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Clinical and Biological Markers Predict the Efficacy of Glucocorticoid- and Macrolide-Based Postoperative Therapy in Patients With Chronic Rhinosinusitis

医学 丙酸氟替卡松 克拉霉素 慢性鼻-鼻窦炎 氟替卡松 耐火材料(行星科学) 内科学 胃肠病学 临床疗效 外科 皮质类固醇 生物 天体生物学 幽门螺杆菌
作者
Ming Zeng,Heng Wang,Bo Liao,Hai Wang,Xiao‐Bo Long,Jin Ma,Jin‐Xin Liu,Zheng Liu
出处
期刊:American Journal of Rhinology & Allergy [SAGE Publishing]
卷期号:35 (5): 596-606 被引量:23
标识
DOI:10.1177/1945892420982236
摘要

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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