Hani Kayyali,1 Ted Bellezza,1 Sarah Weimer,1 Joe Lamont,1 Del Basa,1 Paul Venizelos, M.D2 and Siarhei Ramaniuk2
1. CleveMed, 4415 Euclid Avenue, Cleveland, Ohio 44103,
2. West Region Sleep Center, 15805 Puritas Rd, Cleveland, Ohio 44135.
Study Objective: To assess the feasibility and accuracy of a self-administered wireless technology for comprehensive evaluation of CPAP efficacy in the home.
Introduction: Sleep Disordered Breathing (SDB) affects more than 40 million patients with serious health and economic costs to the patients and the community at large. Effective treatment exists; however, once prescribed therapeutic benefit is not monitored adequately. Many patients, especially those with comorbidities, may require pressure adjustments, supplemental therapy, or different intervention that maybe difficult to identify with current therapy assessment techniques. We describe an easy-to-use wireless technology that interfaces to a commercial CPAP machine and generates a detailed treatment efficacy report.
Methods: The technology consists of a wearable wireless monitor (SleepScout) and a wireless compliance module (S8POD) that plugs in the back of a CPAP machine (ResMed S8). The combined system, which is called Intelligent Quantitative CPAP (IQ-CPAP), requires only three easily-placed sensors on the patient; yet, it can generate a sleep study report with ten (10) cardiorespiratory parameters: airflow, snore, mask pressure, mask leak, chest and abdominal respiratory efforts, pulse ox, body position, Heart rate and PhotoPlethysmography. In order to evaluate accuracy and ease of use, the technology was tested on 18 CPAP users at West Region Sleep Center in Cleveland Ohio. Each patient underwent two (2) consecutive therapy efficacy studies: in-lab full PSG during CPAP on the first night (PSG1) followed by IQ-CPAP study on the second night in the home (Portable2). The following day, the patient returned the equipment with a morning questionnaire to the sleep center where data was downloaded and analyzed.
Results: All 18 studies (100%) generated high fidelity recordings with no loss of data or replacement to any of the sensors. All patients (100%) were able to hook themselves successfully. 72% of the patients (13/18) described the hookup as easy and the rest (28%) as slightly difficult. AHI at-home showed sensitivity of 75% and specificity of 93% when compared to the gold standard (in-lab AHI). Average CPAP usage time in the lab and the home were similar (6.5 hours in-lab vs. 6.3 hrs at-home) suggesting the equipment did not hinder sleep. IQ-CPAP uncovered four patients (22%) in need of re-titrations despite their high compliance with CPAP.
Conclusions: A new enabling wireless technology for comprehensive evaluation of CPAP efficacy was tested with promising results. Unlike other systems that require more extensive hookup or record limited information, this technology is self-administered offering a convenient and cost-effective method to comprehensively evaluate CPAP benefit in the home.
Obstructive Sleep Apnea (OSA) occurs when the upper airway collapses during the night, which fragments sleep and leads to excessive daytime sleepiness. The mechanism for airway occlusions is not yet fully understood. However, it is widely accepted that reduced neck muscle tone combined with abnormal pharyngeal anatomy and excessive fat tissue make the airways vulnerable to collapse during negative inspiratory drive. A report by the National Commission on Sleep Disorders Research1 shows that 12 to 20 million Americans suffer from OSA leading to more than 200,000 car crashes per year. The financial impact is also staggering with direct annual cost for OSA estimated at $16 billion.2,3 OSA has also been linked to cardiovascular and cerebrovascular diseases making the disorder even more alarming than originally thought.4 For example, in a study by Dyken, sleep apnea was found to be five times as frequent in patients with ischemic or hemorrhagic strokes.5 Therefore, sleep disorders in general and OSA in specific present a serious national healthcare concern.
One of the most important and widely used indicators of OSA severity is the Apnea Hypopnea Index (AHI), which is defined as the average number of apneas and hypopneas events per hour of sleep (or per hour of total recording time for ambulatory studies). According to AASM 2007 guidelines, apnea is defined as total cessation of airflow for at least ten seconds, while hypopnea is defined as a drop of 30% or more in airflow or thoracoabdominal effort for at least ten seconds combined with at least 4% oxygen desaturation. Therefore, the proper calculation of AHI requires the measurement of multiple parameters: airflow, respiration effort, and oxygen saturation level. AHI < 5 suggests normal breathing and is used as a major therapy target.
