Summary: Background and aim: Narcolepsy is an uncommon hypothalamic disorder of presumed autoimmune origin that usually requires lifelong treatment. This paper aims to provide evidence‐based guidelines for the management of narcolepsy in both adults and children.Methods: The European Academy of Neurology (EAN), European Sleep Research Society (ESRS) and European Narcolepsy Network (EU‐NN) nominated a task force of 18 narcolepsy specialists. According to the EAN recommendations, 10 relevant clinical questions were formulated in PICO format. Following a systematic review of the literature (performed in Fall 2018 and updated in July 2020) recommendations were developed according to the GRADE approach.Results: A total of 10,247 references were evaluated, 308 studies were assessed and 155 finally included. The main recommendations can be summarized as follows: (i) excessive daytime sleepiness in adults—scheduled naps, modafinil, pitolisant, sodium oxybate (SXB), solriamfetol (all strong), methylphenidate, amphetamine derivates (both weak); (ii) cataplexy in adults—SXB, venlafaxine, clomipramine (all strong) and pitolisant (weak); (iii) excessive daytime sleepiness in children—scheduled naps, SXB (both strong), modafinil, methylphenidate, pitolisant, amphetamine derivates (all weak); (iv) cataplexy in children—SXB (strong), antidepressants (weak). Treatment choices should be tailored to each patient’s symptoms, comorbidities, tolerance and risk of potential drug interactions.Conclusion: The management of narcolepsy involves non‐pharmacological and pharmacological approaches with an increasing number of symptomatic treatment options for adults and children that have been studied in some detail.

Summary: Background and purpose: Narcolepsy is an uncommon hypothalamic disorder of presumed autoimmune origin that usually requires lifelong treatment. This paper aims to provide evidence‐based guidelines for the management of narcolepsy in both adults and children.Methods: The European Academy of Neurology (EAN), European Sleep Research Society (ESRS), and European Narcolepsy Network (EU‐NN) nominated a task force of 18 narcolepsy specialists. According to the EAN recommendations, 10 relevant clinical questions were formulated in PICO format. Following a systematic review of the literature (performed in Fall 2018 and updated in July 2020) recommendations were developed according to the GRADE approach.Results: A total of 10,247 references were evaluated, 308 studies were assessed and 155 finally included. The main recommendations can be summarized as follows: (i) excessive daytime sleepiness (EDS) in adults–scheduled naps, modafinil, pitolisant, sodium oxybate (SXB), solriamfetol (all strong); methylphenidate, amphetamine derivatives (both weak); (ii) cataplexy in adults–SXB, venlafaxine, clomipramine (all strong) and pitolisant (weak); (iii) EDS in children–scheduled naps, SXB (both strong), modafinil, methylphenidate, pitolisant, amphetamine derivatives (all weak); (iv) cataplexy in children–SXB (strong), antidepressants (weak). Treatment choices should be tailored to each patient's symptoms, comorbidities, tolerance and risk of potential drug interactions.Conclusion: The management of narcolepsy involves non‐pharmacological and pharmacological approaches with an increasing number of symptomatic treatment options for adults and children that have been studied in some detail.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Chronotype is the propensity for a person to sleep at a particular time during 24-hour. It is largely regulated by the circadian clock but constrained by work obligations to specific sleep schedules. The discrepancy between biological and social time can be described as social jetlag (SJL), which is highly prevalent in modern society and associated with health problems. SJL and chronotypes have been widely studied in West countries, but never been described in China. Objective: We characterized the chronotypes and SJL in mainland China objectively by analyzing a database of Chinese sleep-wake pattern recorded by up-to-date wearable devices. Methods: We analyzed 71176 anonymous Chinese who were continuously recorded by wearable devices at least for one week between April and July in 2017. The chronotypes were assessed in 49573 subjects by the adjusted mid-point of sleep on free days (MSFsc). Early, intermediate and late chronotypes were defined by arbitrary cut-offs of MSFsc< 3 h, between 3 h and 5 h, and > 5 h. In all subjects, SJL was calculated as the difference between mid-points of sleep on free days and work days. The correlations between SJL and age/ body mass index (BMI)/MSFsc were assessed by Pearson’s correlation. Random forest was used to characterize which factors (age, BMI, sex, nocturnal and daytime sleep durations and exercise) mostly contribute to SJL and MSFsc. Results: The mean total sleep durations of Chinese is about 7-hour, with females sleep on average 17 minutes longer than males. People taking longer naps sleep less during the night, but they have longer total 24-hour sleep durations. MSFsc follows a normal distribution, and the percentages of early, intermediate, and late chronotypes are approximately 26.76% (13266/49573), 58.59% (29045/49573), and 14.64% (7257/49573). Adolescents are later types compared to adults. Age is the most important predictor of MSFsc suggested by our random forest model (relative feature importance: 0.772), and no gender differences are found in chronotypes. SJL follows a normal distribution and 17.07% (12151/71176) of Chinese have SJL larger than 1-hour. 31.53% (22442/71176) Chinese have SJL<0. 53.72% (7127/13266), 25.46% (7396/29045) and 12.71% (922/7257) of the early, intermediate and late chronotypes have SJL<0, respectively. SJL correlates with MSFsc (r=0.54, P<.001), but not with BMI (r=0.004, P=.297). Random forest model suggests that age, nocturnal sleep and daytime nap durations are the features contributing to SJL (their relative feature importance is 0.441, 0.349 and 0.204, respectively). Conclusions: Our data suggest a higher proportion of early compared to late chronotypes in Chinese. Chinese have smaller SJL than the results reported in European populations, and more than half of the early chronotypes have negative SJL. In Chinese population SJL does not associated with BMI. People of later chronotypes and long sleepers suffer more from SJL.

