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dc.contributor.authorSarkar, Rahuldeb
dc.contributor.authorMartin, Christopher
dc.contributor.authorMattie, Heather
dc.contributor.authorGichoya, Judy Wawira
dc.contributor.authorStone, David J
dc.contributor.authorCeli, Leo Anthony G.
dc.date.accessioned2021-04-05T14:25:56Z
dc.date.available2021-04-05T14:25:56Z
dc.date.issued2021-04
dc.identifier.issn2589-7500
dc.identifier.urihttps://hdl.handle.net/1721.1/130358
dc.description.abstractBackground Despite wide use of severity scoring systems for case-mix determination and benchmarking in the intensive care unit (ICU), the possibility of scoring bias across ethnicities has not been examined. Guidelines on the use of illness severity scores to inform triage decisions for allocation of scarce resources, such as mechanical ventilation, during the current COVID-19 pandemic warrant examination for possible bias in these models. We investigated the performance of the severity scoring systems Acute Physiology and Chronic Health Evaluation IVa (APACHE IVa), Oxford Acute Severity of Illness Score (OASIS), and Sequential Organ Failure Assessment (SOFA) across four ethnicities in two large ICU databases to identify possible ethnicity-based bias. Methods Data from the electronic ICU Collaborative Research Database (eICU-CRD) and the Medical Information Mart for Intensive Care III (MIMIC-III) database, built from patient episodes in the USA from 2014–15 and 2001–12, respectively, were analysed for score performance in Asian, Black, Hispanic, and White people after appropriate exclusions. Hospital mortality was the outcome of interest. Discrimination and calibration were determined for all three scoring systems in all four groups, using area under receiver operating characteristic (AUROC) curve for different ethnicities to assess discrimination, and standardised mortality ratio (SMR) or proxy measures to assess calibration. Findings We analysed 166 751 participants (122 919 eICU-CRD and 43 832 MIMIC-III). Although measurements of discrimination were significantly different among the groups (AUROC ranging from 0·86 to 0·89 [p=0·016] with APACHE IVa and from 0·75 to 0·77 [p=0·85] with OASIS), they did not display any discernible systematic patterns of bias. However, measurements of calibration indicated persistent, and in some cases statistically significant, patterns of difference between Hispanic people (SMR 0·73 with APACHE IVa and 0·64 with OASIS) and Black people (0·67 and 0·68) versus Asian people (0·77 and 0·95) and White people (0·76 and 0·81). Although calibrations were imperfect for all groups, the scores consistently showed a pattern of overpredicting mortality for Black people and Hispanic people. Similar results were seen using SOFA scores across the two databases. Interpretation The systematic differences in calibration across ethnicities suggest that illness severity scores reflect statistical bias in their predictions of mortality.en_US
dc.publisherElsevier BVen_US
dc.relation.isversionofhttps://doi.org/10.1016/S2589-7500(21)00022-4en_US
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs Licenseen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.sourceElsevieren_US
dc.titlePerformance of intensive care unit severity scoring systems across different ethnicities in the USA: a retrospective observational studyen_US
dc.typeArticleen_US
dc.identifier.citationSarkar, Rahuldeb et al. "Performance of intensive care unit severity scoring systems across different ethnicities in the USA: a retrospective observational study." Lancet Digital Health 3, 4 (April 2021): e241-e249 © 2021 The Author(s)en_US
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Medical Engineering & Scienceen_US
dc.contributor.departmentHarvard--MIT Program in Health Sciences and Technology. Laboratory for Computational Physiologyen_US
dc.relation.journalLancet Digital Healthen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.date.submission2021-04-01T16:04:17Z
mit.journal.volume3en_US
mit.journal.issue4en_US
mit.licensePUBLISHER_CC


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