Cardiovascular risk and mortality prediction in patients suspected of sleep apnea: a model based on an artificial intelligence system. (29th October 2021)

• Record Type: Journal Article
• Authors: Blanchard, Margaux; Feuilloy, Mathieu; Gervès-Pinquié, Chloé; Trzepizur, Wojciech; Meslier, Nicole; Goupil, François; Pigeanne, Thierry; Racineux, Jean-Louis; Balusson, Frédéric; Oger, Emmanuel; Gagnadoux, Frédéric; Girault, Jean-Marc
• Other Names: collab.
• Is Part Of: Physiological measurement. Volume 42:Number 10(2021)
• Publication Date: 2021-10-29
• Subjects: sleep apnea -- cardiovascular risk -- machine learning -- adaptive boosting; Physiology -- Measurement -- Periodicals; Patient monitoring -- Periodicals; 612
• DOI: 10.1088/1361-6579/ac2a8f
• Languages: English
• ISSNs: 0967-3334

Abstract

Abstract: Objective . Cardiovascular disease (CVD) is one of the leading causes of death worldwide. There are many CVD risk estimators but very few take into account sleep features. Moreover, they are rarely tested on patients investigated for obstructive sleep apnea (OSA). However, numerous studies have demonstrated that OSA index or sleep features are associated with CVD and mortality. The aim of this study is to propose a new simple CVD and mortality risk estimator for use in routine sleep testing. Approach . Data from a large multicenter cohort of CVD-free patients investigated for OSA were linked to the French Health System to identify new-onset CVD. Clinical features were collected and sleep features were extracted from sleep recordings. A machine-learning model based on trees, AdaBoost, was applied to estimate the CVD and mortality risk score. Main results . After a median [inter-quartile range] follow-up of 6.0 [3.5–8.5] years, 685 of 5234 patients had received a diagnosis of CVD or had died. Following a selection of features, from the original 30 features, 9 were selected, including five clinical and four sleep oximetry features. The final model included age, gender, hypertension, diabetes, systolic blood pressure, oxygen saturation and pulse rate variability (PRV) features. An area under the receiver operating characteristic curve (AUC) of 0.78 was reached. Significance . AdaBoost, an interpretable machine-learning model, was applied to predict 6 year CVD and mortality in patients investigated for clinical suspicion of OSA. A mixed set of simple clinical features, nocturnal hypoxemia and PRV features derived from single channel pulse oximetry were used.