Work of Breathing in Fixed and Pressure Relief Continuous Positive Airway Pressure (C-Flex™): A post hoc Analysis.

BACKGROUND: Expiratory pressure relief continuous positive airway pressure (pressure relief CPAP; C-Flex™) causes increases in inspiratory duty cycle and shortening of expiratory time. It has been suggested that these changes are caused by an increase in work of breathing. OBJECTIVES: We studied the effects of C-Flex on work of breathing and intrinsic positive end-expiratory pressure as compared to fixed CPAP. METHODS: Work of breathing was analyzed in 24 patients with obstructive sleep apnea during treatment with fixed CPAP and C-Flex with 3 different pressure relief settings in a randomized order during rapid-eye-movement (REM) and non-REM sleep. Work of breathing was assessed on a breath-by-breath basis using a piezoelectric esophageal pressure catheter and a pneumotachograph for measuring airflow. RESULTS: We found there was no increase in inspiratory work of breathing observed using C-Flex compared to fixed CPAP. Instead, we found a linear decrease in inspiratory work of breathing with increasing pressure relief, with a mean difference of 1.22 J/min between CPAP and maximum pressure release (C-Flex 3; 90% of the value with nasal CPAP); however, the decrease was not statistically significant. The decrease in inspiratory work of breathing associated with C-Flex has a significant inverse correlation with BMI. CONCLUSIONS: The C-Flex technology does not change work of breathing but shows a tendency towards a reduction of inspiratory work of breathing in patients with a lower BMI using higher C-Flex. The effect is probably caused by diminishing airway resistance generated by the positive end-expiratory pressure. Our findings may lead to additional fields of application of the C-Flex technology, such as chronic obstructive pulmonary disease or muscular dystrophy.

C-Flex technology: effects on breathing parameters and inspiratory flow limitation.

BACKGROUND: Expiratory pressure relief continuous positive airway pressure (pressure relief CPAP, C-Flex) is known to be as effective in the treatment of obstructive sleep apnea (OSA) as conventional CPAP while improving overall patients' adherence. However, the effects of C-Flex on ventilation during sleep have not been studied yet. OBJECTIVE: This study investigates the effects of pressure relief CPAP on respiratory parameters and possible inspiratory flow limitation with increased difference between inspiratory and expiratory pressure compared with conventional CPAP. METHODS: In total, 24 patients were investigated both during conventional CPAP and during three C-Flex pressure relief settings in randomized order during rapid-eye-movement (REM) and non-REM (NREM) sleep. Airflow was monitored with a pneumotachograph; inspiratory flow limitation was assessed by analyzing airflow and esophageal pressure swings. RESULTS: Using higher C-Flex gains, expiratory time decreased in favor of the inspiratory duty cycle while there was no significant change in tidal volume. Analysis of inspiratory flow limitation showed no significant difference between conventional CPAP and the C-Flex gains studied. CONCLUSIONS: The increase in the inspiratory duty cycle with C-Flex might either indicate an increase in the work of breathing or a decrease in the work of breathing due to a lower peak end-expiratory pressure and consecutive alleviation of passive expiration. Both treatments appeared equivalent regarding the occurrence of inspiratory flow limitation.

Quantification of tonic and phasic muscle activity in REM sleep behavior disorder.

REM sleep behavior disorder (RBD) is characterized by excessive tone of the chin muscle and limb movement during sleep. In the past, quantification of increased muscle tone in REM sleep has been performed visually, using no stringent criteria. The aim of this study was to develop an automatic analysis, allowing the quantification of muscle activity and its amplitude for all sleep stages, with a focus on REM sleep in patients with RBD. Forty-eight patients (27 male, 21 female) with RBD were included in the analysis. Twenty-one had idiopathic RBD; 28 had narcolepsy plus RBD. Twenty-five patients without confirmed sleep disorder served as control subjects. The amplitude of the EMG was generated from the difference of the upper and lower envelope of the mentalis muscle recordings. By smoothing the amplitude curve, a threshold curve was defined. Any muscle activity beyond the threshold curve was defined as motor activity. The means of the motor activity per second were summarized statistically and calculated for each sleep stage. Due to variable distribution of REM sleep, the latter was assigned to respective quartiles of the recorded night. Muscle activity was defined according to a histogram as short-lasting (<0.5 second) and long-lasting (>0.5 second) activity. No difference in the distribution of REM sleep/quartile and mean muscle tone throughout the sleep cycle could be found within the RBD groups and control subjects. Muscle activity was in the range of 200 ms. No clusters or regular distribution of muscle activity were found. Long muscle activity in the group with manifest clinical RBD was significantly higher than in control subjects, whereas it was nonsignificantly higher in subclinical RBD. The correlation between the frequency of long muscle activity in REM sleep and age was highly significant only for patients with idiopathic RBD. Automatic analysis of muscle activity in sleep is a reliable, easy method that may easily be used in the evaluation for REM sleep behavior disorder, creating indices of muscle activity similar to the indices for sleep apnea or PLMS. Together with the overt behavior, the analyses provides an important tool to get a deeper insight into the pathophysiology of RBD. Long movements appear to represent the motor disinhibition in REM sleep more distinct than short movements. The positive correlation of age and increased motor activity in REM sleep in idiopathic RBD highlights the idea of age dependant motor disinhibition as a continuum of a neurodegenerative disorder, which in narcolepsy patients with RBD only seems to happen as a single temporal event at onset of the disorder.

