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A Tool for Early Prediction of Severe Coronavirus Disease 2019 (COVID-19): A Multicenter Study Using the Risk
Jiao Gong1, Jingyi Ou2, Xueping Qiu3
1Department of Laboratory Medicine, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China.
Insights
A new nomogram can identify hospitalized COVID-19 patients at high risk of severe disease. This tool aids early intervention and management for better patient outcomes in coronavirus disease 2019.
Area of Science:
- Infectious Diseases
- Clinical Medicine
- Biostatistics
Background:
- No reliable tool exists for stratifying severe coronavirus disease 2019 (COVID-19) risk at admission.
- Early identification of high-risk patients is crucial for timely intervention.
Purpose of the Study:
- To construct and evaluate an effective risk prediction model for early identification of severe COVID-19 progression.
Main Methods:
- Retrospective multicenter study of 372 hospitalized nonsevere COVID-19 patients.
- Development of a risk prediction nomogram using baseline data.
- Validation of the nomogram in independent cohorts.
Main Results:
- 72 (19.4%) patients progressed to severe COVID-19.
- Older age and specific biomarkers (LDH, CRP, RDW-CV, BUN, direct bilirubin; albumin) were associated with severe disease.
- The nomogram demonstrated high predictive accuracy (AUC 0.912 training, 0.853 validation) and clinical utility.
Conclusions:
- The developed nomogram facilitates early identification of patients likely to develop severe COVID-19.
- This tool supports centralized management and prompt treatment of severe cases.
Background:
Because there is no reliable risk stratification tool for severe coronavirus disease 2019 (COVID-19) patients at admission, we aimed to construct an effective model for early identification of cases at high risk of progression to severe COVID-19.
Methods:
In this retrospective multicenter study, 372 hospitalized patients with nonsevere COVID-19 were followed for > 15 days after admission. Patients who deteriorated to severe or critical COVID-19 and those who maintained a nonsevere state were assigned to the severe and nonsevere groups, respectively. Based on baseline data of the 2 groups, we constructed a risk prediction nomogram for severe COVID-19 and evaluated its performance.
Results:
The training cohort consisted of 189 patients, and the 2 independent validation cohorts consisted of 165 and 18 patients. Among all cases, 72 (19.4%) patients developed severe COVID-19. Older age; higher serum lactate dehydrogenase, C-reactive protein, coefficient of variation of red blood cell distribution width, blood urea nitrogen, and direct bilirubin; and lower albumin were associated with severe COVID-19. We generated the nomogram for early identifying severe COVID-19 in the training cohort (area under the curve [AUC], 0.912 [95% confidence interval {CI}, .846-.978]; sensitivity 85.7%, specificity 87.6%) and the validation cohort (AUC, 0.853 [95% CI, .790-.916]; sensitivity 77.5%, specificity 78.4%). The calibration curve for probability of severe COVID-19 showed optimal agreement between prediction by nomogram and actual observation. Decision curve and clinical impact curve analyses indicated that nomogram conferred high clinical net benefit.
Conclusions:
Our nomogram could help clinicians with early identification of patients who will progress to severe COVID-19, which will enable better centralized management and early treatment of severe disease.
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