Ensuring Adequate Diagnosis and Treatment: Access, Capacity, and Technology Development
CHAPTER SUMMARY Millions of individuals suffering from sleep disorders remain undiagnosed and untreated. Most American communities do not have adequate health care resources to meet the clinical demands. Further, the current diagnostic and therapeutic capacity is not sufficient for the present demand, let alone the predicted increase in demand arising from the proposed public education campaign. Thus, additional technology development is required. Based on estimates of the prevalence of sleep disorders, millions of individuals are undiagnosed and untreated. As awareness increases, greater investment in the development and validation of new diagnostic and therapeutic technologies will be required to meet the anticipated demand. Numerous technological advances have enhanced the feasibility of portable diagnosis and treatment, but they have not been fully evaluated and validated. Therefore, the committee urges the evaluation and validation of existing diagnostic and therapeutic technologies, as well as the development of new technologies.
Increased awareness among the general public and health care practitioners will present numerous challenges to existing health care providers and researchers who are already stretched too thin. Therefore, as described in the following sections, development and improved capacity through technology development is required.
An increased recognition of sleep disorders has resulted in an increase in demand. In a 3-year period over the late 1990s, demand for a sleep test doubled in the United States (Pack and Gurubhagavatula, 1999). In France the number of patients diagnosed and receiving continuous positive airway pressure (CPAP) treatment is annually increasing by 20 percent (Gagnadoux et al., 2002). Demand has been accompanied by improved patient access to physicians and other clinicians trained in sleep medicine and to facilities where clinical sleep tests, polysomnograms, can be performed. There are currently an estimated 1,292 sleep centers or laboratories in the United States, 39 percent of which were accredited by the American Academy of Sleep Medicine (AASM) (Tachibana et al., 2005). However, resources have not kept up with demand. For example, 80 to 90 percent of obstructive sleep apnea (OSA) cases remain undiagnosed and untreated, which increases the burden of this disorder (Kapur et al., 2002). Narcolepsy, too, is infrequently detected (Singh et al., 2005), but precise rates of under diagnosis are not available because this condition is less common. Similarly, there is poor recognition and treatment of insomnia (Benca, 2005), as well as poor communication between patient and physician. Thus, even with a growth in resources, this issue is of significant importance to the millions of individuals suffering from sleep disorders.
DEVELOPING PORTABLE DIAGNOSTIC TOOLS
Polysomnography, the “gold standard” procedure for the diagnosis of most sleep disorders, is not readily available for everyone who needs it. These procedures employ simultaneous monitoring of numerous physiological parameters including brain wave activity, eye movements, muscle activity (chin and legs), heart rate, body position, and respiratory variables, including oxygen saturation. The test is typically performed overnight in a sleep laboratory with a technician in attendance, requiring an individual to sleep in the laboratory. Thus, this procedure necessitates facilities that accommodate overnight testing (beds and monitoring areas), highly sophisticated equipment, trained staff who are willing to work night shifts, and physicians trained in sleep medicine.
Although there may currently be cost-effective ways to manage sleep disorder, the capacity does not currently exist to diagnose and treat all individuals. Most American communities do not have adequate health care resources to meet the clinical demands of treating the large number of
patients with sleep disorders (Banno and Kryger, 2004; Tachibana et al., 2005). In many health care systems and communities, waiting lists may be as long as 10 weeks (Rodsutti et al., 2004), with even longer waiting times in certain systems such as Veterans Affairs Medical Centers (Sharafkhaneh et al., 2004). Although this is not a problem that is unique to the field, long wait lists cause significant delays in diagnosing and treating individuals (Banno and Kryger, 2004). This is of particular concern for individuals with sleep disorders that lead to increased chance of injury. For example, undiagnosed severe OSA can lead to death or serious harm of self or others due to crashes (George, 2001). Further, long wait times contribute to high no-show rates that in turn increases the length of the wait-lists (Callahan and Redmon, 1987; Olivares, 1990). This also may decrease market share (Christl, 1973; Antle and Reid, 1988). It has been estimated that sleep apnea alone, a diagnosis that necessitates polysomnography to meet current criteria set out by third-party payers, annually requires at least 2,310 polysomnograms per 100,000 population to address the demand for diagnosis and treatment (Flemons et al., 2004). However, on average, only 427 polysomnograms per 100,000 population are performed each year in the United States, a level far below the need. In fact, 32 states annually perform less than 500 polysomnograms (Tachibana et al., 2005). Only Maryland annually performs more than 1,000. This large geographic variability in levels of sleep services is not explained by Medicare reimbursement rates, race, or distribution of OSA risk factors in these areas (Tachibana et al., 2005). Further, such geographical variability suggests the need for more standardized approaches for diagnosis and disease management.
