Objective: To analyze the correlation between drug-induced sleep endoscopy (DISE), results, polysomnography (PSG) indicators, and clinical parameters in patients with obstructive sleep apnea (OSA), and to establish and validate a predictive model for tongue base plane obstruction. Methods: This retrospective study analyzed 117 OSA patients diagnosed via PSG and treated at the Department of Otolaryngology-Head and Neck Surgery, Nanfang Hospital, Southern Medical University, between October 2014 and March 2022. The cohort comprised of 114 males and 3 females, with an age range of 20 to 54 years (mean age 38.1±8.4 years). Data on DISE results, PSG results, and clinical indicators were collected for all 117 patients. Logistic regression analysis was performed to identify relevant indicators, and a predictive model for tongue base plane obstruction was constructed and internally validated using the R programming language. Results: Univariate logistic regression analysis identified four independent risk factors for predicting tongue root plane obstruction: tonsil grading, N2, N3, and rapid eye movement sleep(REM) stage [OR:0.412(0.260~0.652),1.045(1.012~1.079),0.943(0.903~0.984),0.961(0.925~0.998),P <0.05]. Multivariate logistic regression analysis confirmed tonsil grading and N3 sleep stage (12.48±12.22%) as significant predictors. A nomogram model incorporating these factors demonstrated good predictive performance, with an area under curve(AUC) of 0.82 (95%CI: 0.548-1.000), an optimal cutoff of 0.519, a specificity of 80.0%, and a sensitivity of 86.7%. Internal validation of the model in the validation cohort yielded an AUC of 0.751 (95%CI: 0.625-0.876). Conclusions: Tongue base plane obstruction observed during DISE in OSA patients is associated with tonsil grading and N3 sleep stage duration. The predictive model developed for tongue base plane obstruction based on DISE demonstrates good efficacy, as evidenced by its internal validation.
目的： 建立并验证阻塞性睡眠呼吸暂停（obstructive sleep apnea，OSA）患者药物诱导睡眠内镜（drug-induced sleep endoscopy，DISE）下舌根平面塌陷预测模型。 方法： 回顾性分析2014年10月至2022年3月南方医科大学南方医院耳鼻咽喉科收治的经多导睡眠监测（polysomnography，PSG）确诊的117例OSA患者资料，其中男性114例，女性3例，年龄20~54（38.1±8.4）岁。并收集117例患者DISE结果、PSG和临床指标等数据，通过logistic回归分析筛选相关指标，R语言构建预测模型并验证。 结果： 通过单因素logistic回归分析，筛选出4个预测舌根平面阻塞的独立风险因素，分别是扁桃体分度、N2、N3和快动眼睡眠（rapid eye movement sleep，REM）期［比值比分别为：0.412（0.260~0.652）、1.045（1.012~1.079）、0.943（0.903~0.984）和0.961（0.925~0.998），P值均<0.05］。进一步用多因素logistic回归分析筛选，扁桃体分度和N3期睡眠比例（12.48±12.22%）被确定为舌根平面阻塞的预测模型的重要预测因子。我们用其构建了预测舌根平面阻塞的诺模图模型，模型的曲线下面积（area under curve，AUC）为0.82（95%CI：0.548~1.000），最佳截断值为0.519，特异度为80.0%，敏感度86.7%。对模型进行内部验证，验证人群的AUC为0.751（95%CI：0.625~0.876）。 结论： OSA患者睡眠内镜下舌根平面的阻塞与扁桃体分度和N3期存在相关性，构建的DISE下舌根平面阻塞诺模图预测模型效能良好。.