Post-Acute Sequelae of SARS-CoV-2 infection (PASC), also known as Long-COVID, encompasses a variety of complex and varied outcomes following COVID-19 infection that are still poorly understood. We clustered over 600 million condition diagnoses from 14 million patients available through the National COVID Cohort Collaborative (N3C), generating hundreds of highly detailed clinical phenotypes. Assessing patient clinical trajectories using these clusters allowed us to identify individual conditions and phenotypes strongly increased after acute infection. We found many conditions increased in COVID-19 patients compared to controls, and using a novel method to associate patients with clusters over time, we additionally found phenotypes specific to patient sex, age, wave of infection, and PASC diagnosis status. While many of these results reflect known PASC symptoms, the resolution provided by this unprecedented data scale suggests avenues for improved diagnostics and mechanistic understanding of this multifaceted disease.

SARS-CoV-2 感染 （PASC） 的急性后遗症，也称为长期 COVID，包括 COVID-19 感染后各种复杂多样的结果，这些结果仍然知之甚少。我们通过国家 COVID 队列协作组织 （N3C） 聚集了 1400 万患者的 6 亿多例疾病诊断，生成了数百种高度详细的临床表型。使用这些集群评估患者的临床轨迹使我们能够识别急性感染后强烈增加的个体状况和表型。我们发现与对照组相比，COVID-19 患者的许多情况有所增加，并且使用一种新方法随着时间的推移将患者与集群相关联，我们还发现了特定于患者性别、年龄、感染浪潮和 PASC 诊断状态的表型。虽然其中许多结果反映了已知的 PASC 症状，但这种前所未有的数据量表提供的分辨率表明了改进诊断和对这种多方面疾病的机制理解的途径。