Applying Innovative Artificial Intelligence Approaches to a Large Sleep Physiologic Biorepository to Integrate Sleep Disruption in Cardiovascular Risk Calculation

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PROJECT SUMMARY: Cardiovascular disease (CVD) accounts for >800,000 deaths annually, i.e., 32% of all deaths in the US, with total costs projected to reach $2.5 trillion by 2035. Experimental and epidemiologic data identify sleep disorders- -recently recognized in American Heart Association Life’s Essential 8--as independent preventative targets to mitigate downstream major adverse cardiovascular events (MACE). Obstructive sleep apnea (OSA) is the sleep disorder most consistently implicated in CV risk operating via pathways of intermittent hypoxia and sympathetic nervous system activation. Emerging science, however, from our group and others, has identified that other facets of sleep disruption, such as curtailed sleep and sleep architectural disruption, also increase CV risk. Enhanced phenotyping of not only OSA--beyond the limitations of the standardly used apnea-hypopnea index (AHI) --- but also other sleep disorders could refine the ability to characterize sleep-related pathophysiology and MACE prediction. However, overlapping sleep phenotypes contributing to CV risk are difficult to characterize, given the need for large datasets. Moreover, the “sleepy” phenotype of sleep disorders is associated with increased CV risk; however, there is limited understanding of how to integrate this into CV risk prediction. Therefore, we propose leveraging an existing clinical registry of multimodal cardiorespiratory and neurologic physiologic sleep data, i.e.,>186,000 archived sleep studies. The scope of work involves conducting an analysis of biologically plausible aggregate biomarkers of CVD from datasets of polysomnograms (PSG) that combine with artificial intelligence models to identify patterns from structured data and raw PSG signal data to forecast the incidence of MACE (nonfatal myocardial infarction, fatal coronary heart disease, nonfatal, or fatal stroke) and examine the influence of the sleepy phenotype. We will further examine the utility of incorporating automatic PSG analysis in the current clinical CV risk stratification schema. This work will set the stage for external validation work in other clinical cohorts and the NHLBI National Sleep Research Resource, a pooled geographically diverse compilation of >45,000 sleep studies. The proposed work provides an innovative opportunity to assess the ability of sleep study, i.e., PSG biomarkers, to predict individuals at increased risk for CVD using methods established by our group. Innovation also lies in the use of state-of-the-art deep learning strategies, including Transformers models for low-dimensional representation of PSG direct physiological signals. Our group is well-positioned to undertake the following study aims, given the expertise and experience we have in sleep medicine, cardiovascular, and computer science research.