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

Background 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. MethodsNeural 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).Findings 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.Interpretation 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.