ArticleJournal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine2024
Identifying clusters of patient comorbidities associated with obstructive sleep apnea using electronic health records.
Article in Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 5 citations in OpenAlex.
- Phenogroup-based stratification of cardiovascular risk in obstructive sleep apnea.American journal of preventive cardiology · 2026Article
- Distinguishing Age-specific Patterns in Comorbidities of Obstructive Sleep Apnea Using Real-World Data.medRxiv : the preprint server for health sciences · 2026Article
- GLP-1/GIP dual agonist tirzepatide in obstructive sleep apnea syndrome: mechanisms, evidence, and clinical perspectives.Frontiers in medicine · 2026Review
- Big data approaches for novel mechanistic insights on sleep and circadian rhythms: a workshop summary.Sleep · 2025Article
- Comorbidities that modulate temporal risk for incident hypertension among patients with obstructive sleep apnea.Clinical hypertension · 2025Article
- Research progress on the clinical subtyping of obstructive sleep apnea hypopnea syndrome.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2024Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
STUDY
objectivesThe objectives of this study were to understand the relative comorbidity burden of obstructive sleep apnea (OSA), determine whether these relationships were modified by sex or age, and identify patient subtypes defined by common comorbidities.
methodsCases with OSA and noncases (controls) were defined using a validated electronic health record (EHR)-based phenotype and matched for age, sex, and time period of follow-up in the EHR. We compared prevalence of the 20 most common comorbidities between matched cases and controls using conditional logistic regression with and without controlling for body mass index. Latent class analysis was used to identify subtypes of OSA cases defined by combinations of these comorbidities.
resultsIn total, 60,586 OSA cases were matched to 60,586 controls (from 1,226,755 total controls). Patients with OSA were more likely to have each of the 20 most common comorbidities compared with controls, with odds ratios ranging from 3.1 to 30.8 in the full matched set and 1.3 to 10.2 after body mass index adjustment. Associations between OSA and these comorbidities were generally stronger in females and patients with younger age at diagnosis. We identified 5 distinct subgroups based on EHR-defined comorbidities: High Comorbidity Burden, Low Comorbidity Burden, Cardiovascular Comorbidities, Inflammatory Conditions and Less Obesity, and Inflammatory Conditions and Obesity.
conclusionsOur study demonstrates the power of leveraging the EHR to understand the relative health burden of OSA, as well as heterogeneity in these relationships based on age and sex. In addition to enrichment for comorbidities, we identified 5 novel OSA subtypes defined by combinations of comorbidities in the EHR, which may be informative for understanding disease outcomes and improving prevention and clinical care. Overall, this study adds more evidence that OSA is heterogeneous and requires personalized management. CITATION: Te TT, Keenan BT, Veatch OJ, Boland MR, Hubbard RA, Pack AI. Identifying clusters of patient comorbidities associated with obstructive sleep apnea using electronic health records.
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Registered trials
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