The Future of Sleep Disorder Breathing Testing – Baker and Schramm
The Future of Sleep Disorder Breathing Testing
The prevalence of Sleep Disorder Breathing (SDB) has been well described and documented. Awareness of the disorder, its negative impact on health, and the financial burden it creates is growing globally. To address this problem in the United States, the American Academy of Sleep Medicine (AASM) late last year approved non-sleep lab or home testing using only Type III devices for patients suspected of having moderate to severe obstructive sleep apnea.1 In March 2008, the Center for Medicare and Medicaid Services (CMS) ignored the AASM recommendations and decided to reimburse prescribed portable monitoring studies for Type II, Type III and Type IV devices.2
These recent developments all point towards significant growth in the ambulatory sleep diagnostic market.
One objective of home testing is self application of the device by the patient. Therefore the devices must be simple to operate, small and have fewer recording channels compared to full in-lab polysomnograms (PSGs). As a result the devices have sacrificed some of the channels that would otherwise be collected in a conventional sleep laboratory PSG because the electrodes or monitoring devices are too difficult for self-application by patients. The electrodes most commonly eliminated are those recording the electroencephalogram (EEG) channels used currently to define sleep stages. Although the portable devices collect some physiological measures during sleep it accomplishes this without measuring if the patient is really asleep by standard sleep staging criteria.
Nevertheless there are currently many portable devices (Embla markets two: Embletta & Titanium) that collect this type of data and have grown in popularity as the community moves away from traditional sleep labs towards more ambulatory testing. Other factors driving this trend include but are not limited to the high costs of in-lab testing, long waiting lists for a PSG and the high prevalence of SDB in the general population.
Some limitations of home testing with ambulatory devices are that they often require manual interpretation. Although less costly than in-lab procedures, several devices can have a relatively high â€œcost per useâ€ if used with nasal cannulas, oximetry probes and respiratory effort belts. The combination can be technically complicated to apply for some patients which sometimes results in tests having to be repeated due to improper patient application.
Standard Sleep Measures
The current in-lab measure for SDB is the Apnea Hypopnea Index (AHI). It is the number of apneas or hypopneas per hour during sleep. The concept of AHI is based on the qualitative and quantitative identification of breathing events during sleep, using changes in the EEG sleep to measure its disturbance on sleep parameters. The arbitrary rules were developed several decades ago, are very subjective and can have high interrater variability depending on certain factors (i.e. age, pharmacotherapy). There has been no successful proposal to replace the rules. Ambulatory devices generate an approximation of the AHI using a respiration disturbance index (RDI) which is based on the recording time. The RDI may underestimate the number of respiratory events since it inherently does not measure sleep time and includes wake time during the recording period.
Over the last ten years researchers at Beth Israel Hospital in Boston, have been studying how heart and pulmonary activity behaves during sleep. Their findings demonstrate that the ECG can be used as a surrogate for categorizing the sleep state. The technique combines heart rate variability and an ECG-derived respiration signal from the R-wave amplitude fluctuations induced by respiration to generate two data sets. Following artifact extraction and data re-sampling, the cross-power and coherence between both cardiac and respiratory signals are quantified to yield the ratio of coherent cross power in the low-frequency (0.0â€“0.1 Hz) band to that in the high-frequency (0.1â€“0.4 Hz) band.3 A high proportion of power in the low frequency coupling (LFC) band is associated with unstable or periodic sleep behaviors, while a high proportion of power in the high frequency coupling (HFC) band is associated with physiologic respiratory sinus arrhythmia and stable sleep and respiration.4 A third frequency identified as very low-frequency band (0.0â€“0.01) correlates with periods of wake or REM sleep. The resultant spectrogram provides a moving average of the dominant oscillatory ECG frequencies of autonomic drive coupled with respiration in sleep. (See Figure 1).
