Risk-prediction model for incident hypertension in patients with obstructive sleep apnea based on SpO2 signals

This work proposes a method utilizing oxygen saturation (SpO2) for predicting incident hypertension in patients with obstructive sleep apnea (OSA). We extracted time domain features and frequency domain features from the SpO2 signal. For prediction, we employed several machine learning algorithms to establish the 3-year risk prediction model in the Chinese Sleep Health Study, including 250 subjects without baseline hypertension who underwent sleep monitoring. The proposed random forest model achieved an accuracy of 84.4%, a sensitivity of 77.0%, a specificity of 91.5% and an area under the receiver operator characteristic of 84.3% using 10-fold crossvalidation. We show that the model outperformed two sleep medicine specialists using clinical experience to predict hypertension. Furthermore, we applied the prediction results in the public Sleep Heart Health Study database and showed the subjects who were predicted to have hypertension would be at a higher risk in 4-6 years. This work shows the potential of SpO2 signal during sleep for the prediction of hypertension and could be beneficial to the early detection and timely treatment of hypertension in OSA patients.Clinical Relevance-There is no prediction model for incident hypertension in OSA patients in clinical practice. Most patients are unaware of health complexity, symptoms and risk factors before hypertension. Establishing an accurate prediction model can effectively provide early intervention for OSA patients and reduce the prevalence of hypertension.