HypersomniaNCT05443373
A Multi-Signal Based Monitoring System for CNS Hypersomnias
An observational study of Hypersomnia, run by Chang Gung Memorial Hospital at 2 sites in Taiwan.
Trial summary
This is a retrospective and prospective cohort study. There are 600 subjects (age 9-45) will be collected.The purposes of this study are as follows:(1) The main purpose is to use Multi-Signal Based Monitoring System to link with brain image data and perform cross-comparison to find out possible pathological mechanisms… Read more about this study
- Who is it for?
- Ages 9 to 45 yearsHealthy volunteers can take part
- Conditions the study is for
- Hypersomnia
- How many people
- 600 participants
- Where
- 2 sitesTaiwan
- Study dates
- 4 Jun 2020to 31 Jul 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 9 to 45 years
- All sexes
- Healthy volunteers accepted
- Child, Adult
Study population: 9-45 years old subjects with narcolepsy , Kleine-Levin syndrome or Idiopathic Hypersomnia
Who may be able to join
- Patients with narcolepsy , Kleine-Levin syndrome(KLS) or Idiopathic Hypersomnia (IH) diagnosed by a physician who meet the ICSD-3 diagnostic criteria
- Age: 9-45 years old
- Those who agree to participate in this research and can sign the consent form.
Who cannot join
- Patients with epilepsy, head trauma and severe organic brain disease.
- Patients with severe Obstructive Sleep Apnea (OSA) and severe Periodic Limb Movement Disorder (PLMD) who have not received treatment.
- People with narcolepsy due to other physical and brain diseases.
- Those who cannot cooperate with the brain imaging examination and neurocognitive function test.
- Exclude those who have had brain surgery for brain tumor hemangioma, or those who have cerebral blood vessel metal clips.
- Exclude current pacemakers.
Show all 10Show fewer
- Excluded those who had implanted artificial heart metal valve.
- Those who underwent surgery within the last 3 months were excluded.
- rule out claustrophobia
- Those who are unwilling to participate in this research or are unwilling to fill in the consent form.
Trial locations and contacts
2 sites in 1 country.
Taiwan2 sites, 2 recruiting
- Recruiting
Chang Gung Memorial Hospital
Taoyuan, 333423
Yu-Shu Huanh, PhD, +886 3 328200 ext. 3836, yushuhuang1212@gmail.com
- Recruiting
Chang Gung Memorial Hospital, Linkou
Taoyuan City, 333423
Yu-shu Huang, MD.PhD., 886-975365659, yushuhuang1212@gmail.com
About this study
Official title: A Multi-Signal Based Monitoring System for CNS Hypersomnias : A 10-year Longitudinal Study
This is a retrospective and prospective cohort study. There are 600 subjects (age 9-45) will be collected.The purposes of this study are as follows:(1) The main purpose is to use Multi-Signal Based Monitoring System to link with brain image data and perform cross-comparison to find out possible pathological mechanisms of these CNS hypersomnias.(2) Use the Multi-Signal Based Monitoring System to link with brain image data and perform cross-comparison to further screen out these clinically significant biomarkers for CNS hypersomnias, and to find ideal and accurate physiological biomarkers that can monitor the course of the disease.(3) Utilize these precisely monitored biomarkers to track changes in the biomarkers and the long-term course of these CNS hypersomnias, and evaluate the treatment effect and prognosis.(4) Use computer machine learning and other algorithms to analyze and construct a variety of faster and more accurate prediction models for these CNS hypersomnias, thereby achieving the goal of preventive medicine.
Read the detailed description
Excessive daytime sleepiness (EDS) is a common symptom in the general population. The prevalence ranges from 5% to 30%. And daytime drowsiness often brings negative effects, and even the daily function and the quality of life is impaired due to these hypersomnias. In some severe cases, many accidents can occur and endanger life. The current third edition of the International Classification of Sleep Disorders (ICSD 3) specifically classified "Central nervous system disorders of hypersomnolence" as Narcolepsy type 1 and type 2 ; idiopathic hypersomnia(IH), and Kleine-Levin syndrome (KLS). However, so far, except for Narcolepsy type 1, which has a relatively clear pathological mechanism that is related to the reduced secretion of hypocretin, other hypersomnia disorders such as Narcolepsy type 2, IH and KLS, that is no clear neurophysiological diagnosis standard, and the mechanism of these diseases is still not clear. Therefore, the diagnosis can only rely on the clinical symptoms and the clinical experience physicians. That is why the diagnosis of these diseases still has great difficulties and challenges. Therefore, in order to make the diagnosis more accurate, the investigators have to find out the "Biologic and neurophysiologic biomarkers" for these diseases. And let patients receive the correct treatment quickly.
