach could better assess multiple and higher-dimension interactions of comorbidities, and define clusters of β-blocker efficacy in patients with sinus rhythm and atrial fibrillation. Neural network-based variational autoencoders and hierarchical clustering were applied to pooled individual patient data from nine double-blind, randomised, placebo-controlled trials of β blockers. All-cause mortality during median 1·3 years of follow-up was assessed by intention to treat, stratified by electrocardiographic heart rhythm. The number of clusters and dimensions was determined objectively, with results validated using a leave-one-trial-out approach. This study was prospectively registered with ClinicalTrials.gov (NCT00832442) and the PROSPERO database of systematic reviews (CRD42014010012). 15 659 patients with heart failure and LVEF of less than 50% were included, with median age 65 years (IQR 56–72) and LVEF 27% (IQR 21–33). 3708 (24%) patients were women. In sinus rhythm (n=12 822), most clusters demonstrated a consistent overall mortality benefit from β blockers, with odds ratios (ORs) ranging from 0·54 to 0·74. One cluster in sinus rhythm of older patients with less severe symptoms showed no significant efficacy (OR 0·86, 95% CI 0·67–1·10; p=0·22). In atrial fibrillation (n=2837), four of five clusters were consistent with the overall neutral effect of β blockers versus placebo (OR 0·92, 0·77–1·10; p=0·37). One cluster of younger atrial fibrillation patients at lower mortality risk but similar LVEF to average had a statistically significant reduction in mortality with β blockers (OR 0·57, 0·35–0·93; p=0·023). The robustness and consistency of clustering was confirmed for all models (p<0·0001 vs random), and cluster membership was externally validated across the nine independent trials. An artificial intelligence-based clustering approach was able to distinguish prognostic response from β blockers in patients with heart failure and reduced LVEF. This included patients in sinus rhythm with suboptimal efficacy, as well as a cluster of patients with atrial fibrillation where β blockers did reduce mortality. Medical Research Council, UK, and EU/EFPIA Innovative Medicines Initiative BigData@Heart.

• Publisher: Elsevier Ltd,Elsevier Limited,Elsevier
• Subject: Adrenergic beta-Antagonists - therapeutic use / Aged / Artificial intelligence / Atrial Fibrillation - drug therapy / Beta blockers / Bias / Cardiac arrhythmia / Clinical medicine / Clinical trials / Cluster Analysis / Clustering / Comorbidity / Congestive heart failure / Demographics / Double-Blind Method / Drug therapy / Ejection fraction / Electrocardiography / Female / Fibrillation / Health risks / Heart failure / Heart Failure - drug therapy / Heart Failure - mortality / Humans / Learning algorithms / Literature reviews / Machine Learning / Male / Medical prognosis / Medical research / Middle Aged / Mortality / Mortality risk / Neural networks / Patients / Placebos / Principal components analysis / Rhythm / Sinuses / Statistical analysis / Stroke Volume / Ventricle / Ventricular Function, Left