Dataset Information

Quantifying statistical uncertainty in metrics of sleep disordered breathing.

ABSTRACT:

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.

SUBMITTER: Thomas RJ

PROVIDER: S-EPMC6938549 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Publications

Quantifying statistical uncertainty in metrics of sleep disordered breathing.

Thomas Robert J RJ Chen Shuqiang S Eden Uri T UT Prerau Michael J MJ

Sleep medicine 20190613

<h4>Background</h4>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 ...[more]

PMID: 31540785

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Project description:ObjectiveTo examine the association between sleep-disordered breathing and stroke outcomes, and determine the contribution of sleep-disordered breathing to outcome disparities in Mexican Americans.MethodsIschemic stroke patients (n = 995), identified from the population-based Brain Attack Surveillance in Corpus Christi Project (2010-2015), were offered participation in a sleep-disordered breathing study including a home sleep apnea test (ApneaLink Plus). Sleep-disordered breathing (respiratory event index ≥10) was determined soon after stroke. Neurologic, functional, cognitive, and quality of life outcomes were assessed at 90 days poststroke. Regression models were used to assess associations between sleep-disordered breathing and outcomes, adjusted for sociodemographics, prestroke function and cognition, health-risk behaviors, stroke severity, and vascular risk factors.ResultsMedian age was 67 years (interquartile range [IQR] = 59-78); 62.1% were Mexican American. Median respiratory event index was 14 (IQR = 6-25); 62.8% had sleep-disordered breathing. Sleep-disordered breathing was associated with worse functional outcome (mean difference in activities of daily living/instrumental activities of daily living score = 0.15, 95% confidence interval [CI] = 0.01-0.28) and cognitive outcome (mean difference in modified Mini-Mental State Examination = -2.66, 95% CI = -4.85 to -0.47) but not neurologic or quality of life outcomes. Sleep-disordered breathing accounted for 9 to 10% of ethnic differences in functional and cognitive outcome and was associated with cognitive outcome more strongly for Mexican Americans (β = -3.97, 95% CI = -6.63 to -1.31) than non-Hispanic whites (β = -0.40, 95% CI = -4.18 to 3.39, p-interaction = 0.15).InterpretationSleep-disordered breathing is associated with worse functional and cognitive function at 90 days poststroke. These outcomes are reasonable endpoints for future trials of sleep-disordered breathing treatment in stroke. If effective, sleep-disordered breathing treatment may somewhat lessen ethnic stroke outcome disparities. ANN NEUROL 2019;86:241-250.

Project description:BACKGROUND:Sleep-disordered breathing (SDB) is common in pregnancy, but there are limited data on predictors. OBJECTIVES:The objective of this study was to develop predictive models of sleep-disordered breathing during pregnancy. STUDY DESIGN:Nulliparous women completed validated questionnaires to assess for symptoms related to snoring, fatigue, excessive daytime sleepiness, insomnia, and restless leg syndrome. The questionnaires included questions regarding the timing of sleep and sleep duration, work schedules (eg, shift work, night work), sleep positions, and previously diagnosed sleep disorders. Frequent snoring was defined as self-reported snoring ≥3 days per week. Participants underwent in-home portable sleep studies for sleep-disordered breathing assessment in early (6-15 weeks gestation) and mid pregnancy (22-31 weeks gestation). Sleep-disordered breathing was characterized by an apnea hypopnea index that included all apneas, plus hypopneas with ≥3% oxygen desaturation. For primary analyses, an apnea hypopnea index ≥5 events per hour was used to define sleep-disordered breathing. Odds ratios and 95% confidence intervals were calculated for predictor variables. Predictive ability of the logistic models was estimated with area under the receiver-operating-characteristic curves, along with sensitivities, specificities, and positive and negative predictive values and likelihood ratios. RESULTS:Among 3705 women who were enrolled, data were available for 3264 and 2512 women in early and mid pregnancy, respectively. The corresponding prevalence of sleep-disordered breathing was 3.6% and 8.3%, respectively. At each time point in gestation, frequent snoring, chronic hypertension, greater maternal age, body mass index, neck circumference, and systolic blood pressure were associated most strongly with an increased risk of sleep-disordered breathing. Logistic regression models that included current age, body mass index, and frequent snoring predicted sleep-disordered breathing in early pregnancy, sleep-disordered breathing in mid pregnancy, and new onset sleep-disordered breathing in mid pregnancy with 10-fold cross-validated area under the receiver-operating-characteristic curves of 0.870, 0.838, and 0.809. We provide a supplement with expanded tables, integrated predictiveness, classification curves, and an predicted probability calculator. CONCLUSION:Among nulliparous pregnant women, logistic regression models with just 3 variables (ie, age, body mass index, and frequent snoring) achieved good prediction of prevalent and incident sleep-disordered breathing. These results can help with screening for sleep-disordered breathing in the clinical setting and for future clinical treatment trials.

Dataset Information

Quantifying statistical uncertainty in metrics of sleep disordered breathing.

Background

Methods

Results and conclusions

Publications

Quantifying statistical uncertainty in metrics of sleep disordered breathing.

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