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ObjectivesTo highlight the slow-wave sleep (SWS) fragmentation and validate the video-polysomnographic (vPSG) criteria and cutoffs for the diagnosis of disorders of arousal (DOA) in children, as already reported in adults.MethodsOne hundred children (66 boys, 11.0 ± 3.3 years) with frequent episodes of DOA and 50 nonparasomniac children (32 boys, 10.9 ± 3.9 years) underwent vPSG recording to quantify SWS characteristics (number of N3 sleep interruptions, fragmentation index, slow/mixed and fast arousal ratios, and indexes per hour) and associated behaviors. We compared SWS characteristics in the 2 groups and defined the optimal cutoff values for the diagnosis of DOA using receiver operating characteristic curves.ResultsPatients with DOA had higher amounts of N3 and REM sleep, number of N3 interruptions, SWS fragmentation, and slow/mixed arousal indexes than controls. The highest area under the curve (AUC) values were obtained for SWS fragmentation and slow/mixed arousal indexes with satisfactory classification performances (AUC 0.80, 95% confidence interval [CI] 0.73–0.87; AUC 0.82, 95% CI 0.75–0.89). SWS fragmentation index cutoff value of 4.1/h reached a sensitivity of 65.0% and a specificity of 84.0%. Slow/mixed arousal index cutoff of 3.8/h reached a sensitivity of 69.0% and a specificity of 82.0%. At least one parasomniac episode was recorded in 63.0% of patients and none of the controls. Combining behavioral component by vPSG increased sensitivity of both biomarkers to 83% and 89%, respectively.ConclusionsWe confirmed that SWS fragmentation and slow/mixed arousal indexes are 2 relevant biomarkers for the diagnosis of DOA in children, with different cutoffs obtained than those validated in adults.Classification of EvidenceThis study provides Class III evidence that SWS fragmentation and slow/mixed arousal indexes on vPSG accurately identify children with DOA.

ObjectivesTo assess the frequency and determinants of depressive symptoms and suicidal thoughts in adults with narcolepsy type 1 (NT1) and controls, as well as the changes after NT1 management and the risk factors of major depressive episode (MDE) and suicide risk (SR) in NT1.MethodsTwo hundred ninety-seven patients with NT1 (age 39 ± 17 years, 172 drug-free) and 346 controls (age 38 ± 16 years) underwent a comprehensive clinical evaluation including the Beck Depression Inventory-II (BDI-II) self-questionnaire, with 1 item on suicidal thoughts. One hundred one drug-free patients with NT1 completed the BDI-II a second time during treatment. In 162 patients with NT1, the face-to-face Mini International Neuropsychiatric Interview was performed to formally diagnose current MDE and SR.ResultsBDI-II total scores were higher in patients with NT1 than controls and in untreated than treated patients. Patients with moderate to severe BDI-II scores (24.9%) were less educated, were more frequently obese, and had more severe narcolepsy symptoms, more autonomic dysfunctions, and poorer quality of life. Results were unchanged in models adjusted for NT1 medication intake. Suicidal thoughts were more frequent in untreated patients than controls (22.7% vs 12.4%). Patients with suicidal thoughts were more likely to be men and to have more severe narcolepsy symptoms. After narcolepsy management, BDI-II total score and suicidal thoughts decreased. MDE was diagnosed in 29 (18.1%) and SR in 27 (16.9%) patients.ConclusionsDepression, depressive symptoms, suicidal thoughts, and SR were frequent in patients with NT1, especially those without treatment, and were associated with NT1 severity. Depressive symptoms and suicidal thoughts improved after NT1 management.

