© 2002 American Thoracic Society
Response of Automatic Continuous Positive Airway Pressure Devices to Different Sleep Breathing PatternsA Bench StudyUnitat de Biofísica i Bioenginyeria, Facultat de Medicina, Universitat de Barcelona; Servei de Pneumologia i Al lèrgia Respiratòria, Hospital Clinic Provincial; and Institut d'Investigacions Biomèdiques August Pi Sunyer, Barcelona, Spain Correspondence and requests for reprints should be addressed to Dr. Ramon Farré, Unitat de Biofisica i Bioenginyeria, Facultat de Medicina Casanova, 143 E-08036, Barcelona, Spain. E-mail: farre{at}medicina.ub.es
Evaluating the usefulness of automatic continuous positive airway pressure (CPAP) in treating the sleep apnea–hypopnea syndrome (SAHS) is not easy because the algorithms for automatic CPAP implemented in the devices available are not well known and are probably dependent on the device. In addition, at present it is not possible to test the behavior of automatic CPAP devices in response to well-defined breathing patterns. Our aim was to implement a bench test to characterize the responses of automatic CPAP devices by subjecting them to breathing patterns of patients with SAHS. To this end, a variety of typical breathing patterns (normal, apneas, hypopneas, flow limitation, snoring) previously recorded in patients with SAHS during sleep were reproduced by a breathing waveform generator. Five commercially available automatic CPAP devices were tested. The responses of the devices to apneas, hypopneas, flow limitation, and snoring were considerably different. In some devices, the response was modified by air leaks similar to the ones found in patients. Consequently, the effectiveness of automatic CPAP assessed in clinical tests performed by using particular devices has no general validity. Testing automatic CPAP devices in a bench study is a useful first step in evaluating the performance of this new type of device in adjusting nasal pressure for each patient.
Key Words: automatic CPAP devices • sleep apnea • hypopnea • flow limitation • self-regulation
Continuous positive airway pressure (CPAP) is the reference treatment for normalizing sleep in patients with the sleep apnea–hypopnea syndrome (SAHS) (1). A new generation of CPAP devices aimed at automatically adjusting the applied CPAP for each patient has been proposed in recent years. The usefulness of automatic CPAP, both for long-term treatment and for pressure titration, has been evaluated by employing currently available automatic CPAP devices in patient studies (2–10). Although the results published to date are not sufficiently conclusive to recommend the general application of this technology for treating SAHS (11, 12), some recent data suggest that certain subpopulations of patients could benefit from automatic CPAP (13). Moreover, other studies suggest that automatic CPAP devices may be useful for unattended, and hence, simplified, CPAP titration (9). Accordingly, further research is required to demonstrate the potential usefulness of this new generation of CPAP devices. Although the clinical outcome of a given automatic CPAP device can be assessed in a well-defined patient study, evaluating the usefulness of the concept of automatic CPAP with precision is not easy at present. Indeed, the conclusions derived from clinical tests performed by using particular devices have no general validity given that the algorithms for automatic CPAP implemented in the devices available are not well known to the physician and probably depend on the device. In addition, at present it is not possible to comparatively test the behavior of automatic CPAP devices in response to well-defined breathing patterns under laboratory conditions. To facilitate the analysis of the performance of automatic CPAP devices, our aim was to define and implement a bench testing protocol to characterize the response of commercial CPAP devices. To this end, the CPAP devices were connected to a breathing waveform generator reproducing realistic well-defined breathing patterns representative of patients with SAHS. The testing protocol implemented may be a first step in evaluating automatic CPAP devices. Indeed, the bench study allows us to test whether a given device behaves according to the defined strategy for adjusting CPAP to the breathing pattern. Moreover, the bench study allows the comparison of the responses of different devices when they are subjected to exactly the same patterns of disturbed breathing, which is not possible in patients, given the variability in their disturbed breathing patterns.
Automatic CPAP Devices The study was performed on five automatic CPAP devices: DeVilbis AutoAdjust LT (Sunrise Medical, Somerset, PA), Autoset Portable II Plus (Resmed, North Ryde, Australia), Autoset-T (Resmed, North Ryde, Australia), Virtuoso LX (Respironics, Murrysville, PA), and Goodknight 418P (Mallinckrodt, Villers-les-Nancy, France). These devices were identified as D1, D2, D3, D4, and D5, respectively. Each device was equipped with its corresponding exhalation valve and tubing. The minimum and maximum CPAP were set at 4 and 16 cm H2O, respectively. The initial waiting time was reduced to the minimum possible, and any other programmable settings available were kept at their default values.
