Identifying COVID-19 disease severity in real-world data: Implications for medical product effectiveness studies

Identifying COVID-19 disease severity in real-world data: Implications for medical product effectiveness studies | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Pharmacoepidemiology and Drug Safety This is a preprint and has not been peer reviewed. Data may be preliminary. 12 October 2025 V1 Latest version Share on Identifying COVID-19 disease severity in real-world data: Implications for medical product effectiveness studies Authors : Mayura U. Shinde [email protected] , Katherine Shapiro , Laura Hou , Kevin Coughlin , Aaron M. Madow , Fatma M. Shebl , Patricia Bright , … Show All … , Gaia Pocobelli 0000-0002-0481-6168 , James D. Ralston , Vina F. Graham , Margaret Nolan B , Ingrid A. Binswanger , Chih-Ying Pratt , Wei Hua , Noelle M. Cocoros , and Silvia Perez-Vilar 0000-0003-1272-1502 Show Fewer Authors Info & Affiliations https://doi.org/10.22541/au.176028677.72168724/v1 Published Pharmacoepidemiology and Drug Safety Version of record Peer review timeline 374 views 150 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract not-yet-known not-yet-known not-yet-known unknown Purpose: The U.S. Food and Drug Administration (FDA) defined disease severity criteria to assist clinical development of medical products for management of COVID-19. These definitions were translated to code-based algorithms for use in real-world data. We validated the algorithms’ performance in ambulatory settings at three regional integrated healthcare delivery systems contributing data to FDA’s Sentinel System. Methods: We identified cohorts of individuals ≥18 years that met the algorithms’ criteria for mild, moderate, and severe COVID-19 at incident COVID-19 diagnosis or positive SARS-CoV-2 test, and separately, at incident COVID-19 treatment, from January 2022 through April 2023. We validated the algorithms via chart review of a random sample to calculate positive predictive values (PPVs) and 95% CIs. Results: The algorithms identified 33,071 patients at COVID-19 diagnosis or positive test; 26,985 mild (49 chart reviewed), 5,180 moderate (55 reviewed), and 906 severe (56 reviewed). A total of 4,512 patients were identified at COVID-19 treatment; 3,474 mild (56 reviewed), 848 moderate (60 reviewed), and 190 severe (46 reviewed). The PPVs 1) at COVID-19 diagnosis or positive test were: mild 57% (95% CI: 43–71%), moderate 58% (95% CI: 45–71%), and severe 54% (95% CI: 41–67%) and 2) at COVID-19 treatment: mild 57% (95% CI: 44–70%), moderate 70% (95% CI: 58%-82%) and severe 72% (95% CI: 59%-85%). Conclusion: The algorithms had low-to-moderate performance in classifying COVID-19 severity in ambulatory settings, depending on assessment at diagnosis or treatment. Researchers should consider performance of the algorithm when using real-world data to assess COVID-19 severity. Supplementary Material File (pds-25-0829-file001.docx) Download 380.28 KB Information & Authors Information Version history V1 Version 1 12 October 2025 Peer review timeline Published Pharmacoepidemiology and Drug Safety Version of Record 14 Apr 2026 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Pharmacoepidemiology and Drug Safety Keywords algorithms covid-19 positive predictive value real-world data Authors Affiliations Mayura U. Shinde [email protected] Harvard Pilgrim Health Care Institute View all articles by this author Katherine Shapiro Harvard Pilgrim Health Care Institute View all articles by this author Laura Hou Harvard Pilgrim Health Care Institute View all articles by this author Kevin Coughlin Harvard Pilgrim Health Care Institute View all articles by this author Aaron M. Madow Harvard Pilgrim Health Care Institute View all articles by this author Fatma M. Shebl US Food and Drug Administration View all articles by this author Patricia Bright US Food and Drug Administration View all articles by this author Gaia Pocobelli 0000-0002-0481-6168 Kaiser Permanente Washington Health Research Institute View all articles by this author James D. Ralston Kaiser Permanente Washington Health Research Institute View all articles by this author Vina F. Graham Kaiser Permanente Washington Health Research Institute View all articles by this author Margaret Nolan B HealthPartners Institute View all articles by this author Ingrid A. Binswanger Kaiser Permanente Colorado View all articles by this author Chih-Ying Pratt US Food and Drug Administration View all articles by this author Wei Hua US Food and Drug Administration View all articles by this author Noelle M. Cocoros Harvard Pilgrim Health Care Institute View all articles by this author Silvia Perez-Vilar 0000-0003-1272-1502 US Food and Drug Administration View all articles by this author Metrics & Citations Metrics Article Usage 374 views 150 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Mayura U. Shinde, Katherine Shapiro, Laura Hou, et al. Identifying COVID-19 disease severity in real-world data: Implications for medical product effectiveness studies. Authorea . 12 October 2025. 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