Age modulates the predictive value of self-reported sleepiness for all-cause mortality risk: insights from a comprehensive national database of veterans.

Study Objectives: Excessive daytime sleepiness is prevalent and overwhelmingly stems from disturbed sleep. We hypothesized that age modulates the association between excessive daytime sleepiness and increased all-cause mortality.

Methods: We utilized the Veterans' Health Administration data from 1999-2022. We enrolled participants with sleep related 9/10 codes or sleep services. A natural language processing pipeline was developed and validated to extract the Epworth Sleepiness Scale (ESS) as a self-reported tool to measure excessive daytime sleepiness from physician progress notes. The natural language processing's accuracy was assessed through manual annotation of 470 notes. Participants were categorized into normal-ESS (ESS 0-10) and high-ESS (ESS 11-24). We created 3 age groups: < 50 years, 50 to < 65 years, and ≥ 65 years. The adjusted odds ratio of mortality was calculated for age, body mass index, sex, race, ethnicity, and the Charlson Comorbidity Index, using normal-ESS as the reference. Subsequently, we conducted age stratified analysis.

Results: The first ESS records were extracted from 423,087 veterans with a mean age of 54.8 (± 14.6), mean body mass index of 32.6 (± 6.2), and 90.5% male. The adjusted odds ratio across all ages was 17% higher (1.15, 1.19) in the high-ESS category. The adjusted odds ratio s only became statistically significant for individuals aged ≥ 50 years in the high-ESS compared to the normal-ESS category (< 50 years: 1.02 [0.96, 1.08], 50 to < 65 years 1.13[1.10, 1.16]; ≥ 65 years: 1.25 [1.21, 1.28]).

Conclusions: High-ESS predicted increased mortality only in participants aged 50 and older. Further research is required to identify this differential behavior in relation to age.

Citation: Maghsoudi A, Azarian M, Sharafkhaneh A, et al. Age modulates the predictive value of self-reported sleepiness for all-cause mortality risk: insights from a comprehensive national database of veterans. . 2024;20(11):1785-1792.