Long COVID augmented definition publications
Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort study
Created Sep 14, 2023 - Last updated: Aug 25, 2023
We have 2 studies published on the augmented Long COVID definitions from the collaboration with the 4CE consortium. First,
A retrospective cohort analysis leveraging augmented intelligence to characterize long COVID in the electronic health record: A precision medicine framework (link to open access publication), was published by Plos Digital Health, in which we described the methods and validation results on 3 Long COVID sub-types. This was a precursor for 2nd study in Lancet’s eClinical Medicine,
Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort study (link to open access publication).
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. 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.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. 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.