Breathing Waveform Generator
Basic Breathing Events The patterns reproduced by the breathing waveform generator were defined from four types of flow events: apneas, hypopneas, flow limitation, and snoring. Representative samples of these events had been recorded in earlier sleep studies in patients with SAHS. Figure 2 shows examples of the events used to generate the breathing patterns of disturbed flow. The flow patterns in this figure are those of flows actually generated by the breathing flow generator and measured by the pneumotachograph placed at the entrance of the automatic CPAP device under test (Figure 1). Apnea-A and hypopneas Hypo-A, -B, and -C consisted of three to four identical normal cycles followed by five repeated abnormal cycles and ended with three to four cycles with increased tidal volume corresponding to arousal. Hypo-A showed a characteristic U-shaped inspiratory flow contour and a tidal volume equal to 70% of normal cycle. Hypo-B had the same flow contour as Hypo-A but with reduced amplitude (tidal volume = 35% of normal). Hypo-C was a hypopnea with a square-like inspiratory contour and a tidal volume value 70% of normal. The flow limitation index corresponding to Hypo-A, -B, and -C, computed as described by Teschler and coworkers (2), was 0.12, 0.12, and 0.01, respectively. Figure 2 shows another event (Hypo-A + Snoring) consisting of snoring oscillation superimposed on Hypo-A. This figure also shows three more events: Apnea-B and hypopneas Hypo-D and -E, and a section of persistent pattern of flow limitation (PFL). These last four waveforms, in contrast to the previous ones in the figure (which were made by repeating characteristic respiratory cycles), are actual full events recorded in patients. Accordingly, they reflect the variation within cycles found in SAHS: the flow amplitude and shape of the different cycles progressively changed from the beginning to the end of the event.
Patterns of Disturbed Breathing The basic events shown in Figure 2 were used to build a series of waveforms consisting of the successive repetition of apneic or hypopneic events (Apnea-A and hypopneas A to D) or consisting of persistent flow limitation (Hypo-A and Hypo-C, with and without snoring). The flow (V·) tracings on the top of Figures 3 and 4 illustrate two examples of the patterns of disturbed breathing built from the basic events in Figure 2. These patterns of disturbed breathing were used as the first step in analyzing how the automatic CPAP devices modify the applied pressure when they are subjected to a breathing pattern, regardless of the CPAP applied. Furthermore, the breathing waveform generator was also programmed to test the automatic CPAP devices in a more realistic way. To simulate a breathing condition representative of a patient with SAHS, the breathing pattern reproduced by the waveform generator depended on the CPAP applied. Specifically, the generator reproduced apneas (Apnea-B) if CPAP was less than 5 cm H2O, severe hypopneas (Hypo-D) when applied CPAP was between 5 and 7 cm H2O, moderate hypopneas (Hypo-E) if CPAP was between 7 and 10 cm H2O, PFL when CPAP was between 10 and 12 cm H2O, and normal breathing for CPAP greater than 12 cm H2O.
Protocol Each test run was initiated by connecting the CPAP device at minimum CPAP (4 cm H2O) to the breathing waveform generator (Figure 1). After reproducing prolonged normal breathing for a period exceeding the CPAP device waiting time, the breathing waveform generator reproduced each of the predefined disturbed breathing patterns. In addition to investigating the response of the automatic CPAP devices when subjected to disturbed breathing, we also analyzed the time-course of the pressure applied by the CPAP device after normal breathing was achieved. To this end, each CPAP device was subjected to events that induced an automatic increase in pressure up to the maximum value (16 cm H2O), and subsequently, the CPAP device was subjected to a normal flow pattern. Finally, the effects of a realistic air leak in the responses of the automatic CPAP devices were assessed. This was done by comparing their responses before and after opening the leak orifice in the breathing waveform generator (Figure 1). To assess the repeatability of the responses of the automatic CPAP devices, each test was repeated three times after complete resetting (power off/on).
