• Andreas Karwath, Karina V Bunting, Simrat K Gill, Otilia Tica, Samantha Pendleton, Furqan Aziz, Andrey D Barsky, Saisakul Chernbumroong, Jinming Duan, Alastair R Mobley, Victor Roth Cardoso, Karin Slater, John A Williams, Emma-Jane Bruce, Xiaoxia Wang, Marcus D Flather, CoatsAndrew J SAJSWarwick Medical School, University of Warwick, Warwick, UK., Georgios V Gkoutos, Dipak Kotecha, and card AIc group and the Beta-blockers in Heart Failure Collaborative Group.
• Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK; Health Data Research UK Midlands Site, Birmingham, UK.
• Lancet. 2021 Oct 16; 398 (10309): 142714351427-1435.

BackgroundMortality 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).Findings15 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.InterpretationAn 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.FundingMedical Research Council, UK, and EU/EFPIA Innovative Medicines Initiative BigData@Heart.Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.

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