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.
[Abstract] Objective: To develop a pneumonia risk prediction model for SARS-CoV-2 infected patients to reduce unnecessary chest CT scans; Materials andMethods: Retrospective analysis was performed on the clinical data of SARS-CoV-2-positive patients who visited outpatient and emergency clinics and underwent chest CT scans at the Mawangdui Branch of Hunan Provincial People’s Hospital from 20 December 2022 to 23 December 2022 and at the Tianxinge Branch of Hunan Provincial People’s Hospital from 1 January 2023 to 4 January 2023. A retrospective analysis of imaging and clinical data from 205 cases (training cohort) and 94 cases (validation cohort) of SARS-CoV-2-positive patients who visited outpatient and emergency clinics was conducted. The predictor variables were screened using the “univariate and then multivariate logistic regression” and “least absolute shrinkage and selection operator (LASSO)” approaches, and the predictive model was constructed using multifactorial logistic regression and represented as a nomogram. The diagnostic effectiveness of the pneumonia risk model was evaluated using receiver operating characteristic (ROC) curves; the Delong test and Integrated Discrimination Improvement Index (IDI) were used to compare the AUC of the pneumonia risk model with the AUCs for predictors incorporated in the model alone. The calibration of the pneumonia risk model was assessed using calibration curves; Decision curve analysis (DCA) was used to evaluate the clinical validity of the pneumonia risk model. In addition, a smoothed curve was fitted using a generalized additive model (GAM) to explore the relationship between the pneumonia grade and the model’s predicted probability of pneumonia; Results: “univariate and then multivariate logistic regression ” and Lasso regression together show that age, natural log-transformed value (InCRP), Monocytes percentage (%Mon) are valid predictors of pneumonia risk; the AUC of the pneumonia risk model was 0.7820 (95% CI: 0.7254-0.8439) in the training cohort and 0.8432 (95% CI: 0.7588-0.9151) in the validation cohort; at the cut-off value of 0.5, the sensitivity and specificity of the pneumonia risk model were 70.75%, 66.33% (training cohort), 76.09%, and 73.91% (validation cohort), the calibration curves showed that the pneumonia risk model has good calibration accuracy. The decision curve analysis showed that the pneumonia risk model has high clinical value in predicting the probability of pneumonia in SARS-CoV-2 infected patients. Conclusion: The pneumonia risk prediction model developed in this study can be used to predict the risk of pneumonia in SARS-CoV-2 infected patients diagnostically.
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