The purposes of this study are as follows:
- The main purpose is to use Multi-Signal Based Monitoring System to link with brain image data and perform cross-comparison to find out possible pathological mechanisms of these CNS hypersomnias.
- Use the Multi-Signal Based Monitoring System to link with brain image data and perform cross-comparison to further screen out these clinically significant biomarkers for CNS hypersomnias, and to find ideal and accurate physiological biomarkers that can monitor the course of the disease.
- Utilize these precisely monitored biomarkers to track changes in the biomarkers and the long-term course of these CNS hypersomnias, and evaluate the treatment effect and prognosis.
- Use computer machine learning and other algorithms to analyze and construct a variety of faster and more accurate prediction models for these CNS hypersomnias, thereby achieving the goal of preventive medicine.
Research method:
This is a retrospective and prospective cohort study. There are 600 subjects (age 9-45) will be collected. These subjects will be divided into the five groups: (1) experimental group (narcolepsy Type 1, 300 subjects); (2) experimental group (narcolepsy Type 2, 100 subjects); and (3) experimental group (KLS, 100 subjects); and (4) experimental group (IH,50 subjects); and (5) healthy control group (age and gender matched healthy subjects,50 subjects). The investigators will collect all the clinical data for each subject, including clinical characteristics, sleep examination data, actigraphy, HLA typing, and brain imaging data.
Data analysis method:
Use multiple physiological signals to generate real-time quantitative algorithms and find physiological biomarkers related to hypersomnias. Use the aforementioned data were categorized and grouped through data analysis based on computer machine learning, neural network, and other algorithms. Then the investigators will build a predictive model based on the results and write a medical report and publish it.
- Conditions
- Hypersomnia
- Keywords
- Sleep
- Medical subject headings
- Disorders of Excessive Somnolence
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| experimental group (narcolepsy Type 1) | experimental group (narcolepsy Type 1, 300 subjects) | |
| experimental group (narcolepsy Type 2) | experimental group (narcolepsy Type 2, 100 subjects) | |
| experimental group (KLS) | experimental group (KLS, 100 subjects) | |
| experimental group (IH) | experimental group (IH,50 subjects) | |
| healthy control group | healthy control group (age and gender matched healthy subjects,50 subjects) |
What is being measured
The primary outcome is the main question the study is designed to answer.
Polysomnography (PSG)
Change in sleep latency (SL, mins) based on PSG during the study.
Measured: Once a year until the study is completed (up to 3 years)
Multiple sleep latency test (MSLT)
Change in Change in sleep latency (SL, mins) based on MSLT during the study.
Measured: Once a year until the study is completed (up to 3 years)
HLA TYPING
The investigators will use sequence-specific primer - polymerase chain reaction (SSP-PCR) to detect HLA-DQB1 and reverse sequence-specific oligonucleotide probes (SSOPs) to detect HLA-DQA1,and also use Sequencing Based Typing (SBT) and reverse sequence specific oligonucleotide (rSSO) to detect HLA-DRB and HLA-DQB in the lab.
Measured: baseline
Actigraphy
Change in sleep latency (mins) based on actigraphy during the study.
Measured: Once a year until the study is completed (up to 3 years)
PET/MRI
Positron Emission Tomography is a fusion of PET and MRI imaging techniques that can show the spread of diseased cells in soft tissue. The PET/MRI system can scan various parts of the patient and collect PET and MRI images separately for early diagnosis.
Measured: through study completion, an average of 1 year
Secondary outcomes (13)
Conners' Continuous Performance Test (CPT)
The Conners Continuous Performance Test is a computer administered test that is designed to assess problems with attention.Many statistics are computed including omission errors , commission errors, hit reaction time, hit reaction time standard error, detectability, response style, perseverations , hit reaction time by block, standard error by block, reaction time by ISI , and standard error by ISI. These statistics are converted to T-scores and can be interpreted in terms of various aspects of attention including inattention, impulsivity, and vigilance.Higher rates of correct detections indicate better attentional capacity.
