Precision phenotyping for curating research cohorts of patients with unexplained post-acute sequelae of COVID-19.

IF 12.8 Q1 MEDICINE, RESEARCH & EXPERIMENTAL Med Pub Date : 2024-11-02 DOI:10.1016/j.medj.2024.10.009

Alaleh Azhir, Jonas Hügel, Jiazi Tian, Jingya Cheng, Ingrid V Bassett, Douglas S Bell, Elmer V Bernstam, Maha R Farhat, Darren W Henderson, Emily S Lau, Michele Morris, Yevgeniy R Semenov, Virginia A Triant, Shyam Visweswaran, Zachary H Strasser, Jeffrey G Klann, Shawn N Murphy, Hossein Estiri

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Abstract

Background: Scalable identification of patients with post-acute sequelae of COVID-19 (PASC) is challenging due to a lack of reproducible precision phenotyping algorithms, which has led to suboptimal accuracy, demographic biases, and underestimation of the PASC.

Methods: In a retrospective case-control study, we developed a precision phenotyping algorithm for identifying cohorts of patients with PASC. We used longitudinal electronic health records data from over 295,000 patients from 14 hospitals and 20 community health centers in Massachusetts. The algorithm employs an attention mechanism to simultaneously exclude sequelae that prior conditions can explain and include infection-associated chronic conditions. We performed independent chart reviews to tune and validate the algorithm.

Findings: The PASC phenotyping algorithm improves precision and prevalence estimation and reduces bias in identifying PASC cohorts compared to the ICD-10-CM code U09.9. The algorithm identified a cohort of over 24,000 patients with 79.9% precision. Our estimated prevalence of PASC was 22.8%, which is close to the national estimates for the region. We also provide in-depth analyses, encompassing identified lingering effects by organ, comorbidity profiles, and temporal differences in the risk of PASC.

Conclusions: PASC precision phenotyping boasts superior precision and prevalence estimation while exhibiting less bias in identifying patients with PASC. The cohort derived from this algorithm will serve as a springboard for delving into the genetic, metabolomic, and clinical intricacies of PASC, surmounting the constraints of prior PASC cohort studies.

Funding: This research was funded by the US National Institute of Allergy and Infectious Diseases (NIAID).

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通过精准表型，建立 COVID-19 后遗症患者研究队列。

背景：由于缺乏可重复的精确表型算法，对COVID-19急性后遗症（PASC）患者进行规模化鉴定具有挑战性，这导致了次优的准确性、人口统计学偏差和对PASC的低估：在一项回顾性病例对照研究中，我们开发了一种用于识别 PASC 患者队列的精确表型算法。我们使用了马萨诸塞州 14 家医院和 20 个社区医疗中心的 295,000 多名患者的纵向电子健康记录数据。该算法采用了一种注意机制，可同时排除先前病症可解释的后遗症，并纳入与感染相关的慢性病症。我们进行了独立的病历审查，以调整和验证该算法：与ICD-10-CM代码U09.9相比，PASC表型算法提高了精确度和患病率估计，减少了在识别PASC队列时的偏差。该算法识别了超过24000名患者的队列，精确度为79.9%。我们估计的 PASC 患病率为 22.8%，接近该地区的全国估计值。我们还进行了深入分析，包括已确定的器官残留效应、合并症概况以及 PASC 风险的时间差异：结论：PASC 精确表型具有更高的精确度和患病率估计，同时在识别 PASC 患者时偏差较小。从该算法中得出的队列将成为深入研究 PASC 遗传、代谢组学和临床复杂性的跳板，从而突破之前 PASC 队列研究的限制：本研究由美国国家过敏与传染病研究所（NIAID）资助。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Med MEDICINE, RESEARCH & EXPERIMENTAL-

CiteScore

17.70

自引率

0.60%

发文量

102

期刊介绍： Med is a flagship medical journal published monthly by Cell Press, the global publisher of trusted and authoritative science journals including Cell, Cancer Cell, and Cell Reports Medicine. Our mission is to advance clinical research and practice by providing a communication forum for the publication of clinical trial results, innovative observations from longitudinal cohorts, and pioneering discoveries about disease mechanisms. The journal also encourages thought-leadership discussions among biomedical researchers, physicians, and other health scientists and stakeholders. Our goal is to improve health worldwide sustainably and ethically. Med publishes rigorously vetted original research and cutting-edge review and perspective articles on critical health issues globally and regionally. Our research section covers clinical case reports, first-in-human studies, large-scale clinical trials, population-based studies, as well as translational research work with the potential to change the course of medical research and improve clinical practice.