Comprehensive Assessment of CPAP Benefit is Very Important; Yet, It is not Part of the Standard of Care
CPAP is by far the most common and effective treatment of OSA. However, despite its popularity, CPAP once prescribed is monitored only for a short period of time after therapy initiation. Furthermore, when treatment benefit is assessed, it is done in a limited fashion. This leaves many physicians un-certain of their patients’ long-term therapy progress especially for those with comorbidities. A study by Lain et. al. conducted on 1321 OSA patients with COPD on CPAP showed that almost half (42.9%) of the patients had significant nocturnal desaturations and required supplemental oxygen to improve outcome.10 Another study conducted on 47 patients showed similar results.11 CPAP also affects cardiac function. A study by Bradley et. al. showed that while CPAP improved cardiac function in Central Sleep Apnea (CSA) patients with Cheyne-Stokes Respiration (CSR), it was ineffective in CSA patients without CSR.12 More encouraging results on hypertension were found demonstrated by improvement in vascular responsiveness, reduction of nocturnal and daytime systolic blood pressure and restoration of heart rate variability.6,7,8 Another disorder whose response to CPAP remains un-certain is Complex Sleep Apnea (CompSA). While research in this area continues, early investigations at Beth Israel and the Mayo Clinic suggest that some CompSA patients need alternative therapies such as Adaptive Servo Ventilation or CO2-regulating PAP.17,18 Therefore, given the complexity of the disease and the wide-ranging effect of CPAP, it becomes prudent upon us to follow a more extensive approach for the way we determine therapy benefit.
Therapy Monitoring must be Comprehensive
In-lab PSG is the most comprehensive method to assess CPAP benefit; however, it is expensive and inconvenient for many patients especially for multiple follow-up evaluations. Monitoring therapy progress in the home is ideal but present techniques offer limited assessments. The most popular program to document therapy benefit from the home is to download CPAP usage time. However, while CPAP data can demonstrate the extent of compliance with therapy, it does not necessarily indicate that therapy is optimal. Even highly compliant patients may still need adjustments to CPAP pressure, supplemental therapy, or a completely different intervention. Indeed, research conducted by Pusalavidyasagar19 found that CompSA patients used CPAP for at least as long as their uncomplicated OSA counterparts; yet, they required more follow-ups, and exhibited higher “air hunger/dyspnea” rates. Similarly, another research by Kuzniar20 found that nearly half of CompSA patients who were highly compliant with CPAP maintained a persistently elevated AHI (non-responders).
More quantitative therapy evaluation can be achieved by a follow-up sleep test. Some ambulatory home sleep monitors can be adapted to work during CPAP; however, these devices are not suited for self-administration in that application. For example, the presence of the CPAP mask complicates, if not prohibits, the hookup of airflow and snore sensors. Also, these systems need to be wired to the CPAP for data download and synchronization. Some simpler HST devices can be self-administered but exhibit a reduced channel count; thus, limiting their recordings. Moreover, their proprietary outcome measures differ from lab-based indices (such as AHI) that are used for therapy targets; thus, restricting the ability of those systems to track CPAP progress in a fashion comparable to standard guidelines.
Another method to evaluate therapy is to use apnea severity indices generated inside some CPAP’s. However, studies that compared CPAP-derived AHI to in-lab AHI showed marked differences between the two concluding that CPAP’s have insufficient signal quality and could potentially generate” misleading results” especially for mild to moderate OSA patients.13–16 These studies demonstrate that while superior to oximetry alone and therefore a potentially better in-home “screening” tool, CPAP’s without supplemental information can overestimate apneas and underestimate hypopneas. The studies also showed susceptibility to mouth leaks and mask motion artifacts.
Finally, adequate home-based therapy assessment tools are not only needed to improve long-term management of the disease, they may also facilitate CPAP reimbursement. Some payers now require another doctor visit for clinical evaluation within ninety days post CPAP initiation. This can be a hurdle to many patients especially home-bound or rural patients who often live far from their treating physician. Home health tools with extensive data could provide a convenient and effective means to document therapy benefit, which may avoid delay or discontinuation of therapy reimbursement.
The new technology, IQ-CPAP, (Figure 1) consists of two wireless components: a patient monitor (SleepScout manufactured by CleveMed) and the S8-POD (manufactured by CleveMed), which is a small module that attaches to the back of the S8 CPAP (manufactured by ResMed). The S8-POD serves as a wireless interface between the SleepScout and S8 allowing seamless data exchange and synchronization between the two devices. In this research, only three external sensors out of 9 possible SleepScout input channels were used: pulse ox, chest effort, and abdominal effort (the remaining channels such as EKG and EMG, which can be used for expanded follow-up studies for arrhythmia, Bruxism, or restless leg evaluation have not been used here). The three external data channels combined with an internal body position sensor were then merged with real-time data extracted from the S8 like airflow and snore recordings. The ability to access CPAP data simplifies patient hookup because it eliminates sensors like cannula or microphone and strengthens the study interpretation by including interface information like mask pressure and mask leak. The combined data set results in a ten (10) parameter recording saved inside the SleepScout: airflow, snore, SpO2, Plethysmography, Heart Rate, Chest Effort, Abdominal Effort, Body Position, Mask Pressure, and Mask Leak. In order to test accuracy and ease-of-use, the technology was compared with in-lab PSG during CPAP on eighteen (18) patients who were on therapy for at least one month. The studies were done at West Region Sleep Center.
