bluuebunny/crossref_metadata_2025_split huggingface dataset:
0644 Comparison of Baseline Narcolepsy Characteristics Among Participant Age Groups: Analysis from REST-ON
Authors: Roth Thomas , Schweitzer Paula , Thorpy Michael , Gudeman Jennifer , Dauvilliers Yves 
Published: April 2024

Abstract

Introduction
Narcolepsy is a chronic disease with symptom onset frequently occurring between the ages of 10–25 years. Efficacy and safety of once-nightly sodium oxybate (ON-SXB; LUMRYZ™) were demonstrated in the phase 3 REST-ON trial. The objective of this post hoc analysis was to compare baseline narcolepsy characteristics among 3 age groups from REST-ON.

Methods
Participants aged ≥16 years with narcolepsy type 1/2 were enrolled in the randomized, double-blind, placebo-controlled REST-ON trial (NCT02720744). Baseline data were trichotomized post hoc by age subgroups (years, youngest: 16–25; middle: 26–34; oldest: 35–72).

Results
The safety analysis included 212 participants who received ≥1 dose of study drug (youngest, n=73; middle, n=70; oldest, n=69). For youngest, middle, and oldest participants, respectively, mean age was 20.6, 29.6, and 44.0 years; 63.0%, 68.6%, and 72.5%, were female. Respective mean baseline values for excessive daytime sleepiness (EDS) measures were similar across the youngest, middle, and oldest age groups (range of group means, mean sleep latency on the Maintenance of Wakefulness Test: 4.5–5.1, 4.6–5.0, 4.9–5.1 minutes; Clinical Global Impression of Severity for sleepiness rating: 4.8–5.3, 5.1–5.3, 5.1 [both treatment arms]; Epworth Sleepiness Scale score: 16.7–16.9, 15.2–17.9, 17.6–18.1). For objective disrupted nighttime sleep (DNS) measures determined by polysomnography, the middle group (range of group means, 58.1–68.2) and oldest group (63.4–66.3) had more sleep stage shifts to lighter stage of sleep/wake compared to the youngest group (50.5–56.3). The oldest group experienced a higher number of nocturnal arousals (range of group means, 83.5–87.2) compared to the middle (77.6–83.2) and youngest groups (65.4–80.7). Subjective DNS measures were similar across age groups (range of group means, visual analog scale [VAS] sleep quality, 1=did not sleep/100=slept very well: 48.3–60.7; VAS refreshing nature of sleep, 1=not refreshed/100=refreshed: 44.2–53.2).

Conclusion
Baseline measures of EDS were similar among age groups of participants with narcolepsy in the REST-ON trial. Objective but not subjective DNS measures were worse in older participants, suggesting DNS may increase with age.

Support (if any)
Avadel Pharmaceuticals
0645 Comparison of Demographics and Baseline Narcolepsy Symptoms Between Participants with NT1 and NT2 from REST-ON
Authors: Dauvilliers Yves , Roth Thomas , Thorpy Michael , Morse Anne , Kushida Clete , Harsh John , Ortiz Luis , Gudeman Jennifer 
Published: April 2024

Abstract

Introduction
Narcolepsy is classified into 2 subtypes: narcolepsy type 1 (NT1; with cataplexy) and 2 (NT2; without cataplexy). Limited data are available regarding subtype differences in clinical characteristics and disease severity. In the phase 3 REST-ON trial, once-nightly sodium oxybate (ON-SXB; LUMRYZ™) demonstrated significant improvement in the 3 coprimary endpoints of change from baseline in mean sleep latency on the Maintenance of Wakefulness test (MWT), Clinical Global Impression of Improvement rating, and weekly cataplexy episodes (all P&lt; 0.001) in patients with NT1 and NT2. This post hoc analysis compared baseline characteristics between participants with NT1 and NT2.

Methods
REST-ON (NCT02720744) participants were ≥16 years of age with NT1 or NT2, excessive daytime sleepiness, and cataplexy (NT1 only). Use of alerting agents was permitted. Randomization (1:1 to ON-SXB or placebo) was stratified by narcolepsy type; the trial population was oversampled for NT1. Baseline characteristics were compared between narcolepsy types.

