Abstract
Background
Obstructive Sleep Apnea Syndrome (OSAS) is a major global health concern and is typically diagnosed by in-lab polysomnography (PSG). This examination though has high medical manpower costs and alternative portable methods have further limitations. This paper develops a new model for screening the risk of OSAS in different age groups and gender by using body profiles. The effects of body profiles for different subgroups in sleep stage alteration and OSAS severity are also investigated.
Methods
The data is derived from 6614 Han-Taiwanese subjects who have previously undergone PSG in order to assess the severity of OSAS in the sleep center of Taipei Medical University Shuang-Ho Hospital between March 2015 and October 2019. Characteristics of subjects, including age, gender, body mass index (BMI), neck circumference, and waist circumference, were obtained from a questionnaire. Pearson regression was used to evaluate the correlations between body profiles and sleep stages as well as sleep disorder indexes. To develop an age and gender independent model, random forests (RF), which is an ensemble learning method with high explainability, were trained by the four groups by gender and age (older or younger than 50 years old) with ratios of 70% (training dataset) and 30% (testing dataset), respectively. Prediction performance was evaluated by sensitivity, specificity and accuracy. Variable importance was assessed by averaging the impurity decrease to account for the effect of different factors.
Results
Results indicate that high BMI, neck circumference and waist circumference decreased the duration of slow-wave sleep and increased the sleep disorder indices and the percentage of wake and N1. Additionally, screening models for different gender and age utilizing anthropometric features as predictors via RF were established and demonstrated to have high accuracy (75.63% for younger males, 74.72% for elder males, 78.81% for younger females, and 72.10% for elder females). Feature importance indicated that waist circumference was the highest contributing factor in females and elder males, whereas the BMI was the highest contribution in younger males.
Conclusions
The authors recommend the use of the prediction models for those with Han-Taiwanese craniofacial features.
Age and Gender Related Differences in Patient Awareness of Conditions Associated with Untreated Obstructive Sleep Apnea