STUDY OBJECTIVES: To identify the most relevant clinical and video-polysomnographic characteristics of patients with sleep-related dissociative disorders (SRDDs) and to propose a framework for new diagnostic criteria. METHODS: We searched potential SRDD cases from the scientific literature and from a database of patients referred for clinical and video-polysomnographic assessment in a single sleep disorders center for disruptive nocturnal behaviors (n = 731). The most relevant clinical and neurophysiological characteristics of the cases were extracted and a descriptive analysis was performed. RESULTS: Twenty-six SRDD cases (8 new and 18 previously published cases) were reviewed. Almost all cases of SRDDs occurred in a context of past traumatic events or abuse and were associated with at least 1 comorbid psychiatric disorder. We highlighted 4 relevant clinical characteristics of SRDD useful for the differential diagnosis with parasomnias: episodes of long duration of more than 1 hour (90.9%), self-inflicted injuries (83.3%), occurrence while awake close to bedtime (35.7%), and the presence of daytime dissociative symptoms (72.7%). The video-polysomnography documented typical episodes of SRDD with prolonged wakefulness before, during, and after the event in 11/26 cases. New diagnostic criteria for SRDD were proposed, with 3 levels of certainty for the diagnosis based on clinical, video-polysomnographic, and homemade video findings. CONCLUSIONS: More than 30 years after its formal identification, SRDD is not currently recognized as an official diagnostic entity. We better delineate the clinical and neurophysiological features of SRDD and propose a framework for its reinstatement in the next revisions of the sleep and psychiatric disorders classifications. CITATION: Lopez R, Lefevre L, Barateau L, Micoulaud-Franchi J-A, Dauvilliers Y, Schenck CH. A series of 8 cases of sleep-related psychogenic dissociative disorders and proposed updated diagnostic criteria. J Clin Sleep Med. 2022;18(2):563–573.

Background and ObjectivesTo explore the first coronavirus disease 2019 (COVID-19) lockdown effect on sleep symptoms in patients with narcolepsy, idiopathic hypersomnia (IH), and restless legs syndrome (RLS).MethodsBetween March and May 2020, a sample of adult patients regularly followed up in a Reference Hospital Sleep Unit (299 with narcolepsy, 260 with IH, and 254 with RLS) was offered an online survey assessing their sleep-wake habits, daily activities, medication intake, and validated scales: International RLS Study Group questionnaire, Narcolepsy Severity Scale (NSS), IH Severity Scale (IHSS), Epworth Sleepiness Scale (ESS), Insomnia Severity Index, Beck Depression Inventory–II, and European Quality of Life (QoL) scale. The survey was proposed once, and the questions were answered for the prelockdown (recall of the month before the confinement) and the lockdown (time of study) periods.ResultsOverall, 331 patients completed the survey (response rate 40.7%): 102 with narcolepsy, 81 with IH, and 148 with RLS. All patients reported later bedtimes, with reduced differences for time in bed (TIB) and total sleep time (TST) over 24 hours between weekdays and weekends. Patients with narcolepsy spent more TIB and increased TST overnight, with more daytime napping. They had more awakenings, higher ESS scores, lower QoL, and no NSS changes. Patients with IH also increased their TIB, TST overnight and 24 hours on weekdays. Nocturnal sleep latency and the number of awakenings increased but with no change in ESS, QoL, and IHSS scores. Patients with RLS reported longer nocturnal sleep latency, more awakenings, more naps, decreased TIB, and TST overnight. RLS severity increased while QoL decreased. A significant portion of patients reported disease worsening during the lockdown (narcolepsy: 39.4%, IH: 43.6%, and RLS: 32.8%), and some patients stopped or lowered their medication (narcolepsy: 22.5%, IH: 28%, and RLS: 9.5%).DiscussionDuring the lockdown, all patients reported later bedtimes; those with narcolepsy and IH extended their sleep duration unlike patients with RLS. These changes were often associated with negative consequences on QoL. In the current context of recurrent COVID-19 waves, the recent development of teleconsultations should enable physicians to monitor patients with chronic sleep disorders more closely and to recommend optimized sleep schedules and duration, in order to prevent psychological problems and improve their QoL.

International audience; Electron density information, usually obtained from computed tomography (CT) Hounsfield units, is critical for modern radiation therapy treatment planning. However, CT scans have limited ability to differentiate different types of soft tissue, which has motivated the use of magnetic resonance imaging (MRI) to improve treatment accuracy in radiation oncology. While challenging, the generation of synthetic-CT from MRI opens up opportunities for improved patient outcomes, reduced healthcare costs, fusion of functional and structural information, and real-time imaging during treatment through combined MRI-linear accelerators. This chapter presents an overview of synthetic-CT generation, quality assurance, and deployment. © 2022 Elsevier Inc. All rights reserved.

