Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort study - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue EClinicalMedicine Année : 2023

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

Arianna Dagliati
  • Fonction : Auteur
Zachary H Strasser
  • Fonction : Auteur
Zahra Shakeri Hossein Abad
  • Fonction : Auteur
Jeffrey G Klann
  • Fonction : Auteur
Kavishwar B Wagholikar
  • Fonction : Auteur
Rebecca Mesa
  • Fonction : Auteur
Shyam Visweswaran
  • Fonction : Auteur
Michele Morris
  • Fonction : Auteur
Yuan Luo
  • Fonction : Auteur
Darren W Henderson
  • Fonction : Auteur
Bryce W Q Tan
  • Fonction : Auteur
Guillame Verdy
  • Fonction : Auteur
Gilbert S Omenn
  • Fonction : Auteur
Zongqi Xia
  • Fonction : Auteur
Riccardo Bellazzi
  • Fonction : Auteur
Shawn N Murphy
  • Fonction : Auteur
John H Holmes
  • Fonction : Auteur
Hossein Estiri
  • Fonction : Auteur
Romain Griffier
  • Fonction : Collaborateur
Vianney Jouhet
  • Fonction : Collaborateur

Résumé

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.
Fichier principal
Vignette du fichier
BPH_EClinicalMedicine_2023_Dagliati.pdf (1.53 Mo) Télécharger le fichier
Origine Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-04576630 , version 1 (15-05-2024)

Licence

Identifiants

Citer

Arianna Dagliati, Zachary H Strasser, Zahra Shakeri Hossein Abad, Jeffrey G Klann, Kavishwar B Wagholikar, et al.. Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort study. EClinicalMedicine, 2023, 64, pp.102210. ⟨10.1016/j.eclinm.2023.102210⟩. ⟨hal-04576630⟩

Collections

U1219
0 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More