Researchers develop algorithm to predict impact of CPAP therapy on cardiovascular disease

NEW YORK – Researchers at Mount Sinai have created an analytic tool using machine learning that they say can predict cardiovascular disease risk in millions of patients with obstructive sleep apnea, according to findings recently published in Communications Medicine.

The researchers used a machine learning algorithm to create an analysis model that they say 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.

“Our findings represent a significant advancement in personalized medicine, moving away from a one-size-fits-all strategy in the treatment of obstructive sleep apnea,” said co-corresponding author Neomi A. Shah, MD, MPH, MSC, professor of medicine (pulmonary, critical care and sleep medicine), and artificial intelligence and human health, and associate chief for academic affairs in the Division of Pulmonary, Critical Care and Sleep Medicine at the Icahn School of Medicine at Mount Sinai. “This underscores the value of new data-driven approaches like our model to assist clinicians in making informed decisions about CPAP treatment recommendations, enhancing personalized care to meet the individual needs of every patient.”

Researchers analyzed 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.

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 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.

“These results demonstrate the power of machine learning for prediction of treatment effects in an era of precision medicine; however, such models require careful validation to prove their utility in clinical practice,” said co-primary author Oren Cohen, MD, assistant professor of medicine (pulmonary, critical care and sleep medicine) at the Icahn School of Medicine.

• Related: PAP adherence reduces cardiovascular related health events (CVRHU) in patients with obstructive sleep apnea, according to a new study published in Sleep and supported by Resmed.