Objective This study aimed to systematically identify clinic-based predictors of poor proton pump inhibitors (PPIs) response in patients with suspected Laryngopharyngeal reflux disease (LPRD) (based on RSI >13 and RFS >7) and develop a predictive model for individualized management. Methods A retrospective cohort study analyzed suspected adult LPRD patients who were treated with standard-dose PPIs for ≥3 months from 2021-2024. Patients were stratified into PPI-responsive or non-responsive groups. Clinical characteristics, pretreatment Reflux Symptom Index (RSI) score, adherence to lifestyle modifications, findings from electronic laryngoscopy (specifically the presence of chronic hyperplastic/keratotic changes), and objective voice assessment via acoustic analysis (dichotomized as normal/abnormal) were evaluated. Independent predictors were identified via univariate and multivariate logistic regression and used to construct a predictive model. Model performance was assessed using ROC curves, calibration plots, and decision curve analysis in training and internal validation cohorts. Results A total of 301 patients were included. Multivariate analysis identified four independent predictors of poor PPI response: higher pretreatment RSI score (OR=1.180), low adherence to lifestyle modifications (<90%, OR=4.660), the presence of laryngoscopic hyperplastic/keratotic changes (OR=5.440), and abnormal pretreatment voice acoustic parameters (OR=2.755). The predictive model incorporating these variables demonstrated excellent discriminative ability, with an area under the ROC curve (AUC) of 0.856 (95% CI: 0.805-0.907) in the training set and 0.891 (95% CI: 0.826-0.955) in the validation set. The model also showed good calibration and provided positive net benefit across a range of clinical decision thresholds. Conclusion The developed predictive model, based on readily accessible clinical, laryngoscopic, and vocal functional variables, effectively identifies suspected LPRD patients at high risk of poor PPI response. These findings are hypothesis-generating and lay the groundwork for future prospective studies to validate the model’s utility and to determine whether it can facilitate personalized management strategies in clinical practice.
Wang et al. (Thu,) studied this question.