Obstructive sleep apnea and risk of cardiovascular events and all-cause mortality: a decade-long historical cohort study

Abstract

Background: Obstructive sleep apnea (OSA) has been reported to be a risk factor for cardiovascular (CV) disease. Although the apnea-hypopnea index (AHI) is the most commonly used measure of OSA, other less well studied OSA-related variables may be more pathophysiologically relevant and offer better prediction. The objective of this study was to evaluate the relationship between OSA-related variables and risk of CV events.

Methods and findings: A historical cohort study was conducted using clinical database and health administrative data. Adults referred for suspected OSA who underwent diagnostic polysomnography at the sleep laboratory at St Michael's Hospital (Toronto, Canada) between 1994 and 2010 were followed through provincial health administrative data (Ontario, Canada) until May 2011 to examine the occurrence of a composite outcome (myocardial infarction, stroke, congestive heart failure, revascularization procedures, or death from any cause). Cox regression models were used to investigate the association between baseline OSA-related variables and composite outcome controlling for traditional risk factors. The results were expressed as hazard ratios (HRs) and 95% CIs; for continuous variables, HRs compare the 75th and 25th percentiles. Over a median follow-up of 68 months, 1,172 (11.5%) of 10,149 participants experienced our composite outcome. In a fully adjusted model, other than AHI OSA-related variables were significant independent predictors: time spent with oxygen saturation <90% (9 minutes versus 0; HR = 1.50, 95% CI 1.25-1.79), sleep time (4.9 versus 6.4 hours; HR = 1.20, 95% CI 1.12-1.27), awakenings (35 versus 18; HR = 1.06, 95% CI 1.02-1.10), periodic leg movements (13 versus 0/hour; HR = 1.05, 95% CI 1.03-1.07), heart rate (70 versus 56 beats per minute [bpm]; HR = 1.28, 95% CI 1.19-1.37), and daytime sleepiness (HR = 1.13, 95% CI 1.01-1.28).The main study limitation was lack of information about continuous positive airway pressure (CPAP) adherence.

Conclusion: OSA-related factors other than AHI were shown as important predictors of composite CV outcome and should be considered in future studies and clinical practice.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Figure 1. Flow diagram of the final cohort.

*Split night, diagnostic study night when treatment was initiated due to severe OSA.

Figure 2. Unadjusted Kaplan-Meier survival curves by obstructive sleep apnea severity as expressed by the apnea-hypopnea index.

The numbers at risk are presented above the x-axis: from the top to the bottom, AHI30.

Figure 3. Predicted survival by OSA severity, adjusted for traditional CV risk factors

(BMI = 29, age = 50, sex = men, never smoked, without prior hypertension, diabetes, MI, stroke, or heart failure).

Figure 4. Results from multivariable Cox regression model presented as hazard ratios with shading representing confidence levels (99%, 95%, 90%, 80%, and 70%).

AWK, number of awakenings in TST; TST90SaO2, sleep time spent with SaO2 less than 90%; PLMI, periodic leg movement index; HR, mean heart rate during sleep; day sleep, DS, identified by a positive answer to the question “During the day, do you ever fall asleep unintentionally?”.

Figure 5. Predicted survival curves to show the effect of oxygen saturation

(comparing 75th percentile [9 min] to 25th percentile [0 min]) controlling for potential confounders (BMI = 29, age = 50, sex = men, never smoked, without prior hypertension, diabetes, MI, stroke or heart failure, TST = 5.8, AWK = 25, PLMI, 1.2, mean heart rate, 63, without reporting excessive DS).

Figure 6. Clinical nomogram for obstructive sleep apnea patients.

To obtain the nomogram predicted probability of three- and five-year event-free survival and to estimate median event-free survival, locate patient values at each axis, then draw a vertical line to the “Point” scale (axis) to determine how many points are attributed for each predictor. Sum the points for all predictors. Locate the sum on the “Total Points” scale. Draw a vertical line towards the “3-year Survival,” “5-year Survival,” and “Median Survival Time” axes to determine the three-year composite CV outcome-free survival, the five-year event-free survival, and to estimate median survival respectively. PLMI, periodic limb movement index; TST90SaO2, sleep time spent with SaO2 less than 90%.