The coronavirus disease 2019 (COVID-19) is heterogeneous and our understanding of the biological mechanisms of host response to the viral infection remains limited. Identification of meaningful clinical subphenotypes may benefit pathophysiological study, clinical practice, and clinical trials. Here, our aim was to derive and validate COVID-19 subphenotypes using machine learning and routinely collected clinical data, assess temporal patterns of these subphenotypes during the pandemic course, and examine their interaction with social determinants of health (SDoH). We retrospectively analyzed 14418 COVID-19 patients in five major medical centers in New York City (NYC), between March 1 and June 12, 2020. Using clustering analysis, 4 biologically distinct subphenotypes were derived in the development cohort (N = 8199). Importantly, the identified subphenotypes were highly predictive of clinical outcomes (especially 60-day mortality). Sensitivity analyses in the development cohort, and rederivation and prediction in the internal (N = 3519) and external (N = 3519) validation cohorts confirmed the reproducibility and usability of the subphenotypes. Further analyses showed varying subphenotype prevalence across the peak of the outbreak in NYC. We also found that SDoH specifically influenced mortality outcome in Subphenotype IV, which is associated with older age, worse clinical manifestation, and high comorbidity burden. Our findings may lead to a better understanding of how COVID-19 causes disease in different populations and potentially benefit clinical trial development. The temporal patterns and SDoH implications of the subphenotypes may add insights to health policy to reduce social disparity in the pandemic.

2019 年冠状病毒病 (COVID-19) 具有异质性，我们对宿主对病毒感染反应的生物学机制的了解仍然有限。有意义的临床亚表型的鉴定可能有益于病理生理学研究、临床实践和临床试验。在这里，我们的目标是使用机器学习和常规收集的临床数据来推导和验证 COVID-19 亚表型，评估这些亚表型在大流行过程中的时间模式，并检查它们与健康的社会决定因素 (SDoH) 的相互作用。我们回顾性分析了 2020 年 3 月 1 日至 6 月 12 日期间纽约市 (NYC) 五个主要医疗中心的 14418 名 COVID-19 患者。通过聚类分析，在开发队列中得出了 4 种生物学上不同的亚表型 ( N = 8199)。重要的是，确定的亚表型可以高度预测临床结果（尤其是 60 天死亡率）。开发队列中的敏感性分析以及内部 ( N = 3519) 和外部 ( N = 3519) 验证队列中的重新推导和预测证实了亚表型的再现性和可用性。进一步的分析显示，纽约市在疫情高峰期存在不同的亚表型流行率。我们还发现，SDoH 特别影响亚表型 IV 的死亡率结果，这与年龄较大、临床表现较差和合并症负担较高有关。我们的研究结果可能有助于更好地了解 COVID-19 如何在不同人群中引起疾病​​，并可能有利于临床试验的开发。亚表型的时间模式和 SDoH 影响可能会为卫生政策增加见解，以减少大流行中的社会差距。