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• W4324142731 abstract "[Abstract] Objective: To develop a pneumonia risk prediction model for SARS-CoV-2 infected patients to reduce unnecessary chest CT scans; Materials and Methods: 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." @default.
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• W4324142731 date "2023-03-14" @default.
• W4324142731 modified "2023-09-24" @default.
• W4324142731 title "Development and validation of a model for the prediction of the risk of pneumonia in patients with SARS-CoV-2 infection" @default.
• W4324142731 doi "https://doi.org/10.22541/au.167879588.86752560/v1" @default.
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