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Characterization of Long Covid Temporal Sub-Phenotypes by Distributed Learning from Electronic Health Record Data
44 Pages Posted: 12 Jun 2023
More...Abstract
Background: Characterizing Post-Acute Sequelae of COVID (SARS-CoV-2 Infection), or PASC has been challenging due to the multitude of subtypes, temporal attributes, and definitions. Scalable characterization of PASC subtypes can enhance screening capacities, disease management, and treatment planning.
Methods: We conducted a retrospective multi-centre observational cohort study, leveraging longitudinal electronic health record (EHR) data of 30,422 patients from three healthcare systems in the Consortium for the Clinical Characterization of COVID-19 by EHR (4CE). We applied a deductive approach to develop a temporally distributed representation learning process for providing augmented definitions for PASC subtypes.
Findings: Our framework characterized seven PASC subtypes. We estimated that on average 15.7 % of the hospitalized COVID-19 patients were likely to suffer from at least one PASC symptom and almost 5.98 %, on average, had multiple symptoms. Joint pain and dyspnea had the highest prevalence, with an average prevalence of 5.45% and 4.53%, respectively.
Interpretation: We provided a scalable framework to every participating healthcare system for estimating PASC subtypes prevalence and temporal attributes, thus developing a unified model that characterizes augmented subtypes across the different systems.
Funding: The National Institute of Allergy and Infectious Diseases (R01AI165535), the National Institute on Aging (RF1AG074372), the National Center for Advancing Translational Sciences (UL1-TR001878).
Declaration of Interest: Riccardo Bellazzi is shareholder of Biomeris s.r.l.
Ethical Approval: The use of EHR data at each institution was approved by local Institutional Review Boards with waiver of patient consent.
Keywords: Post-acute sequelae of SARS-CoV-2, PASC, COVID-19, SARS-CoV-2
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