Oelsner Elizabeth C, Krishnaswamy Akshaya, Rustamov Rafail, Balte Pallavi P, Ali Tauqeer, Allen Norrina B, Andrews Howard F, Anugu Pramod, Arynchyn Alexander, Bateman Lori A, Cai Jianwen, Chang Harry, Chen Lucas, Elkind Mitchell S V, Floyd James S, Gabriel Kelley Pettee, Gharib Sina A, Gutierrez Jose D, Stukovsky Karen Hinckley, Howard Virginia J, Isasi Carmen R, Jager Lauren, Jin Ling, Judd Suzanne E, Kanaya Alka M, Kandula Namratha R, Kelly Maureen R, Khan Sadiya S, Kucharska-Newton Anna, Lee Joyce S, Levitan Emily B, Lewis Cora E, Make Barry J, Malloy Kimberly, Manly Jennifer J, Mauger David, Min Yuan-I, Murabito Joanne M, Murphy Charles G, Norwood Arnita F, O'Connor George T, Ortega Victor E, Patel Ashmi A, Pirzada Amber, Regan Elizabeth A, Ring Kimberly B, Rosamond Wayne D, Schwartz David A, Shikany James M, Sotres-Alvarez Daniela, Tarlton Cheryl, Tse Janis, Meneses Elman M Urbina, Vankineni Maya, Wenzel Sally E, Woodruff Prescott G, Xanthakis Vanessa, Yang Ji Hyun, Zakai Neil A, Zhang Ying, Post Wendy S
Division of General Medicine, Department of Medicine, Columbia University Irving Medical Center, New York, New York, United States of America.
Department of Medicine, Nassau University Medical Center, East Meadow, New York, United States of America.
PLoS One. 2025 Feb 10;20(2):e0316198. doi: 10.1371/journal.pone.0316198. eCollection 2025.
Robust COVID-19 outcomes classification is important for ongoing epidemiology research on acute and post-acute COVID-19 conditions. Protocolized medical record review is an established method to validate endpoints for clinical trials and cardiovascular epidemiology cohorts; however, a protocol to adjudicate hospitalizations for COVID-19 among epidemiology cohorts was lacking.
We developed a protocol to ascertain and adjudicate hospitalized COVID-19 across a meta-cohort of 14 US prospective cohort studies. This report describes the first three years of protocol implementation (October 1, 2020-October 1, 2023) and evaluates its repeatability and performance compared to classification by administrative codes.
The protocol was adapted from cohort approaches to clinical cardiovascular events ascertainment and adjudication. Potential COVID-19 hospitalizations and deaths were identified by self-/proxy-report and, in some cases, active surveillance. Medical records were requested from hospitals and adjudicated for COVID-19 outcomes by clinically trained personnel according to a standardized rubric. Inter-rater agreement was assessed. The sensitivity and specificity of discharge diagnosis codes was compared to adjudicated diagnoses.
The study obtained medical records for 1,167 potential COVID-19 hospitalizations, which underwent protocolized adjudication. Adjudication confirmed COVID-19 infection was present for 1,030 (88%) events, of which COVID-19 was not the cause of hospitalization for 78 (8%). Of 952 hospitalizations determined by adjudicators to be caused by COVID-19, 319 (34%) participants were critically ill and 210 (22%) died. Pneumonia was confirmed in 822 (86%) and acute kidney injury in 350 (37%); other cardiovascular and thrombotic complications were rare (2-5%). Interrater reliability among adjudicators was high (kappa = 0.85-1.00) except for myocardial infarction (kappa = 0.60). Compared to adjudication, sensitivity of discharge diagnosis codes was higher for pneumonia (84%) and pulmonary embolism (81%) than for other complications (48-70%).
Protocolized adjudication confirmed four out of five COVID-19 hospitalizations in a US meta-cohort and confirmed cases of pneumonia, pulmonary embolism, and other conditions that were not indicated by discharge diagnosis codes. These results highlight the importance of validating health outcomes for use in research on COVID-19 and post-COVID-19 conditions, and some limitations of claims-based data.
强大的新冠病毒疾病结局分类对于正在进行的关于急性和急性后新冠病毒疾病状况的流行病学研究至关重要。规范化的病历审查是验证临床试验和心血管流行病学队列终点的既定方法；然而，缺乏一种用于判定流行病学队列中新冠病毒疾病住院情况的方案。
我们制定了一项方案，以确定并判定美国14项前瞻性队列研究的元队列中新冠病毒疾病住院情况。本报告描述了该方案实施的前三年（2020年10月1日至2023年10月1日），并与行政代码分类相比，评估了其可重复性和性能。
该方案改编自用于临床心血管事件确定和判定的队列方法。通过自我/代理人报告，在某些情况下通过主动监测来识别潜在的新冠病毒疾病住院和死亡情况。向医院索取病历，并由经过临床培训的人员根据标准化的评分标准对新冠病毒疾病结局进行判定。评估了评判者间的一致性。将出院诊断代码的敏感性和特异性与判定诊断进行了比较。
该研究获取了1167例潜在新冠病毒疾病住院病例的病历，并进行了规范化判定。判定确认1030例（88%）事件存在新冠病毒感染，其中78例（8%）新冠病毒不是住院原因。在判定者确定由新冠病毒疾病导致的952例住院病例中，319例（34%）参与者病情危重，210例（22%）死亡。822例（86%）确诊为肺炎，350例（37%）确诊为急性肾损伤；其他心血管和血栓并发症很少见（2%-5%）。除心肌梗死外（kappa = 0.60），判定者间的可靠性较高（kappa = 0.85 - 1.00）。与判定相比，出院诊断代码对肺炎（84%）和肺栓塞（81%）的敏感性高于其他并发症（48%-70%）。
在美国的一个元队列中，规范化判定确认了五分之四的新冠病毒疾病住院病例，并确认了肺炎、肺栓塞和其他出院诊断代码未显示的疾病病例。这些结果凸显了在新冠病毒疾病和新冠后疾病研究中验证健康结局的重要性，以及基于索赔数据的一些局限性。
