Predicting lethal courses in critically ill COVID-19 patients using a machine learning model trained on patients with non-COVID-19 viral pneumonia

dc.contributor.authorLichtner, Gregor
dc.contributor.authorBalzer, Felix
dc.contributor.authorHaufe, Stefan
dc.contributor.authorGiesa, Niklas
dc.contributor.authorSchiefenhövel, Fridtjof
dc.contributor.authorSchmieding, Malte
dc.contributor.authorJurth, Carlo
dc.contributor.authorKopp, Wolfgang
dc.contributor.authorAkalin, Altuna
dc.contributor.authorSchaller, Stefan J.
dc.contributor.authorWeber-Carstens, Steffen
dc.contributor.authorSpies, Claudia
dc.contributor.authorvon Dincklage, Falk
dc.date.accessioned2023-04-24T08:32:52Z
dc.date.available2023-04-24T08:32:52Z
dc.date.issued2021-06-24
dc.date.updated2023-03-28T07:39:12Z
dc.description.abstractIn a pandemic with a novel disease, disease-specific prognosis models are available only with a delay. To bridge the critical early phase, models built for similar diseases might be applied. To test the accuracy of such a knowledge transfer, we investigated how precise lethal courses in critically ill COVID-19 patients can be predicted by a model trained on critically ill non-COVID-19 viral pneumonia patients. We trained gradient boosted decision tree models on 718 (245 deceased) non-COVID-19 viral pneumonia patients to predict individual ICU mortality and applied it to 1054 (369 deceased) COVID-19 patients. Our model showed a significantly better predictive performance (AUROC 0.86 [95% CI 0.86–0.87]) than the clinical scores APACHE2 (0.63 [95% CI 0.61–0.65]), SAPS2 (0.72 [95% CI 0.71–0.74]) and SOFA (0.76 [95% CI 0.75–0.77]), the COVID-19-specific mortality prediction models of Zhou (0.76 [95% CI 0.73–0.78]) and Wang (laboratory: 0.62 [95% CI 0.59–0.65]; clinical: 0.56 [95% CI 0.55–0.58]) and the 4C COVID-19 Mortality score (0.71 [95% CI 0.70–0.72]). We conclude that lethal courses in critically ill COVID-19 patients can be predicted by a machine learning model trained on non-COVID-19 patients. Our results suggest that in a pandemic with a novel disease, prognosis models built for similar diseases can be applied, even when the diseases differ in time courses and in rates of critical and lethal courses.en
dc.identifier.eissn2045-2322
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/18657
dc.identifier.urihttps://doi.org/10.14279/depositonce-17466
dc.language.isoen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.ddc000 Informatik, Informationswissenschaft, allgemeine Werke::000 Informatik, Wissen, Systeme::004 Datenverarbeitung; Informatik
dc.subject.otherinfectious diseasesen
dc.subject.othermachine learningen
dc.subject.otherpredictive medicineen
dc.subject.otherprognosisen
dc.titlePredicting lethal courses in critically ill COVID-19 patients using a machine learning model trained on patients with non-COVID-19 viral pneumoniaen
dc.typeArticle
dc.type.versionpublishedVersion
dcterms.bibliographicCitation.articlenumber13205
dcterms.bibliographicCitation.doi10.1038/s41598-021-92475-7
dcterms.bibliographicCitation.issue1
dcterms.bibliographicCitation.journaltitleScientific Reports
dcterms.bibliographicCitation.originalpublishernameSpringer Nature
dcterms.bibliographicCitation.originalpublisherplaceHeidelberg
dcterms.bibliographicCitation.volume11
dcterms.rightsHolder.referenceCreative-Commons-Lizenz
tub.accessrights.dnbfree
tub.affiliationFak. 4 Elektrotechnik und Informatik::Inst. Softwaretechnik und Theoretische Informatik::FG Unsicherheit, inverse Modellierung und maschinelles Lernen
tub.publisher.universityorinstitutionTechnische Universität Berlin

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