Practice parameters for the indications for polysomnography and related procedures: An update for 2005

Stanford University Center of Excellence for Sleep Disorders, Stanford, CA, USA.
Sleep (Impact Factor: 5.06). 05/2005; 28(4):499-521.
Source: PubMed

ABSTRACT These practice parameters are an update of the previously-published recommendations regarding the indications for polysomnography and related procedures in the diagnosis of sleep disorders. Diagnostic categories include the following: sleep related breathing disorders, other respiratory disorders, narcolepsy, parasomnias, sleep related seizure disorders, restless legs syndrome, periodic limb movement sleep disorder, depression with insomnia, and circadian rhythm sleep disorders. Polysomnography is routinely indicated for the diagnosis of sleep related breathing disorders; for continuous positive airway pressure (CPAP) titration in patients with sleep related breathing disorders; for the assessment of treatment results in some cases; with a multiple sleep latency test in the evaluation of suspected narcolepsy; in evaluating sleep related behaviors that are violent or otherwise potentially injurious to the patient or others; and in certain atypical or unusual parasomnias. Polysomnography may be indicated in patients with neuromuscular disorders and sleep related symptoms; to assist in the diagnosis of paroxysmal arousals or other sleep disruptions thought to be seizure related; in a presumed parasomnia or sleep related seizure disorder that does not respond to conventional therapy; or when there is a strong clinical suspicion of periodic limb movement sleep disorder. Polysomnography is not routinely indicated to diagnose chronic lung disease; in cases of typical, uncomplicated, and noninjurious parasomnias when the diagnosis is clearly delineated; for patients with seizures who have no specific complaints consistent with a sleep disorder; to diagnose or treat restless legs syndrome; for the diagnosis of circadian rhythm sleep disorders; or to establish a diagnosis of depression.

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Available from: Milton Kramer, Jul 26, 2015
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    • "Despite these theoretical advantages, the most recent clinical guidelines for use of PM to diagnose OSA [1] did not consider devices that were capable of measuring sleep. This was because there were no new data available comparing such devices to PSG since previous guidelines [3], stated that evidence was lacking to recommend their clinical use. "
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    ABSTRACT: This study examined the impact of using two abbreviated signal montages on the accuracy, precision and inter-scorer reliability of polysomnography (PSG) sleep and arousal scoring, compared to a standard reference montage, in a cohort of patients investigated for obstructive sleep apnoea (OSA). One abbreviated montage incorporated two signals dedicated to sleep and arousal scoring, and the other incorporated a single signal. Four scorers from two laboratories each scored 15 PSGS four times in random order: once using each abbreviated montage and twice using the reference montage. Use of the two-signal montage resulted in small changes in the distribution of sleep stages, a reduction in the arousal index and resultant reductions in sleep and arousal scoring agreement. For the one-signal montage, although similar magnitude sleep stage distribution changes were observed, there were larger reductions in the arousal index, and sleep and arousal scoring accuracy. Additionally, using the one-signal montage, there were statistically significant reductions in the precision of summary statistics including total sleep time (TST) and the amount of rapid eye movement (REM) sleep scored, and reductions in the inter-scorer reliability of REM sleep and arousal scoring. These findings demonstrate that abbreviated signal montages may result in underestimation of the arousal index and, depending on the montage, poorer precision in TST and REM sleep scoring, with potential consequences for apnoea-hypopnoea index (AHI) measures and OSA diagnosis. The results highlight the importance of careful evaluation of PSG results when using portable devices that have restricted signals, and they offer guidance for future PSG and portable monitoring standards. Copyright © 2014 Elsevier B.V. All rights reserved.
    Sleep Medicine 11/2014; 16(1). DOI:10.1016/j.sleep.2014.11.005 · 3.10 Impact Factor
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    • "Continuous positive airway pressure (CPAP) has been established as the primary treatment for patients with OSA [4]. The CPAP treatment involves using a device that provides pressurized air through a nasal or full-face mask and prevents upper airway collapse [5]. Although the efficacy of CPAP therapy has been consistently demonstrated, adherence to CPAP has been recognized as an important limitation of treatment [6] [7] [8]. "
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    ABSTRACT: Study Objectives Examine whether subtypes of continuous positive airway pressure (CPAP) user profiles could be identified, and determine predictors of CPAP subgroup membership. Design A retrospective, correlational approach was used. Subjects attended clinic where a CPAP download was performed and questionnaires were completed. Additional information was obtained from the electronic medical record. Setting Miami VA Sleep Clinic. Participants Obstructive sleep apnea patients (N=207). Measurements Three adherence variables comprised the profile: % of nights of CPAP use, % of nights of CPAP use >4 hours and average nightly use in minutes. Predictors included age, AHI, time since CPAP therapy was initiated, CPAP pressure, residual AHI, BMI, social-cognitive variables, insomnia, sleepiness, psychiatric and medical comorbidities. Results Latent profile analysis was used to identify CPAP user profiles. Three subgroups were identified and labeled “Non-Adherers”, “Attempters” and “Adherers”. Non-Adherers (37.6% of the sample) used CPAP for an average of 37 minutes nightly, used CPAP 18.2% of nights and used CPAP <4 hour 6.2 % of nights. Attempters (32.9%) used CPAP for 156 minutes on average, used CPAP 68.2% of nights and used CPAP >4 hour 29.3% of nights. Adherers (29.5%) used CPAP for 392 minutes, used CPAP 95.4% of nights and used CPAP >4 hour 86.2% of nights. Self-efficacy, insomnia, AHI, time since CPAP was initiated, and CPAP pressure predicted CPAP subgroup membership. Conclusion Sixty-seven percent of users (Non-Adherers, Attempters) had suboptimal adherence. Understanding CPAP use profiles and their predictors provide identification of those who may require additional intervention to improve adherence.
    Sleep Medicine 09/2014; 16(3). DOI:10.1016/j.sleep.2014.08.013 · 3.10 Impact Factor
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    • "expensive to acquire, to maintain, and to replace. The golden standard in medicine to observe sleep remains polysomnography [7], where the patient has to spent at least one night in a sleeping lab while being monitored through typically more than 20 different sensors. However, such an environment is often uncomfortable to sleep in, as these sensors often need to be wired to the side of the bed and is very different from what the patient is used to at home. "
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    ABSTRACT: The monitoring of sleep by quantifying sleeping time and quality is pivotal in many preventive health care scenarios. A substantial amount of wearable sensing products have been introduced to the market for just this reason, detecting whether the user is either sleeping or awake. Assessing these devices for their accuracy in estimating sleep is a daunting task, as their hardware design tends to be different and many are closed-source systems that have not been clinically tested. In this paper, we present a challenging benchmark dataset from an open source wrist-worn data logger that contains relatively high-frequent (100Hz) 3D inertial data from 42 sleep lab patients, along with their data from clinical polysomnography. We analyse this dataset with two traditional approaches for detecting sleep and wake states and propose a new algorithm specifically for 3D acceleration data, Estimation of Stationary Sleep-segments (ESS). Results show that all three methods generally over-estimate for sleep, with our method performing slightly better (74% overall mean accuracy) than the traditional activity count-based methods.
    IEEE International Conference on Healthcare Informatics 2014 (ICHI 2014), Verona, Italy; 09/2014
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