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A Clinical Risk Score to Predict In-hospital Mortality from COVID-19 in South Korea
Ae Young Her1, Youngjune Bhak2, Eun Jung Jun3
1Division of Cardiology, Department of Internal Medicine, Kangwon National University School of Medicine, Chuncheon, Korea.
Insights
A new COVID-Mortality Score aids in early identification of high-risk patients. This validated clinical tool uses admission data to predict coronavirus disease 2019 (COVID-19) mortality, improving patient care.
Area of Science:
- Infectious Diseases
- Clinical Medicine
- Biostatistics
Background:
- Early identification of high-risk patients with coronavirus disease 2019 (COVID-19) is crucial for effective clinical management and treatment.
- Developing a reliable risk score for predicting mortality upon hospital admission is essential.
Purpose of the Study:
- To develop and validate a clinical risk score for predicting in-hospital mortality in patients with COVID-19.
- To identify key clinical factors and laboratory parameters for mortality risk assessment.
Main Methods:
- A prospective cohort of 5,628 COVID-19 patients was established and divided into development (n=3,940) and validation (n=1,688) sets.
- Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression were used to construct the COVID-Mortality Score.
- Model performance was evaluated using receiver operating characteristic curves (AUC).
Main Results:
- The COVID-Mortality Score incorporates age, sex, BMI, comorbidities, symptoms, and complete blood count (CBC) results.
- The score demonstrated high discriminative power with AUCs of 0.96 in the development set and 0.97 in the validation set.
- Optimized for >90% sensitivity, the model achieved accuracies of 81.0% and 80.2% in the respective sets.
Conclusions:
- The developed and validated COVID-Mortality Score effectively predicts in-hospital mortality for COVID-19 patients.
- The score utilizes readily available clinical data at admission, facilitating clinical decision-making.
- An online risk calculator is publicly available for practical application.
Background:
Early identification of patients with coronavirus disease 2019 (COVID-19) who are at high risk of mortality is of vital importance for appropriate clinical decision making and delivering optimal treatment. We aimed to develop and validate a clinical risk score for predicting mortality at the time of admission of patients hospitalized with COVID-19.
Methods:
Collaborating with the Korea Centers for Disease Control and Prevention (KCDC), we established a prospective consecutive cohort of 5,628 patients with confirmed COVID-19 infection who were admitted to 120 hospitals in Korea between January 20, 2020, and April 30, 2020. The cohort was randomly divided using a 7:3 ratio into a development (n = 3,940) and validation (n = 1,688) set. Clinical information and complete blood count (CBC) detected at admission were investigated using Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression to construct a predictive risk score (COVID-Mortality Score). The discriminative power of the risk model was assessed by calculating the area under the curve (AUC) of the receiver operating characteristic curves.
Results:
The incidence of mortality was 4.3% in both the development and validation set. A COVID-Mortality Score consisting of age, sex, body mass index, combined comorbidity, clinical symptoms, and CBC was developed. AUCs of the scoring system were 0.96 (95% confidence interval [CI], 0.85-0.91) and 0.97 (95% CI, 0.84-0.93) in the development and validation set, respectively. If the model was optimized for > 90% sensitivity, accuracies were 81.0% and 80.2% with sensitivities of 91.7% and 86.1% in the development and validation set, respectively. The optimized scoring system has been applied to the public online risk calculator (https://www.diseaseriskscore.com).
Conclusion:
This clinically developed and validated COVID-Mortality Score, using clinical data available at the time of admission, will aid clinicians in predicting in-hospital mortality.
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