Prognostic factors for polyp recurrence in chronic rhinosinusitis with nasal polyps

Chronic rhinosinusitis with nasal polyps is frequently managed with endoscopic sinus surgery (ESS). Prior studies describe individual clinical variables and eosinophil density measures as prognostic for polyp recurrence (PR). However, the relative prognostic significance of these have not been extensively investigated. We sought to evaluate the impact of PR on measures of disease severity post-ESS and quantify the prognostic value of various clinical variables and biomarkers. Ninety-four patients with chronic rhinosinusitis with nasal polyps and prospectively biobanked polyp homogenates at the time of ESS were recruited 2 to 5 years post-ESS. Patients were evaluated with patient-reported outcome measures and endoscopic and radiographic scoring pre- and post-ESS. Biomarkers in polyp homogenates were measured with ELISA and Luminex. Relaxed least absolute shrinkage and selection operator regression optimized predictive clinical, biomarker, and combined models. Model performance was assessed using receiver-operating characteristic curve and random forest analysis. PR was found in 39.4% of patients, despite significant improvements in modified Lund-Mackay (MLM) radiographic and 22-item Sinonasal Outcomes Test scores (both P < .0001). PR was significantly associated with worse post-ESS MLM, modified Lund-Kennedy, and 22-item Sinonasal Outcomes Test scores. Relaxed least absolute shrinkage and selection operator identified 2 clinical predictors (area under the curve = 0.79) and 3 biomarkers (area under the curve = 0.78) that were prognostic for PR. When combined, the model incorporating these pre-ESS factors: MLM, asthma, eosinophil cationic protein, anti–double-stranded DNA IgG, and IL-5 improved PR predictive accuracy to area under the curve of 0.89. Random forest analysis identified and validated each of the 5 variables as the strongest predictors of PR. PR had strong associations with patient-reported outcome measures, endoscopic and radiographic severity. A combined model comprised of eosinophil cationic protein, IL-5, pre-ESS MLM, asthma, and anti–double-stranded DNA IgG could accurately predict PR.