Abstract
Objective
To characterize age- and sex-stratified prevalence patterns of obstructive sleep apnea and co-morbid conditions corresponding to
CHA
2
DS
2
−
VASc
components using a large, population-based electronic health record dataset.
Study Design
The study cohort included 556,474 unique de-identified OSA patients (ICD-9 code 327.23), mined from HIPAA-compliant Cerner
HealthFacts
®
database (1999–2016). The patient records were mined using Structured Query Language (SQL), stratified by age, gender, and stroke risk comorbidities per
CHA
2
DS
2
−
VASc
, to conduct a population-based cross-sectional epidemiological study.
Methods
Clinical and epidemiological research informatics methodologies are applied to characterize OSA prevalence patterns across age (5-year intervals), gender, and stroke risk comorbidities per
CHA
2
DS
2
−
VASc
criteria. Body-level organ system involvement was examined according to the Major Diagnostic Category (MDC) classification.
Results
Age-stratified cross-sectional analyses showed that prevalence rates for OSA (m: 10.43%, f: 9.28%), hypertension (m: 7.37%, f: 7.11%), along with clinical encounters, peaked at age 55 for both genders, whereas the highest prevalence of cardiovascular comorbidities was observed in older age groups (ages 65–70). Hypertension was most prevalent (57.83%), followed by diabetes (34.7%), congestive heart failure (17.7%), atrial fibrillation (13.31%), vascular disease (12.3%), and prior stroke/TIA (4.78%).
MDC analysis revealed that prevalence is pronounced in ENT, cardiovascular, and musculoskeletal body systems, consistent with the pathophysiology of chronic intermittent hypoxia in OSA patients. These patterns reflect population-level age-specific prevalence differences rather than longitudinal disease progression.
Conclusion
This large cross-sectional study (556,474 patients) provides detailed age- and sex-specific prevalence estimates of OSA-associated stroke-risk comorbidities. While causal or temporal inferences cannot be drawn, the findings highlight population-level prevalence patterns that may inform hypothesis-driven longitudinal studies and future evaluation of stroke-risk models.