Limitations in providing overnight diagnostic sleep laboratory services are attributed to a number of factors. Direct costs associated with having a polysomnogram performed (Chapter 4) are high. In addition, there are high expenses to sleep laboratories, including costs related to the initial investment in equipment (hardware and software) and information technology needed to manage large amounts of digital data. There are considerable personnel costs related to dedicating one to two trained technicians to each patient for a 10- to 12-hour period (for orientation, hookup, and minute-by-minute monitoring) and for scoring of studies (2 to 3 hours per study), overhead for space (which traditionally has used in-patient hospital space and more recently has used space in upscale hotels that contract with health care organizations to provide rooms or floors that serve as “community-based sleep laboratories”), and costs related to consumable supplies used for monitoring. Most insurers require sleep laboratories to be supervised by physicians or other clinicians certified by the American Academy of Sleep Medicine. In addition, many patients are reluctant to undergo somewhat intrusive monitoring and to spend one or more nights away from home. The latter is of special concern to individuals with home care (of their chil-
dren or parents) responsibilities. These factors have contributed to an interest in developing portable, and perhaps simpler, less costly and less intrusive devices that can be used in a patient’s own home, with the goals of improving access and decreasing the cost of sleep studies.
The Potential of Developing Portable Sleep Monitoring
Numerous technological advances have enhanced the feasibility of portable monitoring. These include miniaturization of recording components, efficiencies of digital data storage, remote monitoring capabilities (allowing centrally based technicians to monitor signals at home via wireless or modem communications), and development of new physiological sensors. Advances have been such that essentially the same data that are collected using full polysomnography in the laboratory can be collected in the home with monitors that weigh less than 300 grams. Large-scale epidemiological studies have demonstrated the feasibility of such multichannel recordings done in children and in middle-aged and elderly individuals (Goodwin et al., 2003; Redline et al., 1998). Recent experience in a community sample of almost 3,000 older men, a large percentage of whom had OSA and periodic limb movements, indicates that this approach can yield high quality data in 97 percent of studies performed (personal communication, S. Redline, Case Western Reserve University School of Medicine, December 15, 2005). The improvement in the high quality of data in this study compared to previous studies is largely due to technological advances. A study comparing the quality of data obtained from an in-home to an in-laboratory study demonstrates comparable quality and evidence of slightly less stage 1 sleep (i.e., lighter sleep) in the home, suggesting that patients may sleep better and have more representative sleep at home (Iber et al., 2004). The apnea-hypopnea index (AHI) determined using the two methods were highly correlated; however, a Bland Altman plot showed that at lower AHIs, the AHI tended to be lower in the laboratory than at home, and at higher AHIs, the AHI was higher in the laboratory than home. The latter phenomenon was thought to relate to positional differences in apnea severity, with severely affected patients probably spending more time on their back when sleeping in the typical hospital bed than when studied at home. However, although recent studies suggest low failure rates, there may be significant differences in the failure rates of unattended monitoring in less controlled settings. Thus examination of the efficacy of such technologies should be performed in less controlled settings, as may occur in clinical practice.
Despite the promise of this technology, such comprehensive monitoring, even at home, is probably as burdensome to patients as when performed in the laboratory, requires a technician to travel to the patient’s
home to set up and retrieve the units, and has a higher failure rate due to all the vagaries of using many sensors in an unattended manner. Failure rates between 5 to 20 percent have been reported for ambulatory diagnostic devices (Redline et al., 1998; Whitney et al., 1998; Fry et al., 1998; Mykytyn et al., 1999; Portier et al., 2000); however, since these reports were released there have been many technological improvements. A formal cost-benefit analysis of 12 to 14 multichannel in-home monitoring compared to in-laboratory monitoring has not been performed. Thus, there is interest in use of simpler technology with sufficient predictive value to be used in decision making.
Technological advances also have led to the incorporation and packaging of various groups of sensors, many novel, designed to provide simpler means for quantifying airflow limitation or breathing effort, oxygen desaturation, snoring sounds, movement, heart rate, blood pressure, and vascular tone variability.
Several of these devices are designed to primarily provide estimates of sleep and wake time over 24-hour periods, such as wrist actigraphs (i.e., a movement detector coupled with software that uses movement patterns to provide estimated sleep and wake times) (Ancoli-Israel et al., 1997). These are used more often in research than in clinical settings, although clinically they have been used to enhance evaluation of sleep-wake disorders. These devices provide estimates of sleep time that correlate moderately well to polysomnography-based estimates; however, in certain high-risk subgroups, such as children with attention-deficit/hyperactivity disorder or sleep apnea, they may perform less well (Bader et al., 2003).
