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

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.

Identifying Atrial Fibrillation Mechanisms for Personalized Medicine

“…Others have shown that AF causes electrical remodeling with longer effective refractory periods and slower conduction, predominantly in the PVs, which begets AF [ 101 ]. Together, these studies explain why beta-blockers, which ameliorate autonomic influences, slow the heart rate, and flatten APD restitution [ 102 ], may prevent AF in some patients [ 103 ]. They may also motivate the role of class I agents, which slow atrial conduction [ 104 ].…”

Using machine learning to uncover heterogeneity of beta blocker response in heart failure

“…Karwath et al. 1 in The Lancet investigated the question of whether there are clusters of heart failure patients in both sinus rhythm and atrial fibrillation that exhibit differential benefit from beta blocker medications. Among patients who have heart failure with reduced ejection fraction (HFrEF), beta blockers are a cornerstone medical therapy supported by substantial prior literature.…”

“… Summary A recent study by Karwath et al. 1 in The Lancet applied machine learning-based cluster analysis to pooled data from nine double-blind, randomized controlled trials of beta blockers, identifying subgroups of efficacy in patients with sinus rhythm and atrial fibrillation. …”

Catheter ablation for atrial fibrillation: impact on mortality, morbidity, quality of life, and implications for the future

“…The results of a currently published neural network-based analysis of 15,659 patients with heart failure and a mean left ventricular ejection fraction of 27% reported that the use of beta-blocker medication in the majority of patients with atrial fibrillation does not reduce mortality when compared to patients with sinus rhythm, suggesting that, to date, catheter ablation remains the only mortality-reducing treatment in this very common group of patients (Fig. 1 ; [ 8 ]).

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