Classifying COVID-19 hospitalizations in epidemiology cohort studies: The C4R study

RATIONALE: 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. OBJECTIVES: 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. METHODS: 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. MEASUREMENTS AND MAIN RESULTS: 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%). CONCLUSIONS: 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.

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UNC Libraries
Published
2025-02-20
DOI
https://doi.org/10.17615/frzr-mr12
Primary Topic
Machine Learning in Healthcare
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article
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article

Classifying COVID-19 hospitalizations in epidemiology cohort studies: The C4R study

Howard Andrews, José D. Gutiérrez, Charles Murphy, Elizabeth A. Regan et al.
UNC Libraries
Machine Learning in Healthcare
article

Classifying COVID-19 hospitalizations in epidemiology cohort studies: The C4R study

Howard Andrews, José D. Gutiérrez, Charles Murphy, Elizabeth A. Regan, Lori A. Bateman, Pramod Anugu, Mitchell S.V. Elkind, Cheryl Tarlton, Norrina B. Allen, Lauren Jager, Carmen R. Isasi, Alka M. Kanaya, Joanne M. Murabito, Lukui Chen, James M. Shikany, Kimberly Ring, Victor E. Ortega, Wendy S. Post, Akshaya Krishnaswamy, Maya Vankineni, Barry J. Make, Karen Hinckley Stukovsky, Daniela Sotres‐Alvarez, Arnita F. Norwood, Wayne D. Rosamond, Elizabeth C. Oelsner, Anna Kucharska‐Newton, George T. O’Connor, James S. Floyd, Emily B. Levitan, Vanessa Xanthakis, Sally E. Wenzel, Pallavi Balte, Tauqeer Ali, Prescott G. Woodruff, Jennifer J. Manly, Cora Elizabeth Lewis, Harry Chang, Amber Pirzada, Rafail Rustamov, Alexander Arynchyn, Namratha R. Kandula, Suzanne E. Judd, Maureen R. Kelly, Virginia J. Howard, Kelley Pettee Gabriel, Sina A. Gharib, Janis Tse, Yuan‐I Min, Sadiya S. Khan, David A. Schwartz, Ying Zhang, Joyce Lee, Ling Jin, Joseph Yang, Ashmi A Patel, Neil A. Zakai, Jianwen Cai, D. Mauger, Elman M. Urbina Meneses, Kimberly Malloy
article en

Abstract

RATIONALE: 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. OBJECTIVES: 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. METHODS: 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. MEASUREMENTS AND MAIN RESULTS: 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%). CONCLUSIONS: 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.

UNC Libraries
University of North Carolina at Chapel Hill (US)
U.S. Department of Health and Human Services, Northwestern University, University of Minnesota, Strong, National Institutes of Health, Kaiser Foundation Research Institute, National Institute on Aging, National Heart, Lung, and Blood Institute, National Institute on Deafness and Other Communication Disorders, National Institute of Neurological Disorders and Stroke
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Machine Learning in Healthcare
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