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

Saturday, September 28, 2024

9:42 AM – 9:48 AM EDT

Location: MBCC, 206

Disclosure(s):

Yingqian Zhou, PhD:

faculty for this accredited education activity has no relationship(s) with ineligible companies to disclose.

Jingyuan You, PhD:

faculty for this accredited education activity has no relationship(s) with ineligible companies to disclose.

Jingying Ye, PhD:

faculty for this accredited education activity has no relationship(s) with ineligible companies to disclose.

Introduction: 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.

Methods: All subjects with OSA who underwent velopharyngeal surgery followed for 3-6 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 (SVR), k-nearest neighbor (KNN), random forest (RF), and extreme gradient boosting (XGBoost) were utilized to establish the regression model. Surgical success was defined as a ≥ 50% reduction in apnea-hypopnea index (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, M/F =136/16) were included in this study. The ANN model achieved the highest performance with a coefficient of determination (R2) 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% ± 1.19% 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, 71.74% ± 1.54% and 68.81% ± 2.93%, respectively.

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

Presenting Author(s)

Yingqian Zhou, PhD

Tsinghua University
Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University
Beijing, Beijing, China (People's Republic)

Disclosure(s):
faculty for this accredited education activity has no relationship(s) with ineligible companies to disclose.

Co-Author(s)

JY

Jingyuan You, PhD

Student
Tsinghua University
Beijing, Beijing, China (People's Republic)

Disclosure(s):
faculty for this accredited education activity has no relationship(s) with ineligible companies to disclose.
JL

Juan Li, PhD

Beijing Tsinghua Changgung Hospital, China (People's Republic)

Disclosure information not submitted.
XC

Xin Cao, PhD

Attending Physician
Department of Otolaryngology, Head and Neck Surgery, Beijing Tsinghua Chang Gung Hospital
Beijing, Beijing, China (People's Republic)

Disclosure information not submitted.
YZ

Yuhuan Zhang

Beijing Tsinghua Changgung Hospital, United States

Disclosure information not submitted.
CZ

Chunmei Zhao

Beijing Tsinghua Changgung Hospital, China (People's Republic)

Disclosure information not submitted.

Senior Author(s)

Jingying Ye, PhD

DR.
Beijing Tsinghua Changgung Hospital
Beijing, Beijing, China (People's Republic)

Disclosure(s):
faculty for this accredited education activity has no relationship(s) with ineligible companies to disclose.