Elevated low-frequency coupling (e-LFC) is a subset of LFC that correlates with scored apneas and hypopneas in patients with sleep apnea. Elevated-LFC shows a narrow spectral band in sleep apnea that seems to have a dominant chemo-reflex influence â€“ central sleep apnea (CSA), periodic breathing, or complex sleep apnea syndrome (SAS); a broad spectral band is seen in dominant obstructive sleep apnea (OSA).4 (See Figure 2) It is the construct of e-LFC in the spectrogram that allows the sleep technician or clinician to easily differentiate between obstructive and non-obstructive sleep-disordered breathing.
CPC technology represents a breakthrough in how sleep can be visualized, by presenting a simple and accurate picture of sleep oscillations and interactions instead of the tedious manual sleep stage scoring and counting of respiratory events that has been the standard for the last 40 years. The analysis has been validated using over 10,000 sleep studies at Beth Israel Hospital using its own data and that obtained from the Sleep
Fig. 1. RemLogic CPC analysis spectrograph demonstrating LFC and HFC oscillations in sleep from 9:00 PM to 12:30 PM; a preponderance of LFC or unstable sleep from 12:30 PM to 5:00 PM with a representative segment of the nocturnal PSG in the background (time ~ 2:50 AM) during LFC showing repetitive OSAH.
Fig. 2. RemLogic CPC analysis spectrograph demonstrating elevated-LFC (e-LFC) broad spectral band in sleep from 10:00 to 11:00 PM and 02:30 to 05:00 AM; showing a preponderance of e-LFC broad spectral band that is dominant in obstructive sleep apnea. The representative segment of the nocturnal PSG is shown in the background (time ~ 2:47 AM) showing repetitive OSAH.
Heart Health Study.5 â€œThe CPC technology is not a sleep-stage or respiratory event detector but [it] does provide a dynamic measure of sleep state-modulated cardio-pulmonary interactions,â€ says Dr. Robert Thomas. â€œAs the technique is automated, inter-scorer reliability is eliminated as a problem. The technique can track dynamic sleep physiology, and provide a unique ambulatory measure of sleep quality.â€
ACPC analytical tool is currently being developed by Embla Inc. (Broomfield, CO, USA) and is expected to be available for sleep labs, clinicians and technicians in Q4 of 2008. The CPC analytical software will be demonstrated during the Sleep 2008 convention in Baltimore, MA at the Embla booth. In addition, a new CPC device that records ECG, nocturnal activity, and sleep position will be unveiled at the trade show with demonstrations of its connectivity to and functionality on the Internet. CPC technology will provide a fundamentally new method to aid in SDB differentiation, identification, treatment and assessment of compliance of SDB patients on therapy.
President and CEO of Embla Systems
Preetam Schramm, PhD, RPSGT
Senior Clinical Consultant for Embla Systems
1. Collop NA, Anderson WM, Boehlecke B, et al. Portable Monitoring Task Force on the American Academy of Sleep Medicine. Clinical Guidelines for the Use of Portable Monitoring Devices in the Diagnosis of Obstructive Sleep Apnea in Adults Patients. J Clin Sleep Med, 2007; 3(7):1â€“16.
2. Decision Memo for Continuous Positive Airway Pressure (CPAP) Therapy for Obstructive Sleep Apnea (OSA) (CAG-00093R2) https://www.cms.hhs.gov/mcd/viewdecisionsmemo.asp?from 2=viewdecisionmemo.asp&id=204&.
3. Thomas RJ, Mietus JE, Peng CK, Goldberger AL. An electrocardiogram- based technique to assess cardiopulmonary coupling during sleep. Sleep 2005; 28:1151â€“61.
4. Thomas R, Mietus J, Peng CK, Gilmartin G, Daly RW, Golberger A, Gottleib DJ. Differentiating obstructive from central and complex sleep apnea using an automated electrocardiogram-based method. Sleep 2007; Dec 1, 30(12):1756â€“69.
5. Quan SF, Howard BV, Iber C, et al. The Sleep Heart Health Study: design, rationale, and methods. Sleep 1997; 20(12): 1077â€“1085.
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