J Sleep Med.  2018 Dec;15(2):48-54. 10.13078/jsm.18012.

Predictive Model of Optimal Continuous Positive Airway Pressure for Obstructive Sleep Apnea Patients with Obesity by Using Machine Learning

• Kim SS
• Yang KI

• KMID: 2434746
• DOI: http://doi.org/10.13078/jsm.18012

Abstract

OBJECTIVES
The aim of this study was to develop a predicting model for the optimal continuous positive airway pressure (CPAP) for obstructive sleep apnea (OSA) patient with obesity by using a machine learning
METHODS
We retrospectively investigated the medical records of 162 OSA patients who had obesity [body mass index (BMI) ≥ 25] and undertaken successful CPAP titration study. We divided the data to a training set (90%) and a test set (10%), randomly. We made a random forest model and a least absolute shrinkage and selection operator (lasso) regression model to predict the optimal pressure by using the training set, and then applied our models and previous reported equations to the test set. To compare the fitness of each models, we used a correlation coefficient (CC) and a mean absolute error (MAE).
RESULTS
The random forest model showed the best performance {CC 0.78 [95% confidence interval (CI) 0.43-0.93], MAE 1.20}. The lasso regression model also showed the improved result [CC 0.78 (95% CI 0.42-0.93), MAE 1.26] compared to the Hoffstein equation [CC 0.68 (95% CI 0.23-0.89), MAE 1.34] and the Choi's equation [CC 0.72 (95% CI 0.30-0.90), MAE 1.40].
CONCLUSIONS
Our random forest model and lasso model (26.213+0.084×BMI+0.004×apnea-hypopnea index+0.004×oxygen desaturation index−0.215×mean oxygen saturation) showed the improved performance compared to the previous reported equations. The further study for other subgroup or phenotype of OSA is required.

Keyword

Sleep apnea; Obstructive; Continuous positive airway pressure; Machine learning; Obesity

MeSH Terms

Continuous Positive Airway Pressure*
Forests
Humans
Machine Learning*
Medical Records
Obesity*
Oxygen
Phenotype
Retrospective Studies
Sleep Apnea Syndromes
Sleep Apnea, Obstructive*
Oxygen