Abstract
OBJECTIVE: To establish a prediction model of pneumonia risk in SARS-CoV-2-infected patients to reduce unnecessary chest CT scans. MATERIALS AND METHODS: The model was constructed based on a retrospective cohort study. We selected SARS-CoV-2 test-positive patients and collected their clinical data and chest CT images from the outpatient and emergency departments of Hunan Provincial People's Hospital, China. Univariate and multivariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression were utilized to identify predictors of pneumonia risk for patients infected with SARS-CoV-2. These predictors were then incorporated into a nomogram to establish the model. To ensure its performance, the model was evaluated from the aspects of discrimination, calibration, and clinical validity. In addition, a smoothed curve was fitted using a generalized additive model (GAM) to explore the association between the pneumonia grade and the model's predicted probability of pneumonia. RESULTS: We selected 299 SARS-CoV-2 test-positive patients, of whom 205 cases were in the training cohort and 94 cases were in the validation cohort. Age, CRP natural log-transformed value (InCRP), and monocyte percentage (%Mon) were found to be valid predictors of pneumonia risk. This predictive model achieved good discrimination of AUC in the training and validation cohorts which was 0.7820 (95% CI: 0.7254-0.8439) and 0.8432 (95% CI: 0.7588-0.9151), respectively. At the cut-off value of 0.5, it had a sensitivity and specificity of 70.75% and 66.33% in the training cohort and 76.09% and 73.91% in the validation cohort, respectively. With suitable calibration accuracy shown in calibration curves, decision curve analysis indicated high clinical value in predicting pneumonia probability in SARS-CoV-2-infected patients. The probability of pneumonia predicted by the model was positively correlated with the actual pneumonia classification. CONCLUSION: This study has developed a pneumonia risk prediction model that can be utilized for diagnostic purposes in predicting the probability of pneumonia in patients infected with SARS-CoV-2.