# Undiagnosed Obstructive Sleep Apnea in Primary Care Clinics

> **NIH NIH R01** · OHIO STATE UNIVERSITY · 2024 · $740,271

## Abstract

Obstructive sleep apnea (OSA) is a major public health problem that is commonly seen in primary care clinics
where a majority of outpatient office visits occur. Prior studies suggest that OSA is severely under-diagnosed in
primary care practices, but these studies relied on patient reported symptoms rather than objective diagnosis.
Consequently, the prevalence of OSA and rate of under-diagnosis of OSA among primary care practices are
not currently known. Given the recent studies implicating the excessively sleepy OSA symptom subtype to the
development of cardiovascular events, it is also important to ascertain the prevalence and underdiagnosis rate
of this OSA subtype among patients seen at primary care clinics. In addition to estimating prevalence, it is
equally important to evaluate the factors contributing to the likelihood of underdiagnosis across different
primary care practices. Barriers to OSA diagnosis include patient factors related to lack of awareness and
understanding of the disease, physician perceptions of OSA risk and knowledge about the heterogeneous
presentation of OSA, and system and practice-specific factors such as time constraints. The growth of
electronic health records (EHRs) in recent years also provides the unique ability to identify these undiagnosed
and untreated cases via the application of automated algorithms. Our preliminary data suggest that combining
readily available data in EHRs including comorbidities using machine learning techniques offers an opportunity
to develop a reliable OSA phenotypic risk score that would facilitate OSA case identification in primary care.
New paradigms for identification of OSA and its subtypes centered on primary care and using data from the
EHR, including comorbidities and patient symptoms, are needed to meet the high prevalence of OSA. In Aim 1,
using a two-stage sampling strategy in primary care practices, we will determine the prevalence of OSA and its
subtypes, and robustly estimate the rate of OSA under-diagnosis in primary care clinics. In Aim 2, we intend to
develop a root cause analysis of the underdiagnosis of OSA problem in primary care practice setting using a
systems engineering framework. We will elicit the barriers and facilitators of the OSA diagnostic process within
the work system of the primary care practice setting using stakeholder engagement methods with multi-level
stakeholders. Using these results, we will survey primary care providers and staff to estimate the frequency
and priority rank-order of OSA diagnostic process barriers and facilitators within the primary care work system.
In Aim 3, using a novel machine learning pipeline, we will develop an efficient and accurate tool for determining
OSA risk in primary care participants recruited in Aim 1 and an efficient tool to determine OSA subtype that
could be employed within the context of primary care practice. This proposal will ultimately lead to increased
identification of OSA and its subtypes in primary c...

## Key facts

- **NIH application ID:** 10943154
- **Project number:** 1R01HL175579-01
- **Recipient organization:** OHIO STATE UNIVERSITY
- **Principal Investigator:** ULYSSES J MAGALANG
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $740,271
- **Award type:** 1
- **Project period:** 2024-08-15 → 2029-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10943154

## Citation

> US National Institutes of Health, RePORTER application 10943154, Undiagnosed Obstructive Sleep Apnea in Primary Care Clinics (1R01HL175579-01). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10943154. Licensed CC0.

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