A detailed review of different ambulatory technologies for sleep apnea measurement was recently performed (Flemons et al., 2003; Tice, 2005). Most devices have been designed to screen or diagnose sleep apnea. Several novel portable devices that have been informed by a growing knowledge of physiological correlates of sleep apnea have been developed. A recent review by the AASM has identified the utility of measuring nasal pressure from a sensor placed in the outer nares, which accurately detects airflow limitation (Krieger et al., 2002), the sine qua non of OSA. Several devices combine this sensor with sensors that measure oxygen saturation, snoring, and other sleep apnea correlates. For example, a relatively simple device has been designed to measure nasal pressure, oximetry, head movement, and snoring with a head band containing these sensors that is placed around the forehead and can be self-applied without glue or skin preparation (Westbrook et al., 2005). The AHI derived using an early version of this device tested in both in-home and in-laboratory settings in a large sample showed sensitivities of 92 to 98 percent and specificities of 86 to 100 percent for identification of sleep apnea. An advantage of such technology includes its potential to easily measure sleep over two or more nights (enhancing reliability) and its potential reduced cost
(estimated at 30 to 50 percent of that of in-laboratory polysomnography). There has also been great interest in the use of completely novel sensors that have not been traditionally used in the sleep laboratory, but which are based on growing interests in the autonomic sequelae of sleep apnea. One such device measures peripheral artery tone from a sensor placed on the finger and has been shown to provide estimates of vascular flow, a measure that reflects variations in breathing and sleep-related arousals (Lavie et al., 2000). One wrist-worn device that uses this sensor in combination with sensors measuring oxygen saturation, heart rate, and movement has shown promising utility for sleep apnea detection. Preliminary data from one study showed a 95 percent sensitivity and 100 percent specificity (Pittman et al., 2004). Other studies have also supported this approach (Ayas et al., 2003), including results from a study of almost 100 individuals (Zou et al., 2006). Another exciting advance is the development of oximeters that are relatively resistant to movement artifact, thus improving the accuracy of such data in unattended settings (Barker, 2002).
CHALLENGES TO DEVELOPING AMBULATORY TECHNOLOGIES
Despite the promise of this technology, use of portable monitoring for diagnosis or management of sleep disorders has not yet been endorsed by any professional organization. Dozens of studies have been conducted that evaluate different aspects of technology use (ranging from evaluation of the accuracy of individual sensors to use in epidemiological studies to use in case identification); however, very few studies have met rigorous criteria for endorsement of a new diagnostic test, including comparison to a reference standard, blinded assessments, and use of large samples (Tice, 2005). Although development and evaluation of new and improved sleep monitors are much needed, the industry has failed to invest in conducting such rigorous studies. The National Institutes of Health (NIH) has invested in such assessments mostly through Small Business Innovation Research (SBIR) grants; however, between 2002 to 2005, only 17 SBIR grants were awarded to develop and evaluate new sleep technology, and many of these studies were designed to test feasibility (phase I) rather than efficacy.
There are several challenges to technology development and evaluation that may be fairly specific to sleep medicine. Challenges relate to the underlying uncertainty over: (1) which physiological signals best capture the stresses associated with sleep apnea and thus would most optimally identify patients who are either at increased risk for sleep apnea-related morbidity or who are most likely to require and respond to therapy; and (2) what threshold values, if any, for quantitative data derived from physiological monitoring best identify patients at risk or likely to respond to therapy. The collection of 12 or more channels of physiological data on sleep architec-
ture, cardiovascular responses, and disordered breathing potentially provides the clinician a comprehensive panel of data from which to make treatment decisions. The influences of reducing this panel of data on clinical decisions and short- and long-term disease management are unclear. Emerging data suggest that different sleep apnea-related outcomes may be differentially predicted by alternative indexes of physiological stress captured by polysomnography. One recent cross-sectional study, for example, showed that while indexes of overnight hypoxemia were most strongly associated with glucose impairment, the arousal index best predicted hypertension (Sulit et al., 2006). Thus, monitors that selectively record one set of physiological disturbances may be well suited for predicting some, but not all outcomes. Threshold values may also differ according to the physiological outcome of interest. For example, data from the Sleep Heart Health Study suggest that an increased prevalence of hypertension may be observed at a threshold AHI that is higher than the threshold associated with other cardiovascular manifestations (Nieto et al., 2000; Shahar et al., 2001). Such uncertainties hamper technological efforts at choosing sensor “packages” that are most clinically relevant and evaluation procedures that require clear consensus over affection status to determine sensitivity and specificity.
Implicit in the challenges noted above are the very limited available data that address the clinical utility of the most commonly considered reference standard of polysomnography, coupled with current practice that focuses on specific numbers obtained from this test to make specific diagnoses. However, the latter practice is actually not well supported by evidence, and there is much debate over which threshold levels define “disease” and what combinations of data should be used to construct each metric (Ryan et al., 1988; Redline and Sanders, 1999). Little available research has evaluated the specific contribution of polysomnography over information obtained by other clinical assessments, including history and examination. As mentioned, although multiple physiological variables are captured, there is no clear consensus on how these data are most optimally combined for case identification or for disease assessment. Historically, the field (including third-party insurers) have used a single metric such as the AHI for defining sleep apnea, or the periodic limb movement index (PLMI) for periodic limb movement disorder, defining disease by using a single cutoff value for each (e.g., AHI greater than 5 for sleep apnea or PLMI greater than 5 for periodic limb movement disorder). However, this approach, which emphasizes the centrality of a single number—and which is known to vary from night to night (Quan et al., 2002)—differs from that in other fields where data from physiological tests are used as one of many indices to gauge disease severity and to follow treatment responses, but are not used as the sole diagnostic instrument. For example, asthma, a common chronic inflammatory disease of the airways, is diagnosed predominantly
using a careful history; lung function tests are used to gauge disease severity and treatment responses and sometimes to help differentiate asthma from other respiratory conditions. The issues that plague equipment development and laboratory access in the sleep laboratory have not impeded the development of lung function laboratories. Rather, the development and accreditation of lung function laboratories and lung function equipment (including portable spirometers) are based on collecting reproducible data that meet physiological criteria for accuracy, independent of the role of such equipment as tools evaluated on their ability to independently classify disease status. It is recognized that the latter requires consideration of multiple factors, including symptoms, level of impairment, response to allergic and irritant triggers, and often empiric responses to therapeutic trials.
