Nattapong Jaimchariyatam1, Carlos L Rodriguez, Kumar Budur. 1. Division of Pulmonary and Critical Care Medicine, Department of Medicine, Faculty of Medicine, Chulalongkorn University, 1873 Rama 4 Road, Pathumwan, Bangkok, 10330, Thailand, drboy48@yahoo.com.
Abstract
PURPOSE: The arousal index (AI) quantifies cortical arousal relative to total sleep time and is widely used to determine the severity of sleep fragmentation. It usually includes arousals secondary to respiratory events, limb movements, and spontaneous arousals. No systematic studies have been undertaken to determine AI cutoff in subjects with negative polysomnography. METHODS: Three hundred fifty polysomnograms of subjects ≥18 years of age with no sleep disorders (apnea-hypopnea index (AHI) <5, periodic limb movement index (PLMI) <10, no upper airway resistance syndrome) or minimum oxygen saturation > 90 % and no comorbid health problems were reviewed. RESULTS: Basic sleep architecture appears within normal range, except for increased stage N2 and decreased stage N3. AI significantly correlated with age (r = 0.7), sleep efficiency (r =-0.16), sleep latency (r = 0.14), rapid eye movement (REM) latency (r = 0.12), stage N1 (r = 0.15), stage N2 (r = 0.12), stage N3 (r = -0.27), AHI (r = 0.24), PLMI (r = 0.18), and nadir oxygen saturation (r = -0.17) [p < 0.05 for all]. A significant correlation was noted between age and sleep efficiency (r = -0.19), REM latency (r = 0.13), stage N1 (r = 0.16), stage N2 (r = 0.21), stage N3 (r = -0.39), and nadir oxygen saturation (r = -0.16) [p < 0.05 for all]. Multiple linear regression analysis showed that age is only the independent predictor of AI (R (2) = 0.70, p < 0.01). The prediction equation for the arousal index in subject with negative polysomnography is AI = 0.276 × age (year) + 8.018. CONCLUSIONS: Age is the most important independent factor in predicting increasing AI in subjects with negative polysomnography.
PURPOSE: The arousal index (AI) quantifies cortical arousal relative to total sleep time and is widely used to determine the severity of sleep fragmentation. It usually includes arousals secondary to respiratory events, limb movements, and spontaneous arousals. No systematic studies have been undertaken to determine AI cutoff in subjects with negative polysomnography. METHODS: Three hundred fifty polysomnograms of subjects ≥18 years of age with no sleep disorders (apnea-hypopnea index (AHI) <5, periodic limb movement index (PLMI) <10, no upper airway resistance syndrome) or minimum oxygen saturation > 90 % and no comorbid health problems were reviewed. RESULTS: Basic sleep architecture appears within normal range, except for increased stage N2 and decreased stage N3. AI significantly correlated with age (r = 0.7), sleep efficiency (r =-0.16), sleep latency (r = 0.14), rapid eye movement (REM) latency (r = 0.12), stage N1 (r = 0.15), stage N2 (r = 0.12), stage N3 (r = -0.27), AHI (r = 0.24), PLMI (r = 0.18), and nadir oxygen saturation (r = -0.17) [p < 0.05 for all]. A significant correlation was noted between age and sleep efficiency (r = -0.19), REM latency (r = 0.13), stage N1 (r = 0.16), stage N2 (r = 0.21), stage N3 (r = -0.39), and nadir oxygen saturation (r = -0.16) [p < 0.05 for all]. Multiple linear regression analysis showed that age is only the independent predictor of AI (R (2) = 0.70, p < 0.01). The prediction equation for the arousal index in subject with negative polysomnography is AI = 0.276 × age (year) + 8.018. CONCLUSIONS: Age is the most important independent factor in predicting increasing AI in subjects with negative polysomnography.
Authors: Mario Giovanni Terzano; Liborio Parrino; Arianna Smerieri; Ronald Chervin; Sudhansu Chokroverty; Christian Guilleminault; Max Hirshkowitz; Mark Mahowald; Harvey Moldofsky; Agostino Rosa; Robert Thomas; Arthur Walters Journal: Sleep Med Date: 2002-03 Impact factor: 3.492