BackgroundThe promise of precision population health includes the ability to use robust patient data to tailor prevention and care to specific groups. Advanced analytics may allow for automated detection of clinically informative subgroups that account for clinical, genetic, and environmental variability. This study sought to evaluate whether unsupervised machine learning approaches could interpret heterogeneous and missing clinical data to discover clinically important coronary artery disease subgroups.Methods and ResultsThe Genetic Determinants of Peripheral Arterial Disease study is a prospective cohort that includes individuals with newly diagnosed and/or symptomatic coronary artery disease. We applied generalized low rank modeling and K‐means cluster analysis using 155 phenotypic and genetic variables from 1329 participants. Cox proportional hazard models were used to examine associations between clusters and major adverse cardiovascular and cerebrovascular events and all‐cause mortality. We then compared performance of risk stratification based on clusters and the American College of Cardiology/American Heart Association pooled cohort equations. Unsupervised analysis identified 4 phenotypically and prognostically distinct clusters. All‐cause mortality was highest in cluster 1 (oldest/most comorbid; 26%), whereas major adverse cardiovascular and cerebrovascular event rates were highest in cluster 2 (youngest/multiethnic; 41%). Cluster 4 (middle‐aged/healthiest behaviors) experienced more incident major adverse cardiovascular and cerebrovascular events (30%) than cluster 3 (middle‐aged/lowest medication adherence; 23%), despite apparently similar risk factor and lifestyle profiles. In comparison with the pooled cohort equations, cluster membership was more informative for risk assessment of myocardial infarction, stroke, and mortality.ConclusionsUnsupervised clustering identified 4 unique coronary artery disease subgroups with distinct clinical trajectories. Flexible unsupervised machine learning algorithms offer the ability to meaningfully process heterogeneous patient data and provide sharper insights into disease characterization and risk assessment.RegistrationURL: https://www.clinicaltrials.gov; Unique identifier: NCT00380185.

中文翻译：

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用于自动检测冠状动脉疾病亚组的无监督学习


背景精准人群健康的承诺包括能够使用可靠的患者数据来针对特定群体定制预防和护理。先进的分析可以自动检测临床信息亚组，从而解释临床、遗传和环境变异性。本研究旨在评估无监督机器学习方法是否可以解释异质和缺失的临床数据，以发现临床上重要的冠状动脉疾病亚组。方法和结果外周动脉疾病的遗传决定因素研究是一个前瞻性队列，其中包括新诊断和/或有症状的个体冠状动脉疾病。我们使用来自 1329 名参与者的 155 个表型和遗传变量应用广义低秩模型和 K 均值聚类分析。 Cox比例风险模型用于检查集群与主要不良心脑血管事件和全因死亡率之间的关联。然后，我们比较了基于聚类和美国心脏病学会/美国心脏协会汇总队列方程的风险分层的表现。无监督分析确定了 4 个表型和预后不同的簇。第 1 组的全因死亡率最高（最年长/最常见的共病；26%），而第 2 组的主要不良心脑血管事件发生率最高（最年轻/多种族；41%）。尽管危险因素和生活方式特征明显相似，但组 4（中年/最健康行为）比组 3（中年/最低药物依从性；23%）经历了更多的主要不良心脑血管事件（30%）。 与汇总队列方程相比，聚类成员资格对于心肌梗塞、中风和死亡率的风险评估提供了更多信息。结论无监督聚类确定了 4 个具有不同临床轨迹的独特冠状动脉疾病亚组。灵活的无监督机器学习算法能够有效地处理异构患者数据，并为疾病特征和风险评估提供更清晰的见解。RegistrationURL：https://www.clinicaltrials.gov；唯一标识符：NCT00380185。