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Article Dans Une Revue Journal of the American Medical Informatics Association Année : 2021

Validation of an Internationally Derived Patient Severity Phenotype to Support COVID-19 Analytics from Electronic Health Record Data

Jeffrey G. Klann
  • Fonction : Auteur
Griffin M. Weber
  • Fonction : Auteur
Hossein Estiri
Bertrand Moal
Paul Avillach
Chuan Hong
Victor Castro
  • Fonction : Auteur
Thomas Maulhardt
  • Fonction : Auteur
Amelia L. M. Tan
  • Fonction : Auteur
Alon Geva
  • Fonction : Auteur
Brett K. Beaulieu-Jones
  • Fonction : Auteur
Alberto Malovini
Andrew M. South
  • Fonction : Auteur
Shyam Visweswaran
Gilbert S. Omenn
  • Fonction : Auteur
Kee Yuan Ngiam
Kenneth D. Mandl
  • Fonction : Auteur
Martin Boeker
  • Fonction : Auteur
Karen L. Olson
  • Fonction : Auteur
Danielle L. Mowery
  • Fonction : Auteur
Michele Morris
Robert W. Follett
  • Fonction : Auteur
David A. Hanauer
  • Fonction : Auteur
Riccardo Bellazzi
Jason H. Moore
  • Fonction : Auteur
Ne-Hooi Will Loh
  • Fonction : Auteur
Douglas S. Bell
  • Fonction : Auteur
Kavishwar B. Wagholikar
  • Fonction : Auteur
Luca Chiovato
Valentina Tibollo
Siegbert Rieg
  • Fonction : Auteur
Anthony L. L. J. Li
  • Fonction : Auteur
Vianney Jouhet
Emily Schriver
Malarkodi J. Samayamuthu
  • Fonction : Auteur
Zongqi Xia
Meghan Hutch
Yuan Luo
Isaac S. Kohane
  • Fonction : Auteur
Gabriel A. Brat
  • Fonction : Auteur
Shawn N. Murphy
  • Fonction : Auteur

Résumé

INTRODUCTION: The Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing COVID-19 with federated analyses of electronic health record (EHR) data. OBJECTIVE: We sought to develop and validate a computable phenotype for COVID-19 severity. METHODS: Twelve 4CE sites participated. First we developed an EHR-based severity phenotype consisting of six code classes, and we validated it on patient hospitalization data from the 12 4CE clinical sites against the outcomes of ICU admission and/or death. We also piloted an alternative machine-learning approach and compared selected predictors of severity to the 4CE phenotype at one site. RESULTS: The full 4CE severity phenotype had pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of individual code categories for acuity had high variability - up to 0.65 across sites. At one pilot site, the expert-derived phenotype had mean AUC 0.903 (95% CI: 0.886, 0.921), compared to AUC 0.956 (95% CI: 0.952, 0.959) for the machine-learning approach. Billing codes were poor proxies of ICU admission, with as low as 49% precision and recall compared to chart review. DISCUSSION: We developed a severity phenotype using 6 code classes that proved resilient to coding variability across international institutions. In contrast, machine-learning approaches may overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold-standard outcomes, possibly due to heterogeneous pandemic conditions. CONCLUSION: We developed an EHR-based severity phenotype for COVID-19 in hospitalized patients and validated it at 12 international sites.
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hal-03188882 , version 1 (02-04-2021)

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Paternité - Pas d'utilisation commerciale

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Jeffrey G. Klann, Griffin M. Weber, Hossein Estiri, Bertrand Moal, Paul Avillach, et al.. Validation of an Internationally Derived Patient Severity Phenotype to Support COVID-19 Analytics from Electronic Health Record Data. Journal of the American Medical Informatics Association, 2021, ⟨10.1093/jamia/ocab018⟩. ⟨hal-03188882⟩

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