Measured: Once a year until the study is completed (up to 3 years)
Wisconsin Card Sorting Test (WCST)
The Wisconsin Card Sorting Test (WCST) is a neuropsychological test that is frequently used to measure such higher-level cognitive processes as attention, perseverance,working memory, abstract thinking and set shifting.
Measured: Once a year until the study is completed (up to 3 years)
Epworth Sleepoiness Scale (ESS)
Epworth Sleepoiness Scale (ESS) assesses the responder's propensity to doze or fall asleep during 8 common daily activities, such as: sitting and reading; sitting inactive in a public place; sitting and talking to someone; sitting quietly after a lunch without alcohol; or in a car, while stopped for a few minutes in traffic. An ESS score >10 suggests excessive daytime sleepiness (EDS); ESS score ≥16 suggests a high level of EDS.
Measured: Once a year until the study is completed (up to 3 years)
Pediatric Daytime Sleepiness Scale (PDSS)
The pediatric daytime sleepiness questionnaire is a 5 points Likert scale (0-4) for 8 questions concerning to sleepiness. Scores ranged from 0 to 32.Higher scores on PDSS were associated with reduced total sleep time, poorer school achievement, poorer anger control, and frequent illness.
Measured: Once a year until the study is completed (up to 3 years)
Short Form-36 (SF-36)
36-Item Short-Form Health Survey (SF-36) includes 11 major questions that evaluate eight components (0-100), with higher scores indicating better outcome.These components include physical functioning, role limitations due to physical health, role limitations due to emotional problems, energy/fatigue, emotional wellbeing, social functioning, pain, and general health.
Measured: Once a year until the study is completed (up to 3 years)
Polysomnography (PSG)-SE
Change in sleep efficiency (SE, %)based on PSG during the study.
Measured: Once a year until the study is completed (up to 3 years)
Polysomnography (PSG)-TST
Change in total sleep time (TST, mins) based on PSG during the study.
Measured: Once a year until the study is completed (up to 3 years)
Polysomnography (PSG)-WASO
Change in slow wave sleep (SWS, %) based on PSG during the study.
Measured: Once a year until the study is completed (up to 3 years)
Polysomnography (PSG)-REM
Change in REM sleep (%) based on PSG during the study.
Measured: Once a year until the study is completed (up to 3 years)
Polysomnography (PSG)-SWS
Change in slow wave sleep (SWS, %) based on PSG during the study.
Measured: Once a year until the study is completed (up to 3 years)
Actigraphy-TST
Total sleep time (TST, mins) based on actigraphy during the study.
Measured: Once a year until the study is completed (up to 3 years)
Actigraphy-SE
Sleep efficiency (SE, %) based on actigraphy during the study.
Measured: Once a year until the study is completed (up to 3 years)
Actigraphy-WASO
Wake after sleep onset (WASO) based on actigraphy during the study.
Measured: Once a year until the study is completed (up to 3 years)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Jul 2022
- Study start4 Jun 2020
- Primary completion31 Jul 2023 (estimated)
- Study completion31 Jul 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (17)Papers the sponsor has linked to this study.
- Other trials for HypersomniaStudies of the same condition, open and under way.
- More trials run by Chang Gung Memorial HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Enrolment
- 600 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Chang Gung Memorial Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Yu-Shu Huang, PhD, Study director, Principal Investigator
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05443373
- Sponsor's protocol ID
- 201902163A3
- Status last verified
- May 2022
- First submitted
- 30 May 2022
- Last updated
- 5 Jul 2022
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Abe K. Lithium prophylaxis of periodic hypersomnia. Br J Psychiatry. 1977 Mar;130:312-3. doi: 10.1192/bjp.130.3.312. No abstract available.
PubMed 843779Anderson KN, Pilsworth S, Sharples LD, Smith IE, Shneerson JM. Idiopathic hypersomnia: a study of 77 cases. Sleep. 2007 Oct;30(10):1274-81. doi: 10.1093/sleep/30.10.1274.
PubMed 17969461Arnulf I, Rico TJ, Mignot E. Diagnosis, disease course, and management of patients with Kleine-Levin syndrome. Lancet Neurol. 2012 Oct;11(10):918-28. doi: 10.1016/S1474-4422(12)70187-4.
