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

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. and Kotecha, Dipak (2021) Redefining β-blocker response in heart failure patients with sinus rhythm and atrial fibrillation: a machine learning cluster analysis. The Lancet, 398 (10309). pp. 1427-1435. ISSN 0140-6736

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Abstract

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.

Item Type: Article
Additional Information: Acknowledgements: We thank the four pharmaceutical companies that provided access to full trial data: Merck, Menarini, AstraZeneca, and GlaxoSmithKline. This work was funded by an MRC Rutherford Fellowship (MR/S003991/1), MRC HDRUK (HDRUK/CFC/01), and EU/EFPIA Innovative Medicines Initiative (BigData@Heart 116074), and supported by the British Heart Foundation Accelerator Award to the University of Birmingham Institute of Cardiovascular Sciences (AA/18/2/34218). The Beta-blockers in Heart Failure Collaborative Group received an unrestricted research grant for start-up administration from Menarini Farmaceutica Internazionale and a collaborative research grant from IRCCS San Raffaele. The opinions expressed in this paper are those of the authors and do not represent any of the listed organisations.
Uncontrolled Keywords: medicine(all) ,/dk/atira/pure/subjectarea/asjc/2700
Faculty \ School: Faculty of Medicine and Health Sciences > Norwich Medical School
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Depositing User: LivePure Connector
Date Deposited: 24 Nov 2021 03:11
Last Modified: 24 Nov 2021 03:11
URI: https://ueaeprints.uea.ac.uk/id/eprint/82294
DOI: 10.1016/S0140-6736(21)01638-X

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