Understanding Post-Acute Sequelae of SARS-CoV-2 Infection through Data-Driven Analysis with Longitudinal Electronic Health Records: Findings from the RECOVER Initiative

Abstract

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.

Racial/Ethnic Disparities in Post-acute Sequelae of SARS-CoV-2 Infection in New York: an EHR-Based Cohort Study from the RECOVER Program

“…Finally, we used an inverse probability-weighted Cox proportional hazard model to compare the incidence risk of each of the 137 PASC conditions between COVID-19-positive and COVID-19-negative cohorts. 21 We identified 24 PASC conditions with adjusted hazard ratios greater than 1 (meaning that COVID-19-positive patients were at higher risk of developing these conditions). These conditions were categorized into eight groups corresponding to organ systems (nervous, skin, respiratory, circulatory, blood, endocrine, digestive, and general), and were consistent with conditions reported in other studies of PASC.…”

Data-driven identification of post-acute SARS-CoV-2 infection subphenotypes

“…We examined the robustness of the identified PASC subphenotypes in a more restricted set of PASC conditions. Specifically, with a high-dimensional propensity score (PS) adjustment pipeline 20,21 and existing research into PASC 10,21,22 , we identified 44 PASC conditions (Supplementary Table 7) in the INSIGHT cohort that demonstrated statistically significant higher risk in the follow-up period for patients who tested positive versus negative for SARS-CoV-2 infection. Then, we implemented the same subphenotyping process based on these 44 PASC conditions.…”

“…We leveraged our high-dimensional PS adjustment pipeline 20,21 to identify the potential PASC conditions that showed significantly higher risk during 30-180 days after lab-confirmed SARS-CoV-2 infection compared to patients who tested negative for SARS-CoV-2, after adjusting for a comprehensive list of hypothetical confounders, including demographics, baseline medical utilizations and comorbidities, and severity during the acute infection phase. More details can be foud in Zang et al 21 , and the results on different PASC conditions are provided in Supplementary Table 7. We selected a particular PASC condition if either of the following two conditions was satisfied.…”

Identifying Contextual and Spatial Risk Factors for Post-Acute Sequelae of SARS-CoV-2 Infection: An EHR-based Cohort Study from the RECOVER Program

“…We included 23 PASC symptoms and conditions that were identified from our previous study based on existing literature, input from clinical experts, and data-driven analytics (Zang et al, 2022). A detailed description of methods of identifying these PASC symptoms and conditions was reported separately (Zang et al, 2022).…”

“…We included 23 PASC symptoms and conditions that were identified from our previous study based on existing literature, input from clinical experts, and data-driven analytics (Zang et al, 2022). A detailed description of methods of identifying these PASC symptoms and conditions was reported separately (Zang et al, 2022). These symptoms and conditions are categorized into the following eight organ systems: nervous system (encephalopathy, dementia, cognitive problems, sleep disorders, and headache), skin (hair loss and pressure ulcer of skin), respiratory system (pulmonary fibrosis, dyspnea, and acute pharyngitis), circulatory system (pulmonary embolism, thromboembolism, chest pain, and abnormal heartbeat), blood (anemia), endocrine (malnutrition, diabetes mellitus, fluid disorders, and edema), digestive system (constipation and abdominal pain), and general signs and symptoms (malaise and fatigue and joint pain).…”