Data-Driven Sleep Biomarkers of Brain Health, Heart Health, and Mortality

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Abstract: Data-Driven Sleep Biomarkers of Brain Health, Heart Health, and Mortality Sleep state signals encode critical biological information about brain and cardiovascular health. However, present approaches to polysomnography data (“sleep studies”) discard most of the collected information, instead providing, using visual analysis and rules from the 1960s, relatively unsophisticated metrics (e.g., 30-second sleep stages, apnea-hypopnea index). Visual scoring is also limited by interscorer inconsistencies. Recent advances in computational science and Machine Learning (ML) / Artificial Intelligence (AI) open the way for 1) standard scoring with unparalleled precision and consistency; 2) new data-driven, quantitative measures. There is a critical unmet need for new tools, algorithms and datasets that leverage recent advances in data science to develop robust sleep-based biomarkers of brain and cardiovascular health. We propose to create a Complete AI Sleep Report (CAISR) algorithm for all standard sleep measures, and a progressively accumulating library of novel analytics. We are ideally positioned to close this gap. We have access between our four collaborating institutions to sleep data from >80K patients (35,000 already assembled), and at least >20K more during the project; experience curating large clinical physiology and electronic medical records data for research; progress already underway with building a scalable public data sharing portal; expertise in basic and translational sleep science; and an established record of successfully developing and validating novel deep learning tools and algorithms to analyze sleep data. Our long-term goal is to increase the value of sleep data by replacing manual analysis by open-source data- driven AI approaches. Our central hypothesis is that sleep signals carry measurable latent information about mortality and brain and heart health. Our specific aims are: 1) Create an online public portal with de-identified polysomnograms (PSG) and cross-sectional and longitudinal electronic health records (EHR) data for 100K adult and pediatric patients; 2) Implement CAISR and validated that it generalizes across age, gender, and race. CAISR will also be externally validated on >13,000 PSGs from public research cohorts; 3) Develop AI algorithms that a) differentiate patients with vs. without existing brain and heart disease; b) predict primary outcomes of all cause and cardiovascular mortality; secondary outcomes of heart disease (coronary artery disease, myocardial infarction, congestive heart failure, atrial fibrillation, hypertension); and brain disease (dementia, stroke, intracranial hemorrhage). Completing these aims will lead to these expected outcomes: (1) sleep data across the lifespan, (2) sleep scoring AI algorithms validated across age, gender, and ethnicity; (3) predictors of mortality and brain and heart health. These outcomes will lead to new testable hypotheses, make sleep diagnostics more accessible to socially and biologically underserved groups, and stimulate progress in data-driven sleep research.