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dc.contributor.authorBertsimas, Dimitris J
dc.contributor.authorLukin, Galit
dc.contributor.authorMingardi, Luca
dc.contributor.authorNohadani, Omid
dc.contributor.authorOrfanoudaki, Agni
dc.contributor.authorStellato, Bartolomeo
dc.contributor.authorWiberg, Holly
dc.contributor.authorGonzalez-Garcia, Sara
dc.contributor.authorParra-Calderón, Carlos Luis
dc.contributor.authorRobinson, Kenneth
dc.contributor.authorSchneider, Michelle
dc.contributor.authorStein, Barry
dc.contributor.authorEstirado, Alberto
dc.contributor.authora Beccara, Lia
dc.contributor.authorCanino, Rosario
dc.contributor.authorDal Bello, Martina
dc.contributor.authorPezzetti, Federica
dc.contributor.authorPan, Angelo
dc.contributor.authorHellenic COVID-19 Study Group
dc.date.accessioned2020-12-11T19:05:36Z
dc.date.available2020-12-11T19:05:36Z
dc.date.issued2020-12
dc.date.submitted2020-07
dc.identifier.issn1932-6203
dc.identifier.urihttps://hdl.handle.net/1721.1/128817
dc.description.abstractTimely identification of COVID-19 patients at high risk of mortality can significantly improve patient management and resource allocation within hospitals. This study seeks to develop and validate a data-driven personalized mortality risk calculator for hospitalized COVID-19 patients. De-identified data was obtained for 3,927 COVID-19 positive patients from six independent centers, comprising 33 different hospitals. Demographic, clinical, and laboratory variables were collected at hospital admission. The COVID-19 Mortality Risk (CMR) tool was developed using the XGBoost algorithm to predict mortality. Its discrimination performance was subsequently evaluated on three validation cohorts. The derivation cohort of 3,062 patients has an observed mortality rate of 26.84%. Increased age, decreased oxygen saturation (≤ 93%), elevated levels of C-reactive protein (≥ 130 mg/L), blood urea nitrogen (≥ 18 mg/dL), and blood creatinine (≥ 1.2 mg/dL) were identified as primary risk factors, validating clinical findings. The model obtains out-of-sample AUCs of 0.90 (95% CI, 0.87–0.94) on the derivation cohort. In the validation cohorts, the model obtains AUCs of 0.92 (95% CI, 0.88–0.95) on Seville patients, 0.87 (95% CI, 0.84–0.91) on Hellenic COVID-19 Study Group patients, and 0.81 (95% CI, 0.76–0.85) on Hartford Hospital patients. The CMR tool is available as an online application at https:/www.covidanalytics.io/mortality_calculator and is currently in clinical use. The CMR model leverages machine learning to generate accurate mortality predictions using commonly available clinical features. This is the first risk score trained and validated on a cohort of COVID-19 patients from Europe and the United States.en_US
dc.publisherPublic Library of Science (PLoS)en_US
dc.relation.isversionofhttps://doi.org/10.1371/journal.pone.0243262en_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourcePLoSen_US
dc.titleCOVID-19 mortality risk assessment: An international multi-center studyen_US
dc.typeArticleen_US
dc.identifier.citationBertsimas, Dimitris et al. "COVID-19 mortality risk assessment: An international multi-center study." PLoS One 15, 12: e0243262 © 2020 Bertsimas et al.en_US
dc.contributor.departmentSloan School of Managementen_US
dc.relation.journalPLoS Oneen_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.submission2020-12-11T16:25:38Z
mit.journal.volume15en_US
mit.journal.issue12en_US
mit.licensePUBLISHER_CC
mit.metadata.statusComplete


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