Treating Narcolepsy and Idiopathic Hypersomnia

Teleworking during a pandemic: perspective of an idiopathic hypersomnia patient.

Xywav (calcium, magnesium, potassium, and sodium oxybates), a CNS depressant distributed by Jazz Pharmaceuticals, was approved in August 2021 for treatment of Idiopathic Hypersomnia (IH), a condition characterized by recurrent...

To determine clinicians' patterns of use of the International Classification of Sleep Disorders (ICSD) and their ratings of its utility and organization. A telephone survey was conducted in one clinician practicing at each of 206 sleep centers accredited by the American Academy of Sleep Medicine (AASM). Descriptive analyses were performed, and ratings for various sleep classifications were compared with ANOVA and post- hoc tests. Clinicians at 230/251 (92%) of contacted centers agreed to participate, and data from 206/251 (82%) were actually collected. The ICSD was used by 91.7% of clinicians for clinical decision making but by lower percentages for billing or medical record keeping. Features of the ICSD pertaining to clinical descriptions and differential diagnosis were rated as most useful. The Association of Sleep Disorders Centers (ASDC) classification was rated more highly than ICSD in organization and ease of use. The ICSD and ASDC systems were rated more highly than other sleep disorders classifications in terms of organization, "fit" to patients, and ease of use. The ICSD is used widely by clinicians, primarily for diagnostic purposes, and is preferred over more general sleep disorders classification systems. However, more favorable ratings for the ASDC compared to ICSD indicate the value clinicians place on easy-to-use, symptom-oriented classifications.

Clinical Guidelines for the Use of Unattended Portable Monitors in the Diagnosis of Obstructive Sleep Apnea in Adult Patients

IntroductionThe multiple sleep latency test (MSLT) was originally validated as a diagnostic test for narcolepsy.1 Sleep disorders clinics use the MSLT to investigate a variety of presenting complaints. There is...

The recognition and growth of sleep medicine as a clinical specialty is manifested by the inclusion of sleep disorders in disease classification systems from the 1970s. The task of developing a classification system for sleep disorders has been undertaken by three organizations, namely, the World Health Organization (WHO), the American Psychiatric Association (APA) and associations and societies involved in sleep medicine as a clinical discipline, most recently the American Academy of Sleep Medicine (AASM). Three different classification systems have evolved: first, with sleep disorders being added to a general coding system, the International Classification of Diseases (ICD)(World Health Organization 1977, 1994, 2011); second, designed for use by mental health professionals, the Diagnostic and Statistical Manual of Mental Disorders (DSM)(American Psychiatric Association 1994, 2013); and third, developed by and for specialists in sleep medicine, the International Classification of Sleep Disorders (ICSD)(Association of Sleep Disorders Centers 1979, 1990, 1997; American Academy of Sleep Medicine 2005, 2014). A summary of the development of these three nosologies from the 1970s to the present is summarized in Table 2.1. In examining their development, it is important to recognize how the classification systems should, and do, evolve in response to other classification systems, as well as advances in scientific knowledge and clinical experience.

Etiologies of Sleep-Related Movement Disorders (SRMD)

Excessive daytime sleepiness (EDS) is a frequent cause for consultation and a defining symptom of narcolepsy and idiopathic hypersomnia (IH). The associated mechanisms remain unclear. Lipocalin-type prostaglandin D synthase (LPGDS) is a plausible sleep-inducing candidate. This study is to compare cerebral spinal fluid (CSF) and serum LPGDS levels in patients group with hypersomnia of central origin, including those with narcolepsy type 1 (NT1) and type 2 (NT2) and IH, to those in healthy controls (Con). Serum LPGDS, CSF LPGDS, and CSF hypocretin-1(Hcrt-1) levels were measured by ELISA in 122 narcolepsy patients (106 NT1 and 16 NT2), 27 IH, and 51Con. LPGDS levels in CSF (p = 0.02) and serum (p < 0.001) were 22%-25% lower in control subjects than in patients with EDS complaints, including NT1, NT2, and IH. In contrast to significant differences in CSF Hcrt-1 levels, CSF L-PGDS levels and serum L-PGDS were comparable among NT1, NT2, and IH (p > 0.05), except for slightly lower serum LPGDS in IH than in NT1 (p = 0.01). Serum L-PGDS correlated modestly and negatively to sleep latency on MSLT (r = -0.227, p = 0.007) in hypersomnia subjects. As a somnogen-producing enzyme, CSF/serum LPGDS may serve as a new biomarker for EDS of central origin and imply a common pathogenetic association, but would complement rather than replaces orexin markers.

Successful treatment with levothyroxine for idiopathic hypersomnia patients with subclinical hypothyroidism

Types of Hypersomnia

The aim of the study was to inspect the acoustic properties and sleep characteristics of a preapneic snoring sound. The feasibility of forecasting upcoming respiratory events by snoring sound was also investigated. Participants with habitual snoring or a heavy breathing sound during sleep were recruited consecutively. Polysomnography was conducted, and snoring-related breathing sound was recorded simultaneously. Acoustic features and sleep features were extracted from 30-second samples, and a machine learning algorithm was used to establish 2 prediction models. A total of 74 eligible participants were included. Model 1, tested by 5-fold cross-validation, achieved an accuracy of 0.92 and an area under the curve of 0.94 for respiratory event prediction. Model 2, with acoustic features and sleep information tested by Leave-One-Out cross-validation, had an accuracy of 0.78 and an area under the curve of 0.80. Sleep position was found to be the most important among all sleep features contributing to the performance of the 2 models. Preapneic sound presented unique acoustic characteristics, and snoring-related breathing sound could be deployed as a real-time apneic event predictor. The models, combined with sleep information, serve as a promising tool for an early warning system to forecast apneic events. Wang B, Yi X, Gao J, etal. Real-time prediction of upcoming respiratory events via machine learning using snoring sound signal. J Clin Sleep Med. 2021;17(9):1777-1784.