Sleep medicine as a scenario for medical grid application.

Sleep medicine is gaining more and more interest and importance both within medical research and clinical routine. The investigation of sleep and associated disorders requires the overnight acquisition of a huge amount of biosignal data derived from various sensors (polysomnographic recording) as well as consecutive time-consuming manual analysis (polysomnographic analysis). Therefore, the development of automatic analysis systems has become a major focus in sleep research in the recent years, resulting in the development of algorithms for the analysis of different biosignals (EEG, ECG, EMG, breathing signals). In this study, an open source algorithm published by Hamilton et al. was used for ECG analysis, whereas the analysis of breathing signals was done using an algorithm published by Clark et al. using also variations of the intra-thoracic pressure for the detection of breathing disorders. The electromyogram (EMG) analysis was done with a self-made algorithm, whereas EEG analyses are currently under development, using both frequency analysis modules and pattern recognition procedures. Although all these algorithms have proved to be quite useful, their validity and reliability still needs to be verified in future studies. Taking into account that during a standard polysomnographic recording data from approximately 8 hours of sleep are collected, it is imaginable that processing this amount of data by the described algorithms very often exceeds the calculating capacity of current standard computers. Using Grid technology, this limitation can be transcended by splitting biosignal data and distributing it to several analysis computers. Therefore, Grid based automatic analysis systems may improve the effectiveness of polysomnographic investigations and thereby diminish the costs for health care providers.

Effect of nasal continuous positive airway pressure treatment on blood pressure in patients with obstructive sleep apnea.

BACKGROUND: There is increasing evidence that obstructive sleep apnea (OSA) is an independent risk factor for arterial hypertension. Because there are no controlled studies showing a substantial effect of nasal continuous positive airway pressure (nCPAP) therapy on hypertension in OSA, the impact of treatment on cardiovascular sequelae has been questioned altogether. Therefore, we studied the effect of nCPAP on arterial hypertension in patients with OSA. METHODS AND RESULTS: Sixty consecutive patients with moderate to severe OSA were randomly assigned to either effective or subtherapeutic nCPAP for 9 weeks on average. Nocturnal polysomnography and continuous noninvasive blood pressure recording for 19 hours was performed before and with treatment. Thirty two patients, 16 in each group, completed the study. Apneas and hypopneas were reduced by approximately 95% and 50% in the therapeutic and subtherapeutic groups, respectively. Mean arterial blood pressure decreased by 9.9+/-11.4 mm Hg with effective nCPAP treatment, whereas no relevant change occurred with subtherapeutic nCPAP (P=0.01). Mean, diastolic, and systolic blood pressures all decreased significantly by approximately 10 mm Hg, both at night and during the day. CONCLUSIONS: Effective nCPAP treatment in patients with moderate to severe OSA leads to a substantial reduction in both day and night arterial blood pressure. The fact that a 50% reduction in the apnea-hypopnea index did not result in a decrease in blood pressure emphasizes the importance of highly effective treatment. The drop in mean blood pressure by 10 mm Hg would be predicted to reduce coronary heart disease event risk by 37% and stroke risk by 56%.

Detection of sleep disordered breathing by automated ECG analysis.

Sleep related breathing disorders are a highly prevalent disease associated with increased risk of cardiovascular complications like chronic arterial hypertension, myocardial infarction or stroke. Gold standard diagnostics (polysomnography) are complex and expensive; the need for simplified diagnostics is therefore obvious. As the ECG can be easily conducted during the night, the detection of sleep related breathing disorders by ECG analysis provides an easy and cheap approach. Using a combination of well known biosignals processing algorithms, we trained the algorithm on 35 pre-scored overnight recordings. We then applied the algorithm on 35 control recordings, achieving a diagnostic accuracy of 77%. We believe that with further improvements in ECG analysis this algorithm can be used for screening diagnostics of obstructive sleep apnea.