Objective: The objective of this study was to design and validate a nomogram of intranasal corticosteroid (INCS) insensitivity for adult patients with allergic rhinitis (AR).
Methods: Training and validation datasets comprised randomly divided groups of AR patients diagnosed between 2019 and 2022, with a 7 : 3 ratio. These patients were categorized according to their INCS insensitivity status, and LASSO and multivariate logistic regression analyses were conducted to identify associated risk factors. These factors were incorporated into a nomogram for predicting INCS insensitivity. The performance of the nomogram was assessed using receiver operating characteristic (ROC) curves, calibration curves, and discrimination techniques.
Results: In this study, 313 patients were included, of which 120 (38.3%) showed INCS insensitivity. The type of AR, comorbidities, family history of AR, and duration of AR were identified as predictors and incorporated into the nomogram using least absolute shrinkage and selection operator and multivariate logistic regression. The calibration curves showed excellent agreement between predicted and actual probabilities of INCS insensitivity in both the training and validation sets. The area under the curve values observed in the validation set were 0.918 (95% confidence interval, 0.859-0.943), and 0.932 (95% confidence interval, 0.849-0.953) in the training set, indicating strong performance on both sets. Decision curve analysis showed that the constructed nomogram yielded a net clinical benefit for AR patients.
Conclusion: The nomogram constructed from risk predictors of INCS insensitivity in patients with AR demonstrated strong predictive power and enabled clinicians to identify high-risk patients, aiding them in developing an optimal treatment plan for AR.
Copyright © 2023 Deping Sun et al.