Excessive daytime sleepiness is an increasingly frequent condition among older adults with comorbidities and living in nursing homes (NHs). This study investigated associations between participants’ characteristics and excessive daytime sleepiness (EDS); the ability of the Epworth Sleepiness Scale (ESS) scores, EDS, and EDS severity levels to predict mortality at 12 months of follow-up; and the optimal cut-off for ESS to predict mortality among NH residents.
Prospective and cross-sectional analysis in a prospective study.
Older adults permanently residing in 12 NHs from South Australia.
Baseline characteristics including the ESS were collected and mortality at 12 months was assessed. Logistic regression analyzed associations between participants’ characteristics and EDS (ESS >10). Kaplan-Meier cumulative survival estimates followed by log-rank and adjusted Cox proportional hazards models explored associations of ESS scores, EDS, and EDS severity levels with time-to-incident death. Receiver operator curve analysis assessed the best cut-off for ESS to predict mortality risk.
A total of 550 participants [mean (SD) age, 87.7 (7.2) years; 968 (50.9%) female]. Malnutrition [adjusted odds ratio (aOR) 2.02, 95% confidence interval (CI) 1.13‒3.61], myocardial infarction (aOR 1.91, 95% CI 1.20‒3.03), heart failure (aOR 2.85, 95% CI 1.68‒4.83), Parkinson’s disease (aOR 2.16, 95% CI 1.04‒4.47) and severe dementia (aOR 8.57, 95% CI 5.25‒14.0) were associated with EDS. Kaplan-Meier analyses showed reduced survival among participants with EDS (log-rank test: χ = 25.25, P 10) was the best cut point predicting mortality risk (area under the curve = 0.62).
EDS predicts mortality risk and is associated with age-related comorbidities in NH residents. Screening for EDS is a simple strategy to identify NH residents at higher risk of adverse outcomes, triggering an assessment for reversibility or conversations about end-of-life care.

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