Other challenges relate to designing studies that specifically address a number of distinct potential applications of portable sleep monitoring. These include screening—which is often population-based, and intended to detect cases independent of symptoms; clinical case definition— identification of cases among patients referred because of health concerns; disease management in which sleep monitoring provides quantitative data on progression or regression of disease severity; and epidemiological studies—in which sleep monitoring is used to provide a quantitative assessment of a physiological exposure or outcome. It is important that any given evaluation study of new technologies be designed to address a specific question or related series of questions.
Scoring and Processing of Sleep Studies
Current scoring approaches use a system of epoch by epoch scoring (30 seconds per epoch) developed over 40 years ago when polysomnography used only paper-based systems based on analog data. This approach is recognized to be both labor-intensive and time-consuming. Further, reliance on human scorers using visual pattern recognition requires intensive and ongoing training to achieve high reliability (Whitney et al., 1998), which may be lower than that potentially attained by automated methods (which also have their limitations). Visual scoring also may not maximally utilize the spectral components of the electrophysiological data, which may provide useful information on sleep architecture. Furthermore, there is a shortage of trained sleep technicians. Currently there are only 2,198 certified technicians to monitor and score sleep tests, far below the need (Association of Polysomnographic Technologists, 1999). Recognizing these issues, the AASM convened a task force in 2004 to reassess current scoring approaches, critically evaluate both sensors and scoring algorithms, and update scoring approaches as appropriate to include digital analysis of electrophysiological data. This report, scheduled
for release in 2006, should provide important advances for the diagnosis of chronic sleep loss and sleep disorders.
Summary of Formal Evaluation Reviews
Three recent in-depth reviews have been performed to examine the effectiveness of portable monitoring devices (Ross et al., 2000; ATS, 2004; Tice, 2005). As described above, these reports were largely aimed at evaluating the literature regarding the accuracy of clinical diagnosis relative to reference in lab polysomnography, with some attempt at also evaluating the literature relative to cost-effectiveness and clinical prediction. In 1998, the Agency for Healthcare Research and Quality performed a literature review and meta-analysis on studies of portable monitoring for OSA. The review concluded that at the time there was insufficient evidence to make firm recommendations for use of portable monitoring for the diagnosis of sleep apnea (Ross et al., 2000).
An executive summary on the systematic review and practice parameters for portable monitoring in the investigation of suspected sleep apnea in adults was published in 2004 by an evidence review committee consisting of members from the American Thoracic Society, the American College of Chest Physicians, and the American Academy of Sleep Medicine (ATS, 2004; Flemons and Littner, 2003). In that summary, the following recommendations were made:
Given the available data, the use of portable device was not recommended for general screening.
The use of portable devices was not recommended in patients with comorbid conditions or secondary sleep complaints.
The use of portable devices should require review of raw data by trained sleep specialists.
The review committee also recognized the need for further development of portable devices and suggested several goals for future research. It was found that most studies on portable monitoring were performed primarily on white males with OSA who had few comorbidities. The evidence review committee recommended that future studies should include more diverse populations, other than patients with sleep apnea, that are not subject to selection bias. Additional recommendations were that future studies should address clinical predictive algorithms in combination with portable monitoring in the diagnosis of sleep apnea, and study design should assess the cost-effectiveness and outcomes associated with different diagnostic and management strategies.
The California Technology Assessment Forum most recently evaluated the evidence that supported use of ambulatory devices over in-laboratory procedures for the purposes of diagnosing sleep apnea (Tice, 2005). The following five technology assessment criteria were identified:
The technology must have final approval from government regulatory bodies.
The scientific evidence must permit conclusions concerning the effectiveness of the technology regarding health outcomes.
The technology must improve health outcomes.
The technical must be as beneficial as any established alternatives.
The technology must be attainable outside of the investigational setting.
They determined that only the first two criteria had been met, but the last three were not. This review also identified the paucity of data regarding the “reference standard” (laboratory polysomnography) as improving health outcomes and suggested that a therapeutic trial of CPAP therapy may be a more efficient and clinically relevant approach than use of either in-home or in-laboratory sleep monitoring.
