0845 INTERPRETING IN-HOME SLEEP BIOMARKERS BASED ON POLYSOMNOGRAPHY REFERENCE VALUES

Abstract

Introduction: Abnormal sleep patterns have been associated with increased risk for cardiovascular disease, diabetes, and neuro-degeneration. This study investigates whether references values obtained from polysomnography (PSG) can be usefully compared to sleep architecture and sleep continuity biomarkers obtained with limited- montage recordings. Methods: This retrospective analysis was conducted with frontopolar recordings made in-home with Sleep Profiler™ (Advanced Brain Monitoring). Studies were auto-staged, expert edited, and the twonights averaged. A normative cohort was selected from records of those who did not have insomnia, daytime sleepiness, depression or anxiety (ISI <55 years (nine males, five females, median=27 years) and Older>69 years (14 males, 15 females, median=75 years). Records from a cohort of insomnia patients were also stratified into Young (four males, 11 females, median=44 years) and Older (ten males, eight females, median=73 years) groups. Reference values were obtained from the Health Heart Study using the percentiles to estimate the age-stratified distributions. Systemic differences were recognized when the PSG distributions were significantly different from both the normative and insomnia cohorts using the Welch's t-test (p<0.05). Results: Frontopolar staging exhibited a systemic bias toward reduced stage N3 in the Older but not the Young group (Older: reference= 18.0%, normative=5.2%, insomnia=7.3%; Young: reference= 19.4%, normative=19.5%, insomnia=17.1%). A similar age-related pattern was observed in the percent time REM (Older: reference=20.7%, normative=16.9%, insomnia=17.1%). A systemic increase in stage N1 and awakenings was observed in both age groups (N1-Young: reference=4.1%, normative=6.5%, insomnia=8.3%; Older: reference=4.8%, normative=12.2%, insomnia=11.5%; Awakenings/h-Young: reference=3.3, normative=5.7, insomnia=6.5; Older: reference=3.4, normative=7.0, insomnia=7.5). The magnitude of the N1 discrepancies impacted the sleep efficiencies in the Older group (reference=81.8%, normative=74.7%, insomnia=76.2%). Conclusion: Discrepancies in the N3 sleep detected in the elderly may be explained by the application of different scoring rules (Rechtschaffen/Kales vs. AASM-emulated auto-scoring) applied to age and site-specific attenuation in slow wave sleep. Staging N1 and REM from frontopolar recordings likely contributed to age-specific systemic differences. All other sleep biomarker measures were equivalent.