Establishment and Verification of a Risk Prediction Model for Chronic Rhinosinusitis

<b>Objective:</b> Factors influencing chronic rhinosinusitis (CRS) are usually studied in terms of genetics and environment; however, clinical indicators have not been reported. This case-control study was conducted in Ningbo, China, to explore new independent risk factors for CRS. <b>Methods:</b> A total of 695 participants, including 440 healthy controls and 255 patients with CRS, were included in this study. Clinical data were retrieved from questionnaires and electronic medical record systems of hospitals. Independent risk factors were screened using logistic regression and 10-fold cross-validation combined with the least absolute shrinkage and selection operator. A CRS risk prediction model was established using logistic regression, and nomograms were visualized. The model was validated and evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). <b>Results:</b> Ten independent risk factors, including alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, creatinine, triglyceride, total cholesterol, red blood cell count, hemoglobin, lymphocyte percentage, and monocyte percentage were screened. ROC analysis showed that the area under the curve of the training set was 0.890, indicating that the predictive model had excellent discriminant ability. The calibration curves showed that the fitting curves of the training set were close to the reference curves, indicating that the model had a good fit. The DCA showed that the threshold probability range of the training set was 1% to 89%. <b>Conclusions:</b> Independent risk factors for CRS were screened, and a prediction model was constructed, which is of significance for the prevention, control, and treatment of the disease.