Inclusion of blood pressure parameter increases predictive capability of severe obstructive sleep apnea: A decision tree approach

Abstract

Few studies included objective blood pressure (BP) to construct the predictive model of severe obstructive sleep apnea (OSA). This study used binary logistic regression model (BLRM) and the decision tree method (DTM) to constructed the predictive models for identifying severe OSA, and to compare the prediction capability between the two methods. Totally 499 adult patients with severe OSA and 1421 non-severe OSA controls examined at the Sleep Medicine Center of a tertiary hospital in southern Taiwan between October 2016 and April 2019 were enrolled. OSA was diagnosed through polysomnography. Data on BP, demographic characteristics, anthropometric measurements, comorbidity histories, and sleep questionnaires were collected. BLRM and DTM were separately applied to identify predictors of severe OSA. The performance of risk scores was assessed by area under the receiver operating characteristic curves (AUCs). In BLRM, body mass index (BMI) ≥27 kg/m², and Snore Outcomes Survey score ≤55 were significant predictors of severe OSA (AUC 0.623). In DTM, mean SpO₂ <96%, average systolic BP ≥135 mmHg, and BMI ≥39 kg/m² were observed to effectively differentiate cases of severe OSA (AUC 0.718). The AUC for the predictive models produced by the DTM was higher in older adults than in younger adults (0.807 vs. 0.723) mainly due to differences in clinical predictive features. In conclusion, DTM, using a different set of predictors, seems more effective in identifying severe OSA than BLRM. Differences in predictors ascertained demonstrated the necessity for separately constructing predictive models for younger and older adults.

Keywords: binary logistic regression model; blood pressure; decision tree method; obstructive sleep apnea.