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Development and external validation of a COVID-19 mortality risk prediction algorithm: a multicentre retrospective
Jin Mei1, Weihua Hu2, Qijian Chen3
1Central Laboratory, Ningbo First Hospital, Zhejiang University, Ningbo, China.
Insights
This study developed and validated COVID-19 mortality risk prediction models. The models accurately identify patients at high risk of death within 60 days, aiding clinical decisions.
Area of Science:
- Medical Informatics
- Epidemiology
- Public Health
Background:
- The COVID-19 pandemic necessitated accurate tools for predicting patient mortality.
- Risk stratification is crucial for effective management of severe cases.
Purpose of the Study:
- To develop and externally validate a predictive algorithm for COVID-19 mortality.
- To provide a tool for acute risk classification of COVID-19 patients.
Main Methods:
- A retrospective cohort study involving 1364 adult COVID-19 patients from Hubei province, China.
- Development of two models (full and simple) using data from 1088 patients and external validation on 276 patients.
- Assessment of model discrimination using AUC and calibration using calibration plots and decision curve analysis.
Main Results:
- The full model achieved an AUC of 0.96 in the derivation cohort and 0.97 in the validation cohort.
- The simple model achieved an AUC of 0.92 in the derivation cohort and 0.88 in the validation cohort.
- Both models demonstrated good calibration accuracy in both cohorts.
Conclusions:
- The developed prediction models effectively identify COVID-19 patients at high risk of 60-day mortality.
- These models show potential utility for acute risk classification and clinical decision-making.
- A freely accessible web calculator is available for practical application.
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/).
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