Chinh D Nguyen1,2, Jong Won Kim1, Ronald R Grunstein1,3, Cindy Thamrin1, David Wang1,3,4. 1. Woolcock Institute of Medical Research and Sydney Medical School, University of Sydney, Glebe, New South Wales, Australia. 2. Neuroscience Research Australia (NeuRA), Randwick, New South Wales, Australia. 3. Department of Respiratory and Sleep Medicine, Royal Prince Alfred Hospital, Sydney Local Health District, Central Clinical School, University of Sydney, Camperdown, New South Wales, Australia. 4. Department of Respiratory and Sleep Disorders Medicine, Western Hospital, University of Melbourne, Victoria, Australia.
Abstract
STUDY OBJECTIVES: Methadone maintenance treatment (MMT) patients have a high prevalence of central sleep apnea and ataxic breathing related to damage to central respiratory rhythm control. However, the quantification of sleep apnea indices requires laborious manual scoring, and ataxic breathing pattern is subjectively judged by visual pattern recognition. This study proposes a semi-automated technique to characterize respiratory variability in MMT patients. METHODS: Polysomnography, blood, and functional outcomes of sleep questionnaire (FOSQ) from 50 MMT patients and 20 healthy subjects with matched age, sex, and body mass index, were analyzed. Inter-breath intervals (IBI) were extracted from the nasal cannula pressure signal. Variability of IBI over 100 breaths was quantified by standard deviation (SD), coefficient of variation (CV), and scaling exponent (α) from detrended fluctuation analysis. The relationships between these variability measures and blood methadone concentration, central sleep apnea index (CAI), apnea-hypopnea index (AHI), and clinical outcome (FOSQ), were then examined. RESULTS: MMT patients had significantly higher SD and CV during all sleep stages. During NREM sleep, SD and CV were correlated with blood methadone concentration (Spearman R = 0.52 and 0.56, respectively; p < 0.01). SD and CV were also correlated with CAI (R = 0.63 and 0.71, p < 0.001, respectively), and AHI (R = 0.45 and 0.58, p < 0.01, respectively). Only α showed significant correlation with FOSQ (R = -0.33, p < 0.05). CONCLUSIONS: MMT patients have a higher respiratory variability during sleep than healthy controls. Semi-automated variability measures are related to apnea indices obtained by manual scoring and may provide a new approach to quantify opioid-related sleep-disordered breathing.
STUDY OBJECTIVES:Methadone maintenance treatment (MMT) patients have a high prevalence of central sleep apnea and ataxic breathing related to damage to central respiratory rhythm control. However, the quantification of sleep apnea indices requires laborious manual scoring, and ataxic breathing pattern is subjectively judged by visual pattern recognition. This study proposes a semi-automated technique to characterize respiratory variability in MMTpatients. METHODS: Polysomnography, blood, and functional outcomes of sleep questionnaire (FOSQ) from 50 MMTpatients and 20 healthy subjects with matched age, sex, and body mass index, were analyzed. Inter-breath intervals (IBI) were extracted from the nasal cannula pressure signal. Variability of IBI over 100 breaths was quantified by standard deviation (SD), coefficient of variation (CV), and scaling exponent (α) from detrended fluctuation analysis. The relationships between these variability measures and blood methadone concentration, central sleep apnea index (CAI), apnea-hypopnea index (AHI), and clinical outcome (FOSQ), were then examined. RESULTS:MMTpatients had significantly higher SD and CV during all sleep stages. During NREM sleep, SD and CV were correlated with blood methadone concentration (Spearman R = 0.52 and 0.56, respectively; p < 0.01). SD and CV were also correlated with CAI (R = 0.63 and 0.71, p < 0.001, respectively), and AHI (R = 0.45 and 0.58, p < 0.01, respectively). Only α showed significant correlation with FOSQ (R = -0.33, p < 0.05). CONCLUSIONS:MMTpatients have a higher respiratory variability during sleep than healthy controls. Semi-automated variability measures are related to apnea indices obtained by manual scoring and may provide a new approach to quantify opioid-related sleep-disordered breathing.
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