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Redefining β-blocker response in heart failure patients with sinus rhythm and atrial fibrillation: a machine learning cluster analysis.
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- Author(s): Karwath, Andreas1,2 (AUTHOR); Bunting, Karina V2,3,4 (AUTHOR); Gill, Simrat K4 (AUTHOR); Tica, Otilia2,4 (AUTHOR); Pendleton, Samantha1 (AUTHOR); Aziz, Furqan1,2 (AUTHOR); Barsky, Andrey D1,2 (AUTHOR); Chernbumroong, Saisakul1 (AUTHOR); Duan, Jinming5 (AUTHOR); Mobley, Alastair R2,3,4 (AUTHOR); Cardoso, Victor Roth1,2,4 (AUTHOR); Slater, Luke1,2 (AUTHOR); Williams, John A1,2 (AUTHOR); Bruce, Emma-Jane2,3,4 (AUTHOR); Wang, Xiaoxia2,3,4 (AUTHOR); Flather, Marcus D6 (AUTHOR); Coats, Andrew J S7 (AUTHOR); Gkoutos, Georgios V1,2,3 (AUTHOR) ; Kotecha, Dipak2,3,4 (AUTHOR) ; card AIc group and the Beta-blockers in Heart Failure Collaborative Group (CORPORATE AUTHOR)
- Source:
Lancet. Oct2021, Vol. 398 Issue 10309, p1427-1435. 9p.
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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.Funding: Medical Research Council, UK, and EU/EFPIA Innovative Medicines Initiative BigData@Heart. [ABSTRACT FROM AUTHOR]
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