Development and validation of a model for the prediction of the risk of
pneumonia in patients with SARS-CoV-2 infection
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.