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SLEEP MEDICINE | V. ISETTA ET AL.
Introduction Continuous positive airway pressure (CPAP) is the treatment of choice for patients with obstructive sleep apnoea (OSA), regardless of disease severity [1, 2]. CPAP has been shown to decrease elevated blood pressure, improve cardiovascular disease outcomes, and reduce the risk of fatal and nonfatal cardiovascular events [3 – 5]. It also improves excessive daytime sleepiness and restores impaired cognitive function [6]. Adherence to CPAP therapy is necessary for achieving satisfactory treatment outcomes [7]. However, CPAP compliance tends to be suboptimal, both in terms of treatment acceptance and hours of CPAP use per night, with compliance reported to be as low as 30 – 60% [8]. Although therapy acceptance depends on patient characteristics, equipment-related factors are crucial in determining CPAP adherence. Automatic CPAP (APAP) is an alternative treatment for CPAP-intolerant OSA patients [1] and was developed to improve compliance. APAP devices reduce mean nocturnal pressure by automatically adjusting the delivered pressure based on the changing requirements of the patient [9 – 14]. Accordingly, APAP therapy might be more suitable for patients who have a variety of pressure demands during sleep based on factors such as body posture, sleep stage or variability between nights [15 – 19]. However, data on the advantage of APAP over CPAP for the general population of OSA patients remain controversial [20, 21]. APAP does seem to be better suited for specific OSA phenotypes, although defining the optimal target population for this therapy requires further research. Given the technical complexity of APAP engineering, each manufacturer designs its own solution to detect disturbed breathing events and potential artefacts selectively, and to define a strategy for automatic adaptation of nasal pressure. Therefore, the use of different proprietary algorithms in APAP devices usually leads to distinct responses to the same sleep-related breathing conditions. As a result, devices from different manufacturers cannot be considered equal, particularly with respect to clinical performance. Despite existing studies comparing APAP technologies, the ongoing development of more technologically advanced devices and their availability on the market means that objective analyses are required to provide reliable data on which to base clinical decisions. Thus, the aim of this study was to compare the responses of several currently available APAP devices using a bench test simulation of OSA disturbed breathing patterns. Methods The bench test method used in this study was described in detail previously [22, 23]. Briefly, the bench test simulator comprised a flow generator controlled by a computer. The piston-based flow generator can reproduce mathematically designed breathing flows or replicate respiratory flows of patients recorded during polysomnography (PSG). A computer-controlled obstruction valve allows the simulation of central or obstructive events. The bench platform is equipped with two sensors (one for pressure and one for flow) and data are recorded on the computer for subsequent analysis. Conventional tubing connects the APAP device under test to the simulated OSA patient. The disturbed breathing events employed in this study were extracted from real PSG recordings of OSA patients as previously described [22]. The simulator was set to reproduce the breathing events depending on the positive airway pressure (PAP) applied (figure 1): 1) apnoeas with obstruction for PAP <5 cmH 2 O; 2) severe hypopnoeas for PAP between 5 and 7 cmH 2 O; 3) mild hypopneas for PAP between 7 and 10 cmH 2 O; 4) prolonged flow limitation for PAP between 10 and 12 cmH 2 O; and 5) normal breathing for PAP >12 cmH 2 O. Each test started with 15 min of normal breathing to simulate the time before sleep onset, followed by 2 h of simulated OSA. The study was performed on seven APAP devices currently in clinical use: AirSense 10 (A) manufactured by ResMed (San Diego, CA, USA), Dreamstar (B) by SEFAM (Villers-lès-Nancy, France), Icon (C) by Fisher & Paykel (Auckland, New Zealand), Resmart (D) by BMC (Beijing, China), Somnobalance (E) by Weinmann (Hamburg, Germany), System One (F) by Respironics (Murrysville, PA, USA) and XT-Auto (G) by Apex (New Taipei City, Taiwan). For the AirSense 10 device, two different inbuilt algorithms were tested, standard (A1) and response (A2) settings, with the Expiratory Pressure Relief setting off. Each device was equipped with its corresponding tubing. The minimum and maximum pressures were set at 4 and 20 cmH 2 O, respectively. Any other programmable settings were left at their default values. Ramp time was off for all devices and no humidification was used during any of the testing. Each test was repeated twice and the corresponding average result is reported. Results The responses of the assessed APAP devices are summarised in table 1. There was considerable variation among devices, particularly with respect to the mean and maximum nasal pressures applied, the time to reach maximum nasal pressure, and the residual apnoea – hypopnoea index (number of residual obstructive events per hour). More than five residual obstructive events per hour were observed with devices B, D, F
ERJ Open Res 2015; 1: 00031 ‐ 2015 | DOI: 10.1183/23120541.00031-2015
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