Zhongxing Zhang,1 Yves Dauvilliers,2– 4 Giuseppe Plazzi,5,6 Geert Mayer,7 Gert Jan Lammers,8,9 Joan Santamaria,10 Markku Partinen,11 Sebastiaan Overeem,12,13 Rafael del Rio Villegas,14 Karel Sonka,15 Rosa Peraita-Adrados,16 Raphaël Heinzer,17 Aleksandra Wierzbicka,18 Birgit Högl,19 Mauro Manconi,20 Eva Feketeova,21 Antonio Martins da Silva,22 Jitka Bušková,23 Claudio LA Bassetti,24,25 Lucie Barateau,2– 4 Fabio Pizza,5,26 Elena Antelmi,5,6 Jari K Gool,8,9 Rolf Fronczek,8,9 Carles Gaig,10 Ramin Khatami1,24 1Center for Sleep Medicine, Sleep Research and Epileptology, Klinik Barmelweid AG, Barmelweid, Aargau, Switzerland; 2Sleep-Wake Disorders Unit, Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier, Montpellier, France; 3National Reference Centre for Orphan Diseases, Narcolepsy, Idiopathic Hypersomnia, and Kleine-Levin Syndrome, Montpellier, France; 4Institute for Neurosciences of Montpellier INM, Univ Montpellier, INSERM, Montpellier, France; 5Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy; 6IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy; 7Neurology Department, Hephata Klinik, Schwalmstadt, Germany; 8Sleep Wake Center SEIN Heemstede, Stichting Epilepsie Instellingen Nederland, Heemstede, the Netherlands; 9Department of Neurology and Clinical Neurophysiology, Leiden University Medical Center, Leiden, the Netherlands; 10Neurology Service, Institut de Neurociències Hospital Clínic, University of Barcelona, Barcelona, Spain; 11Helsinki Sleep Clinic, Vitalmed Research Center, Helsinki, Finland; 12Sleep Medicine Center Kempenhaeghe, Heeze, the Netherlands; 13Eindhoven University of Technology, Eindhoven, the Netherlands; 14Neurophysiology and Sleep Disorders Unit, Hospital Vithas Nuestra Señora de América, Madrid, Spain; 15Neurology Department and Centre of Clinical Neurosciences, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic; 16Sleep and Epilepsy Unit – Clinical Neurophysiology Service, University General Hospital Gregorio Marañón, Research Institute Gregorio Marañón, University Complutense of Madrid, Madrid, Spain; 17Center for Investigation and Research in Sleep, Lausanne University Hospital, Lausanne, Vaud, Switzerland; 18Department of Clinical Neurophysiology, Institute of Psychiatry and Neurology, Warsaw, Poland; 19Neurology Department, Sleep Disorders Clinic, Innsbruck Medical University, Innsbruck, Austria; 20Neurology Department, EOC, Ospedale Regionale di Lugano, Lugano, Ticino, Switzerland; 21Neurology Department, Medical Faculty of P. J. Safarik University, University Hospital of L. Pasteur Kosice, Kosice, Slovak Republic; 22Serviço de Neurofisiologia, Hospital Santo António/Centro Hospitalar Universitário do Porto and UMIB-Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, Porto, Portugal; 23Department of Sleep Medicine, National Institute of Mental Health, Klecany, Czech Republic; 24Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland; 25Department of Neurology, Sechenov First Moscow State University, Moscow, Russia; 26Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, ItalyCorrespondence: Yves Dauvilliers, National Reference Network for Narcolepsy, Sleep-Disorders Center, Department of Neurology, Hopital Gui de Chauliac, INSERM U1061, Montpellier, UM1, France, Email ydauvilliers@yahoo.fr Ramin Khatami, Center for Sleep Medicine, Sleep Research and Epileptology, Klinik Barmelweid AG, Barmelweid, CH-5017, Switzerland, Email ramin.khatami@barmelweid.chPurpose: 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.Keywords: cataplexy, diagnostic delay, misdiagnosis, symptom onset, machine learning

