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      Enabling endpoint development for interventional clinical trials in individuals with Angelman syndrome: a prospective, longitudinal, observational clinical study (FREESIAS)

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          Abstract

          Background

          Angelman syndrome (AS) is a rare neurodevelopmental disorder characterized by the absence of a functional UBE3A gene, which causes developmental, behavioral, and medical challenges. While currently untreatable, comprehensive data could help identify appropriate endpoints assessing meaningful improvements in clinical trials. Herein are reported the results from the FREESIAS study assessing the feasibility and utility of in-clinic and at-home measures of key AS symptoms.

          Methods

          Fifty-five individuals with AS (aged < 5 years: n = 16, 5–12 years: n = 27, ≥ 18 years: n = 12; deletion genotype: n = 40, nondeletion genotype: n = 15) and 20 typically developing children (aged 1–12 years) were enrolled across six USA sites. Several clinical outcome assessments and digital health technologies were tested, together with overnight 19-lead electroencephalography (EEG) and additional polysomnography (PSG) sensors. Participants were assessed at baseline (Clinic Visit 1), 12 months later (Clinic Visit 2), and during intermittent home visits.

          Results

          The participants achieved high completion rates for the clinical outcome assessments (adherence: 89–100% [Clinic Visit 1]; 76–91% [Clinic Visit 2]) and varied feasibility of and adherence to digital health technologies. The coronavirus disease 2019 (COVID-19) pandemic impacted participants’ uptake of and/or adherence to some measures. It also potentially impacted the at-home PSG/EEG recordings, which were otherwise feasible. Participants achieved Bayley-III results comparable to the available natural history data, showing similar scores between individuals aged ≥ 18 and 5–12 years. Also, participants without a deletion generally scored higher on most clinical outcome assessments than participants with a deletion. Furthermore, the observed AS EEG phenotype of excess delta-band power was consistent with prior reports.

          Conclusions

          Although feasible clinical outcome assessments and digital health technologies are reported herein, further improved assessments of meaningful AS change are needed. Despite the COVID-19 pandemic, remote assessments facilitated high adherence levels and the results suggested that at-home PSG/EEG might be a feasible alternative to the in-clinic EEG assessments. Taken altogether, the combination of in-clinic/at-home clinical outcome assessments, digital health technologies, and PSG/EEG may improve protocol adherence, reduce patient burden, and optimize study outcomes in AS and other rare disease populations.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s11689-023-09494-w.

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          Most cited references42

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          The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research

          Despite the prevalence of sleep complaints among psychiatric patients, few questionnaires have been specifically designed to measure sleep quality in clinical populations. The Pittsburgh Sleep Quality Index (PSQI) is a self-rated questionnaire which assesses sleep quality and disturbances over a 1-month time interval. Nineteen individual items generate seven "component" scores: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. The sum of scores for these seven components yields one global score. Clinical and clinimetric properties of the PSQI were assessed over an 18-month period with "good" sleepers (healthy subjects, n = 52) and "poor" sleepers (depressed patients, n = 54; sleep-disorder patients, n = 62). Acceptable measures of internal homogeneity, consistency (test-retest reliability), and validity were obtained. A global PSQI score greater than 5 yielded a diagnostic sensitivity of 89.6% and specificity of 86.5% (kappa = 0.75, p less than 0.001) in distinguishing good and poor sleepers. The clinimetric and clinical properties of the PSQI suggest its utility both in psychiatric clinical practice and research activities.
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            Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L)

            Purpose This article introduces the new 5-level EQ-5D (EQ-5D-5L) health status measure. Methods EQ-5D currently measures health using three levels of severity in five dimensions. A EuroQol Group task force was established to find ways of improving the instrument’s sensitivity and reducing ceiling effects by increasing the number of severity levels. The study was performed in the United Kingdom and Spain. Severity labels for 5 levels in each dimension were identified using response scaling. Focus groups were used to investigate the face and content validity of the new versions, including hypothetical health states generated from those versions. Results Selecting labels at approximately the 25th, 50th, and 75th centiles produced two alternative 5-level versions. Focus group work showed a slight preference for the wording ‘slight-moderate-severe’ problems, with anchors of ‘no problems’ and ‘unable to do’ in the EQ-5D functional dimensions. Similar wording was used in the Pain/Discomfort and Anxiety/Depression dimensions. Hypothetical health states were well understood though participants stressed the need for the internal coherence of health states. Conclusions A 5-level version of the EQ-5D has been developed by the EuroQol Group. Further testing is required to determine whether the new version improves sensitivity and reduces ceiling effects.
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              A new method for measuring daytime sleepiness: the Epworth sleepiness scale.

