Development and external validation of a COVID-19 mortality risk prediction algorithm: a multicentre retrospective cohort study

Objective This study aimed to develop and externally validate a COVID-19 mortality risk prediction algorithm.Design Retrospective cohort study.Setting Five designated tertiary hospitals for COVID-19 in Hubei province, China.Participants We routinely collected medical data of 1364 confirmed adult pat...

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Autores principales:	Bin Li, Jin Mei, Weihua Hu, Qijian Chen, Chang Li, Zaishu Chen, Yanjie Fan, Shuwei Tian, Zhuheng Zhang, Qifa Ye, Jiang Yue, Qiao-Li Wang
Formato:	article
Lenguaje:	EN
Publicado:	BMJ Publishing Group 2020
Materias:	Medicine R
Acceso en línea:	https://doaj.org/article/8692cbd82c4646d885199cff8b44e248
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spelling	oai:doaj.org-article:8692cbd82c4646d885199cff8b44e2482021-11-10T20:00:05ZDevelopment and external validation of a COVID-19 mortality risk prediction algorithm: a multicentre retrospective cohort study10.1136/bmjopen-2020-0440282044-6055https://doaj.org/article/8692cbd82c4646d885199cff8b44e2482020-12-01T00:00:00Zhttps://bmjopen.bmj.com/content/10/12/e044028.fullhttps://doaj.org/toc/2044-6055Objective This study aimed to develop and externally validate a COVID-19 mortality risk prediction algorithm.Design Retrospective cohort study.Setting Five designated tertiary hospitals for COVID-19 in Hubei province, China.Participants We routinely collected medical data of 1364 confirmed adult patients with COVID-19 between 8 January and 19 March 2020. Among them, 1088 patients from two designated hospitals in Wuhan were used to develop the prognostic model, and 276 patients from three hospitals outside Wuhan were used for external validation. All patients were followed up for a maximal of 60 days after the diagnosis of COVID-19.Methods The model discrimination was assessed by the area under the receiver operating characteristic curve (AUC) and Somers’ D test, and calibration was examined by the calibration plot. Decision curve analysis was conducted.Main outcome measures The primary outcome was all-cause mortality within 60 days after the diagnosis of COVID-19.Results The full model included seven predictors of age, respiratory failure, white cell count, lymphocytes, platelets, D-dimer and lactate dehydrogenase. The simple model contained five indicators of age, respiratory failure, coronary heart disease, renal failure and heart failure. After cross-validation, the AUC statistics based on derivation cohort were 0.96 (95% CI, 0.96 to 0.97) for the full model and 0.92 (95% CI, 0.89 to 0.95) for the simple model. The AUC statistics based on the external validation cohort were 0.97 (95% CI, 0.96 to 0.98) for the full model and 0.88 (95% CI, 0.80 to 0.96) for the simple model. Good calibration accuracy of these two models was found in the derivation and validation cohort.Conclusion The prediction models showed good model performance in identifying patients with COVID-19 with a high risk of death in 60 days. It may be useful for acute risk classification.Web calculator We provided a freely accessible web calculator (https://www.whuyijia.com/).Bin LiJin MeiWeihua HuQijian ChenChang LiZaishu ChenYanjie FanShuwei TianZhuheng ZhangQifa YeJiang YueQiao-Li WangBMJ Publishing GrouparticleMedicineRENBMJ Open, Vol 10, Iss 12 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R
spellingShingle	Medicine R Bin Li Jin Mei Weihua Hu Qijian Chen Chang Li Zaishu Chen Yanjie Fan Shuwei Tian Zhuheng Zhang Qifa Ye Jiang Yue Qiao-Li Wang Development and external validation of a COVID-19 mortality risk prediction algorithm: a multicentre retrospective cohort study
description	Objective This study aimed to develop and externally validate a COVID-19 mortality risk prediction algorithm.Design Retrospective cohort study.Setting Five designated tertiary hospitals for COVID-19 in Hubei province, China.Participants We routinely collected medical data of 1364 confirmed adult patients with COVID-19 between 8 January and 19 March 2020. Among them, 1088 patients from two designated hospitals in Wuhan were used to develop the prognostic model, and 276 patients from three hospitals outside Wuhan were used for external validation. All patients were followed up for a maximal of 60 days after the diagnosis of COVID-19.Methods The model discrimination was assessed by the area under the receiver operating characteristic curve (AUC) and Somers’ D test, and calibration was examined by the calibration plot. Decision curve analysis was conducted.Main outcome measures The primary outcome was all-cause mortality within 60 days after the diagnosis of COVID-19.Results The full model included seven predictors of age, respiratory failure, white cell count, lymphocytes, platelets, D-dimer and lactate dehydrogenase. The simple model contained five indicators of age, respiratory failure, coronary heart disease, renal failure and heart failure. After cross-validation, the AUC statistics based on derivation cohort were 0.96 (95% CI, 0.96 to 0.97) for the full model and 0.92 (95% CI, 0.89 to 0.95) for the simple model. The AUC statistics based on the external validation cohort were 0.97 (95% CI, 0.96 to 0.98) for the full model and 0.88 (95% CI, 0.80 to 0.96) for the simple model. Good calibration accuracy of these two models was found in the derivation and validation cohort.Conclusion The prediction models showed good model performance in identifying patients with COVID-19 with a high risk of death in 60 days. It may be useful for acute risk classification.Web calculator We provided a freely accessible web calculator (https://www.whuyijia.com/).
format	article
author	Bin Li Jin Mei Weihua Hu Qijian Chen Chang Li Zaishu Chen Yanjie Fan Shuwei Tian Zhuheng Zhang Qifa Ye Jiang Yue Qiao-Li Wang
author_facet	Bin Li Jin Mei Weihua Hu Qijian Chen Chang Li Zaishu Chen Yanjie Fan Shuwei Tian Zhuheng Zhang Qifa Ye Jiang Yue Qiao-Li Wang
author_sort	Bin Li
title	Development and external validation of a COVID-19 mortality risk prediction algorithm: a multicentre retrospective cohort study
title_short	Development and external validation of a COVID-19 mortality risk prediction algorithm: a multicentre retrospective cohort study
title_full	Development and external validation of a COVID-19 mortality risk prediction algorithm: a multicentre retrospective cohort study
title_fullStr	Development and external validation of a COVID-19 mortality risk prediction algorithm: a multicentre retrospective cohort study
title_full_unstemmed	Development and external validation of a COVID-19 mortality risk prediction algorithm: a multicentre retrospective cohort study
title_sort	development and external validation of a covid-19 mortality risk prediction algorithm: a multicentre retrospective cohort study
publisher	BMJ Publishing Group
publishDate	2020
url	https://doaj.org/article/8692cbd82c4646d885199cff8b44e248
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Development and external validation of a COVID-19 mortality risk prediction algorithm: a multicentre retrospective cohort study

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