A Predictive Model for Recurrence After Endoscopic Sinus Surgery in Chronic Rhinosinusitis Without Nasal Polyps
Abstract
Objective: Recurrence of chronic rhinosinusitis without nasal polyps (CRSsNP) after endoscopic sinus surgery (ESS) remains a clinically important challenge, with reported rates varying widely across follow-up durations. This study aimed to identify clinical predictors of recurrence and develop a prognostic model to estimate recurrence risk at 3, 5, 10, and 15 years post-ESS.
Material and Methods: In this retrospective, single-institution cohort study, 106 CRSsNP patients who underwent ESS between January 1, 2002, and December 31, 2024, were included, and time-to-recurrence was analyzed. Candidate predictors, including age, sex, smoking, asthma, NSAIDs hypersensitivity, symptom duration, blood eosinophil count (BEC), Modified Lund-Kennedy (MLK) score, and Lund-Mackay (LM) score, were evaluated using a least absolute shrinkage and selection operator (LASSO)-pooled logistic model. A nomogram was constructed and assessed using time-dependent AUROC, Brier scores, calibration curves, internal validation with 1,000 bootstrap resamples, and decision curve analysis (DCA).
Results: Recurrence occurred in 17.9%. Smoking, asthma, BEC, and LM score were significant predictors. The nomogram showed good discrimination, with AUROCs of 0.759, 0.780, 0.773, and 0.742 at 3, 5, 10, and 15 years post-ESS, respectively, and low prediction error, with corresponding Brier scores of 0.137, 0.132, 0.108, and 0.097. Internal validation demonstrated stable performance. Calibration curves showed strong agreement between predicted and observed outcomes. DCA demonstrated a meaningful net benefit, particularly at the threshold probabilities of 0.2–0.7 for 3- and 5-year predictions and 0.1–0.6 for 10- and 15-year predictions.
Conclusion: The LASSO-based pooled logistic nomogram provides a well-calibrated, robust, and clinically applicable tool for individualized long-term recurrence risk prediction in patients with CRSsNP after ESS.
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