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Development and Validation of a Prognostic Risk Score System for COVID-19 Inpatients: A Multi-Center Retrospective
Ye Yuan1, Chuan Sun1, Xiuchuan Tang2
1School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China.
Insights
A new risk score accurately predicts COVID-19 patient mortality using simple blood biomarkers. This tool helps clinicians identify high-risk individuals early for better outcomes in the pandemic.
Area of Science:
- Medical diagnostics
- Infectious diseases
- Biomarkers
Background:
- Coronavirus disease 2019 (COVID-19) presents a significant global health challenge with high mortality rates in hospitalized patients.
- There is a critical need for accessible tools to identify patients at high risk of mortality from COVID-19.
Purpose of the Study:
- To develop and validate a simple, practical risk score for predicting mortality in hospitalized COVID-19 patients.
- To utilize readily available clinical biomarkers for early risk stratification.
Main Methods:
- A risk score was developed using clinical data from 1479 inpatients (Tongji Hospital, Wuhan).
- External validation was performed using data from 141 inpatients (Jinyintan Hospital, Wuhan) and 432 inpatients (Third People's Hospital of Shenzhen).
- The score is based on three routine blood biomarkers and assessed for predictive accuracy and patient stratification.
Main Results:
- The developed risk score accurately predicts mortality in COVID-19 patients over 12 days in advance with >90% accuracy across all cohorts.
- Kaplan-Meier analysis demonstrated clear differentiation of patients into low, intermediate, and high-risk groups upon admission (AUC = 0.9551).
- The risk score is based on easily obtainable biomarkers from routine blood samples.
Conclusions:
- A validated, simple risk score effectively predicts death in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.
- The risk score demonstrates robust performance and generalizability across independent hospital cohorts.
- This tool offers a practical method for early identification of high-risk COVID-19 patients, aiding clinical decision-making.
Abstract:
Coronavirus disease 2019 (COVID-19) has become a worldwide pandemic. Hospitalized patients of COVID-19 suffer from a high mortality rate, motivating the development of convenient and practical methods that allow clinicians to promptly identify high-risk patients. Here, we have developed a risk score using clinical data from 1479 inpatients admitted to Tongji Hospital, Wuhan, China (development cohort) and externally validated with data from two other centers: 141 inpatients from Jinyintan Hospital, Wuhan, China (validation cohort 1) and 432 inpatients from The Third People's Hospital of Shenzhen, Shenzhen, China (validation cohort 2). The risk score is based on three biomarkers that are readily available in routine blood samples and can easily be translated into a probability of death. The risk score can predict the mortality of individual patients more than 12 d in advance with more than 90% accuracy across all cohorts. Moreover, the Kaplan-Meier score shows that patients can be clearly differentiated upon admission as low, intermediate, or high risk, with an area under the curve (AUC) score of 0.9551. In summary, a simple risk score has been validated to predict death in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); it has also been validated in independent cohorts.