Evaluating Daytime Sleepiness
There is also a need to improve diagnostic procedures aiming at the quantification of excessive daytime sleepiness and the diagnosis of narcolepsy and hypersomnia. The current gold standard is the clinical Multiple Sleep Latency Test (MSLT), conducted after nocturnal polysomnography is performed (Littner et al., 2005). Sleepiness is considered consistent with hypersomnia or narcolepsy when a mean sleep latency less or equal to 8 minutes is observed (AASM, 2005). The observation of multiple sleep onset rapid eye movement (REM) periods (SOREMPs) during five naps is considered diagnostic for narcolepsy (see Chapter 3). Problematically however, the MSLT is sensitive to sleep deprivation and sleep-disordered breathing; thus, the test is often difficult to interpret. Population-based studies with the experimental MSLT, a modified version of the MSLT where mean sleep latency, but not SOREMP are measured, suggest that a large portion of the population has a short sleep latency (Kim and Young, 2005) and that the test correlates only partially with subjective measures of excessive daytime sleepiness. This has led to the revised diagnostic criteria that suggest that the MSLT should only be interpreted in the absence of sleep apnea and sufficient sleep prior to the MSLT (total sleep time equal to or greater than 6 hours) (AASM, 2005). Very limited clinical MSLT data are available in population samples, but data to date suggest that 3.9 percent of individuals may be positive for SOREMPs independent of daytime sleepiness (Singh et
al., 2005). Similarly, the Maintenance of Wakefulness Test (MWT), a test in which the subject is asked to try not to fall asleep in naps and sleep latency is measured, has been used to objectively measure alertness in drug trials but is not validated to demonstrate an ability to stay awake for patients at risk, for example in medicolegal cases and the evaluation of driving abilities (Littner et al., 2005).
As conducted, the MSLT and the MWT are time-consuming and expensive, and validation in the general population sample is lacking. How sleep apnea and prior sleep time in and outside the laboratory affects the occurrence of SOREMPs in MSLTs is not established. It is also unknown whether these tests may not be more valid after a night at home and verification of sleep with actigraphy or other procedures, a modification that would reduce cost in some cases. Finally, performance tests such as the psychomotor vigilance task, used commonly to evaluate performance after sleep deprivation, may have applications in this area (Dauvilliers and Buguet, 2005), especially if those tests can be adjusted to be used in ambulatory situations. Biochemical and imaging research aiming at discovering biomarkers of sleep debt and sleepiness is also needed.
Other Diagnostic Technologies
In addition to the development of ambulatory strategies, efforts are also currently under way to utilize other techniques to diagnose individuals who suffer chronic sleep loss or sleep disorders. These strategies include the development of genetic and biochemical tests for narcolepsy, magnetic resonance imaging (MRI) to visualize the upper airway in children with OSA, and acoustic reflectometry (a noninvasive ultrasound technique) of the upper airway to quantify anatomic obstruction of the upper airway in children (Mignot et al., 2002; Arens et al., 2003; Monahan et al., 2002; Donnelly et al., 2004; Abbott et al., 2004). Tests such as the standardized immobilization test or biochemical/imaging measures of brain iron metabolism are being developed to assist in the diagnosis and quantification of severity in restless leg syndrome (Allen and Earley, 2001; Garcia-Borreguero et al., 2004; Trenkwalder et al., 2005). Actigraphy and other methods are also used to estimate leg movement frequency in outpatients (Kazenwadel et al., 1995; Sforza et al., 2005). Video technologies may also be of value, especially in the diagnosis of individuals with night terrors. Finally, there is a need to establish novel procedures to objectively identify abnormalities in insomnia beyond the changes generally observed using sleep questionnaires, logs, and polysomnography (Roth and Drake, 2004). These may involve the use of spectral analysis (Perlis et al., 2001), microstructural cyclic alternating patterns analysis (Parrino et al., 2004), and functional neuroimaging (Drummond et al., 2004; Nofzinger, 2005).
The development of polysomnograms that are performed in a local hospital and telemonitored by a central sleep laboratory could allow for a single technician to monitor multiple studies from a central location. However, the reliability of these procedures varies (Gagnadoux et al., 2002).
Given the cumbersome nature and cost of the diagnosis and treatment of sleep disorders and sleep loss, the resultant inequities with regard to access, and in order to ensure future quality care, greater investment in the development of new, and validation of existing, diagnostic and therapeutic technologies is required. Improvement in portable monitoring techniques will likely enhance access to sleep diagnostic services. With the inadequate availability of sleep centers and sleep technicians, not only in the United States but more so worldwide, access to portable diagnostic screening procedures and streamlining initiation of treatment would clearly be advantageous. In particular, portable monitoring at level III (limited channel polysomnogram of four or more cardiopulmonary bioparameters) or level IV (testing of only one or two cardiopulmonary bioparameters) would help lower health costs and shorten waiting lists. In selected patient populations, portable monitoring in conjunction with inpatient split-night polysomnography or unattended autotitration of nasal CPAP could prove to be the most cost-effective and rational approach to most patients with a clinical profile for moderate to severe sleep apnea syndrome. Research in the design and evaluation of existing and novel diagnostic technologies is also needed in the area of insomnia, hypersomnia, and restless legs syndrome and periodic limb movements.