Results
The safety population included 212 participants (NT1, n=162; NT2, n=50). At baseline, demographic characteristics of participants with NT1 vs NT2 were: mean (SD) age, 32.1 (11.1) vs 28.3 (10.0) years, respectively; sex, 72.8% vs 52.0% female; body mass index, 28.9 (7.5) vs 25.7 (5.6) kg/m2; and use of concurrent alerting agents, 59.2% vs 68.0%. Mean (SD) baseline clinical characteristics in participants with NT1 vs NT2 were: mean sleep latency (MWT), 4.9 (2.9) vs 4.9 (2.9) minutes; Clinical Global Impression rating (CGI-Severity), 5.2 (1.1) vs 4.7 (1.1); ESS scores, 17.6 (4.0) vs 15.4 (3.2); number of sleep stage shifts to lighter stage of sleep or wake measured by polysomnography (PSG), 61.5 (22.2) vs 55.8 (23.5); number of nocturnal arousals by PSG, 81.5 (42.4) vs 73.2 (35.9); sleep quality (visual analog scale [VAS; 1 = did not sleep and 100 = slept very well]), 54.5 (22.0) vs 55.8 (20.8); and refreshing nature of sleep (VAS; 1 = not refreshed and 100 = refreshed), 49.9 (22.6) vs 42.6 (21.6).

Conclusion
Limited data are available comparing NT1 and NT2 populations; these data indicate a similar level of disease severity at baseline.

Support (if any)
Avadel Pharmaceuticals
0657 Understanding the Debilitating Nature of Narcolepsy in Patients’ Own Words: A Social Listening Analysis
Authors: Morse Anne , Lavender Maggie , Horsnell Matthew , Krahn Lois , Ortiz Luis , Cronin Dianna , Schneider Beth , Gudeman Jennifer 
Published: April 2024

Abstract

Introduction
To further characterize the many struggles and unmet needs of people with narcolepsy (PWN), passive social listening was used to explore how PWN describe the condition using their own words.

Methods
MyNarcolepsyTeam is a social network where members can organically share their experiences living with narcolepsy with one another. Following an online survey, organic posts, comments, questions, and answers posted from January 2022 to present were analyzed.

Results
Of 110 survey respondents, 31% reported that the time from symptom onset to diagnosis was ≥10 years. Social listening highlighted both misdiagnoses (eg, depression) and “missed” diagnoses (eg, sleep apnea but not narcolepsy). Almost half (43%) of respondents reported pain as a comorbidity. Social listening revealed the burden of painful comorbidities (eg, fibromyalgia, migraines, neuropathy), which often lead to additional medications and further sleep disruption. While 90% of respondents reported excessive daytime sleepiness, 81% also reported sleep disturbances. Nighttime disruptions experienced by PWN included poor sleep quality, vivid dreams, frequent awakenings, sleep paralysis, and abnormal REM cycles. Structured routines helped improve sleep for some PWN. Additionally, 33% of respondents reporting cataplexy always experienced full body cataplexy; 21% always experienced localized cataplexy; 43% experienced a mix of both, with the remaining answering ‘not sure.’ Social listening highlighted the dangers of cataplexy, including falls/fractures. Organic conversations also brought to bear the full range of emotions that can trigger an attack – from laughter, to being startled, to feeling stressed. In total, 65% of respondents were taking ≥2 medications to treat daytime and/or nighttime narcolepsy symptoms. Organic conversations highlighted the challenges PWN experience managing complex treatment regimens in an attempt to cope with the full spectrum of narcolepsy symptoms.

Conclusion
Insight into how narcolepsy is experienced in patients’ own words and how patients try to mitigate their symptoms will help sleep specialists better understand the challenges and needs of PWN, which can lead to both faster diagnosis and more effective, individualized treatment.

Support (if any)
Avadel Pharmaceuticals
0660 In and Between Nap MSLT Features in Hypersomnia
Authors: Andersen Louise , Brink-Kjaer Andreas , Sum-Ping Oliver , Mignot Emmanuel 
Published: April 2024

Abstract

Introduction
This study aimed to identify novel markers of NT1 using between-nap and in-nap features of Multiple Sleep Latency Test (MSLT) recordings. We hypothesized that sleep-wake instability observed in NT1, alongside quantifiable patterns of sleepiness during wakefulness, may contain biomarkers for differentiating NT1 from other hypersomnia disorders between naps. Further, we wanted to explore if NT1 and NT2 MSLT could be distinguished beyond the common presence of a mean sleep latency below 8 minutes and multiple SOREMPs.

Methods
We analyzed in-between nap periods of MSLT recordings, extracting features describing sleepiness, microsleep patterns, and sleep-stage mixing, in three different sleep centers, based on the hypodensities obtained from U-sleep. 107 features were extracted from 178 patients diagnosed with NT1, NT2, Idiopathic Hypersomnia (IH), and Subjective IH (sIH), most with CSF hypocretin levels available. Second, we trained a model to distinguish NT1 from sIH and to differentiate NT1 from other hypersomnolence disorders, including IH plus NT2. A cross-cohort validation strategy was employed, involving training the model on combinations of two cohorts and validating on the third.

