Moderate-severe OSA screening based on support vector machine of the Chinese population facio-cervical measurements dataset: A cross-sectional study

Objectives Obstructive sleep apnea (OSA) has received much attention as a risk factor for perioperative complications and 68.5% of OSA patients remain undiagnosed before surgery. Facio-cervical characteristics may screen OSA for Asians due to smaller upper airways compared to Caucasians. Thus, our study aimed to explore a machine-learning model to screen moderate-severe OSA based on facio-cervical and anthropometric measurements.

Design A cross-sectional study.

Setting Data were collected from the Shanghai Jiao Tong University School of Medicine affiliated Ruijin Hospital between February 2019 and August 2020.

Participants A total of 481 Chinese participants were included in the study.

Primary and secondary outcome (1) Identification of moderate-severe OSA with apnea-hypopnea index (AHI)15 events·h−1. (2) Verification of the machine learning model.

Results The SABIHC2 model (Sex-Age-Body mass index-maximum Interincisal distance-ratio of Height to thyro-sternum distance-ne...