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Data-driven analysis to understand long COVID using electronic health records from the RECOVER initiative

Author

Listed:
  • Chengxi Zang

    (Weill Cornell Medicine)

  • Yongkang Zhang

    (Weill Cornell Medicine)

  • Jie Xu

    (University of Florida)

  • Jiang Bian

    (University of Florida)

  • Dmitry Morozyuk

    (Weill Cornell Medicine)

  • Edward J. Schenck

    (Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine)

  • Dhruv Khullar

    (Weill Cornell Medicine)

  • Anna S. Nordvig

    (Weill Cornell Medicine)

  • Elizabeth A. Shenkman

    (University of Florida)

  • Russell L. Rothman

    (Vanderbilt University Medical Center)

  • Jason P. Block

    (Harvard Pilgrim Health Care Institute, Harvard Medical School)

  • Kristin Lyman

    (Louisiana Public Health Institute)

  • Mark G. Weiner

    (Weill Cornell Medicine)

  • Thomas W. Carton

    (Louisiana Public Health Institute)

  • Fei Wang

    (Weill Cornell Medicine)

  • Rainu Kaushal

    (Weill Cornell Medicine)

Abstract

Recent studies have investigated post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) using real-world patient data such as electronic health records (EHR). Prior studies have typically been conducted on patient cohorts with specific patient populations which makes their generalizability unclear. This study aims to characterize PASC using the EHR data warehouses from two large Patient-Centered Clinical Research Networks (PCORnet), INSIGHT and OneFlorida+, which include 11 million patients in New York City (NYC) area and 16.8 million patients in Florida respectively. With a high-throughput screening pipeline based on propensity score and inverse probability of treatment weighting, we identified a broad list of diagnoses and medications which exhibited significantly higher incidence risk for patients 30–180 days after the laboratory-confirmed SARS-CoV-2 infection compared to non-infected patients. We identified more PASC diagnoses in NYC than in Florida regarding our screening criteria, and conditions including dementia, hair loss, pressure ulcers, pulmonary fibrosis, dyspnea, pulmonary embolism, chest pain, abnormal heartbeat, malaise, and fatigue, were replicated across both cohorts. Our analyses highlight potentially heterogeneous risks of PASC in different populations.

Suggested Citation

  • Chengxi Zang & Yongkang Zhang & Jie Xu & Jiang Bian & Dmitry Morozyuk & Edward J. Schenck & Dhruv Khullar & Anna S. Nordvig & Elizabeth A. Shenkman & Russell L. Rothman & Jason P. Block & Kristin Lyma, 2023. "Data-driven analysis to understand long COVID using electronic health records from the RECOVER initiative," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37653-z
    DOI: 10.1038/s41467-023-37653-z
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    References listed on IDEAS

    as
    1. Yan Xie & Benjamin Bowe & Ziyad Al-Aly, 2021. "Burdens of post-acute sequelae of COVID-19 by severity of acute infection, demographics and health status," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    2. Ziyad Al-Aly & Yan Xie & Benjamin Bowe, 2021. "High-dimensional characterization of post-acute sequelae of COVID-19," Nature, Nature, vol. 594(7862), pages 259-264, June.
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