Respiratory Variability during Sleep in Methadone Maintenance Treatment Patients

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.

Keywords: central apnea; detrended fluctuation analysis; inter-breath interval; methadone; narcotic drugs.

Figure 1. Extraction of inter-breath interval from flow signal.

The raw flow signal was filtered and breath detection algorithm was applied to extract the inter-breath interval (IBI) signal. Closed circles represent the starts of inspiration.

Figure 2. Mean and variability measures of inter-breath interval signals.

Mean and variability measures (standard deviation [SD], coefficient of variation [CV], and slope exponent [α]) of inter-breath interval (IBI) signals in stable methadone maintenance treatment (MMT) patients and healthy controls. Boxplots represent median and 5th–95th percentile. **p < 0.01, ***p < 0.001, ****p < 0.0001.

Figure 3. Relationship between blood methadone concentration, respiratory variability measures and apnea indices.

Relationship between blood methadone concentration and respiratory variability measures (standard deviation [SD], coefficient of variation [CV], and slope exponent [α]) calculated from inter-breath intervals (IBI) and apnea indices in methadone maintenance treatment (MMT) patients during NREM. Data of CAI, AHI, and blood methadone concentration were transformed using base-10 logarithm transformation.

Figure 4. Relationship between respiratory variability measures and apnea indices.

Relationship between respiratory variability measures (standard deviation [SD], coefficient of variation [CV], and slope exponent [α]) calculated from inter-breath intervals (IBI) and apnea indices in methadone maintenance treatment (MMT) patients during NREM. Data of CAI and AHI were transformed using base-10 logarithm transformation.

Figure 5. Relationship between the slope exponent α and FOSQ.

Relationship between the slope exponent α obtained by detrended fluctuation analysis and FOSQ index in healthy controls (closed circles) and methadone maintenance treatment (MMT) patients (open circles) during NREM.