International audience; Meeting Abstract PO-1615

International audience; Meeting Abstract PO-1611

Purpose Deep learning methods (DLMs) have recently been proposed to generate pseudo-CT (pCT) for magnetic resonance imaging (MRI) based dose planning. This study aims to evaluate and compare DLMs (U-Net and generative adversarial network [GAN]) using various loss functions (L2, single-scale perceptual loss [PL], multiscale PL, weighted multiscale PL) and a patch-based method (PBM). Methods and Materials Thirty-nine patients received a volumetric modulated arc therapy for prostate cancer (78 Gy). T2-weighted MRIs were acquired in addition to planning CTs. The pCTs were generated from the MRIs using 7 configurations: 4 GANs (L2, single-scale PL, multiscale PL, weighted multiscale PL), 2 U-Net (L2 and single-scale PL), and the PBM. The imaging endpoints were mean absolute error and mean error, in Hounsfield units, between the reference CT (CTref) and the pCT. Dose uncertainties were quantified as mean absolute differences between the dose volume histograms (DVHs) calculated from the CTref and pCT obtained by each method. Three-dimensional gamma indexes were analyzed. Results Considering the image uncertainties in the whole pelvis, GAN L2 and U-Net L2 showed the lowest mean absolute error (≤34.4 Hounsfield units). The mean errors were not different than 0 (P ≤ .05). The PBM provided the highest uncertainties. Very few DVH points differed when comparing GAN L2 or U-Net L2 DVHs and CTref DVHs (P ≤ .05). Their dose uncertainties were ≤0.6% for the prostate planning target Volume V95%, ≤0.5% for the rectum V70Gy, and ≤0.1% for the bladder V50Gy. The PBM, U-Net PL, and GAN PL presented the highest systematic dose uncertainties. The gamma pass rates were >99% for all DLMs. The mean calculation time to generate 1 pCT was 15 s for the DLMs and 62 min for the PBM. Conclusions Generating pCT for MRI dose planning with DLMs and PBM provided low-dose uncertainties. In particular, the GAN L2 and U-Net L2 provided the lowest dose uncertainties together with a low computation time.

Whether depression and suicide thoughts relate to restless legs syndrome (RLS) or comorbidities associated with RLS remain unclear.To determine frequency of depressive symptoms and suicidal thoughts in patients with RLS and their change after RLS treatment, associated clinical and polysomnographic factors, and current major depressive episode (MDE) frequency and suicide risk in RLS.Overall, 549 untreated patients with RLS and 549 age-, sex-, and education level-matched controls completed a standardized evaluation, including the Beck Depression Inventory-II that has one item on suicide thoughts. Patients underwent a polysomnographic recording and completed the Urgency, Premeditation, Perseverance, Sensation Seeking Impulsive Behavior scale. In a subgroup of 153 patients, current MDE and suicide risk were assessed with the face-to-face Mini-International Neuropsychiatric Interview (MINI). A subgroup of 152 patients were evaluated in untreated and treated conditions.The frequency of depressive symptoms (32.5%) and suicidal thoughts (28%) was 10-fold and 3-fold higher, respectively, in patients with RLS than controls. Current MDE (10.5%) and suicidal risk (19.9%) (MINI) were also high. Moderate-to-severe depressive symptoms were associated with young age, female sex, insomnia symptoms, and urgency dimension. The suicide risk was associated with depression, impulsiveness, and RLS severity. RLS treatment improved depressive symptoms but not suicidal thoughts.The rate of depressive symptoms, depression, and suicidal thoughts/risk was higher in patients with RLS, with key associations with insomnia symptoms, urgency dimension, and RLS severity. These results emphasize the importance of detecting these symptoms in current practice and of evaluating their change after treatment, especially in young women, to improve RLS management. © 2022 International Parkinson and Movement Disorder Society.

