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Identifying clusters of patient comorbidities associated with obstructive sleep apnea using electronic health records
by
Te, Tue T.
, Veatch, Olivia J.
, Pack, Allan I.
, Keenan, Brendan T.
, Boland, Mary Regina
, Hubbard, Rebecca A.
in
Body mass index
/ Comorbidity
/ Electronic Health Records
/ Female
/ Humans
/ Obesity - complications
/ Patients
/ Scientific Investigations
/ Sleep apnea
/ Sleep Apnea, Obstructive - diagnosis
2024
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Identifying clusters of patient comorbidities associated with obstructive sleep apnea using electronic health records
by
Te, Tue T.
, Veatch, Olivia J.
, Pack, Allan I.
, Keenan, Brendan T.
, Boland, Mary Regina
, Hubbard, Rebecca A.
in
Body mass index
/ Comorbidity
/ Electronic Health Records
/ Female
/ Humans
/ Obesity - complications
/ Patients
/ Scientific Investigations
/ Sleep apnea
/ Sleep Apnea, Obstructive - diagnosis
2024
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Do you wish to request the book?
Identifying clusters of patient comorbidities associated with obstructive sleep apnea using electronic health records
by
Te, Tue T.
, Veatch, Olivia J.
, Pack, Allan I.
, Keenan, Brendan T.
, Boland, Mary Regina
, Hubbard, Rebecca A.
in
Body mass index
/ Comorbidity
/ Electronic Health Records
/ Female
/ Humans
/ Obesity - complications
/ Patients
/ Scientific Investigations
/ Sleep apnea
/ Sleep Apnea, Obstructive - diagnosis
2024
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Identifying clusters of patient comorbidities associated with obstructive sleep apnea using electronic health records
Journal Article
Identifying clusters of patient comorbidities associated with obstructive sleep apnea using electronic health records
2024
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Overview
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.
Publisher
Springer International Publishing,Springer Nature B.V,American Academy of Sleep Medicine
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