Results
Analysis indicated an increased probability of REM sleep during between-nap periods in the MSLT of NT1 patients, along with a greater prevalence of sleep stage mixing between REM and N1 sleep stages in microsleep episodes. In distinguishing NT1 from NT2, IH, and Subjective IH using features solely from between-nap periods of the MSLT, sensitivity was 0.72, specificity 0.70, and F1-score 0.62 across three validation cohorts. Distinguishing NT1 versus NT2 with between-nap features resulted in sensitivity 0.75, specificity 0.59 and F1-score 0.66 across three validation cohorts. When using features from the in-nap part of the MSLT, NT1 was distinguished from NT2 with sensitivity of 0.75, specificity of 0.82, and F1-score of 0.78 on average across the three validation cohorts.

Conclusion
The findings in this study support the hypothesis that NT1 patients exhibit increased sleepiness and sleep stage mixing during wake periods compared to other hypersomnolence disorders. Further, NT1 and NT2 could be partially distinguished using in-nap features, even so these two groups of subjects have positive MSLTs. This work supports the use of new analytical methods in MSLT.

Support (if any)
0664 Development of a Novel Patient-Reported Outcome Measure of Functional Impacts for Narcolepsy Types 1 and 2
Authors: Romero Heather , Savva Yulia , Crawford Stephen , McPherson Peter , Doll Helen , Howerter Amy , Symonds Tara , Swick Todd , Dauvilliers Yves 
Published: April 2024

Abstract

Introduction
Narcolepsy, a rare, chronic neurologic disorder that affects sleep-wake stability, is associated with detrimental effects on functioning, quality of life, and productivity. There is a need for a fit-for-purpose clinical outcome assessment that captures the functional impacts of narcolepsy in various clinical settings to complement the Narcolepsy Severity Scale. The objective was to develop a new patient-reported outcome (PRO) measure, the Functional Impacts of Narcolepsy Instrument (FINI), for evaluating treatment response in patients with narcolepsy type 1 (NT1) or type 2 (NT2).

Methods
Initial item generation was derived using concept elicitation (patients with NT1, n=21), cognitive debriefing (NT1, n=20; NT2, n=15), and item debriefing with medical experts. A mixed-methods approach was used to determine the final PRO using two independent datasets: a cross-sectional, US observational NT1 study (n=126) and a global Phase 2 study (NT1 and NT2, n=125).

Results
Concept elicitation interviews were conducted with patients with NT1 to understand meaningful symptoms and impacts and generate items deemed relevant by patients and medical experts. The original instrument version was then refined and cognitively debriefed with patients in two stand-alone studies resulting in a 48-item pilot version. All items have a 7-day recall period on a 5-point Likert scale of frequency from “never” to “always” or severity from “not at all” to “very much.” The four a priori factors were expanded to a six-factor structure based on exploratory factor analysis results and confirmed to be multidimensional on Rasch analysis, resulting in a final 28-item version with 6-domains (tiredness, cognitive functioning, cataplexy [for NT1], social activities, everyday activities, and everyday responsibilities) (FINI v1.0). Summary scores for each of 6 domains for NT1 and 5 domains for NT2 were calculated and transformed to a standardized scale from 0 (best health) to 100 (worst health).

Conclusion
The FINI is a modular, domain-specific assessment of functional impacts that is fit-for-purpose for patients with NT1 (FINI) and NT2 (FINI-NT2). It has demonstrated good content validity and psychometric methods support scoring and interpretation. The FINI can be used to generate evidence in clinical trials or practice for functional recovery in patients with NT1 and NT2.

Support (if any)
Funded by Takeda
0666 Idiopathic Hypersomnia in the Sleep Apnea Population
Authors: Molin Nicole , DeKloe Jefferson , Doghramji Karl 
Published: April 2024

Abstract

Introduction
Hypersomnia in OSA can have a significant impact on patient’s quality of life. While hypersomnia tends to improve with treatment, there remains a portion of patients where this persists. This can often be attributed to inadequately controlled OSA, however the prevalence of idiopathic hypersomnia (IH) within the adequately treated OSA population remains unknown. With the emergence of new management strategies for IH, this becomes important to assess. We aim to evaluate the prevalence of patients with adequately treated OSA at risk for idiopathic hypersomnia using the idiopathic hypersomnia severity scale.

