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dc.creatorRazavian, Narges
dc.creatorMajor, Vincent J.
dc.creatorSudarshan, Mukund
dc.creatorBurk-Rafel, Jesse
dc.creatorStella, Peter
dc.creatorRandhawa, Hardev
dc.creatorBilaloglu, Seda
dc.creatorChen, Ji
dc.creatorNguy, Vuthy
dc.creatorWang, Walter
dc.creatorZhang, Hao
dc.creatorReinstein, Ilan
dc.creatorKudlowitz, David
dc.creatorZenger, Cameron
dc.creatorCao, Meng
dc.creatorZhang, Ruina
dc.creatorDogra, Siddhant
dc.creatorHarish, Keerthi B.
dc.creatorBosworth, Brian
dc.creatorFrancois, Fritz
dc.creatorHorwitz, Leora I.
dc.creatorRanganath, Rajesh
dc.creatorAustrian, Jonathan
dc.creatorAphinyanaphongs, Yindalon
dc.date.accessioned2020-10-13T15:07:56Z
dc.date.available2020-10-13T15:07:56Z
dc.date.created2020
dc.identifier.issn2398-6352spa
dc.identifier.otherhttps://doi.org/10.1038/s41746-020-00343-xspa
dc.identifier.urihttp://hdl.handle.net/20.500.12010/14373
dc.description.abstractThe COVID-19 pandemic has challenged front-line clinical decision-making, leading to numerous published prognostic tools. However, few models have been prospectively validated and none report implementation in practice. Here, we use 3345 retrospective and 474 prospective hospitalizations to develop and validate a parsimonious model to identify patients with favorable outcomes within 96 h of a prediction, based on real-time lab values, vital signs, and oxygen support variables. In retrospective and prospective validation, the model achieves high average precision (88.6% 95% CI: [88.4–88.7] and 90.8% [90.8–90.8]) and discrimination (95.1% [95.1–95.2] and 86.8% [86.8–86.9]) respectively. We implemented and integrated the model into the EHR, achieving a positive predictive value of 93.3% with 41% sensitivity. Preliminary results suggest clinicians are adopting these scores into their clinical workflows.spa
dc.format.extent13 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherDigital medicinespa
dc.sourcereponame:Expeditio Repositorio Institucional UJTLspa
dc.sourceinstname:Universidad de Bogotá Jorge Tadeo Lozanospa
dc.subjectCOVID-19spa
dc.subjectHospitalizedspa
dc.subjectPatientsspa
dc.titleA validated, real-time prediction model for favorable outcomes in hospitalized COVID-19 patientsspa
dc.type.localArtículospa
dc.subject.lembSíndrome respiratorio agudo gravespa
dc.subject.lembCOVID-19spa
dc.subject.lembSARS-CoV-2spa
dc.subject.lembCoronavirusspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersionspa
dc.rights.localAbierto (Texto Completo)spa
dc.identifier.doihttps://doi.org/10.1038/s41746-020-00343-xspa
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1spa


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