Department of Otolaryngology-Head and Neck Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China.
Am J Rhinol Allergy. 2021 Sep;35(5):596-606. doi: 10.1177/1945892420982236. Epub 2020 Dec 21.
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.
We aimed to develop prediction models for the response to glucocorticoid- and macrolide-based postoperative therapy in CRS patients.
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.
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.
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.
尽管慢性鼻-鼻窦炎（CRS）患者在鼻窦手术后需要进行后续的抗炎治疗，但很少有研究探讨影响术后抗炎治疗效果的因素。
我们旨在为接受糖皮质激素和大环内酯类药物联合术后治疗的 CRS 患者建立预测模型。
我们对先前一项比较丙酸氟替卡松和克拉霉素在 CRS 患者术后治疗效果的研究进行了回顾性分析，收集了临床特征和治疗结果信息。此外，对手术中获得的病变鼻-鼻窦黏膜组织进行生物标志物 34 种蛋白水平的 Bio-Plex 分析。基于临床特征和生物标志物，为接受丙酸氟替卡松或克拉霉素治疗的患者建立预测难治性 CRS 的分类树。使用随机森林算法对形成分类树的判别因素进行验证。
术后 1 年，接受丙酸氟替卡松治疗的患者中有 22.7%（17/75）和接受克拉霉素治疗的患者中有 24.3%（18/74）被诊断为难治性 CRS。丙酸氟替卡松组中鼻组织白细胞介素-8 和 IgG3 水平以及头痛视觉模拟量表评分，克拉霉素组中鼻组织 IgG4 水平和总体症状视觉模拟量表评分，被确定为预测难治性 CRS 的分类树形成的判别因素。该模型对丙酸氟替卡松和克拉霉素为基础的术后治疗的总体预测准确率分别为 89.3%和 87.8%。
使用临床和生物学参数构建的分类树可帮助识别对丙酸氟替卡松和克拉霉素为基础的术后治疗反应不佳的患者。
