You Jingyuan, Li Juan, Zhou Yingqian, Cao Xin, Zhao Chunmei, Zhang Yuhuan, Ye Jingying
School of Biomedical Engineering Tsinghua University Beijing China.
Department of Otolaryngology-Head Neck Surgery Sleep Medicine Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine Tsinghua University Beijing China.
OTO Open. 2025 Jan 7;9(1):e70061. doi: 10.1002/oto2.70061. eCollection 2025 Jan-Mar.
To investigate machine learning-based regression models to predict the postoperative apnea-hypopnea index (AHI) for evaluating the outcome of velopharyngeal surgery in adult obstructive sleep apnea (OSA) subjects.
A single-center, retrospective, cohort study.
Sleep medical center.
All subjects with OSA who underwent velopharyngeal surgery followed for 3 to 6 months were enrolled in this study. Demographic, polysomnographic, and anatomical variables were analyzed. Compared with traditional stepwise linear regression (LR) algorithm, machine learning algorithms including artificial neural network (ANN), support vector regression, K-nearest neighbor, random forest, and extreme gradient boosting were utilized to establish the regression model. Surgical success was defined as a ≥50% reduction in AHI to a final AHI of <20 events/h.
A total of 152 OSA adult patients (median [interquartile range] age = 40 [35, 48] years, male/female = 136/16) were included in this study. The ANN model achieved the highest performance with a coefficient of determination ( ) of 0.23 ± 0.05, a root mean square error of AHI of 10.71 ± 1.01 events/h, an accuracy for outcomes classification of 81.3% ± 1.2% and an area under the receiver operating characteristic of 74.6% ± 1.9%, whereas for LR model, they were 0.094 ± 0.06, 11.61 ± 0.76 events/h, 71.7% ± 1.5% and 68.8% ± 2.9%, respectively.
The machine learning-based model exhibited excellent performance for predicting postoperative AHI, which is helpful in guiding patient selections and improving surgery outcomes.
研究基于机器学习的回归模型，以预测术后呼吸暂停低通气指数（AHI），从而评估成年阻塞性睡眠呼吸暂停（OSA）患者腭咽手术的效果。
单中心、回顾性队列研究。
睡眠医学中心。
纳入所有接受腭咽手术并随访3至6个月的OSA患者。分析人口统计学、多导睡眠图和解剖学变量。与传统逐步线性回归（LR）算法相比，利用包括人工神经网络（ANN）、支持向量回归、K近邻、随机森林和极端梯度提升在内的机器学习算法建立回归模型。手术成功定义为AHI降低≥50%，最终AHI<20次/小时。
本研究共纳入152例成年OSA患者（年龄中位数[四分位间距]=40[35,48]岁，男/女=136/16）。ANN模型表现最佳，决定系数（ ）为0.23±0.05，AHI的均方根误差为10.71±1.01次/小时，结果分类准确率为81.3%±1.2%，受试者工作特征曲线下面积为74.6%±1.9%；而LR模型的相应指标分别为0.094±0.06、11.61±0.76次/小时、71.7%±1.5%和68.8%±2.9%。
基于机器学习的模型在预测术后AHI方面表现出色，有助于指导患者选择并改善手术效果。
