# Breathing patterning in obstructive sleep apnea

> **NIH VA IK2** · LOUIS STOKES CLEVELAND VA MEDICAL CENTER · 2024 · —

## Abstract

Obstructive sleep apnea (OSA), the most common sleep disorder, is a major problem in the Veteran
community that poses risks for decreased physical and mental health. The predominant treatment for OSA is
positive airway pressure (PAP) therapy. While PAP therapy is highly effective, adherence is suboptimal with
approximately 50% stopping therapy by year 2. Our group has shown that breathing patterning is predictive of
long-term adherence, but the method used requires a 15-minute procedure while awake. The goal of this
project is to refine breathing patterning metrics and enhance clinical integration by repurposing clinical data
obtained as part of routine OSA evaluation (sleep studies) and treatment (PAP machine data).
 This retrospective analysis will first apply an already-developed breathing pattering metric to be more
applicable for both in-lab polysomnography and home sleep apnea testing. Comparison of the breathing
patterning metric in sleep and wake will determine applicability for home sleep apnea testing, which does not
gather sleep staging data. Because PAP machines only record flow, we will also evaluate whether the
breathing patterning metric has similar performance characteristics in different respiratory signals
(plethysmography and flow) for predicting adherence by comparing concordance index. We will then evaluate
for the optimal breathing patterning metric and machine learning algorithm based on accuracy in predicting
PAP adherence. Breathing patterning can be measured in multiple ways; to optimize adherence, a panel of
breathing patterning metrics will be evaluated. Multiple machine learning algorithms will be compared to
determine which has the best discrimination for predicting PAP adherence. Breathing patterning changes with
age will be evaluated. In addition, we will evaluate the added utility of breathing patterning to a model that
predicts adherence using patient demographics, past medical history, and sleep study summary data. This
project could provide a practical, cost-effective method to identify patients that are likely to become
nonadherent.
 My short-term career goal is to develop a foundation of focused research in optimizing obstructive sleep
apnea (OSA) treatment via patient-centered solutions that will inform my clinical practice. To fulfill this goal, the
developed research and career development goals of this CDA-2 project complement each other. Additional
instruction in modern machine learning techniques will allow for adherence model development and validation.
Coursework in clinical informatics will assist me in optimizing the design and data collection from the VA
electronic medical record. Instruction on managing research records will help with managing the large
databases necessary for this project. Proposed training in research team leadership and responsible conduct
of research is particularly relevant to a transdisciplinary project such as this and will have long-term career
benefits. Over the course of...

## Key facts

- **NIH application ID:** 11116828
- **Project number:** 5IK2CX001882-06
- **Recipient organization:** LOUIS STOKES CLEVELAND VA MEDICAL CENTER
- **Principal Investigator:** Anna Michelle May
- **Activity code:** IK2 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2024
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2019-07-01 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11116828

## Citation

> US National Institutes of Health, RePORTER application 11116828, Breathing patterning in obstructive sleep apnea (5IK2CX001882-06). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/11116828. Licensed CC0.

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