Methods
Surveys were administered to patients with OSA on CPAP demonstrating compliance of at least 80%. Surveys included IHSS, insomnia severity scale (ISI) and Epworth sleepiness scale (ESS). Patients were included if they used CPAP &gt;6h/night, had an ISI&lt; 14, no prior diagnosis of central hypersomnia and not on stimulant medication. IHSS &gt;22 was considered high risk for IH based on prior validation studies.

Results
54 patients met inclusion criteria; 7 (13%) were high risk for IH, 4 (57%) of which had ESS&lt; 10. Age, sex, race, ethnicity and CPAP use were similar between high and low risk patients. Patients with IHSS&gt;22 (high risk) had significantly higher BMI (p&lt; 0.05).

Conclusion
Of patients who have adequately controlled OSA and are compliant with CPAP, our data suggests that 13% of them may be at high risk for idiopathic hypersomnia. Of these patients 57% of them would not have been identified using ESS alone. This may be related to the symptom specific questions included on the IHSS such as those related to sleep inertia, total sleep time etc. While further testing with PSG/MSLT is needed to confirm a diagnosis of IH, which is currently being offered to the high risk patients, this preliminary data suggests that increased screening for IH in this population may be beneficial.

Support (if any)
0670 Patterns of Nocturnal Sleep Disruption: Differentiating IH from NT2 Using Cluster Analysis
Authors: Cairns Alyssa , Mysliwiec Vincent , Hanif Umaer , Trotti Lynn Marie 
Published: April 2024

Abstract

Introduction
Disambiguation of type 2 narcolepsy (NT2) from idiopathic hypersomnia (IH) is notably challenging, given the absence of biomarkers and testing (MSLT) limitations. The utility of nocturnal sleep features for diagnostic differentiation of NT2 vs. IH has yielded some promising findings. However, data are limited by the reliance on the MSLT to define CNS subtypes, yielding critical weaknesses in generalizability and inference. The goals of this study were to (1) quantify and qualify patterns of sleep disruption in patients being evaluated for hypersomnia, agnostic of MSLT outcomes [blinded] and (2) evaluate congruence between nocturnal endotypes and unblinded MSLT outcomes/diagnoses.

Methods
This study used BioSerenity’s archival database of patients being evaluated for hypersomnia with in-lab PSG-MSLT. A total of N=697 PSGs met inclusion criteria. Records were scored by senior RPSGTs and spectral power was computed on raw tracings using FFT with Welch’s method. Data for each 30-s epoch were exported for tabulation summary variables, state sequences, and transition indices. Data were ingested into a statistical program for unsupervised cluster analysis.

Results
A two-step unsupervised cluster analysis was performed on 18 PSG features; 5 were retained for cohesion: # wake-REM sequences, sleep-wake and REM transition indices, WASO, and arousal index (p&lt;.001). The model identified 3 clusters of patients with homogeneous patterns of sleep disruption. Cluster 1 (n=88) had notably disrupted sleep and REM, cluster 2 (n=210) had sleep (not REM) disruption with WASO, and cluster 3 (n=399) had well-consolidated sleep. A multinomial logistic regression indicated similar MSLT outcomes for clusters 1 and 3 (p=.385), whereas cluster 2 had the fewest &gt;2 MSLT REMs (17% vs. 27%; p&lt;.001) and longest MSL (8.7 min vs. 7.1 min; p&lt;.001).

Conclusion
This data-driven approach produced 3 distinct groups of nocturnal features that, in theory, were compatible with the conceptual distinction of NT2 from IH. However, MSLT outcomes of NT2 and IH did not reflect a differential pattern of cluster membership. While further data are needed to evaluate the clinical utility of nocturnal sleep/REM features in predicting outcomes, PSG features of sleep/REM disruption, when adjudicated against MSLT outcomes, may provide nuance to guide interpretation and additional testing.

Support (if any)
Jazz Pharmaceuticals (IST-19-11124)
0673 The Burden of Living with Narcolepsy : Patient Perspectives from In-Depth Qualitative Interviews
Authors: Doane Michael , O'Connor Meaghan , Lauher-Charest Miranda , Waldman Laura Tesler , Williams Wilbur 
Published: April 2024

Abstract

Introduction
Narcolepsy is a chronic neurological disorder characterized by excessive daytime sleepiness, among other symptoms. This rare condition may be associated with negative impacts to patients’ lives, including impaired mental and physical health, increased stigma, and difficulty obtaining an education or maintaining employment. Previous studies that have identified the burden associated with narcolepsy have largely relied on quantitative methods (e.g., surveys), providing limited insights into the patient experience. This study used qualitative, in-depth interviews to characterize the burden of narcolepsy.