However, the rational application of technology needs to be coupled with the following:
A reexamination of the role of diagnostic testing in case identification and disease management, clarifying optimal use of objective physiological monitoring data (including data obtained from portable monitors) in clinical diagnostic and management algorithms.
Recognition that the development of new physiological monitoring tools needs to be guided by research that clarifies the short- and long-term clinical predictive information of specific channels (including responses to clinical interventions), or combinations of data. This should include consideration of the extent to which data from new technologies complement those from other techniques.
Standardization of diagnostic and treatment criteria, language, and technologies.
Investigation of how information from laboratory and portable diagnosis may interface as complementary rather than competitive technologies.
Investment by industry and the NIH in rigorous evaluation and outcome studies that are designed to test specific questions regarding technology applications in improving the efficiency of screening, case identification, and disease management.
Assessment of technologies utilizing indexes to examine their cost-effectiveness.
Development of technologies keeping in mind that treatment of sleep disorders requires a chronic care management scheme (see Chapter 9).
Specific efforts to develop and modify technologies for children.
Recommendation 6.1: The National Institutes of Health and the Agency for Healthcare Research and Quality should support the validation and development of existing and new diagnostic and therapeutic technologies.
The National Center on Sleep Disorders Research—working with the Trans-NIH Sleep Research Coordinating Committee, the Agency for Health Care Policy and Research, other federal agencies, and private industry—should support the evaluation and validation of existing diagnostic and therapeutic technologies. Further, development of new technologies such as ambulatory monitoring, biological markers, and imaging techniques should be vigorously supported.
AASM (American Academy of Sleep Medicine). 2005. The International Classification of Sleep Disorders. Westchester, IL: AASM.
Abbott MB, Donnelly LF, Dardzinski BJ, Poe SA, Chini BA, Amin RS. 2004. Obstructive sleep apnea: MR imaging volume segmentation analysis. Radiology 232(3):889–895.
Allen RP, Earley CJ. 2001. Restless legs syndrome: A review of clinical and pathophysiologic features. Journal of Clinical Neurophysiology 18(2):128–147.
Ancoli-Israel S, Clopton P, Klauber MR, Fell R, Mason W. 1997. Use of wrist activity for monitoring sleep/wake in demented nursing-home patients. Sleep 20(1):24–27.
Antle DW, Reid RA. 1988. Managing service capacity in an ambulatory care clinic. Hospital and Health Services Administration 33(2):201–211.
Arens R, McDonough JM, Corbin AM, Rubin NK, Carroll ME, Pack AI, Liu J, Udupa JK. 2003. Upper airway size analysis by magnetic resonance imaging of children with obstructive sleep apnea syndrome. American Journal of Respiratory and Critical Care Medicine 167(1):65–70.
Association of Polysomnographic Technologists. 1999. The APT Demographic, Salary, and Educational Needs Survey. Lenexa, KS: APT.
ATS (American Thoracic Society). 2004. Executive summary on the systematic review and practice parameters for portable monitoring in the investigation of suspected sleep apnea in adults. American Journal of Respiratory and Critical Care Medicine 169(10):1160– 1163.
Ayas NT, Pittman S, MacDonald M, White DP. 2003. Assessment of a wrist-worn device in the detection of obstructive sleep apnea. Sleep Medicine 4(5):435–442.
Bader G, Gillberg C, Johnson M, Kadesjö B, Rasmussen P. 2003. Activity and sleep in children with ADHD. Sleep 26:A136.
Banno K, Kryger MH. 2004. Factors limiting access to services for sleep apnea patients. Sleep Medicine Reviews 8(4):253–255.
Barker SJ. 2002. “Motion-resistant” pulse oximetry: A comparison of new and old models. Anesthesia and Analgesia 95(4):967–972.
Benca RM. 2005. Diagnosis and treatment of chronic insomnia: A review. Psychiatry Services 56(3):332–343.
Callahan NM, Redmon WK. 1987. Effects of problem-based scheduling on patient waiting and staff utilization of time in a pediatric clinic. Journal of Applied Behavioral Analysis 20(2):193–199.
Christl HL. 1973. Some methods of operations research applied to patient scheduling problems. Medical Progress Through Technology 2(1):19–27.
Dauvilliers Y, Buguet A. 2005. Hypersomnia. Dialogues in Clinical Neuroscience 7(4):347–356.
Donnelly LF, Shott SR, LaRose CR, Chini BA, Amin RS. 2004. Causes of persistent obstructive sleep apnea despite previous tonsillectomy and adenoidectomy in children with Down syndrome as depicted on static and dynamic cine MRI. American Journal of Roentgenology 183(1):175–181.
Drummond SP, Smith MT, Orff HJ, Chengazi V, Perlis ML. 2004. Functional imaging of the sleeping brain: Review of findings and implications for the study of insomnia. Sleep Medicine Reviews 8(3):227–242.
Flemons WW, Littner MR. 2003. Measuring agreement between diagnostic devices. Chest 124(4):1535–1542.
