Abstract
Background
Although subsequent anti-inflammatory treatments are indispensable for patients with chronic rhinosinusitis (CRS) undergoing sinus surgery, few studies have explored the factors influencing the efficacy of postoperative anti-inflammatory treatment.
Objective
We aimed to develop prediction models for the response to glucocorticoid- and macrolide-based postoperative therapy in CRS patients.
Methods
We performed a post-hoc analysis of our previous study comparing the efficacy of fluticasone propionate and clarithromycin in the postoperative treatment of CRS patients. Clinical characteristics and treatment outcome information were collected. In addition, diseased sinonasal mucosal tissues obtained during surgery were processed for Bio-Plex analysis of protein levels of 34 biomarkers. Classification trees were built to predict refractory CRS based on clinical characteristics and biological markers for patients treated with fluticasone propionate or clarithromycin. A random forest algorithm was used to confirm the discriminating factors that formed the classification trees.
Results
One year after surgery, 22.7% of the patients (17/75) treated with fluticasone propionate, and 24.3% of those (18/74) treated with clarithromycin were diagnosed with refractory CRS. Nasal tissue IL-8 and IgG3 levels and headache VAS scores in the fluticasone propionate group, and nasal tissue IgG4 levels and overall burden of symptoms VAS scores in the clarithromycin group, were identified as discriminating factors forming the classification tree to predict refractory CRS. The overall predictive accuracy of the model was 89.3% and 87.8% for fluticasone propionate- and clarithromycin-based postsurgical treatment, respectively.
Conclusions
Classification trees built using clinical and biological parameters could be helpful in identifying patients with poor response to fluticasone propionate- and clarithromycin-based postoperative treatment.
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