A COVID-19 Pulmonary Outcome Clinical Prediction Rule Using Epigenetics

项目总结/摘要 虽然大多数SARS-CoV-2感染患者病情轻微，但少数患者病情严重， 肺结果。COVID-19肺炎和相关呼吸衰竭的发病机制仍然存在 不太了解。与社区获得性肺炎患者不同， 感染的放射学证据，COVID-19肺炎患者从开始有几天的间隔 从感染症状到放射学上明显的肺炎住院。预测哪些患者 最初出现轻度症状的患者将保持最低程度的症状，而那些进展到 严重的肺部结果目前是不可能的。这是一个关键的知识缺口，因为这些患者 可以通过早期关键干预措施来改善结果并保护有限的资源。的 本项目的目标是建立模型并验证临床预测规则， 临床变量和表观遗传标记来自我们的电子病历数据仓库， 制定COVID-19严重程度临床预测规则（COPR）。核心假设是，在患者中， 最初表现为最小症状，COPR将预测谁将保持最小症状， 会发展成严重的肺部疾病因此，具体目标包括：（1）确定临床 变量和表观遗传标志物，以预测进展为严重的COVID-19肺部结局，以及（2） 内部验证该临床预测规则。这项研究有助善用医疗资源 通过在病程早期识别受感染个体并预测严重的 最小症状期间的肺部结局。通过这个项目，我将学习如何：1）开发 并验证临床决策规则; 2）将“组学”应用于临床研究。这种结合临床- 表观遗传变量方法也可能有利于预测其他病毒性肝炎的临床结局。 感染，并可能被改造，验证，并部署为下一次大流行。

PROJECT SUMMARY/ABSTRACT Although most SARS-CoV-2 infected patients develop mild illness, a minority progress to develop severe pulmonary outcomes. The pathogenesis of COVID-19 pneumonia and associated respiratory failure remains poorly understood. Unlike patients with community-acquired pneumonia, who rapidly develop clinical and radiologic evidence of infection, patients with COVID-19 pneumonia have a several-day interval from the start of infective symptoms to hospitalization with radiographically apparent pneumonia. Predicting which patients who initially present with mild symptoms will remain minimally symptomatic versus those who progress to severe pulmonary outcomes is currently impossible. This is a critical knowledge gap because these patients could be targeted with early critical interventions to improve outcomes and preserve limited resources. The objective of this project is to model and validate a clinical prediction rule that incorporates existing, detailed clinical variables and epigenetic markers derived from our electronic medical record data warehouse to develop the COVID-19 severity clinical prediction rule (COPR). The central hypothesis is that, in patients initially presenting with minimal symptoms, the COPR will predict who will remain minimally symptomatic and who will progress to severe pulmonary outcomes. The Specific Aims therefore include: (1) to identify clinical variables and epigenetic markers to predict progression to severe COVID-19 pulmonary outcomes, and (2) to internally validate this clinical prediction rule. This study will facilitate the efficient use of healthcare resources through the identification of infected individuals early in their disease course and prediction of severe pulmonary outcomes during periods of minimal symptoms. Through this project I will learn how to: 1) develop and validate clinical decision rules, and 2) apply `omics to clinical investigation. This combination clinical- epigenetic variable approach could also be beneficial for the prediction of clinical outcomes in other viral infections and may be remodeled, validated, and deployed for the next pandemic.