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Diagnosed sleep apnea and cardiovascular disease in atrial fibrillation patients

The role of measurement error from administrative data

Ogilvie, Rachel P.1,2; MacLehose, Richard F.2; Alonso, Alvaro3; Norby, Faye L.2; Lakshminarayan, Kamakshi2; Iber, Conrad4; Chen, Lin Y.5; Lutsey, Pamela L.2

doi: 10.1097/EDE.0000000000001049
Original Article: PDF Only

Background: Atrial fibrillation (AF) and obstructive sleep apnea (OSA) are common conditions, but little is known about OSA and cardiovascular risk among AF patients.

Methods: Using the Truven Health MarketScan databases we constructed a prospective cohort of AF patients from 2007-2014. AF, OSA, stroke, myocardial infarction (MI), and confounders were defined using International Classification of Disease (ICD)-9-CM codes. We matched individuals with an OSA diagnosis with up to five individuals without a diagnosis by age, sex, and enrollment date. Cox proportional hazards models adjusted for confounders and high-dimensional propensity scores. We included migraines as a control outcome. Bias analysis used published sensitivities and specificities to generate rate ratios adjusted for OSA misclassification.

Results: We matched 56,969 individuals with an OSA diagnosis to 323,246 without. During a mean follow-up of 16 months, 3,234 incident strokes and 4,639 incident MIs occurred. After adjustment, OSA diagnosis was strongly associated with reduced risk of incident stroke (hazard ratio =0.48, 95% confidence interval [0.43, 0.53]) and MI (0.40, [0.37, 0.44]) and a smaller reduced risk of migraines (0.82 [0.68, 0.99]). Bias analysis produced wide ranging or inestimable rate ratios adjusted for misclassification of OSA.

Conclusions: OSA diagnosis in AF patients was strongly associated with reduced risk of incident cardiovascular disease. We discuss misclassification, selection bias, and residual confounding as potential explanations.

1.Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA

2.Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN

3.Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA

4. epartment of Medicine, University of Minnesota Medical School, Minneapolis, MN

5.Cardiac Arrhythmia Center, Cardiovascular Division, Department of Medicine, University of Minnesota Medical School, Minneapolis, MN

Conflicts of Interest: None declared

Funding: Research reported in this publication was supported by the National Heart, Lung, And Blood Institute of the National Institutes of Health under Award Number R01HL122200 and the American Heart Association award 16EIA26410001 (Alonso). R.P. Ogilvie was also supported by provided by NIH grants T32HL007779 and T32HL082610.

Due to copyright, the data for this paper cannot be shared; however, it is commercially available.

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the American Heart Association.

SAS Macros for the high dimensional propensity scores are available at Bias analysis spreadsheets are available at

Corresponding author: Rachel P. Ogilvie, PhD, 3609 Forbes Avenue, Second Floor, Pittsburgh, PA 15213, Phone: 412-383-0629, Email:

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