Patient-Reported Outcome Measures as Predictive Tools for Disease Control in Chronic Rhinosinusitis With Nasal Polyps: A Prospective Study

Abstract

Background

Chronic rhinosinusitis with nasal polyps (CRSwNP) significantly impairs the quality of life, and disease control is now considered the primary treatment goal. Although patient-reported outcome measures (PROMs) such as the 22-item Sinonasal Outcome Test (SNOT-22) and CRS-PRO are widely used, their utility in predicting long-term postoperative disease control remains limited.

Methods

This prospective follow-up study aimed to evaluate postoperative recovery and identify the predictors of suboptimal disease control in patients with CRSwNP by integrating preoperative PROMs with objective clinical features. A total of 102 patients with CRSwNP undergoing functional endoscopic sinus surgery (FESS) were enrolled, of whom 89 completed at least 12 months of follow-up. Preoperative and postoperative PROMs were compared across disease control groups classified based on the European Position Paper on Rhinosinusitis and Nasal Polyps 2020 criteria. Least absolute shrinkage and selection operator regression was applied to select objective clinical predictors, which were then combined with either CRS-PRO or SNOT-22 item scores to develop and compare the nine machine learning models. Model performance was assessed using area under the curve (AUC), decision curve analysis, sensitivity, specificity, and other metrics.

Results

Eosinophil and neutrophil counts were identified as key objective predictors of suboptimal disease control after FESS. Among all models, logistic regression incorporating CRS-PRO scores and selected clinical features achieved the best performance, yielding an AUC of 0.866, accuracy of 83.3%, sensitivity of 72.7%, specificity of 89.5%, and F1-score of 76.2%. This model demonstrated a strong discriminatory ability and potential utility in individualized clinical decision-making.

Conclusion

Integrating preoperative CRS-PRO item scores with selected objective clinical parameters enables the accurate prediction of suboptimal disease control in patients with CRSwNP following FESS. This approach supports personalized risk stratification and postoperative management strategies.