Flemons WW, Littner MR, Rowley JA, Gay P, Anderson WM, Hudgel DW, McEvoy RD, Loube DI. 2003. Home diagnosis of sleep apnea: A systematic review of the literature. An evidence review cosponsored by the American Academy of Sleep Medicine, the American College of Chest Physicians, and the American Thoracic Society. Chest 124(4):1543–1579.
Flemons WW, Douglas NJ, Kuna ST, Rodenstein DO, Wheatley J. 2004. Access to diagnosis and treatment of patients with suspected sleep apnea. American Journal of Respiratory and Critical Care Medicine 169(6):668–672.
Fry JM, DiPhillipo MA, Curran K, Goldberg R, Baran AS. 1998. Full polysomnography in the home. Sleep 21(6):635–642.
Gagnadoux F, Pelletier-Fleury N, Philippe C, Rakotonanahary D, Fleury B. 2002. Home unattended vs hospital telemonitored polysomnography in suspected obstructive sleep apnea syndrome: A randomized crossover trial. Chest 121(3):753–758.
Garcia-Borreguero D, Larrosa O, de la Llave Y, Granizo JJ, Allen R. 2004. Correlation between rating scales and sleep laboratory measurements in restless legs syndrome. Sleep Medicine 5(6):561–565.
George CF. 2001. Reduction in motor vehicle collisions following treatment of sleep apnoea with nasal CPAP. Thorax 56(7):508–512.
Goodwin JL, Kaemingk KL, Fregosi RF, Rosen GM, Morgan WJ, Sherrill DL, Quan SF. 2003. Clinical outcomes associated with sleep-disordered breathing in Caucasian and Hispanic children—the Tucson Children’s Assessment of Sleep Apnea Study (TuCASA). Sleep 26(5):587–591.
Iber C, Redline S, Kaplan Gilpin AM, Quan SF, Zhang L, Gottlieb DJ, Rapoport D, Resnick HE, Sanders M, Smith P. 2004. Polysomnography performed in the unattended home versus the attended laboratory setting—Sleep Heart Health Study methodology. Sleep 27(3):536–540.
Kapur V, Strohl KP, Redline S, Iber C, O’Connor G, Nieto J. 2002. Underdiagnosis of sleep apnea syndrome in U.S. communities. Sleep and Breathing 6(2):49–54.
Kazenwadel J, Pollmacher T, Trenkwalder C, Oertel WH, Kohnen R, Kunzel M, Kruger HP. 1995. New actigraphic assessment method for periodic leg movements (PLM). Sleep 18(8):689–697.
Kim H, Young T. 2005. Subjective daytime sleepiness: Dimensions and correlates in the general population. Sleep 28(5):625–634.
Krieger J, McNicholas WT, Levy P, De Backer W, Douglas N, Marrone O, Montserrat J, Peter JH, Rodenstein D, European Respiratory Society Task Force. 2002. Public health and medicolegal implications of sleep apnoea. European Respiratory Journal 20(6):1594– 1609.
Lavie P, Schnall RP, Sheffy J, Shlitner A. 2000. Peripheral vasoconstriction during REM sleep detected by a new plethysmographic method. Nature Medicine 6(6):606.
Littner MR, Kushida C, Wise M, Davila DG, Morgenthaler T, Lee-Chiong T, Hirshkowitz M, Daniel LL, Bailey D, Berry RB, Kapen S, Kramer M. 2005. Practice parameters for clinical use of the multiple sleep latency test and the maintenance of wakefulness test. Sleep 28(1):113–121.
Mignot E, Lammers GJ, Ripley B, Okun M, Nevsimalova S, Overeem S, Vankova J, Black J, Harsh J, Bassetti C, Schrader H, Nishino S. 2002. The role of cerebrospinal fluid hypocretin measurement in the diagnosis of narcolepsy and other hypersomnias. Archives of Neurology 59(10):1553–1562.
Monahan KJ, Larkin EK, Rosen CL, Graham G, Redline S. 2002. Utility of noninvasive pharyngometry in epidemiologic studies of childhood sleep-disordered breathing. American Journal of Respiratory and Critical Care Medicine 165(11):1499–1503.
Mykytyn IJ, Sajkov D, Neill AM, McEvoy RD. 1999. Portable computerized polysomnography in attended and unattended settings. Chest 115(1):114–122.
Nieto FJ, Young TB, Lind BK, Shahar E, Samet JM, Redline S, D’Agostino RB, Newman AB, Lebowitz MD, Pickering TG. 2000. Association of sleep-disordered breathing, sleep apnea, and hypertension in a large community-based study. Sleep Heart Health Study. Journal of the American Medical Association 283(14):1829–1836.
Nofzinger EA. 2005. Functional neuroimaging of sleep. Seminars in Neurology 25(1):9–18.
Olivares VE. 1990. Scheduling strategies. Radiology Management 12(3):29–30.
Pack AI, Gurubhagavatula I. 1999. Economic implications of the diagnosis of obstructive sleep apnea. Annals of Internal Medicine 130(6):533–534.