18 patients previously diagnosed with OSA at West Region Sleep Center were recruited. After describing the study and obtaining consent, each patient underwent two consecutive studies: one in the lab followed by one in the home. For the first night, an in-lab full PSG (Rembrandt N7000) was conducted during CPAP set at the original therapeutic level. In the morning, the patient was given the SleepScout monitor with the S8/S8-POD and hookup instructions on how to attach the three external sensors (both belts and the pulse ox). The patients were also instructed to attach one additional sensor that offered an independent airflow measurement. For that hookup, the patient attached a dual lumen tube connected in-line with the CPAP air hose on one end and to the Sleep-Scout dual pressure ports on the other. The “gold standard” airflow signal generated by this additional sensor served as a benchmark to compare the IQ-CPAP’s airflow. In the morning, the patient returned the equipment to the sleep lab with a questionnaire filled regarding system’s ease-of-use. Data were downloaded from the SleepScout SD card and manually scored by the same registered sleep technologist who also scored the in-lab PSG. All studies were interpreted by the same board-certified sleep physician.
11 of 18 patients were male (61%). Age ranged from 31 to 75 years old (mean age was 53 years). Participants were on CPAP for more than 1 month. All recordings (18/18) were completed successfully; no sensors fell off or needed replacement. All patients were able to hook themselves in the home per the provided instructions with 72% of the patients (13/18) describing the hookup as easy. The remainder of the patients (28%) described the hookup as slightly difficult, which could e partly attributed to the additional hookup of the airflow sensor (that sensor will not be part of the ultimate device use). 11 of 18 patients (61%) preferred the home study over the lab, 2 of 18 patients (11%) rated the home and the lab equally convenient. 5 of 18 patients (28%) preferred the lab and cited the availability of a technician during the night as the reason; although, they also acknowledged that none of the sensors fell off and a technician was not called.
A typical screen shot of an IQ-CPAP recording is shown in Figure 2 with repeated apneas and hypopneas scored per accepted clinical guidelines for ambulatory studies. Figure 2 also shows two events with marked airflow reductions that may have been misidentified as hypopneas without confirming pulse oximetry information further highlighting the benefit of multi-channel recording. Figure 3 compares the conventional airflow and snore signals with those recorded by IQ-CPAP showing nearly identical traces. Although this result was expected, it further validates that the traditional sensors typically used to generate these signals can be replaced with CPAP data; thus simplifying patient hookup in the home. Atypical IQ-CPAP patient report is shown in Figure 4, which includes recording time, respiratory analysis by body position, desaturation summary, and heart rate summary. Finally, a summary of the patient’s all-night trends of heart rate, respiratory events, oxygen desaturation levels and body position are summarized in Figure 5, which can be used to offer more insight of the patient’s response to CPAP.
AHI-at-home when compared with the gold standard (AHIin-lab) showed sensitivity of 75% and specificity of 93% (using a cutoff of AHI = 5). Four (4) patients (22%) exhibited elevated AHI (average AHI-in-lab=25.5, averageAHI-at-home=14.4), which maybe partly attributed to the influence of comorbidities; all 4 patients suffered from one or more confounding conditions like hypertension, asthma, COPD, and CHF. All four patients were highly compliant with CPAP (average 6.2 hr/night) suggesting that reliance on usage time alone to determine therapy benefit is not sufficient. Overall, the average CPAP usage time for all patients in the home was high (6.3 hrs), which indicates that the IQ-CPAP monitor did not hinder sleep.
Apnea and Hyponea counts calculated inside the S8 (AHIS8) using its own proprietary event detection algorithms is another measure sometimes used to document CPAP performance. When compared with the in-lab and at-home AHI indices, the AHI-S8 over-estimated severity in 5 patients (28%). Average AHI’s for those five patients were as follows: 2.2 in-lab, 2.8 at-home, and 8.8 for S8. One possible factor that can artificially elevate CPAP’s hypopnea count maybe mask leaks which was significant in these patients (average 95% percentile mask leak was 47.5 l/min).
