Understanding Post-Acute Sequelae of SARS-CoV-2 Infection through Data-Driven Analysis with the Longitudinal Electronic Health Records: Findings from the RECOVER Initiative
Chengxi Zang; Yongkang Zhang; Jie Xu; Jiang Bian; Dmitry Morozyuk; Edward J. Schenck; Dhruv Khullar; Anna S. Nordvig; Elizabeth A. Shenkman; Russel L. Rothman; Jason P. Block; Kristin Lyman; Mark Weiner; Thomas W. Carton; Fei Wang; Rainu Kaushal.
Preprint
em Inglês
| PREPRINT-MEDRXIV | ID: ppmedrxiv-22275420
Recent studies have investigated post-acute sequelae of SARS-CoV-2 infection (PASC) using real-world patient data such as electronic health records (EHR). Prior studies have typically been conducted on patient cohorts with small sample sizes1 or specific patient populations2,3 limiting generalizability. This study aims to characterize PASC using the EHR data warehouses from two large national patient-centered clinical research networks (PCORnet), INSIGHT and OneFlorida+, which include 11 million patients in New York City (NYC) and 16.8 million patients in Florida respectively. With a high-throughput causal inference pipeline using high-dimensional inverse propensity score adjustment, we identified a broad list of diagnoses and medications with significantly higher incidence 30-180 days after the laboratory-confirmed SARS-CoV-2 infection compared to non-infected patients. We found more PASC diagnoses and a higher risk of PASC in NYC than in Florida, which highlights the heterogeneity of PASC in different populations.
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