OSApredictor: A tool for prediction of moderate to severe obstructive sleep apnea-hypopnea using readily available patient characteristics

Abstract

Sleep apnea is a common sleep disorder. The availability of an easy-to-use sleep apnea predictor would provide a public health benefit by promoting early diagnosis and treatment. Our goal was to develop a prediction tool that used commonly available variables and was accessible to the public through a web site. Using data from polysomnography (PSG) studies that measured the apnea-hypopnea index (AHI), we built a machine learning tool to predict the presence of moderate to severe obstructive sleep apnea (OSA) (defined as AHI ≥15). Our tool employs only seven widely available predictor variables: age, sex, weight, height, pulse oxygen saturation, heart rate and respiratory rate. As a preliminary step, we used 16,958 PSG studies to examine eight machine learning algorithms via five-fold cross validation and determined that XGBoost exhibited superior predictive performance. We then refined the XGBoost predictor by randomly partitioning the data into a training and a test set (13,566 and 3392 PSGs, respectively) and repeatedly subsampling from the training set to construct 1000 training subsets. We evaluated each of the resulting 1000 XGBoost models on the single set-aside test set. The resulting classification tool correctly identified 72.5 % of those with moderate to severe OSA as having the condition (sensitivity) and 62.8 % of those without moderate to-severe OSA as not having it (specificity); overall accuracy was 66 %. We developed a user-friendly publicly available website (https://manticore.niehs.nih.gov/OSApredictor). We hope that our easy-to-use tool will serve as a screening vehicle that enables more patients to be clinically diagnosed and treated for OSA.