The responses of the investigated automatic CPAP devices when subjected to well-defined testing breathing patterns were considerably different (Table 1). Figure 3 shows the initial evolution of the CPAP automatically applied by the devices after being subjected to a pattern of repetitive apneas. Two devices (D1 and D4) did not modify CPAP. The three responding devices progressively increased CPAP at different rates: D2 increased CPAP up to 16 cm H2O in 8 minutes and then maintained this pressure; D3 increased pressure up to a final CPAP value of 10 cm H2O in 6 minutes; and D5 achieved a final CPAP value of 10 cm H2O in 3 minutes. None of the devices increased pressure when subjected to events based on Hypo-A, which had an inspiratory contour that was clearly abnormal but a relatively high tidal volume. When snoring was superimposed on this pattern, all the devices except D5 increased CPAP, though at a considerably different rate. Only one device (D5) responded when subjected to the events based on Hypo-B, which were characterized by considerably reduced tidal volumes (35% of normal). As shown in Figure 4, only two devices (D2 and D3) increased CPAP when subjected to the events based on Hypo-C. Three devices (D1–D3) responded when subjected to the repetitive pattern based on Hypo-D. The response to the inspiratory pattern of Hypo-A and Hypo-C was the same regardless of inclusion in repetitive events (normal, hypopneas, and arousal breathing cycles) or in prolonged flow limitation. However, all devices increased CPAP when snoring was included in the PFL patterns.
When the automatic CPAP devices were subjected to a breathing pattern that, as in the case of patients, depended on the CPAP applied, we also observed marked differences among the tested devices (Table 1). Three devices (D1, D4, and D5) did not respond to the initial apneas (Apnea-B) and, therefore, remained at the initial constant level of CPAP of 4 cm H2O. By contrast, devices D2 and D3 responded by increasing pressure in a similar way. Figure 5 shows that after two initial apneic events CPAP automatically reached the threshold of 5 cm H2O. Then, the generator reproduced severe hypopneas (two events) until the applied CPAP reached 7 cm H2O, and then the breathing pattern of the generator changed to moderate hypopneas. During this period, the pressure applied by the automatic CPAP device was still progressively increasing until CPAP was greater than 10 cm H2O. Then, the breathing waveform generator exhibited a pattern of continuous flow limitation. At this point, the pressure applied by the automatic CPAP device did not increase because it did not respond to the pattern of persistent flow limitation applied (PFL, Figure 2). As the CPAP device interpreted the breathing pattern not to be abnormal, the pressure applied started a slow decrease (Figure 5). Consequently, devices D2 and D3 automatically increased pressure to avoid apneas and hypopneas, but they were unable to induce normal breathing (CPAP > 12 cm H2O according to the defined breathing behavior).
The rates of automatic CPAP reduction after normalizing the breathing pattern at maximum pressure were also different in the investigated devices. D1 and D2 did not modify CPAP during the first 10 minutes after normalizing the breathing cycle. D3 started a linear CPAP reduction immediately after normalizing breathing (from 16 to 11.5 cm H2O in 10 minutes). By contrast, D4 and D5 maintained CPAP for 2 and 4 minutes, respectively, and then CPAP decreased in steps: 1 cm H2O every 4 minutes in D4 and 0.5 cm H2O every minute in D5. The investigated devices exhibited different behavior when an air leak with a clinically reasonable magnitude was mimicked in the patient simulator. Depending on the device and breathing pattern, the response of the automatic CPAP device was either affected or not by the presence of the leak. Figure 6 shows two examples where the behavior of the CPAP device was modified by the air leak. One of the devices (D3), which in the absence of leak increased CPAP when subjected to apneas and hypopneas (left in Figure 6), did not respond to the apneas but responded to the hypopneas when the air leak was present (right in Figure 6). In contrast, the positive response of another device (D2) to the apneas was not affected by the air leak, but the rate of CPAP increase in response to the hypopneas was considerably decreased.
Besides the specific response to the defined breathing patterns, we found that the ability of the investigated devices to keep a constant pressure regardless of the breathing flow was clearly different, as illustrated by Figures 3–6. For instance, the peak-to-peak amplitude of the pressure oscillation during normal breathing at minimum CPAP ranged from 3.3 cm H2O in D2 to 6.4 cm H2O in D1. Each CPAP device investigated exhibited exactly the same response when it was repeatedly subjected to the same flow pattern.
