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Development and validation of a clinical prediction model to estimate the risk of critical patients with COVID-19
Wenyu Chen1, Ming Yao2, Lin Hu3
1Department of Respiration, Affiliated Hospital of Jiaxing University/The First Hospital of Jiaxing, Jiaxing, China.
Insights
This study developed a COVID-19 prediction model using clinical features to identify patients at high risk of critical illness. The model, incorporating age and comorbidities, aids early clinical diagnosis and resource allocation.
Area of Science:
- Medical Informatics
- Infectious Diseases
- Clinical Prediction Modeling
Background:
- The COVID-19 pandemic has severely impacted global healthcare systems.
- Predicting disease progression is crucial for managing patient outcomes and resource allocation.
Purpose of the Study:
- To develop and validate a clinical prediction model for estimating the risk of COVID-19 patients progressing to critical illness upon admission.
- To identify key clinical features associated with severe COVID-19 progression.
Main Methods:
- Retrospective cohort study of 390 COVID-19 patients admitted between January 16 and March 10, 2020.
- Utilized Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify significant predictors.
- Model validation using area under the curve (AUC), specificity, and sensitivity.
Main Results:
- A prediction model was developed using 6 key variables: age, number of comorbidities, CT severity score, lymphocyte count, aspartate aminotransferase, and albumin.
- The model achieved an AUC of 0.898 in the training set, with 89.7% specificity and 75.5% sensitivity.
- The nomogram demonstrated potential utility in assessing the risk of severe and critical illness.
Conclusions:
- The developed prediction model and nomogram may assist clinicians in identifying COVID-19 patients at high risk of critical illness at the time of admission.
- This tool can inform clinical decision-making and potentially improve patient management strategies.
Abstract:
The outbreak of coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality. This study was aimed to develop and validate a prediction model based on clinical features to estimate the risk of patients with COVID-19 at admission progressing to critical patients. Patients admitted to the hospital between January 16, 2020, and March 10, 2020, were retrospectively enrolled, and they were observed for at least 14 days after admission to determine whether they developed into severe pneumonia. According to the clinical symptoms, all patients were divided into four groups: mild, normal, severe, and critical. A total of 390 patients with COVID-19 pneumonia were identified, including 212 severe patients and 178 nonsevere patients. The least absolute shrinkage and selection operator (LASSO) regression reduced the variables in the model to 6, which are age, number of comorbidities, computed tomography severity score, lymphocyte count, aspartate aminotransferase, and albumin. The area under curve of the model in the training set is 0.898, and the specificity and sensitivity were 89.7% and 75.5%. The prediction model, nomogram might be useful to access the onset of severe and critical illness among COVID-19 patients at admission, which is instructive for clinical diagnosis.
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