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Development and Validation of a Web-Based Severe COVID-19 Risk Prediction Model.
Sang H Woo1, Arturo J Rios-Diaz2, Alan A Kubey3
1Department of Medicine, Division of Hospital Medicine, Thomas Jefferson University, Philadelphia, PA, USA.
The American Journal of the Medical Sciences
|May 24, 2021
Summary
A new web-based tool predicts severe COVID-19 risk using patient data. This model aids in early identification of high-risk patients for better resource allocation and treatment.
Area of Science:
- Medical Informatics
- Epidemiology
- Clinical Prediction Models
Background:
- Coronavirus disease 2019 (COVID-19) presents a significant global health challenge with high morbidity and mortality.
- Identifying patients at risk for severe outcomes is crucial for effective patient management and resource allocation.
Purpose of the Study:
- To develop and validate a web-based risk prediction model for identifying patients likely to develop severe COVID-19.
- Severe COVID-19 is defined as intensive care unit (ICU) admission, mechanical ventilation, or death.
Main Methods:
- A retrospective cohort study of 415 patients admitted to urban medical centers.
- Multivariable logistic regression was used to identify predictors of severe COVID-19.
- Models were developed on a derivation cohort (n=311) and validated on a separate cohort (n=104).
Main Results:
- Eight key predictors were identified: age, sex, dyspnea, diabetes mellitus, troponin, C-reactive protein, D-dimer, and aspartate aminotransferase.
- The prediction models demonstrated excellent discriminative ability for severe COVID-19 (validation AUC=0.82) and mortality (validation AUC=0.81).
- The validated models were integrated into a user-friendly, mobile-compatible website.
Conclusions:
- A validated web-based risk prediction tool for severe COVID-19 is now available.
- The model utilizes data commonly available at patient presentation for bedside use.
- This tool can significantly aid in the clinical decision-making process for COVID-19 patients.
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