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Clinical risk score to predict in-hospital mortality in COVID-19 patients: a retrospective cohort study
Carlo Fumagalli1, Renzo Rozzini2, Matteo Vannini3
1Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.
Insights
A new COVID-19 mortality risk score (COVID-19MRS) accurately predicts in-hospital death using simple clinical and lab data. This tool aids in early patient risk stratification and care assignment for COVID-19 patients.
Area of Science:
- Medical research
- Clinical risk assessment
- Infectious disease epidemiology
Background:
- COVID-19 is associated with significant physiological abnormalities and increased mortality.
- Early identification of high-risk patients is crucial for effective management.
Purpose of the Study:
- To develop and validate a clinical risk score for predicting in-hospital mortality in COVID-19 patients.
- To identify key variables available at hospital admission that correlate with mortality.
Main Methods:
- Retrospective cohort study of 516 COVID-19 patients from two Italian hospitals.
- Analysis of simple clinical and laboratory variables readily available post-triage.
- Development of the COVID-19 in-hospital mortality risk score (COVID-19MRS) using Cox regression.
Main Results:
- Key predictors of mortality included age, number of chronic diseases, respiratory rate, PaO2/FiO2 ratio, serum creatinine, and platelet count.
- The developed COVID-19MRS demonstrated high accuracy (AUC 0.90) in predicting in-hospital mortality.
- The score effectively stratified patients into low, intermediate, and high-risk categories for in-hospital death.
Conclusions:
- The COVID-19MRS is a rapid, objective, and cost-effective tool for predicting mortality in COVID-19 patients.
- This risk score can assist clinicians in triage decisions and appropriate patient care allocation.
- Early risk stratification using COVID-19MRS can optimize resource utilization and patient outcomes.
Objectives:
Several physiological abnormalities that develop during COVID-19 are associated with increased mortality. In the present study, we aimed to develop a clinical risk score to predict the in-hospital mortality in COVID-19 patients, based on a set of variables available soon after the hospitalisation triage.
Setting:
Retrospective cohort study of 516 patients consecutively admitted for COVID-19 to two Italian tertiary hospitals located in Northern and Central Italy were collected from 22 February 2020 (date of first admission) to 10 April 2020.
Participants:
Consecutive patients≥18 years admitted for COVID-19.
Main Outcome Measures:
Simple clinical and laboratory findings readily available after triage were compared by patients' survival status ('dead' vs 'alive'), with the objective of identifying baseline variables associated with mortality. These were used to build a COVID-19 in-hospital mortality risk score (COVID-19MRS).
Results:
Mean age was 67±13 years (mean±SD), and 66.9% were male. Using Cox regression analysis, tertiles of increasing age (≥75, upper vs <62 years, lower: HR 7.92; p<0.001) and number of chronic diseases (≥4 vs 0-1: HR 2.09; p=0.007), respiratory rate (HR 1.04 per unit increase; p=0.001), PaO2/FiO2 (HR 0.995 per unit increase; p<0.001), serum creatinine (HR 1.34 per unit increase; p<0.001) and platelet count (HR 0.995 per unit increase; p=0.001) were predictors of mortality. All six predictors were used to build the COVID-19MRS (Area Under the Curve 0.90, 95% CI 0.87 to 0.93), which proved to be highly accurate in stratifying patients at low, intermediate and high risk of in-hospital death (p<0.001).
Conclusions:
The COVID-19MRS is a rapid, operator-independent and inexpensive clinical tool that objectively predicts mortality in patients with COVID-19. The score could be helpful from triage to guide earlier assignment of COVID-19 patients to the most appropriate level of care.
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