Deep learning of awake and sleep electrocardiography to identify atrial fibrillation risk in sleep apnea

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Project Summary Atrial fibrillation (AF) is the most common cardiac arrhythmia responsible for significant morbidity and mortality burden. Obstructive sleep apnea (OSA) is a common sleep disorder but disproportionately more common in patients with AF. OSA has been proposed as a risk for AF. However, clarifying the association between the OSA and AF has been challenging due to many commonly shared risk factors such as obesity. No studies have demonstrated whether information about OSA improves prediction of future risk of AF. In particular, identifying who “among those with OSA” would be at risk for AF is unclear. Better identification of the group most vulnerable to developing AF among those with OSA will inform clinicians and patients of critical information needed for therapeutic decision making. One major challenge in OSA evaluation is that conventional metrics used in the evaluation, such as the apnea hypopnea index (AHI) do not adequately capture downstream cardiovascular (CV) responses. We and others have identified promising physiologically- driven polysomnography (PSG) markers that better capture the severity of OSA and improve CV risk stratification. Specifically related to AF, our preliminary study shows that heart rate response (HRR) to OSA events, but not AHI, is associated with incident AF in community dwelling elderly men. Electrocardiography (ECG) is a readily available diagnostic tool that captures electrical activity of the heart. Deep learning (DL) has shown great promise in detection and risk prediction of various clinical outcomes including AF from `awake' ECGs alone. `Sleep' ECG is affected by sleep state, respiration and particularly by pathological respiration such as OSA events. Based on this, we propose Aim 1: To evaluate whether novel HRR-based OSA metrics improves risk prediction of AF beyond the current AF risk prediction model. We will use a combined prospective cohort of Atherosclerosis Risk in Communities Study (ARIC)-Sleep Heart Health Study (SHHS), Cardiovascular Health Study (CHS)-SHHS and Multi-Ethnic Study of Atherosclerosis (MESA) (N~5000, AF events~800). Aim 2: To develop and test the DL model using an awake ECG (10 sec 12 lead) and sleep ECG (single lead) to predict a new onset AF in general population “with OSA”. We will develop a convolutional neural network (CNN) model utilizing ARIC + CHS cohorts (combined N with OSA~1500, AF events ~400) and externally validate in MESA cohort (OSA~1000, AF events ~100). The performance will be compared with the CHARGE-AF risk prediction model. Aim 3: Same as Aim 2 except it will be the DL model in prediction of new onset AF patients with OSA in clinical practice. Building upon the CNN model from Aim 2, we will develop a separate CNN model using clinical ECG data from a single academic medical center (N= 2000, AF~200) that may be more relevant in real world clinical practice. 50% of the dataset will be used for training and 50% for validation. The findings of this study will provide critical information about the future application of DL in improving CV risk stratification of people with OSA.