Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort study

BACKGROUND: Characterizing Post-Acute Sequelae of COVID (SARS-CoV-2 Infection), or PASC has been challenging due to the multitude of sub-phenotypes, temporal attributes, and definitions. Scalable characterization of PASC sub-phenotypes 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). From the total cohort, we applied a deductive approach on 12,424 individuals with follow-up data and developed a distributed representation learning process for providing augmented definitions for PASC sub-phenotypes. FINDINGS: Our framework characterized seven PASC sub-phenotypes. 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 sub-phenotypes prevalence and temporal attributes, thus developing a unified model that characterizes augmented sub-phenotypes across the different systems. FUNDING: Authors are supported by 10.13039/100000060National Institute of Allergy and Infectious Diseases, 10.13039/100000049National Institute on Aging, 10.13039/100006108National Center for Advancing Translational Sciences, 10.13039/501100001349National Medical Research Council, 10.13039/100000065National Institute of Neurological Disorders and Stroke, 10.13039/501100000780European Union, 10.13039/100000002National Institutes of Health, 10.13039/100006108National Center for Advancing Translational Sciences.