To assess the impact of the American Academy of Sleep Medicine (AASM) 2012Rec criteria on the surgical success rate and polysomnography (PSG) parameters of patients with obstructive sleep apnea (OSA). A prospective clinical trial. A total of 60 patients who performed surgery for treatment of OSA at the sleep breathing disorder center of a tertiary referral university hospital from October 1, 2015, to September 30, 2016, were enrolled. Preoperative and postoperative PSG were performed and scored using AASM 2007Rec and AASM 2012Rec . Surgical success was determined by criteria 1 (postoperative Apnea-Hypopnea Index [AHI] <20% and 50% reduction) and criteria 2 (postoperative AHI <10), and surgical success rates according to AASM 2007Rec and AASM 2012Rec were compared. PSG parameters including AHI, hypopnea index, respiratory effort-related arousals, respiratory disturbance index, and supine AHI scored significantly higher with AASM 2012Rec compared to AASM 2007Rec criteria, both preoperatively and postoperatively (all P < 0.05). Furthermore, when compared to the AASM 2007Rec criteria, AASM 2012Rec resulted in increased proportion of patients with severe OSA (70.0% vs. 95.0%; P < 0.001). For the surgical outcomes, AASM 2012Rec showed significantly lower surgical success rate both in criteria 1 (51.7% vs. 38.3%) and criteria 2 (35.0% vs. 18.3%) compared to the AASM 2007Rec , respectively (P < 0 .05). This study demonstrated that AASM 2012Rec tends to decrease the success rate of surgical outcome for OSA patients, suggesting that defining success of surgical treatment for OSA could be changed dramatically according to the hypopnea scoring criteria. 4 Laryngoscope, 130:825-831, 2020.

One of the goals of the American Academy of Sleep Medicine (AASM) is to provide clear, evidence-based recommendations in our clinical practice guidelines. Periodically, the AASM will assess and update the process by which these guidelines are developed so that it is in line with the standards currently being used for guideline development. The AASM is now taking the next step forward by fully adopting GRADE (Grading of Recommendation Assessment, Development and Evaluation) as the methodology used for evaluating evidence and forming clinical practice guidelines recommendations. Starting this year, AASM recommendations will be based on the following four interdependent domains: 1. quality of evidence; 2. balance of desirable and undesirable consequences; 3. patients' values and preferences; and 4. resource use (when known). AASM strengths of recommendations will be dichotomized into two categories: "Strong" and "Weak," either for or against a patient-care strategy. In an effort to provide clarity and transparency, all AASM recommendations will be actionable statements that include the specific patient population for which the patient-care strategy is recommended, and clearly define the comparator against which the patient-care strategy was evaluated. In some recommendations, the comparator will be an alternative patient-care strategy (e.g., a "gold standard" or previously available alternative), while in other recommendations the comparator will be a placebo or no treatment; this is determined by the availability of evidence, and analyses decisions made by the AASM task force. Implementation of the complete GRADE criteria by the AASM allows us the best path forward towards continuing to provide high quality clinical practice guidelines.

Overview of recent development in the diagnosis and treatment of the periodic limb movement disorder (PLMD). The last years have seen several changes concerning PLMD diagnosis, detailed in the third edition of the International Classification of Sleep Disorders (ICSD-3) in 2014 as well as in periodic limb movements during sleep (PLMS) scoring criteria published by the American Academy of Sleep Medicine and by the World Association of Sleep Medicine, with these publications motivated by the need to distinguish clinically relevant and specific observations from incidental findings. Information on the treatment of PLMD continues to be largely absent in adults but slowly accumulating for the treatment of PLMD in children. PLMD remains an understudied sleep disorder. While PLMS are frequent, the delineation of a form where these PLMS are clinically relevant remains a challenge in clinical practice and the scientific study of this disorder.

As a high proportion of patients referred to the sleep clinic are evaluated for the possibility of sleep-disordered breathing, it is important to perform a comprehensive assessment of the patient’s baseline pulmonary status by obtaining a thorough medical history and performing a complete physical examination geared toward pulmonary-based issues. If clinical suspicion of impaired pulmonary function is suspected based on initial evaluation, then testing to assess the type and severity of pulmonary disorders is warranted. The American Academy of Sleep Medicine (AASM) International Classification of Sleep Disorders: Diagnostic & Coding Manual 3 outlines criteria and definitions for a vast array of sleep-related hypoventilation disorders. The diagnostic criteria for these disorders incorporate the degree of hypoxia as ascertained from oxygen saturation monitoring during polysomnography and hypercapnia determined by carbon dioxide level based on arterial blood gas measurements obtained during sleep or the use of acceptable surrogates including the end-tidal CO2 or transcutaneous CO2, thereby highlighting the need to understand respiratory monitoring methods and techniques. This chapter focuses upon specific aspects of diagnostic testing performed in the evaluation of pulmonary disease that may be of concern in the context of sleep-disordered breathing (SDB) assessment and also reviews facets of respiratory monitoring performed during polysomnographic appraisal of sleep disorders.