Methods
Participant recruitment included convenience and snowball sampling. Sixty-minute individual interviews were conducted online by trained qualitative researchers. Interviewers used a concept elicitation approach with a semi-structured interview guide to elicit descriptions of patients’ experiences. Interview transcripts were coded and thematically analyzed using inductive and deductive approaches.

Results
Twenty-two adults with narcolepsy (type 1 [NT1] =12; type 2 [NT2=10]) participated in this study (average age: NT1=35; NT2=44). Most identified as female (NT1=83%; NT2=70%) and white (NT1=75%; NT2=60%). Approximately half of participants were employed (NT1=58%; NT2=50%). Average time since diagnosis was 7 years for NT1 and 11 years for NT2. Participants described several ways that narcolepsy has negatively impacted their lives. All or nearly all participants cited impacts on: work and school activities (e.g., trouble concentrating on tasks, forgetting information, falling asleep during meetings or conversations; NT1=100%; NT2=100%), mental health (e.g., depression, frustration, anxiety, embarrassment, lack of motivation; NT1=100%; NT2=90%), and instrumental activities of daily living (e.g., home maintenance, cooking/preparing meals, driving; NT1=92%; NT2=100%). Most participants also described negative impacts on their relationships with family, friends, and romantic partners (e.g., reduced time spent with children, strain on marriage; NT1=83%; NT2=80%) and activities of daily living (e.g., eating, bathing, dressing/grooming, toileting; NT1=92%; NT2=60%).

Conclusion
Results from this study demonstrate the wide breadth of impacts narcolepsy has on patients’ lives and, in so doing, helps to fill a gap in the literature by providing rich insights into the patient experience of narcolepsy.

Support (if any)
This study was sponsored by Alkermes, Inc. and conducted in partnership with QualityMetric. The authors thank Megan Patsch for her contributions.
0677 Winding down for the Night: Changes in Thalamocortical Connectivity Before Bed Are Associated with Deeper Sleep
Authors: Killgore William , Henderson-Arredondo Kymberly , Jankowski Samantha , Patel Salma , Hildebrand Lindsey , Huskey Alisa , Negelspach David , Grandner Michael A , Reich-Fuehrer Melissa Melissa , Franca Gabriela , Wellman Camryn , Dailey Natalie 
Published: April 2024

Abstract

Introduction
According to the arousal hypothesis, insomnia is often the result of excessive cognitive and somatic arousal during the sleep period. Individuals with insomnia show greater cortico-thalamic functional connectivity during waking hours than healthy sleepers. However, prior studies have only examined such connectivity at a static time point without considering dynamic changes in cortico-thalamic connectivity in the hours prior to sleep. Here, we examined the association between changes in cortico-thalamic connectivity across the 4-hours prior to sleep and subsequent overnight sleep parameters measured by polysomnography (PSG) among individuals with insomnia.

Methods
Twenty participants (12 female; age=26.9, SD=6.6 years) with insomnia symptoms completed two serial resting-state functional connectivity (rsFC) MRI sessions in the early evening (1900-2000 and 2100-2200 hrs.) followed by PSG monitored sleep from 2300 to 0700 in a controlled sleep laboratory. Sleep parameters, including time in wake, N1, N2, N3, total sleep time (TST), sleep efficiency (SE), and rapid eye movement (REM) sleep were entered into a series of seed-to-voxel whole-brain analyses using the CONN Toolbox (v21.a). The seed region comprised bilateral thalami as defined by the Automated Anatomical Labeling Atlas. The correlation between the change in cortico-thalamic connectivity and subsequent time in each sleep stage was calculated. Data were analyzed with peak threshold of p&lt;.005 (uncorrected) and p&lt;.05 False-Discovery-Rate (FDR) cluster correction.

Results
Greater declines in cortico-thalamic connectivity prior to bedtime were associated with significantly more N3 sleep (i.e., anticorrelated connectivity to left orbitofrontal cortex, middle frontal gyrus, putamen, insula, and medial prefrontal/paracingulate gyrus), and more REM sleep (i.e., anticorrelated connectivity to the left lateral occipital cortex). Interestingly, greater cortico-thalamic connectivity to the bilateral visual cortex and frontal pole during this same pre-sleep period was associated with more time in stage N2 sleep. Changes in connectivity were not associated with wake, N1, TST, or SE.

Conclusion
Among individuals with insomnia, greater declines in cortico-thalamic connectivity, particularly regions associated with emotion, interoception, and sensory-motor processes, predicted more time in slow-wave sleep, also known as the ‘deep sleep’ stage. Findings support the arousal hypothesis and suggest that interventions that facilitate cortico-thalamic gating may prove useful in facilitating sleep and treating insomnia.

Support (if any)
USAMRAA: W81XWH2010173