21 May ATS24: Mount Sinai Study Develops Algorithm to Distinguish Central vs Obstructive Sleep Apnea

MedicalResearch.com Interview with:

Dr. Parekh

Ankit Parekh, PhD
Director of the Sleep And Circadian Analysis (SCAN) Group
Assistant Professor of Medicine
(Pulmonary, Critical Care and Sleep Medicine)
Icahn School of Medicine at Mount Sinai

MedicalResearch.com: What is the background for this study? What are the main findings?

Response: Sleep apnea is associated with incident cardiovascular disease, and is a common chronic condition affecting over a billion people worldwide. In diagnosing and treating sleep apnea, it is imperative to establish the type of sleep apnea—whether it is obstructive or central sleep apnea. The differential contribution of central vs. obstructive sleep apnea toward incidental cardiovascular disease in those with significant sleep apnea has not been well studied.

Our group has developed an automated algorithm that deduces on a breath-by-breath level whether reductions in airflow are predominantly due to obstructive or central phenomena. Our algorithm uses several features that are known to be key in distinguishing the type of events and derives a probability of obstruction across each “small” (reduced amplitude) breath. The breath-by-breath probability is then used to determine whether a patient’s burden of sleep apnea is predominantly obstructive or central.

In this work, we analyzed sleep study data from The Osteoporotic Fractures in Men (MrOS) cohort (N=2793) consisting of elderly men, across two visits separated on average by 6.5 years, and derived the probability of obstruction on a breath-by-breath level. The median probability of obstruction for each subject was computed and analyzed against outcomes of cardiovascular disease. We also assessed the stability of the metric in those without any prevalent cardiovascular disease. We find that median probability of obstruction was stable across the two visits, and those with any incident cardiovascular disease had a lower median probability of obstruction: patients with incident cardiovascular outcomes had a significant burden of sleep apnea that was predominantly “central” in nature.

MedicalResearch.com: What should readers take away from your report?

Response: Currently, in sleep clinics, distinguishing central from obstructive sleep apnea is not trivial and requires invasive methods. Using an automated algorithm that utilizes routine sleep studies, we can deduce the burden of central vs. obstructive sleep apnea in a given patient. The automated algorithm derives a probability of obstruction and we show in a large dataset that those with incident cardiovascular events had a tendency toward significant central sleep apnea as deduced by the probability of obstruction.

MedicalResearch.com: What recommendations do you have for future research as a result of this study?

Response: As with any scientific study, we are validating our findings in other datasets as well as exploring metrics that amalgamate breath-by-breath probabilities to a single measure, apart from the median, that is clinically meaningful. Further research is needed as to the importance of central sleep apnea on progression of cardiovascular disease using methods that better discriminate central from obstructive sleep apnea, such as our automated algorithm.

 Disclosures: Response: The study was funded in part by NIH R21HL165320, K25HL151912. Dr. Parekh reports no disclosures.

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Last Updated on May 21, 2024 by Marie Benz MD FAAD