Sleep fragmentation indices as predictors of daytime sleepiness and nCPAP response in obstructive sleep apnea.

Journal article

Sleep fragmentation and respiratory disturbance measures are used in the assessment of obstructive sleep apnea (OSA) but have proved to be disappointingly poor correlates of daytime sleepiness. This study investigates the ability of electroencephalograph (EEG) and non-EEG sleep fragmentation indices to predict both presenting sleepiness and the improvement in sleepiness with subsequent nasal continuous positive airway pressure (nCPAP) therapy (nCPAP responsive sleepiness). Forty-one patients (36 men, 5 women), ranging from nonsnorers to severe OSA (> 4% O2 dip rate, median 11.1, range 0.4 to 76.5), had polysomnography with microarousal scoring, computerized EEG analysis, autonomic arousal detection, and body movement analysis. All patients received a trial of nCPAP regardless of sleep study outcome. Spearman's correlation analysis showed significant and similar associations between all sleep fragmentation indices with both pretreatment and nCPAP responsive sleepiness. There was no deterioration in sleepiness on nCPAP in the nonsnorers. Using stepwise multiple regression analysis, the best predictor of nCPAP responsive subjective and objective sleepiness was body movement index, explaining 38% and 43% of the variance, respectively. Variability in EEG sleep depth, quantified from computerized EEG analysis, was the only other index to contribute to these models. Together these indices explained 44% and 51% of the subjective and objective response to nCPAP, respectively. These results suggest that sleep fragmentation indices are useful for identifying OSA patients with sleepiness likely to respond to nCPAP.

Authors: Bennett, L; Langford, B; Stradling, JR; Davies, R

• Publication status: Published
• Journal: American journal of respiratory and critical care medicine
• Volume: 158
• Issue: 3
• Pages: 778-786
• Publication date: 1998-09-01
• DOI: 10.1164/ajrccm.158.3.9711033
• EISSN: 1535-4970
• ISSN: 1073-449X
• Language: English
• Keywords: Movement; Snoring; Oxygen; Adult; Humans; Neural Networks (Computer); Electroencephalography; Positive-Pressure Respiration; Regression Analysis; Forecasting; Follow-Up Studies; Middle Aged; Male; Signal Processing, Computer-Assisted; Circadian Rhythm; Sleep Apnea Syndromes; Female; Heart Rate; Polysomnography; Sleep; Sleep Stages; Arousal