AimsPatients experiencing ischaemic heart failure with reduced ejection fraction (HFrEF) represent a diverse group. We hypothesize that machine learning clustering can help separate distinctive patient phenotypes, paving the way for personalized management.Methods and resultsA total of 8591 ischaemic HFrEF patients pooled from the EPHESUS and CAPRICORN trials (64 ± 12 years; 28% women) were included in this analysis. Clusters were identified using both clinical and biological variables. Association between clusters and the composite of (i) heart failure hospitalization or all‐cause death, (ii) cardiovascular (CV) hospitalization or all‐cause death, and (iii) major adverse CV events was assessed. The derived algorithm was applied in the COMMANDER‐HF trial (n = 5022) for external validation. Five clinical distinctive clusters were identified: Cluster 1 (n = 2161) with the older patients, higher prevalence of atrial fibrillation and previous CV events; Cluster 2 (n = 1376) with the higher prevalence of older hypertensive women and smoking habit; Cluster 3 (n = 1157) with the higher prevalence of diabetes and peripheral artery disease; Cluster 4 (n = 2073) with relatively younger patients, mostly men and with the higher left ventricular ejection fraction; Cluster 5 (n = 1824) with the younger patients and lower CV events burden. Cluster membership was efficiently predicted by a random forest algorithm. Clusters were significantly associated with outcomes in derivation and validation datasets, with Cluster 1 having the highest risk, and Cluster 4 the lowest. Mineralocorticoid receptor antagonist benefit on CV hospitalization or all‐cause death was magnified in clusters with the lowest risk of events (Clusters 2 and 4).ConclusionClustering reveals distinct risk subgroups in the heterogeneous array of ischaemic HFrEF patients. This classification, accessible online, could enhance future outcome predictions for ischaemic HFrEF cases.

中文翻译：

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机器学习方法识别射血分数降低的缺血性心力衰竭患者的表型


Aims射血分数降低的缺血性心力衰竭 （HFrEF） 患者代表了一个多样化的群体。我们假设机器学习聚类可以帮助分离不同的患者表型，为个性化管理铺平道路。方法和结果本分析共纳入了 EPHESUS 和 CAPRICORN 试验中合并的 8591 名缺血性 HFrEF 患者 （64 ± 12 岁;28% 为女性）。使用临床和生物变量识别集群。评估了集群与 （i） 心力衰竭住院或全因死亡，（ii） 心血管 （CV） 住院或全因死亡，以及 （iii） 主要不良 CV 事件的复合关联。该衍生算法应用于 COMMANDER-HF 试验 （n = 5022） 进行外部验证。确定了 5 个临床上独特的集群： 集群 1 （n = 2161） 患者年龄较大，心房颤动患病率较高，既往 CV 事件;聚类 2 （n = 1376） 老年高血压女性和吸烟习惯的患病率较高;第 3 组 （n = 1157） 糖尿病和外周动脉疾病的患病率较高;第 4 组 （n = 2073） 患者相对年轻，以男性为主，左心室射血分数较高;第 5 组 （n = 1824） 患者较年轻，CV 事件负担较低。通过随机森林算法有效地预测集群成员资格。集群与推导和验证数据集中的结果显著相关，其中集群 1 的风险最高，集群 4 的风险最低。盐皮质激素受体拮抗剂对 CV 住院或全因死亡的益处在事件风险最低的集群（集群 2 和 4）中被放大。结论聚类揭示了缺血性 HFrEF 患者异质性阵列中不同的风险亚组。这种分类可在线访问，可以增强缺血性 HFrEF 病例的未来结果预测。