Parrino L, Ferrillo F, Smerieri A, Spaggiari MC, Palomba V, Rossi M, Terzano MG. 2004. Is insomnia a neurophysiological disorder? The role of sleep EEG microstructure. Brain Research Bulletin 63(5):377–383.
Perlis ML, Smith MT, Andrews PJ, Orff H, Giles DE. 2001. Beta/Gamma EEG activity in patients with primary and secondary insomnia and good sleeper controls. Sleep 24(1): 110–117.
Pittman SD, Ayas NT, MacDonald MM, Malhotra A, Fogel RB, White DP. 2004. Using a wrist-worn device based on peripheral arterial tonometry to diagnose obstructive sleep apnea: In-laboratory and ambulatory validation. Sleep 27(5):923–933.
Portier F, Portmann A, Czernichow P, Vascaut L, Devin E, Benhamou D, Cuvelier A, Muir JF. 2000. Evaluation of home versus laboratory polysomnography in the diagnosis of sleep apnea syndrome. American Journal of Respiratory and Critical Care Medicine 162(3 Pt 1):814–818.
Quan SF, Griswold ME, Iber C, Nieto FJ, Rapoport DM, Redline S, Sanders M, Young T. 2002. Short-term variability of respiration and sleep during unattended nonlaboratory polysomnography—the Sleep Heart Health Study. Sleep 25(8):843–849.
Redline S, Sanders M. 1999. A quagmire for clinicians: When technological advances exceed clinical knowledge. Thorax 54(6):474–475.
Redline S, Sanders MH, Lind BK, Quan SF, Iber C, Gottlieb DJ, Bonekat WH, Rapoport DM, Smith PL, Kiley JP. 1998. Methods for obtaining and analyzing unattended polysomnography data for a multicenter study. Sleep Heart Health Research Group. Sleep 21(7):759–767.
Rodsutti J, Hensley M, Thakkinstian A, D’Este C, Attia J. 2004. A clinical decision rule to prioritize polysomnography in patients with suspected sleep apnea. Sleep 27(4):694–699.
Ross SD, Sheinhait IA, Harrison KJ, Kvasz M, Connelly JE, Shea SA, Allen IE. 2000. Systematic review and meta-analysis of the literature regarding the diagnosis of sleep apnea. Sleep 23(4):519–532.
Roth T, Drake C. 2004. Evolution of insomnia: Current status and future direction. Sleep Medicine (suppl 1):S23–S30.
Ryan KL, Fedullo PF, Davis GB, Vasquez TE, Moser KM. 1988. Perfusion scan findings understate the severity of angiographic and hemodynamic compromise in chronic thromboembolic pulmonary hypertension. Chest 93(6):1180–1185.
Sforza E, Johannes M, Claudio B. 2005. The PAM-RL ambulatory device for detection of periodic leg movements: A validation study. Sleep Medicine 6(5):407–413.
Shahar E, Whitney CW, Redline S, Lee ET, Newman AB, Javier Nieto F, O’Connor GT, Boland LL, Schwartz JE, Samet JM. 2001. Sleep-disordered breathing and cardiovascular disease: Cross-sectional results of the Sleep Heart Health Study. American Journal of Respiratory and Critical Care Medicine 163(1):19–25.
Sharafkhaneh A, Richardson P, Hirshkowitz M. 2004. Sleep apnea in a high risk population: A study of Veterans Health Administration beneficiaries. Sleep Medicine 5(4):345–350.
Singh M, Drake C, Roehrs T, Koshorek G, Roth T. 2005. The prevalence of SOREMPs in the general population. Sleep 28(abstract suppl):A221.
Sulit L, Storfer-Isser A, Kirchner HL, Redline S. 2006. Differences in polysomnography predictors for hypertension and impaired glucose tolerance. Sleep 29(6):777–783.
Tachibana N, Ayas TA, White DP. 2005. A quantitative assessment of sleep laboratory activity in the United States. Journal of Clinical Sleep Medicine 1(1):23–26.
Tice JA. 2005. Portable Devices for Home Testing for Obstructive Sleep Apnea. San Francisco: California Technology Assessment Forum.
Trenkwalder C, Paulus W, Walters AS. 2005. The restless legs syndrome. Lancet Neurology 4(8):465–475.
Westbrook PR, Levendowski DJ, Cvetinovic M, Zavora T, Velimirovic V, Henninger D, Nicholson D. 2005. Description and validation of the apnea risk evaluation system: A novel method to diagnose sleep apnea-hypopnea in the home. Chest 128(4):2166–2175.
Whitney CW, Gottlieb DJ, Redline S, Norman RG, Dodge RR, Shahar E, Surovec S, Nieto FJ. 1998. Reliability of scoring respiratory disturbance indices and sleep staging. Sleep 21(7):749–757.
Zou D, Grote L, Peker Y, Lindblad U, Hedner J. 2006. Validation a portable monitoring device for sleep apnea diagnosis in a population based cohort using synchronized home polysomnography. Sleep 29(3):367–374.