Mount Sinai Researchers to Develop and Study AI-Powered Models That Identify Risk for Cardiovascular Disease and Treatment Response in Patients With Obstructive Sleep Apnea
Team studying predictive models for serious sleep condition awarded $3 million NIH grant
Mount Sinai researchers are developing and studying models powered by artificial intelligence (AI) to identify the risk of cardiovascular disease events in patients with obstructive sleep apnea. The prediction models, using machine-learning techniques, will also help classify patients who may benefit from the most common treatment option for the disorder.
The researchers said their personalized tools will provide a novel approach to enhancing management of obstructive sleep apnea by optimizing the best decisions for treatment plans and improving cardiovascular outcomes. The study is supported by a four-year, $3 million grant from the National Heart, Lung, and Blood Institute of the National Institutes of Health (NIH).
Obstructive sleep apnea is a serious and chronic condition in which the upper airway becomes blocked, leading to brief pauses in breathing during sleep. It affects more than 1 billion people worldwide. The most common treatment for obstructive sleep apnea is use of a breathing device called a continuous positive airway pressure (CPAP) machine, which provides air pressure throughout the upper airway to keep it open and help with breathing while asleep. Previous studies have established the prevalence of obstructive sleep apnea and its association to cardiovascular disease. However, little research has demonstrated the benefits of continuous CPAP use on the rate of cardiovascular events.
In response to the NIH Sleep Research Plan’s call for further research in critical and high-priority areas, Mount Sinai experts will use machine-learning techniques on comprehensive multi-modal datasets to identify patients at enhanced risk for atherosclerosis progression, or buildup of fats and cholesterol in the artery walls, and heightened risk for cardiovascular events such as heart attack and stroke. Researchers said the approach will also predict cardiovascular treatment effectiveness of CPAP therapy for patients with the sleep disorder who scored as “non-sleepy” on a standard clinical test, helping to identify patients who would benefit most from using CPAP as well as patients who should avoid CPAP use.
The foundation of this work is the team’s recently published study which revealed the potential harm of CPAP therapy to non-sleepy patients with obstructive sleep apnea and acute coronary syndrome, such as an increased risk of stroke, heart attack, and cardiovascular death. Those findings underscored the importance of identifying apnea patients who could benefit from CPAP and steered the team towards more personalized treatment strategies, said primary Principal Investigator Neomi Shah, MD, MPH, MSc, Associate Dean for Faculty Career Advancement, Vice Chair for Faculty Affairs in the Mount Sinai Health System Department of Medicine, and Professor of Medicine (Pulmonary, Critical Care and Sleep Medicine) at the Icahn School of Medicine at Mount Sinai.
“Supported by a transformative grant, I’m thrilled to lead a project that stands at the intersection of cutting-edge artificial intelligence and sleep medicine,” Dr. Shah said. “Our work will epitomize the wealth of expertise and collaborative effort across the Mount Sinai Health System to both enrich our understanding of the condition and improve patient care, impacting millions in the United States. We are committed to validating our AI tools within Mount Sinai’s clinical dataset to translate our research into real-world practice, thereby, effectively bridging the research to practice gap.”
The research will use data from two cohorts: the Multi-Ethnic Study of Atherosclerosis (MESA) cohort of more than 6,000 ethnically diverse, generally healthy non-sleepy participants, and the Sleep Apnea Cardiovascular Endpoints (SAVE) randomized clinical trial of more than 2,500 non-sleepy participants with moderate to severe obstructive sleep apnea and established cardiovascular disease. They will use these datasets to identify key variables that predict atherosclerosis progression and cardiovascular events such as heart attack and stroke, and to identify subgroups with differential treatment effects with CPAP for cardiovascular events based on demographics or risk characteristics, as well as validation of the models within the Mount Sinai Health System using clinical data from the electronic health record.
“We are inspired by the transformative potential of machine learning techniques in health care, particularly in analyzing vast amounts of complex data to personalize treatment strategies,” said a Principal Investigator of the project, Girish Nadkarni, MD, MPH, Irene and Dr. Arthur M. Fishberg Professor of Medicine, Director of The Charles Bronfman Institute of Personalized Medicine, and System Chief of Data-Driven and Digital Medicine at Icahn Mount Sinai. “Our study has the potential to revolutionize the management of obstructive sleep apnea by offering decision support tools that optimize treatment plans, improve patient outcomes, and reduce the burden of sleep apnea-related cardiovascular disease events on both individuals and health care systems.”
"Through precision medicine, we are prioritizing rigorous intervention to enhance cardiovascular disease risk reduction,” said a Principal Investigator of the project, Mayte Suarez-Farinas, PhD, Associate Director of the Center for Biostatistics, and Professor of Population Health Science and Policy, and Genetics and Genomic Sciences at Icahn Mount Sinai. “Health care providers will be equipped with innovative tools to identify patients at heightened risk for heart attack or stroke and be able to predict treatment outcomes of CPAP therapy in sleep apnea patients. This personalized approach will enable clinicians to tailor treatment strategies to individual patient needs, optimizing CPAP adherence and efficacy.”
Researchers from the University of California-San Diego, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, University of Washington, and Columbia University will contribute to the study. The grant number is 1R01HL168897-01A1.
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