Validation of an internationally derived patient severity phenotype to support COVID-19 analytics from electronic health record data.

Autor:	Klann JG; Laboratory of Computer Science, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA., Estiri H; Laboratory of Computer Science, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA., Weber GM; Department of Biomedical Informatics, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA., Moal B; IAM Unit, Public Health Department , Bordeaux University Hospital, Bordeaux, France., Avillach P; Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA., Hong C; Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA., Tan ALM; Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA., Beaulieu-Jones BK; Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA., Castro V; Research Information Science and Computing, Mass General Brigham, Boston, Massachusetts, USA., Maulhardt T; Institute of Medical Biometry and Statistics, Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany., Geva A; Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Boston, Massachusetts, USA.; Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA., Malovini A; Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy., South AM; Section of Nephrology, Department of Pediatrics, Brenner Children's Hospital, Wake Forest School of Medicine, Winston Salem, North Carolina, USA., Visweswaran S; Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA., Morris M; Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA., Samayamuthu MJ; Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA., Omenn GS; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA., Ngiam KY; Department of Biomedical Informatics-WisDM, National University Health System, Singapore., Mandl KD; Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA., Boeker M; Institute of Medical Biometry and Statistics, Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany., Olson KL; Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA., Mowery DL; Department of Biostatistics, Epidemiology, and Informatics, Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA., Follett RW; Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California, USA., Hanauer DA; Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, USA., Bellazzi R; Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy.; Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy., Moore JH; Department of Biostatistics, Epidemiology, and Informatics, Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA., Loh NW; Division of Critical Care, National University Health System, Singapore., Bell DS; Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California, USA., Wagholikar KB; Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA., Chiovato L; Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy.; Department of Internal Medicine and Medical Therapy, University of Pavia, Pavia, Italy., Tibollo V; Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy., Rieg S; Division of Infectious Diseases, Department of Medicine II, Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany., Li ALLJ; National Center for Infectious Diseases, Tan Tock Seng Hospital, Singapore., Jouhet V; ERIAS-INSERM U1219 BPH, Bordeaux University Hospital, Bordeaux, France., Schriver E; Data Analytics Center, Penn Medicine, Philadelphia, Pennsylvania, USA., Xia Z; Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA., Hutch M; Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA., Luo Y; Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA., Kohane IS; Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA., Brat GA; Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA., Murphy SN; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.; Research Information Science and Computing , Mass General Brigham, Boston, Massachusetts, USA.
Jazyk:	angličtina
Zdroj:	Journal of the American Medical Informatics Association : JAMIA [J Am Med Inform Assoc] 2021 Jul 14; Vol. 28 (7), pp. 1411-1420.
DOI:	10.1093/jamia/ocab018
Abstrakt:	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. (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association.)
Databáze:	MEDLINE
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