Excessive Daytime Sleepiness is Associated with Increased Health Care Utilization Among Patients Referred for Assessment of OSA
Bibliographic record
Abstract
STUDY OBJECTIVES: Excessive daytime sleepiness is an important public health concern associated with increased morbidity and mortality. However, in the absence of sleep diagnostic testing, it is difficult to separate the independent effects of sleepiness from those of intrinsic sleep disorders such as obstructive sleep apnea (OSA). The objective of this study was to determine if excessive daytime sleepiness was independently associated with increased health care utilization among patients referred for assessment of OSA. DESIGN: Cross-sectional study. SETTING/PARTICIPANTS: 2149 adults referred for sleep diagnostic testing between July 2005 and August 2007. INTERVENTIONS: N/A. MEASUREMENTS: Subjective daytime sleepiness was defined as an Epworth Sleepiness Scale score ≥10. Health care use (outpatient physician visits, all-cause hospitalizations, and emergency department visits) was determined from Alberta Health and Wellness administrative databases for the 18-month period preceding their sleep study. Rates of health resource use were analyzed using negative binomial regression, with predictors of increased health care use determined using logistic regression. RESULTS: excessive daytime sleepiness was associated with an increased rate of outpatient physician visits after adjustment for demographic variables, sleep medication use, hypertension, diabetes, depression, and OSA severity (rate ratio [RR]: 1.09 (95% confidence interval [CI]: 1.01, 1.18, P = 0.02) compared to non-sleepy subjects. There was an interaction between severe OSA and sleepiness (RR: 1.22 [95% CI: 1.06, 1.41]), although OSA was not an independent predictor of health care use. Also, sleepy patients with treated depression had a lower likelihood of outpatient visits (RR: 0.95 [95% CI: 0.86, 1.05]). Finally, sleepiness was an independent predictor of increased health care use for outpatient physician visits (odds ratio [OR]: 1.25 [95% CI: 1.00, 1.57; P = 0.048]) and all-cause hospitalizations (OR: 3.94 [95% CI: 1.03, 15.04; P = 0.046]). CONCLUSIONS: Excessive daytime sleepiness is associated with increased health care utilization among patients referred for assessment of OSA. Further investigation is required to determine whether the findings are related to direct effects of sleepiness, or in part, to interactions with other comorbidity such as OSA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".