Journal of Otolaryngology and Ophthalmology of Shandong University ›› 2025, Vol. 39 ›› Issue (6): 78-86.doi: 10.6040/j.issn.1673-3770.0.2024.536

• Original Article • Previous Articles    

Construction and validation of risk prediction model for nasal septal deviation complicated with chronic sinusitis

DU Kangli1, ZHENG Zhenyu1, XU Zhanjiang1, ZHANG Yu1, CHEN Lu1, LU Mengyao2   

  1. 1. Department of Otorhinolaryngology, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou 450014, Henan, China2. Department of Otorhinolaryngology & Head and Neck Surgery, He'nan Children's Hospital, Children's Hospital Affiliated to Zhengzhou University, Zhengzhou 450018, Henan, China
  • Published:2025-11-19

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Abstract: Objective Nasal septal deviation(NSD)and chronic rhinosinusitis(CRS)are two common otolaryngology conditions. While existing research has primarily focused on analyzing their correlation, there is a lack of predictive tools to guide clinical decision-making. This study analyzed patients' clinical and imaging data to develop a highly accurate and interpretable predictive tool, and provide novel, evidence-based support for personalized interventions, Using traditional logistic regression, we constructed a risk prediction model and performed internal validation. Methods A total of 310 patients diagnosed with NSD between October 1,2022, and June 1,2024, were enrolled in the study, and their clinical data was collected. After preprocessing the data, 285 patients were included in the analysis and randomly assigned to a training set(199 cases, or 70%)and a validation set(86 cases, or 30%)in a 7∶3 ratio. Independent risk factors were identified through logistic regression analysis, and based on these results, a nomogram risk prediction model was constructed. The model's performance was then evaluated using a receiver operating characteristic(ROC)curve, a calibration plot(with bootstrapping), and a decision curve analysis(DCA). Results In the training set, the area under the ROC curve(AUC)was 0.760(95% confidence interval ［CI］): 0.686-0.834). In the validation set, the AUC was 0.800(95% CI: 0.689-0.911). The mean absolute error(MAE)for the calibration plots was 0.017 in the training set and 0.015 in the validation set. Decision curve analysis(DCA)showed that the model has potential clinical utility. Conclusion Age, history of allergic rhinitis, degree of septal deviation, and location of deviation were identified as independent risk factors for concurrent CRS in NSD patients. The nomogram prediction model developed, which is based on these factors, can be used to identify high-risk patients with NSD complicated by CRS in clinical practice.

Key words: Nasal septum deviation, Chronic sinusitis, Nomogram, Allergic rhinitis

CLC Number: 

  • R765
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