Department of Otorhinolaryngology- Head and Neck Surgery, OLVG, Jan Tooropstraat 164, Amsterdam, 1061AE, The Netherlands.
Department of Oral Kinesiology, MOVE Research Institute Amsterdam, ACTA, University of Amsterdam and VU University Amsterdam, Amsterdam, The Netherlands.
Sleep Breath. 2024 Nov 29;29(1):22. doi: 10.1007/s11325-024-03172-4.
Drug-induced sleep endoscopy (DISE) helps select patients with obstructive sleep apnea (OSA) for surgery by identifying upper airway collapse patterns. This study aimed to predict the probability of complete concentric collapse at the palatal level (CCCp) during DISE based on patient characteristics, specifically body mass index (BMI).
We retrospectively reviewed records of OSA patients who underwent DISE from January 2018 to July 2023. Logistic regression with receiver operating characteristic (ROC) analysis and classification and regression tree (CART) analysis were used to assess the diagnostic efficiency of BMI and other predictors for CCCp.
A consecutive series of 1761 eligible patients was included for analysis. CCCp was observed in 22.3% of cases. The CCCp group had significantly higher BMI, neck circumference, apnea-hypopnea index (AHI) and height as well as increase in partial and complete collapse at oropharyngeal level. The ROC analysis for predicting CCCp for a BMI cut-off was similar for males and females 29.4 kg/m² and 29.5 kg/m², with an area under the curve (AUC) of 0.65 and 0.73, respectively. Adding predictors like tonsils, AHI, height and neck circumference improved the model's performance.
Although confirming an association between increasing BMI and presence of CCCp, we were unable to define an accurate BMI cut-off value for predicting CCCp. The multifactorial nature of this collapse pattern challenges BMI's efficacy as a sole predictor. Our findings underscore the continued importance of DISE in evaluating CCCp and other collapse patterns for clinical decision-making in patients considered for hypoglossal nerve stimulation therapy.
药物诱导睡眠内镜(DISE)通过识别上呼吸道塌陷模式帮助选择阻塞性睡眠呼吸暂停(OSA)患者进行手术。本研究旨在根据患者特征,特别是体重指数(BMI),预测 DISE 时腭部完全同心塌陷(CCCp)的概率。
我们回顾性分析了 2018 年 1 月至 2023 年 7 月期间接受 DISE 的 OSA 患者的记录。使用逻辑回归与接收者操作特征(ROC)分析和分类回归树(CART)分析来评估 BMI 和其他预测因子对 CCCp 的诊断效率。
共纳入了 1761 例符合条件的连续病例进行分析。22.3%的病例观察到 CCCp。CCCp 组的 BMI、颈围、呼吸暂停低通气指数(AHI)和身高以及口咽水平部分和完全塌陷的增加显著更高。用于预测 BMI 截断值的 CCCp 的 ROC 分析对于男性和女性均相似(男性为 29.4 kg/m²,女性为 29.5 kg/m²),曲线下面积(AUC)分别为 0.65 和 0.73。添加预测因子,如扁桃体、AHI、身高和颈围,可提高模型的性能。
尽管证实了 BMI 增加与 CCCp 存在之间的关联,但我们无法确定用于预测 CCCp 的准确 BMI 截断值。这种塌陷模式的多因素性质挑战了 BMI 作为单一预测因子的有效性。我们的研究结果强调了 DISE 在评估 CCCp 和其他塌陷模式以指导考虑舌下神经刺激治疗的患者的临床决策方面的持续重要性。