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Klann, Jeffrey G; Estiri, Hossein; Weber, Griffin M; Moal, Bertrand; Avillach, Paul; Hong, Chuan; Tan, Amelia L M; Beaulieu-Jones, Brett K; Castro, Victor; Maulhardt, Thomas; Geva, Alon; Malovini, Alberto; South, Andrew M; Visweswaran, Shyam; Morris, Michele; Samayamuthu, Malarkodi J; Omenn, Gilbert S; Ngiam, Kee Yuan; Mandl, Kenneth D; Boeker, Martin; Olson, Karen L; Mowery, Danielle L; Follett, Robert W; Hanauer, David A; Bellazzi, Riccardo; Moore, Jason H; Loh, Ne-Hooi Will; Bell, Douglas S; Wagholikar, Kavishwar B; Chiovato, Luca; Tibollo, Valentina; Rieg, Siegbert; Li, Anthony L L J; Jouhet, Vianney; Schriver, Emily; Xia, Zongqi; Hutch, Meghan; Luo, Yuan; Kohane, Isaac S; Brat, Gabriel A; Murphy, Shawn N
Journal of the American Medical Informatics Association : JAMIA, 07/2021, Letnik: 28, Številka: 7Journal Article
Abstract Objective The Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing coronavirus disease 2019 (COVID-19) with federated analyses of electronic health record (EHR) data. We sought to develop and validate a computable phenotype for COVID-19 severity. Materials and Methods Twelve 4CE sites participated. First, we developed an EHR-based severity phenotype consisting of 6 code classes, and we validated it on patient hospitalization data from the 12 4CE clinical sites against the outcomes of intensive care unit (ICU) admission and/or death. We also piloted an alternative machine learning approach and compared selected predictors of severity with the 4CE phenotype at 1 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 area under the curve of 0.903 (95% confidence interval, 0.886-0.921), compared with an area under the curve of 0.956 (95% confidence interval, 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 with 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 owing to heterogeneous pandemic conditions. Conclusions We developed an EHR-based severity phenotype for COVID-19 in hospitalized patients and validated it at 12 international sites.
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Leto | Faktor vpliva | Izdaja | Kategorija | Razvrstitev | ||||
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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
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Vir: Osebne bibliografije
in: SICRIS
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