Postoperative Apnea-Hypopnea Index Prediction of Velopharyngeal Surgery Based on Machine Learning.

Abstract

Objective: 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.

Study design: A single-center, retrospective, cohort study.

Setting: Sleep medical center.

Methods: 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.

Results: 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 (R ²) 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.

Conclusion: The machine learning-based model exhibited excellent performance for predicting postoperative AHI, which is helpful in guiding patient selections and improving surgery outcomes.