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Article: Development and validation of prognosis model of mortality risk in patients with COVID-19

TitleDevelopment and validation of prognosis model of mortality risk in patients with COVID-19
Authors
KeywordsCOVID-19
machine-learning methods
mortality risk
prognosis
Random Forest
Issue Date2020
PublisherCambridge University Press. The Journal's web site is located at http://journals.cambridge.org/action/displayJournal?jid=HYG
Citation
Epidemiology and Infection, 2020, v. 148, p. article no. e168 How to Cite?
AbstractThis study aimed to identify clinical features for prognosing mortality risk using machine-learning methods in patients with coronavirus disease 2019 (COVID-19). A retrospective study of the inpatients with COVID-19 admitted from 15 January to 15 March 2020 in Wuhan is reported. The data of symptoms, comorbidity, demographic, vital sign, CT scans results and laboratory test results on admission were collected. Machine-learning methods (Random Forest and XGboost) were used to rank clinical features for mortality risk. Multivariate logistic regression models were applied to identify clinical features with statistical significance. The predictors of mortality were lactate dehydrogenase (LDH), C-reactive protein (CRP) and age based on 500 bootstrapped samples. A multivariate logistic regression model was formed to predict mortality 292 in-sample patients with area under the receiver operating characteristics (AUROC) of 0.9521, which was better than CURB-65 (AUROC of 0.8501) and the machine-learning-based model (AUROC of 0.4530). An out-sample data set of 13 patients was further tested to show our model (AUROC of 0.6061) was also better than CURB-65 (AUROC of 0.4608) and the machine-learning-based model (AUROC of 0.2292). LDH, CRP and age can be used to identify severe patients with COVID-19 on hospital admission.
Persistent Identifierhttp://hdl.handle.net/10722/287673
ISSN
2019 Impact Factor: 2.152
2015 SCImago Journal Rankings: 1.320
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorMa, X-
dc.contributor.authorNg, M-
dc.contributor.authorXu, S-
dc.contributor.authorXu, Z-
dc.contributor.authorQiu, H-
dc.contributor.authorLiu, Y-
dc.contributor.authorLyu, J-
dc.contributor.authorYou, J-
dc.contributor.authorZhao, P-
dc.contributor.authorWang, S-
dc.contributor.authorTang, Y-
dc.contributor.authorCui, H-
dc.contributor.authorYu, C-
dc.contributor.authorWang, F-
dc.contributor.authorShao, F-
dc.contributor.authorSun, P-
dc.contributor.authorTang, Z-
dc.date.accessioned2020-10-05T12:01:33Z-
dc.date.available2020-10-05T12:01:33Z-
dc.date.issued2020-
dc.identifier.citationEpidemiology and Infection, 2020, v. 148, p. article no. e168-
dc.identifier.issn0950-2688-
dc.identifier.urihttp://hdl.handle.net/10722/287673-
dc.description.abstractThis study aimed to identify clinical features for prognosing mortality risk using machine-learning methods in patients with coronavirus disease 2019 (COVID-19). A retrospective study of the inpatients with COVID-19 admitted from 15 January to 15 March 2020 in Wuhan is reported. The data of symptoms, comorbidity, demographic, vital sign, CT scans results and laboratory test results on admission were collected. Machine-learning methods (Random Forest and XGboost) were used to rank clinical features for mortality risk. Multivariate logistic regression models were applied to identify clinical features with statistical significance. The predictors of mortality were lactate dehydrogenase (LDH), C-reactive protein (CRP) and age based on 500 bootstrapped samples. A multivariate logistic regression model was formed to predict mortality 292 in-sample patients with area under the receiver operating characteristics (AUROC) of 0.9521, which was better than CURB-65 (AUROC of 0.8501) and the machine-learning-based model (AUROC of 0.4530). An out-sample data set of 13 patients was further tested to show our model (AUROC of 0.6061) was also better than CURB-65 (AUROC of 0.4608) and the machine-learning-based model (AUROC of 0.2292). LDH, CRP and age can be used to identify severe patients with COVID-19 on hospital admission.-
dc.languageeng-
dc.publisherCambridge University Press. The Journal's web site is located at http://journals.cambridge.org/action/displayJournal?jid=HYG-
dc.relation.ispartofEpidemiology and Infection-
dc.rightsEpidemiology and Infection. Copyright © Cambridge University Press.-
dc.rightsThis article has been published in a revised form in [Journal] [http://doi.org/XXX]. This version is free to view and download for private research and study only. Not for re-distribution, re-sale or use in derivative works. © copyright holder.-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectCOVID-19-
dc.subjectmachine-learning methods-
dc.subjectmortality risk-
dc.subjectprognosis-
dc.subjectRandom Forest-
dc.titleDevelopment and validation of prognosis model of mortality risk in patients with COVID-19-
dc.typeArticle-
dc.identifier.emailNg, M: michael.ng@hku.hk-
dc.identifier.authorityNg, M=rp02578-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1017/S0950268820001727-
dc.identifier.pmid32746957-
dc.identifier.pmcidPMC7426607-
dc.identifier.scopuseid_2-s2.0-85089612740-
dc.identifier.hkuros315746-
dc.identifier.volume148-
dc.identifier.spagearticle no. e168-
dc.identifier.epagearticle no. e168-
dc.identifier.isiWOS:000559193000001-
dc.publisher.placeUnited Kingdom-
dc.identifier.issnl0950-2688-

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