In this study, we have defined a bench approach to test automatic CPAP devices in response to patterns of disturbed breathing, and we have applied this procedure to analyze the behavior of commercially available automatic CPAP devices. Although some variability between the different devices was expected, the results obtained under controlled laboratory conditions indicated considerable differences in the response of the investigated automatic CPAP systems when subjected to the same breathing patterns consisting of combinations of normal breathing, apneas, hypopneas, and flow limitation, with or without snoring. We also found that the response of automatic CPAP devices may be affected by air leaks similar to those observed in clinical practice. To test the automatic CPAP devices, we connected them to a breathing waveform generator that was able to reproduce characteristic breathing patterns observed in patients with SAHS. To better mimic the actual conditions found in practice, some of the defined breathing patterns included the variability actually observed within the different cycles in a given event (Apnea-B, Hypo-D and Hypo-E, and PFL). The defined patterns of abnormal breathing were reproduced by a system based on a flow generator and a loudspeaker (Figure 1) that allowed an exact reproduction of the waveforms recorded in patients. A possible alternative to our experimental setting could be to simulate the patient by means of a collapsible tube (Starling resistor) and a pump (16). Nevertheless, we discarded this approach because it did not allow the exact reproduction of the breathing waveforms recorded in patients. The only signals that conventional automatic CPAP devices sense from a patient are pressure and flow. Accordingly, when connected to the experimental setup employed in this study, the automatic CPAP devices were subjected to the same conditions as in a patient application. However, the bench system can be adjusted to test automatic CPAP devices designed to respond to other signals (e.g., forced oscillation [17, 18]). We defined a variety of reasonably representative breathing waveforms and patterns. It can be argued, however, that we did not cover the full spectrum of all the possible variants. We limited our analysis to a restricted number of breathing patterns because we did not try to extensively study each of the devices. Instead, our aim was to propose a procedure for systematically testing automatic CPAP devices under controlled conditions and to illustrate the considerable differences found in the responses of some currently available devices. However, any particular breathing pattern (artificially synthesized in the computer or actually recorded in patients) can be accurately reproduced by the breathing waveform generator. This approach of testing medical instrumentation by using well-defined reference signals characterizing the target pathology is similar to the one employed in the field of electrocardiography (19) or forced spirometry (20). The investigated devices showed marked differences in their responses when subjected to simple but representative breathing patterns. It was particularly remarkable that two devices did not react when subjected to the pattern of repetitive apneas and that not all the devices increased CPAP when subjected to the different patterns, including hypopneic and flow limitation events (Figures 3–5). Although Hypo-A exhibits a contour that is clearly abnormal, which is commonly observed in patients with SAHS, its corresponding flow limitation index is perhaps not low enough to classify this event as severe flow limitation, which may explain the absence of response in the devices in contrast to the response to Hypo-C and Hypo-D. Probably, more precise indices to quantify the abnormality of inspiratory pattern could be useful (21). It was remarkable that only one device responded to Hypo-B, which is characterized by a considerably low tidal volume (35% of normal breathing). Interestingly, all the devices rapidly responded in the presence of the high-frequency oscillation characterizing snoring (22). The fact that the automatic CPAP devices investigated did not react to some types of flow limitation (Table 1) might not constitute a major problem in clinical applications, given that in patients there is usually coexistence of flow limitation and snoring in the same event. The procedure for automatically reducing CPAP after normalizing the breathing pattern, which is probably an important issue in the clinical application, was clearly different among the devices: two of them did not reduce pressure during the first minutes after normalization, whereas one of them immediately started to decrease CPAP. The fact that the response of current devices may be affected by the presence of air leaks of magnitude similar to the ones found in patients may also have clinical implications, particularly under unattended CPAP treatment at home. Regarding the results reported in this work, it should be mentioned that each device was only tested at its default settings. Consequently, it could be expected that the results in a given device would be different when testing it after modifying any selectable response threshold to central/obstructive apneas, hypopneas, flow limitation, and snoring. As demonstrated by the results obtained in this study, the algorithms to modify pressure implemented in each commercial device are different. Some of these differences could be irrelevant, but others may have an impact on the clinical outcome. Therefore, the conclusions derived from clinical studies performed to date with different commercial devices should be interpreted accordingly. Indeed, it is difficult to evaluate the effectiveness of continuously adjusting CPAP if the procedure is not well-defined. It seems that automatic CPAP is another example where technologic advances exceed clinical knowledge (23) because current automatic CPAP devices with selectable settings provide the physician with a sophisticated tool whose clinical effectiveness is still unknown. Regardless of the algorithm defined to automatically adjust CPAP, subjecting the device to reference breathing patterns at the bench is a first step for evaluating the performance of the hardware/software implemented in the device. Further clinical studies should determine whether a given automatic CPAP approach is useful for treating different subpopulations of patients with SAHS (24).
: The authors wish to thank Miguel A. Rodríguez and Marta Puig for their assistance. Supported in part by Comisión Interministerial de Ciencia y Tecnología (CICYT; SAF99-0001), SEPAR (2001), and Fondo de Investigación Sanitaria (FIS-00/575). Received in original form November 27, 2001; accepted in final form May 21, 2002
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