Prognostic factors for polyp recurrence in chronic rhinosinusitis with nasal polyps

Abstract

Background: 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.

Objectives: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Keywords: Chronic rhinosinusitis with nasal polyps; PROMs; biomarker; clinical variables; polyp recurrence; random forest; relaxed LASSO.

FIG 1.

Study flowchart illustrating predictor and outcome variables collected, timing for collection, and onesanalyzed for predicting of PR. ^aClinical variables and ^bbiomarkervariables considered in the modelsfor prediction of PR. AERD, Aspirin exacerbated respiratory disease.

FIG 2.

Evaluation of disease severity at pre- and post-ESS time points. A, Comparisons between matched pairs of pre- and post-ESS radiographic severity measured by MLM. B, Comparisons between matched pairs of pre- and post-ESS levels of SNOT-22. Wilcoxon matched-pairs signed-rank test was used for the comparisons. C, Correlation between post-ESS MLM and SNOT-22 was analyzed by Spearman correlation analysis. **P< .01 and ****P< .0001. Median of the measurements were indicated as red and blue lines for pre- and post-ESS, respectively.

FIG 3.

Associations between PR and post-ESS clinical outcomes. Post-ESS SNOT-22, CRS-PRO, MLK, and MLM were compared between non-PR (PR-, n = 57) and PR (PR+, n = 37). Mann-Whitney U test was used for the comparisons. **P< .01 and ****P< .0001. Median with interquartile range of the measurements are indicated as blue and red lines for PR- and PR+, respectively.

FIG 4.

Feature selections for prediction of PR using relaxed LASSO and the prediction efficacy assessed using ROC curves. LASSO coefficient plot showing the relationship between the number of computed variables and goodness of fit (binomial deviance). A, For LASSO models considering only clinical variables, using 2 variables (log [λ] = −2): pre-ESS MLM and asthma optimized goodness of fit across a range of λ and γ penalties, whereas permitting consideration of all 11 clinical variables (log [λ] = −7) decreased goodness of fit. B, ROC curves for predicting PR using the LASSO identified clinical variables had an AUC of 0.79. C, For LASSO models considering only biomarker variables, 3 variables—ECP, dsDNA-IgG, and IL-5—were selected with optimal λ and γ penalty measures (log [λ] = −2), whereas increasing the number of considered variables to all 17 measured biomarkers (log [λ] = −8) decreased goodness of fit. D, ROC curves for predicting PR using the LASSO identified biomarker variables had an AUC of 0.78. E, For LASSO models considering both clinical and biomarker variables, 5 variables (log [λ] = −2) — pre-ESS MLM, asthma, ECP, dsDNA-IgG, and IL-5—were selected with optimal λ and γ penalty measures. F, ROC curves for predicting PR in the combined model with AUC of 0.89.

FIG 5.

RF analysis of predictors for PR in patients with CRSwNP. Variable importance was determined by the percentage increase in prediction error when that specific variable was excluded as compared to the model with all variables. The importance was demonstrated by Gini coefficient in Gini plots. The mean decrease in the Gini coefficient demonstrated the most important variables in the combined prediction model. ^★The 6 most important variables for PR prediction are log (ECP), pre-ESS MLM, asthma, log (IL-5), log (anti-dsDNA IgG), and log (IL-13).