A self-administered technology (IQ-CPAP) that allows detailed therapy evaluation in the home was developed and tested with high sensitivity and specificity to the gold-standard (in-lab PSG). The majority of the patients categorized the self-hookup as easy and preferred the home study over in-lab PSG. All recordings were successful with no loss of data. The technology records raw data allowing epoch by epoch scoring and generates all-night cardiorespiratory summary reports while maintaining a minimal patient sensor hookup.
Also, this research compared IQ-CPAP results with two methods currently used to evaluate treatment benefit: therapy usage and CPAP-generated AHI. IQ-CPAP, supported by in-lab PSG, uncovered persistently elevated AHI in 4 of 18 patients despite their high compliance with CPAP. On the other hand, home studies, also with agreement from in-lab testing, showed effective therapy in 5 of 18 patients when their CPAP’s continued to identify them as mild apneics. These findings suggest that existing CPAP monitoring techniques may not be sufficient to properly document efficacy and that supplemental multi-channel home sleep test during CPAP is needed.
This research supports the use of a more extensive evaluation to document CPAP benefit. Recent discussions in the medical and payer communities have focused mostly on baseline sleep diagnosis; however, therapy monitoring has received little attention. Easy-to-use, comprehensive home technologies designed for CPAP follow-up are possible and could play a crucial role in the long-term management of Sleep Disordered Breathing.
This research effort was supported by grants from the National Institutes of Health (NIH) – National Institute of Neurological Disorders and Stroke (NINDS) and National Heart Lung and Blood Institute (NHLBI).
1. Wake up America: A National Sleep Alert. Report of the National Commission on Sleep Disorders Research, 1993.
2. Frost and Sullivan Marketing Report, A071-56, 2001.
3. Feedback Research Services, Sleep Screening and Testing Markets, Marketing report, August 2001.
4. Kryger, “Principles and Practice of Sleep Medicine”, Third Edition, Saunders, 2000.
5. Dyken et. al., “Investigating the relationship between stroke and sleep apnea”, Stroke 1996;27: 401–407.
6. Bradley TD et. al., “Refractory hypertension and sleep apnea: effect of CPAP on blood pressure and baroreflex”, Eur Respir J 2003 Feb;21 (2):241–247.
7. Zidek W et.al., “effect of CPAP therapy on 24-hour blood pressure in patients with OSAS”, Am J Hypertens 2002 Mar; 15(3):251–257.
8. Berry et. al.,” Spectral indices of cardiac autonomic function in OSA”, Sleep, 1999 Vol 22 (4);443–451.
9. Fletcher et. al., “Sympathetic over activity in the etiology of hypertension of obstructive sleep apnea”, Sleep. 2003 Feb 1;26(1):15–19.
10. Lain et. al., “Nocturnal oxygen desaturation in OSA using CPAP”, ATS 2003 99th International Conference, Seattle.
11. Lain et. al., “Oxygen Desaturation during nocturnal CPAP use for OSA”, Chest, Vol 120, p:204S–205S.
12. Bradley et. al., Effects of continuous positive airway pressure on cardiovascular outcomes in heart failure patients with and without Cheyne-Stokes respiration. Circulation, 2000 Jul 4;102(1):61–66.
13. Fleury B., et. al., A laboratory validation of the diagnostic mode of the AutoSet system for sleep-related respiratory disorders, Sleep 1996, 19(6):502–505.
14. Gugger M, et. al., Accuracy of an intelligent CPAP machine with in-built diagnostic abilities in detecting apnoeas: a comparison with PSG, Thorax 1995 Nov; 50(11):1199–1201.
15. Gugger M, Comparison of Resmed Autoset (version 3.03) with PSG in the diagnosis of the sleep apnea/hypopnea syndrome, Eur. Respir J. 1997 Mar; 10(3): 587–591.
16. Wang H, et. al., Comparison of Autoset with PSG in the diagnosis of sleep syndrome, Zhonghua Jie He He Hu Xi Za Zhi. 1999 Jun;22(6):344–346.
17. Morgenthaler TI, Treatment of complex sleep apnea syndrome, Curr Treat Options Neurol., 2008 Sep;10(5):336–341.
18. Thomas RJ, Effect of added dead space to positive airway pressure for treatment of complex sleep-disordered breathing, Sleep Med. 2005 Mar;6(2):177-8. Epub 2005 Jan 25.
19. Pusalavidyasagar SS, Treatment of complex sleep apnea syndrome: a retrospective comparative review. Sleep Med. 2006 Sep;7(6):474–479. Epub 2006 Aug 23.
20. Kuzniar TJ, Natural course of complex sleep apnea-a retrospective study, Sleep Breath, 2008, May, 12(2): 135–139.