Redefining β-blocker response in heart failure patients with sinus rhythm and atrial fibrillation: a machine learning cluster analysis. Issue 10309 (16th October 2021)

• Record Type: Journal Article
• Authors: Karwath, Andreas; Bunting, Karina V; Gill, Simrat K; Tica, Otilia; Pendleton, Samantha; Aziz, Furqan; Barsky, Andrey D; Chernbumroong, Saisakul; Duan, Jinming; Mobley, Alastair R; Cardoso, Victor Roth; Slater, Luke; Williams, John A; Bruce, Emma-Jane; Wang, Xiaoxia; Flather, Marcus D; Coats, Andrew J S; Gkoutos, Georgios V; Kotecha, Dipak
• Is Part Of: Lancet. Volume 398:Issue 10309(2021)
• Page Start: 1427
• Page End: 1435
• Publication Date: 2021-10-16
• Subjects: Medicine -- Periodicals; Medicine -- Periodicals; Medicine; Medicine; Electronic journals; Periodicals; 610.5
• DOI: 10.1016/S0140-6736(21)01638-X
• Languages: English
• ISSNs: 0140-6736

Abstract

Summary: 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. Methods: 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). 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. Funding: Medical Research Council, UK, and EU/EFPIA Innovative Medicines Initiative BigData@Heart.