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Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort study.
Dagliati, Arianna; Strasser, Zachary H; Hossein Abad, Zahra Shakeri; Klann, Jeffrey G; Wagholikar, Kavishwar B; Mesa, Rebecca; Visweswaran, Shyam; Morris, Michele; Luo, Yuan; Henderson, Darren W; Samayamuthu, Malarkodi Jebathilagam; Tan, Bryce W Q; Verdy, Guillame; Omenn, Gilbert S; Xia, Zongqi; Bellazzi, Riccardo; Murphy, Shawn N; Holmes, John H; Estiri, Hossein.
Afiliación
  • Dagliati A; Department of Electrical Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
  • Strasser ZH; Department of Medicine, Massachusetts General Hospital, Boston, United States.
  • Hossein Abad ZS; University of Toronto, Dalla Lana School of Public Health, Toronto, Canada.
  • Klann JG; Department of Medicine, Massachusetts General Hospital, Boston, United States.
  • Wagholikar KB; Department of Medicine, Massachusetts General Hospital, Boston, United States.
  • Mesa R; Department of Electrical Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
  • Visweswaran S; Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, United States.
  • Morris M; Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, United States.
  • Luo Y; Department of Preventive Medicine, Northwestern University, Chicago, United States.
  • Henderson DW; University of Kentucky, Center for Clinical and Translational Science, Lexington, United States.
  • Samayamuthu MJ; Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, United States.
  • Tan BWQ; National University Hospital, Singapore Department of Medicine, Singapore.
  • Verdy G; Bordeaux University Hospital, IAM Unit, Bordeaux, France.
  • Omenn GS; University of Michigan, Department of Computational Medicine and Bioinformatics, Internal Medicine, Human Genetics, and School of Public Health, Ann Arbor, United States.
  • Xia Z; University of Pittsburgh Department of Neurology, Pittsburgh, United States.
  • Bellazzi R; Department of Electrical Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
  • Murphy SN; Department of Neurology, Massachusetts General Hospital, Boston, United States.
  • Holmes JH; University of Pennsylvania Perelman School of Medicine, Department of Biostatistics, Epidemiology, and Informatics, Institute for Biomedical Informatics, Philadelphia, United States.
  • Estiri H; Department of Medicine, Massachusetts General Hospital, Boston, United States.
EClinicalMedicine ; 64: 102210, 2023 Oct.
Article en En | MEDLINE | ID: mdl-37745021
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 National Institute of Allergy and Infectious Diseases, National Institute on Aging, National Center for Advancing Translational Sciences, National Medical Research Council, National Institute of Neurological Disorders and Stroke, European Union, National Institutes of Health, National Center for Advancing Translational Sciences.
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Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: EClinicalMedicine Año: 2023 Tipo del documento: Article País de afiliación: Italia

Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: EClinicalMedicine Año: 2023 Tipo del documento: Article País de afiliación: Italia