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

> **NIH NIH R21** · CLEVELAND CLINIC LERNER COM-CWRU · 2024 · $120,750

## Abstract

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.

## Key facts

- **NIH application ID:** 10912772
- **Project number:** 5R21HL170206-02
- **Recipient organization:** CLEVELAND CLINIC LERNER COM-CWRU
- **Principal Investigator:** Matheus Lima Diniz Araujo
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $120,750
- **Award type:** 5
- **Project period:** 2023-09-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10912772

## Citation

> US National Institutes of Health, RePORTER application 10912772, Applying Innovative Artificial Intelligence Approaches to a Large Sleep Physiologic Biorepository to Integrate Sleep Disruption in Cardiovascular Risk Calculation (5R21HL170206-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10912772. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*