Purpose: Narcolepsy type-1 (NT1) is a rare chronic neurological sleep disorder with excessive daytime sleepiness (EDS) as usual first and cataplexy as pathognomonic symptom. Shortening the NT1 diagnostic delay is the key to reduce disease burden and related low quality of life. Here we investigated the changes of diagnostic delay over the diagnostic years (1990– 2018) and the factors associated with the delay in Europe. Patients and Methods: We analyzed 580 NT1 patients (male: 325, female: 255) from 12 European countries using the European Narcolepsy Network database. We combined machine learning and linear mixed-effect regression to identify factors associated with the delay. Results: The mean age at EDS onset and diagnosis of our patients was 20.9± 11.8 (mean ± standard deviation) and 30.5± 14.9 years old, respectively. Their mean and median diagnostic delay was 9.7± 11.5 and 5.3 (interquartile range: 1.7− 13.2 years) years, respectively. We did not find significant differences in the diagnostic delay over years in either the whole dataset or in individual countries, although the delay showed significant differences in various countries. The number of patients with short (≤ 2-year) and long (≥ 13-year) diagnostic delay equally increased over decades, suggesting that subgroups of NT1 patients with variable disease progression may co-exist. Younger age at cataplexy onset, longer interval between EDS and cataplexy onsets, lower cataplexy frequency, shorter duration of irresistible daytime sleep, lower daytime REM sleep propensity, and being female are associated with longer diagnostic delay. Conclusion: Our findings contrast the results of previous studies reporting shorter delay over time which is confounded by calendar year, because they characterized the changes in diagnostic delay over the symptom onset year. Our study indicates that new strategies such as increasing media attention/awareness and developing new biomarkers are needed to better detect EDS, cataplexy, and changes of nocturnal sleep in narcolepsy, in order to shorten the diagnostic interval.

Narcolepsy is a rare life-long disease that exists in two forms, narcolepsy type-1 (NT1) or type-2 (NT2), but only NT1 is accepted as clearly defined entity. Both types of narcolepsies belong to the group of central hypersomnias (CH), a spectrum of poorly defined diseases with excessive daytime sleepiness as a core feature. Due to the considerable overlap of symptoms and the rarity of the diseases, it is difficult to identify distinct phenotypes of CH. Machine learning (ML) can help to identify phenotypes as it learns to recognize clinical features invisible for humans. Here we apply ML to data from the huge European Narcolepsy Network (EU-NN) that contains hundreds of mixed features of narcolepsy making it difficult to analyze with classical statistics. Stochastic gradient boosting, a supervised learning model with built-in feature selection, results in high performances in testing set. While cataplexy features are recognized as the most influential predictors, machine find additional features, e.g. mean rapid-eye-movement sleep latency of multiple sleep latency test contributes to classify NT1 and NT2 as confirmed by classical statistical analysis. Our results suggest ML can identify features of CH on machine scale from complex databases, thus providing ‘ideas’ and promising candidates for future diagnostic classifications.