Study Objectives Sleep inertia is a frequent and disabling symptom in idiopathic hypersomnia (IH), but poorly defined and without objective measures. The study objective was to determine whether the psychomotor vigilance task (PVT) can reliably measure sleep inertia in patients with IH or other sleep disorders (non-IH). Methods A total of 62 (51 women, mean age: 27.7 ± 9.2) patients with IH and 140 (71 women, age: 33.3 ± 12.1) with non-IH (narcolepsy = 29, non-specified hypersomnolence [NSH] = 47, obstructive sleep apnea = 39, insomnia = 25) were included. Sleep inertia and sleep drunkenness in the last month (M-sleep inertia) and on PVT day (D-sleep inertia) were assessed with three items of the Idiopathic Hypersomnia Severity Scale (IHSS), in drug-free conditions. The PVT was performed four times (07:00 pm, 07:00 am, 07:30 am, and 11:00 am) and three metrics were used: lapses, mean 1/reaction time (RT), and slowest 10% 1/RT. Results Sleep inertia was more frequent in patients with IH than non-IH (56.5% and 43.6% with severe sleep inertia in the past month, including 24% and 12% with sleep drunkenness). Lapse number increase and slowest 10% 1/RT decrease, particularly at 07:00 am and 07:30 am, were proportional with M-sleep inertia severity, but regardless of sleep drunkenness and sleep disorders. Similar results were obtained when PVT results were compared in patients with/without D-sleep inertia, with the largest increase of the lapse number at 07:00 am and 07:30 am associated with severe sleep inertia and sleep drunkenness. Conclusions PVT is a reliable and objective measure of sleep inertia that might be useful for its characterization, management, and follow-up in patients with IH.

Adaptive radiotherapy (ART) is a complexe image-guided radiotherapy modality that comprises multiple planning to account for anatomical variations occurring during irradiation. Schematically, two strategies of RTA can be distinguished and combined according to tumor locations. One or more replanning can be proposed to correct systematic variations such as tumor shrinkage. A library of treatment plans with day-to-day plan selection from cone-beam CT imaging can also be proposed to correct random variations such as uterine motion or bladder/rectum volume changes. Because of strong anatomical variations occurring during irradiation, RTA appears therefore particularly justified in head and neck, lung, bladder, cervical and rectum and pancreas tumors, and to a lesser extent for prostate tumors and other digestive tumors. For these tumor locations, ART provides a fairly clear dosimetric benefit but a clinical benefit not yet formally demonstrated. ART cannot be proposed in a routine practice but must be evaluated medico-economically in the context of prospective trials. A rigorous quality control must be associated.

Purpose Methods have been recently developed to generate pseudo–computed tomography (pCT) for dose calculation in magnetic resonance imaging (MRI)–only radiation therapy. This study aimed to propose an original nonlocal mean patch-based method (PBM) and to compare this PBM to an atlas-based method (ABM) and to a bulk density method (BDM) for prostate MRI-only radiation therapy. Materials and Methods Thirty-nine patients received a volumetric modulated arc therapy for prostate cancer. In addition to the planning computed tomography (CT) scans, T2-weighted MRI scans were acquired. pCTs were generated from MRIs using 3 methods: an original nonlocal mean PBM, ABM, and BDM. The PBM was performed using feature extraction and approximate nearest neighbor search in a training cohort. The PBM accuracy was evaluated in a validation cohort by using imaging and dosimetric endpoints. Imaging endpoints included mean absolute error and mean error between Hounsfield units of the pCT and the reference CT (CTref). Dosimetric endpoints were based on dose-volume histograms calculated from the CTref and the pCTs for various volumes of interest and on 3-dimensional gamma analyses. The PBM uncertainties were compared with those of the ABM and BDM. Results The mean absolute error and mean error obtained from the PBM were 41.1 and –1.1 Hounsfield units. The PBM dose-volume histogram differences were 0.7% for prostate planning target volume V95%, 0.5% for rectum V70Gy, and 0.2% for bladder V50Gy. Compared with ABM and BDM, PBM provided significantly lower dose uncertainties for the prostate planning target volume (70-78 Gy), the rectum (8.5-29 Gy, 40-48 Gy, and 61-73 Gy), and the bladder (12-78 Gy). The PBM mean gamma pass rate (99.5%) was significantly higher than that of ABM (94.9%) or BDM (96.1%). Conclusions The proposed PBM provides low uncertainties with dose planned on CTref. These uncertainties were smaller than those of ABM and BDM and are unlikely to be clinically significant.