Mount Sinai Researchers Develop Automated Measure to Assess Severity and Mortality Risk in Sleep Apnea Patients: A Game-Changer in Sleep Disorder Management
Researchers at Mount Sinai have made a groundbreaking discovery in the management of sleep apnea, a chronic sleep disorder that affects millions of people in the United States. They have developed an automated measure that can accurately assess the severity and risk of mortality in patients with obstructive sleep apnea.
The study, published in the American Journal of Respiratory and Critical Care Medicine, introduces a novel breath-by-breath measure called ventilatory burden. Unlike current methods that rely on oxygen levels in the blood and awakenings, this new measure is not dependent on those factors. By analyzing routine sleep studies, researchers can determine the proportion of small breaths and assess the variability of ventilatory burden from night to night. This measure also predicts short- and long-term consequences associated with sleep apnea, including potentially life-threatening conditions like coronary heart disease.
The traditional method used to diagnose obstructive sleep apnea is the Apnea-Hypopnea Index (AHI), which calculates the average number of breathing interruptions per hour during sleep. However, AHI does not predict subsequent risks and is influenced by various variables. It also fails to describe the long-term consequences of respiratory events that occur overnight.
To develop the automated measure, researchers analyzed data from more than 5,000 participants and over 34 million breaths. They established the normal range of ventilatory burden, assessed its relationship to upper airway obstruction, and determined its connection to mortality risks, including cardiovascular diseases. The results showed that ventilatory burden effectively assesses the severity of obstructive sleep apnea, remains consistent from one night to another, and predicts mortality associated with cardiovascular diseases.
The implications of this research are significant. The automated measure provides clinicians with a better tool for managing sleep apnea, allowing for earlier detection and treatment. Current methods often rely on arbitrary rules and thresholds that do not accurately predict the risk of cardiovascular diseases or mortality. With the new measure, patients can receive more precise and effective care.
The Mount Sinai team plans to further their research by developing an artificial intelligence algorithm to replace AHI and identify which patients would benefit from continuous positive airway pressure (CPAP) treatment, the primary therapy for sleep apnea. They also aim to include a more diverse patient dataset to ensure equitable and effective treatment for all.
The study was supported by funding and grants from the National Institute of Health’s National Heart, Lung, and Blood Institute, the American Academy of Sleep Medicine Foundation, and the Foundation for Research in Sleep Disorders.
The development of this automated measure marks a breakthrough in sleep disorder management and offers hope for improved care and outcomes for patients with sleep apnea. With further research and implementation, this game-changing approach has the potential to revolutionize the field and significantly enhance the quality of life for millions of individuals suffering from this prevalent sleep disorder.
