Development and external validation of a COVID-19 mortality risk prediction algorithm : a multicentre retrospective cohort study
(2020) In BMJ Open 10(12).- Abstract
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... (More)
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/).
(Less)
- author
- Mei, Jin
; Hu, Weihua
; Chen, Qijian
; Li, Chang
; Chen, Zaishu
; Fan, Yanjie
; Tian, Shuwei
; Zhang, Zhuheng
; Li, Bin
; Ye, Qifa
; Yue, Jiang
and Wang, Qiao-Li
LU
(Less) - publishing date
- 2020-12-24
- type
- Contribution to journal
- publication status
- published
- keywords
- Algorithms, COVID-19/mortality, China/epidemiology, Follow-Up Studies, Hospitalization/statistics & numerical data, Humans, Pandemics, Prognosis, ROC Curve, Retrospective Studies, Risk Assessment/methods, Risk Factors, SARS-CoV-2, Survival Rate/trends
- in
- BMJ Open
- volume
- 10
- issue
- 12
- article number
- e044028
- publisher
- BMJ Publishing Group
- external identifiers
-
- scopus:85098321918
- pmid:33361083
- ISSN
- 2044-6055
- DOI
- 10.1136/bmjopen-2020-044028
- language
- English
- LU publication?
- no
- additional info
- © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
- id
- 87c7001e-be3e-411d-9eeb-995200e6cc96
- date added to LUP
- 2025-05-12 17:01:46
- date last changed
- 2025-06-24 07:46:23
@article{87c7001e-be3e-411d-9eeb-995200e6cc96,
abstract = {{<p>OBJECTIVE: This study aimed to develop and externally validate a COVID-19 mortality risk prediction algorithm.</p><p>DESIGN: Retrospective cohort study.</p><p>SETTING: Five designated tertiary hospitals for COVID-19 in Hubei province, China.</p><p>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.</p><p>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.</p><p>MAIN OUTCOME MEASURES: The primary outcome was all-cause mortality within 60 days after the diagnosis of COVID-19.</p><p>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.</p><p>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.</p><p>WEB CALCULATOR: We provided a freely accessible web calculator (https://www.whuyijia.com/).</p>}},
author = {{Mei, Jin and Hu, Weihua and Chen, Qijian and Li, Chang and Chen, Zaishu and Fan, Yanjie and Tian, Shuwei and Zhang, Zhuheng and Li, Bin and Ye, Qifa and Yue, Jiang and Wang, Qiao-Li}},
issn = {{2044-6055}},
keywords = {{Algorithms; COVID-19/mortality; China/epidemiology; Follow-Up Studies; Hospitalization/statistics & numerical data; Humans; Pandemics; Prognosis; ROC Curve; Retrospective Studies; Risk Assessment/methods; Risk Factors; SARS-CoV-2; Survival Rate/trends}},
language = {{eng}},
month = {{12}},
number = {{12}},
publisher = {{BMJ Publishing Group}},
series = {{BMJ Open}},
title = {{Development and external validation of a COVID-19 mortality risk prediction algorithm : a multicentre retrospective cohort study}},
url = {{http://dx.doi.org/10.1136/bmjopen-2020-044028}},
doi = {{10.1136/bmjopen-2020-044028}},
volume = {{10}},
year = {{2020}},
}