Quantifying statistical uncertainty in metrics of sleep disordered breathing.

BACKGROUND: The apnea-hypopnea index (AHI) (or one of its derivatives) is the primary clinical metric for characterizing sleep disordered breathing-the value of which with respect to a threshold determines severity of diagnosis and eligibility for treatment reimbursement. The index value, however, is taken as a perfect point estimate, with no measure of statistical uncertainty. Thus, current practice does not robustly account for variability in diagnosis/eligibility due to chance. In this paper, we quantify the statistical uncertainty associated with respiratory event indices for sleep disordered breathing and the effect of uncertainty on treatment eligibility.
METHODS: We develop an empirical estimate of uncertainty using a non-parametric bootstrap on the interevent times, as well as a theoretical Poisson estimate reflecting the current formulation of the AHI. We then apply these methods to estimate AHI uncertainty for 2049 subjects (954/1095 M/F, age: mean 69 ± 9.1) from the Multi-Ethnic Study of Atherosclerosis (MESA). RESULTS AND
CONCLUSIONS: The mean 95% empirical confidence interval width was 11.500 ± 6.208 events per hour and the mean 95% theoretical Poisson confidence interval width was 5.998 ± 2.897 events per hour, suggesting that uncertainty is likely a major confounding factor within the current diagnostic framework. Of the 278 subjects in the symptomatic population (ESS>10), 27% (76/278) had uncertain diagnoses given the 95% empirical confidence interval. Of the 2049 subjects in the full population, 43% (880/2049) had uncertain diagnoses given the 95% empirical confidence interval. The inclusion of subjects with uncertain diagnoses increases the number of eligible patients by 21.3% for the symptomatic population and by 84.8% for the full population. The exclusion of subjects with uncertain diagnoses given the 95% empirical confidence interval decreases the number of eligible patients by 12.4% for the symptomatic population and by 34.8% for full population. Additional analyses suggest that it is practically infeasible to gain diagnostic statistical significance through additional testing for a broad range of borderline cases. Overall, these results suggest that AHI uncertainty is a vital additional piece of information that would greatly benefit clinical practice, and that the inclusion of uncertainty in epidemiological analysis might help improve the ability for researchers to robustly link AHI with co-morbidities and long-term outcomes.