# Reimagining the diagnosis of obstructive sleep apnea beyond the apnea-hypopnea index

> **NIH NIH R21** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2022 · $126,750

## Abstract

PROJECT SUMMARY/ABSTRACT
Obstructive sleep apnea (OSA) is a common chronic condition variably associated with neurocognitive
impairment, hypertension, and incident cerebrovascular and cardiovascular disease. OSA is currently defined
by measuring the frequency of obstructive events (apnea-hypopnea index [AHI]) defined by the number/hour of
obstructions to breathing lasting at least 10 seconds and associated with cortical arousals and/or oxygen
desaturation during sleep. The AHI has been only modestly useful for diagnosis of disease severity and for
predicting short- and long-term consequences in large epidemiologic studies and mounting evidence of AHI's
limitations on both physiological as well as methodological grounds has led to an outcry for rethinking the way
we currently diagnose OSA. This project proposes to extract, from standard sleep studies, metrics to characterize
the burden of OSA in three distinct domains: ventilatory, hypoxic and arousal. The ventilatory burden will be
quantitated automatically from the distribution of breath sizes in a whole night combined with the probability that
each breath is obstructive. Hypoxic burden is quantitated in an automated fashion from overnight oxygen
desaturation (area between baseline saturation and desaturation events). Arousal burden is quantitated using
EEG metrics that indirectly relate to sleep depth and/or arousability overnight. The two overarching aims of this
project are: 1) use ventilatory burden to identify presence of OSA better than AHI, and 2) use combination of
ventilatory/hypoxic/arousal burdens, where the equation for combination is machine-learned, to predict risk of
short- (daytime sleepiness) and long-term (cardiovascular mortality, stroke, and incident congestive heart
failure/myocardial infarction) consequences of OSA. By using existing data (in-lab and at-home
polysomnography) from >9000 subjects, the proposed project will harness the power of big data to refine and
improve the way we diagnose OSA and assess risk of adverse outcomes.

## Key facts

- **NIH application ID:** 10515525
- **Project number:** 1R21HL165320-01
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Ankit Ashok Parekh
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $126,750
- **Award type:** 1
- **Project period:** 2022-08-15 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10515525

## Citation

> US National Institutes of Health, RePORTER application 10515525, Reimagining the diagnosis of obstructive sleep apnea beyond the apnea-hypopnea index (1R21HL165320-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10515525. Licensed CC0.

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