              The development and use of a new scale, the Epworth sleepiness scale (ESS), is described. This is a simple, self-administered questionnaire which is shown to provide a measurement of the subject's general level of daytime sleepiness. One hundred and eighty adults answered the ESS, including 30 normal men and women as controls and 150 patients with a range of sleep disorders. They rated the chances that they would doze off or fall asleep when in eight different situations commonly encountered in daily life. Total ESS scores significantly distinguished normal subjects from patients in various diagnostic groups including obstructive sleep apnea syndrome, narcolepsy and idiopathic hypersomnia. ESS scores were significantly correlated with sleep latency measured during the multiple sleep latency test and during overnight polysomnography. In patients with obstructive sleep apnea syndrome ESS scores were significantly correlated with the respiratory disturbance index and the minimum SaO2 recorded overnight. ESS scores of patients who simply snored did not differ from controls.
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                Author and article information

                Contributors
                jorrit.tjeertes@roche.com
                Elizabeth_Berry-Kravis@rush.edu
                Journal
                J Neurodev Disord
                J Neurodev Disord
                Journal of Neurodevelopmental Disorders
                BioMed Central (London )
                1866-1947
                1866-1955
                26 July 2023
                26 July 2023
                2023
                : 15
                : 22
                Affiliations
                [1 ]GRID grid.417570.0, ISNI 0000 0004 0374 1269, F. Hoffmann-La Roche Ltd, ; Grenzacherstrasse 124, 4070 Basel, Switzerland
                [2 ]GRID grid.39382.33, ISNI 0000 0001 2160 926X, Department of Molecular and Human Genetics, , Baylor College of Medicine, ; Houston, TX USA
                [3 ]GRID grid.416975.8, ISNI 0000 0001 2200 2638, Texas Children’s Hospital, ; Houston, TX USA
                [4 ]COMBINEDBrain, Brentwood, TN USA
                [5 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Department of Pediatrics, , University of California San Diego, ; San Diego, CA USA
                [6 ]GRID grid.286440.c, ISNI 0000 0004 0383 2910, Division of Dysmorphology/Genetics, , Rady Children’s Hospital, ; San Diego, CA USA
                [7 ]GRID grid.282569.2, ISNI 0000 0004 5879 2987, Ionis Pharmaceuticals Inc, ; Carlsbad, CA USA
                [8 ]GRID grid.239546.f, ISNI 0000 0001 2153 6013, Children’s Hospital Los Angeles, ; Los Angeles, CA USA
                [9 ]GRID grid.42505.36, ISNI 0000 0001 2156 6853, Keck School of Medicine of USC, ; Los Angeles, CA USA
                [10 ]Biogen, Cambridge, MA USA
                [11 ]GRID grid.240684.c, ISNI 0000 0001 0705 3621, Departments of Pediatrics, Division of Developmental-Behavioral Pediatrics, , Rush University Medical Center, ; Chicago, IL USA
                [12 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Neurology, , Boston Children’s Hospital, Harvard Medical School, ; Boston, MA USA
                [13 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Psychiatry and Behavioral Services, , Boston Children’s Hospital, Harvard Medical School, ; Boston, MA USA
                [14 ]GRID grid.410711.2, ISNI 0000 0001 1034 1720, Carolina Institute for Developmental Disabilities & UNC Neuroscience Center, , University of North Carolina, ; Chapel Hill, NC USA
                [15 ]GRID grid.38142.3c, ISNI 000000041936754X, Division of Genetics and Genomics, , Boston Children’s Hospital, Harvard Medical School, ; Boston, MA USA
                [16 ]GRID grid.10698.36, ISNI 0000000122483208, Carolina Institute for Developmental Disabilities, ; Carrboro, NC USA
                [17 ]GRID grid.62562.35, ISNI 0000000100301493, RTI International, ; Durham, NC USA
                [18 ]GRID grid.240684.c, ISNI 0000 0001 0705 3621, Departments of Pediatrics, Neurological Sciences, Anatomy and Cell Biology, , Rush University Medical Center, ; 1725 W Harrison St, Suite 718, Chicago, IL 60612 USA
                Author information
                http://orcid.org/0000-0001-7099-1522
                Article
                9494
                10.1186/s11689-023-09494-w
                10373389
                37495977
                82ec69df-0d02-4f53-9575-ef198c8b5f2b
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 17 January 2023
                : 4 July 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100007013, F. Hoffmann-La Roche;
                Funded by: FundRef http://dx.doi.org/10.13039/100005614, Biogen;
                Funded by: FundRef http://dx.doi.org/10.13039/100013669, Ionis Pharmaceuticals;
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2023

                Neurosciences
                angelman syndrome,endpoint development,eeg,sleep,digital health technology,clinical outcome assessments,natural history,clinical trials,ube3a

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