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. 2024 Apr 1;20(4):521-533.
doi: 10.5664/jcsm.10930.

Identifying clusters of patient comorbidities associated with obstructive sleep apnea using electronic health records

Tue T Te  1 , Brendan T Keenan  1 , Olivia J Veatch  2 , Mary Regina Boland  3 , Rebecca A Hubbard  3 , Allan I Pack  1
Affiliations

Identifying clusters of patient comorbidities associated with obstructive sleep apnea using electronic health records

Tue T Te et al. J Clin Sleep Med. .

Abstract

Study objectives: The 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.

Methods: Cases 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.

Results: In 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.

Conclusions: Our 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. J Clin Sleep Med. 2024;20(4):521-533.

Keywords: cardiovascular comorbidities; cluster analysis; comorbidities; data mining; electronic health record; obstructive sleep apnea; personalized treatment.

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Conflict of interest statement

All authors have seen and approved the manuscript. Work for this study was performed at the University of Pennsylvania. The authors report no conflicts of interest.

Figures

Figure 1
Figure 1. Flow diagram of included analysis sample and matched pairs.
The number of patients initially eligible for inclusion in the study, reasons (not mutually exclusive) for exclusion from the analysis set, and final number of pairs included in the specific analysis samples are illustrated. BMI = body mass index, EHR = electronic health record.
Figure 2
Figure 2. Yearly comorbidity prevalence over time among matched sample of cases and controls.
EHR = electronic health record, GERD = gastroesophageal reflux disease.
Figure 3
Figure 3. Heatmap illustrating the prevalence of EHR-defined comorbidities within and between subtypes of OSA patients determined using cluster analysis.
EHR = electronic health record, GERD = gastroesophageal reflux disease, OSA = obstructive sleep apnea.
See this image and copyright information in PMC

References

    1. Kryger MH , Roth T , Dement WC . Principles and Practice of Sleep Medicine. 6th ed . Philadelphia, PA: : Elsevier; ; 2017. .
    1. Leger D , Bayon V , Laaban JP , Philip P . Impact of sleep apnea on economics . Sleep Med Rev. 2012. ; 16 ( 5 ): 455 – 462 . - PubMed
    1. Pang KP , Terris DJ . Screening for obstructive sleep apnea: an evidence-based analysis . Am J Otolaryngol. 2006. ; 27 ( 2 ): 112 – 118 . - PubMed
    1. Marshall NS , Wong KK , Cullen SR , Knuiman MW , Grunstein RR . Sleep apnea and 20-year follow-up for all-cause mortality, stroke, and cancer incidence and mortality in the Busselton Health Study cohort . J Clin Sleep Med. 2014. ; 10 ( 4 ): 355 – 362 . - PMC - PubMed
    1. Nieto FJ , Young TB , Lind BK , et al. . Association of sleep-disordered breathing, sleep apnea, and hypertension in a large community-based study. Sleep Heart Health Study . JAMA. 2000. ; 283 ( 14 ): 1829 – 1836 . - PubMed

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