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.

中文翻译：

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重新定义窦性心律和心房颤动心力衰竭患者的β受体阻滞剂反应：机器学习聚类分析

 背景

尽管治疗方法取得了进步，但患有心力衰竭和左心室射血分数（LVEF）降低的患者的死亡率仍然高得令人无法接受。我们假设一种新的人工智能方法可以更好地评估合并症的多个和更高维度的相互作用，并定义 β 受体阻滞剂对窦性心律和心房颤动患者的疗效集群。

 方法

基于神经网络的变分自动编码器和层次聚类被应用于来自九项β受体阻滞剂双盲、随机、安慰剂对照试验的汇总个体患者数据。中位1·3年​​随访期间的全因死亡率根据治疗意向进行评估，并根据心电图心律进行分层。客观地确定聚类的数量和维度，并使用留一试验方法验证结果。本研究前瞻性地在 ClinicalTrials.gov (NCT00832442) 和 PROSPERO 系统评价数据库 (CRD42014010012) 注册。

 发现

纳入 15 659 名心力衰竭且 LVEF 低于 50% 的患者，中位年龄 65 岁（IQR 56-72），LVEF 27%（IQR 21-33）。 3708 名 (24%) 患者为女性。在窦性心律 (n=12 822) 中，大多数集群表现出 β 受体阻滞剂一致的总体死亡率获益，比值比 (OR) 范围为 0·54 至 0·74。一组症状不太严重的老年窦性心律患者没有表现出显着疗效（OR 0·86，95% CI 0·67–1·10；p=0·22）。在心房颤动 (n=2837) 中，五组中的四组与 β 阻滞剂与安慰剂相比的总体中性效果一致（OR 0·92、0·77–1·10；p=0·37）。一组死亡风险较低但 LVEF 与平均水平相似的年轻房颤患者在使用 β 受体阻滞剂后死亡率显着降低（OR 0·57、0·35–0·93；p=0·023）。所有模型的聚类稳健性和一致性都得到了确认（p<0·0001与随机），并且聚类成员资格在九个独立试验中进行了外部验证。

 解释

基于人工智能的聚类方法能够区分心力衰竭和 LVEF 降低患者的预后反应与 β 受体阻滞剂。其中包括疗效不佳的窦性心律患者，以及一组使用β受体阻滞剂确实降低死亡率的心房颤动患者。

 资金

英国医学研究委员会和 EU/EFPIA 创新药物计划 BigData@Heart。