Mount Sinai scientists have created an analytic tool using machine learning that can predict cardiovascular disease risk in millions of patients with obstructive sleep apnea. This is a serious sleep disorder.
Apnea is a disorder where breathing stops and starts repeatedly during sleep, often leading to reduced oxygen levels in the blood and fragmented sleep. Each pause can last from a few seconds to over a minute and may occur multiple times per hour.
The researchers indicate that their study is the first to provide estimates of whether continuous positive airway pressure (CPAP), a widely used therapy for obstructive sleep apnea, will increase or decrease an individual’s cardiovascular risk.
In addition, the study highlights the potential for precision medicine and varied approaches to tailor clinical care and reduce cardiovascular disease risk in vulnerable patients.
Obstructive sleep apnea is a common, serious condition in which breathing repeatedly stops and starts during sleep. It is associated with elevated risks for cardiovascular disease, including stroke and heart disease. CPAP, which provides a continuous stream of pressurized air through a mask and helps eliminate breathing disturbances during sleep, remains the most effective treatment for sleep apnea. However, prior large studies have not shown that CPAP lowers risks for cardiovascular disease in patients with this disease.
Machine learning
The researchers used a machine learning algorithm to create an analysis model that predicts how CPAP could affect an individual’s cardiovascular health—estimating each patient’s likeliness of benefit or harm from the therapy, based on their sleep and health information.
The findings represent a significant advancement in personalized medicine, moving away from a one-size-fits-all strategy in the treatment of obstructive sleep apnea.
The Mount Sinai team analysed data from the Sleep Apnea Cardiovascular Endpoints (SAVE) trial, the largest clinical cohort evaluating CPAP for cardiovascular disease prevention with more than 2,600 participants from 89 sites in seven countries, to estimate individualized treatment effect scores. They considered more than 100 predictors from sleep and health information to establish 23 key baseline features, such as prior medical conditions and smoking status, in their analysis model.
The researchers found that treatment response significantly varied across the cohort. The model identified a subgroup who were expected to have improved cardiovascular risk with CPAP treatment; participants in this subgroup who were randomly assigned to receive the therapy experienced a 100-fold improvement in future cardiac risk compared with patients from this subgroup on usual care.
Conversely, those in a subgroup predicted to be harmed by the therapy experienced a greater than 100-fold increase in cardiovascular disease outcomes, including recurrent strokes and heart attacks, when receiving CPAP compared with usual care.
The research appears in the journal Communications Medicine, titled “Individualized treatment effects of CPAP on secondary cardiovascular outcomes in non-sleepy obstructive sleep apnea patients.”
