749 Is daytime sleepiness a risk factor for depression and the prevalence of depression in patients with EDS referred for PSG
Abstract
Abstract Introduction Excessive daytime sleepiness affects 18.9–27% of the general population. [1] Patients with excessive daytime sleepiness have a higher prevalence of major depressive episodes. [2] Depression is also associated with poor medical compliance which may further affect management of excessive day time sleepiness. [3] We therefore are interested in the prevalence of depression in patients undergoing in-lab testing for excessive sleepiness and the association of positive depression screening. Methods Retrospective review of diagnostic polysomnograms (PSG) in a tertiary care facility was conducted on patients over 18 years of age. The elements collected include: gender, age, BMI, Depression scale defined by PHQ-9, EDS defined by Epworth’s Sleepiness scale, AHI = Apnea Hypopnea Index, PLMS = Periodic Limb Movements in Sleep. Results There were a total of 32 subjects. Median age was 49 with 43% male and average BMI of 35. The mean PHQ-9 score was 5.8. The mean Epworth score was 8.5. The correlation of the two values was r= 0.078. 12 patients are classified as having EDS. 7 patients (58.3%) in the EDS group were positive for depression screen. 18 patients (90%) without EDS were positive for depression screen. There is no statistical difference 2 groups (58.3% vs 90%, p=1). The mean PHQ-9 score in EDS group is 5.5. The mean PHQ-9 score in non EDS group is 5.95. There is no statistical difference between 2 groups PHQ-9(5.5 vs 5.95, p=0.779). Conclusion Our study…
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 703 participants or records.
Read the paper
Related studies
- The Matrisome: In Silico Definition and In Vivo Characterization by Proteomics of Normal and Tumor Extracellular Matrices
- The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities
- In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling
- In silico prediction of splice-altering single nucleotide variants in the human genome
- In silico clinical trials: how computer simulation will transform the biomedical industry
- In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.