Treatment efficacy prediction model for patients with concurrent allergic rhinitis and laryngopharyngeal reflux

To construct and validate a prediction model based on the treatment efficacy of patients with laryngopharyngeal reflux (LPR) and allergic rhinitis (AR), and to provide a quantitative tool for individualized treatment decision-making.

A total of 106 AR patients admitted to the hospital from January 2023 to November 2024 were included. They were randomly divided into a training set (n = 73) and a validation set (n = 33) at a ratio of 7:3. Independent influencing factors were screened by univariate and multivariate Logistic regression to construct a Nomogram model. The model performance was evaluated using receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).

The baseline data of the training set and the validation set were balanced (p > 0.05). Multivariate analysis showed that BMI, duration of AR, number of allergens, and total serum IgE were independent predictors of treatment efficacy (p < 0.05). The areas under the ROC curve (AUC) were 0.809 (95% CI: 0.693-0.924) and 0.823 (95% CI: 0.630-1.000) respectively in the training set and validation set, and the calibration curve showed a good fit.

The constructed Nomogram model can effectively predict the treatment response of patients with AR complicated by LPR and guide clinical stratified intervention.