Mount Sinai researchers have been awarded a five-year, $4.1 million grant from the National Heart, Lung, and Blood Institute at the National Institutes of Health to develop and study an artificial intelligence (AI)-powered model that predicts adverse outcomes of obstructive sleep apnea. The experts say their model will better reflect the underlying physiology of the condition and the ways it impairs sleep, improving patient care and treatment.
Sleep apnea is a chronic condition of intermittent airflow blockage and improper breathing during sleep that affects nearly 1 billion people worldwide. The current diagnostic tool for obstructive sleep apnea is the apnea hypopnea index, which measures the frequency of apneas, or number of times a person stops breathing while asleep, and hypopneas, or periods of reduced airflow. The metric has limitations and lacks accuracy in predicting the outcomes of these respiratory events.
In response to an international call to better diagnose and manage sleep apnea beyond the standard scale, the researchers at Mount Sinai developed an AI-powered approach that examines the sleep functions apnea is known to impair-;breathing, oxygen levels, and sleep stages-;and combines these categories into a probability score that predicts the risk of short- and long-term outcomes of the disorder. Adverse outcomes of sleep apnea can range from short-term conditions such as excessive daytime sleepiness to long-term conditions such as neurocognitive impairment, hypertension, or cardio-cerebrovascular morbidity.
Our proposal uses a state-of-the-art artificial intelligence model that risk-profiles sleep apnea patients using data from routine sleep studies. Our study will assess the real-world performance of an AI approach and offer crucial evidence needed to translate metrics that go beyond the apnea-hypopnea in assessing severity of obstructive sleep apnea into clinical practice. Achieving this would leave us poised to shift the paradigm in clinical management of obstructive sleep apnea."
Ankit Parekh, PhD, Principal Investigator, Director of the Sleep And Circadian Analysis (SCAN) Group and Assistant Professor of Medicine (Pulmonary, Critical Care and Sleep Medicine) at the Icahn School of Medicine at Mount Sinai
The AI-powered method combines fully automated metrics across possibly independent ventilatory, hypoxic, or arousal categories with data-driven weights to determine risk of adverse outcomes. Mount Sinai experts say preliminary data from three cohorts of nearly 11,000 participants suggests the machine-learning model could predict the probability of sleepiness due to apnea with an accuracy of about 87 percent. In contrast, the model using the existing apnea hypopnea index predicted sleepiness at about 54 percent precision.
Using data from a cohort of more than 4,700 participants, the machine-learned sleep apnea probability of cardiovascular disease could predict cardiovascular mortality with an accuracy of nearly 81 percent, compared to a regression model with the existing index that predicted cardiovascular death at about 58 percent accuracy.
The research team plans to test their two machine-learning models on a group of Mount Sinai Integrative Sleep Center patients who will undergo polysomnogram sleep studies that record brain waves, oxygen levels, heart rates, and breathing during sleep. The findings will be retrospectively validated against sleep data for statistical analysis. The patients will be monitored for three months while keeping digital sleep diaries as they progress through their clinical care.
The grant number is 1R01HL171813-0.
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