PubMed 22995695Ali M, Auger RR, Slocumb NL, Morgenthaler TI. Idiopathic hypersomnia: clinical features and response to treatment. J Clin Sleep Med. 2009 Dec 15;5(6):562-8.
PubMed 20465024Bassetti C, Aldrich MS. Idiopathic hypersomnia. A series of 42 patients. Brain. 1997 Aug;120 ( Pt 8):1423-35. doi: 10.1093/brain/120.8.1423.
PubMed 9278632Brankack J, Kukushka VI, Vyssotski AL, Draguhn A. EEG gamma frequency and sleep-wake scoring in mice: comparing two types of supervised classifiers. Brain Res. 2010 Mar 31;1322:59-71. doi: 10.1016/j.brainres.2010.01.069. Epub 2010 Feb 1.
PubMed 20123089CRITCHLEY M. Periodic hypersomnia and megaphagia in adolescent males. Brain. 1962 Dec;85:627-56. doi: 10.1093/brain/85.4.627. No abstract available.
PubMed 14023898Engstrom M, Hallbook T, Szakacs A, Karlsson T, Landtblom AM. Functional magnetic resonance imaging in narcolepsy and the kleine-levin syndrome. Front Neurol. 2014 Jun 25;5:105. doi: 10.3389/fneur.2014.00105. eCollection 2014.
PubMed 25009530Fraiwan L, Lweesy K, Khasawneh N, Wenz H, Dickhaus H. Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier. Comput Methods Programs Biomed. 2012 Oct;108(1):10-9. doi: 10.1016/j.cmpb.2011.11.005. Epub 2011 Dec 16.
PubMed 22178068Frenette E, Kushida CA. Primary hypersomnias of central origin. Semin Neurol. 2009 Sep;29(4):354-67. doi: 10.1055/s-0029-1237114. Epub 2009 Sep 9.
PubMed 19742411Grimaldi D, Pierangeli G, Barletta G, Terlizzi R, Plazzi G, Cevoli S, Franceschini C, Montagna P, Cortelli P. Spectral analysis of heart rate variability reveals an enhanced sympathetic activity in narcolepsy with cataplexy. Clin Neurophysiol. 2010 Jul;121(7):1142-7. doi: 10.1016/j.clinph.2010.01.028. Epub 2010 Feb 23.
PubMed 20181520Grosse-Wentrup M, Liefhold C, Gramann K, Buss M. Beamforming in noninvasive brain-computer interfaces. IEEE Trans Biomed Eng. 2009 Apr;56(4):1209-19. doi: 10.1109/TBME.2008.2009768.
PubMed 19423426Guilleminault C, Lopes MC, Hagen CC, da Rosa A. The cyclic alternating pattern demonstrates increased sleep instability and correlates with fatigue and sleepiness in adults with upper airway resistance syndrome. Sleep. 2007 May;30(5):641-7. doi: 10.1093/sleep/30.5.641.
PubMed 17552380Hadjiyannakis K, Ogilvie RD, Alloway CE, Shapiro C. FFT analysis of EEG during stage 2-to-REM transitions in narcoleptic patients and normal sleepers. Electroencephalogr Clin Neurophysiol. 1997 Nov;103(5):543-53. doi: 10.1016/s0013-4694(97)00064-3.
PubMed 9402885Jaussent I, Morin CM, Ivers H, Dauvilliers Y. Incidence, worsening and risk factors of daytime sleepiness in a population-based 5-year longitudinal study. Sci Rep. 2017 May 2;7(1):1372. doi: 10.1038/s41598-017-01547-0.
PubMed 28465612
Study at a glance
From the registry record, last updated by the sponsor on 5 Jul 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Multi-Signal Based Monitoring System for CNS Hypersomnias : A 10-year Longitudinal Study
- ClinicalTrials.gov ID
- NCT05443373
- Acronym / sponsor ID
- 201902163A3
- Conditions
- Hypersomnia
- Keywords
- Sleep
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 600 participants (estimated)
- Lead sponsor
- Chang Gung Memorial HospitalUniversity, hospital or other
- Study start
- 4 Jun 2020 (actual)
- Primary completion
- 31 Jul 2023 (estimated)
- Study completion
- 31 Jul 2023 (estimated)
- First posted
- 5 Jul 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This is a retrospective and prospective cohort study. There are 600 subjects (age 9-45) will be collected.The purposes of this study are as follows:(1) The main purpose is to use Multi-Signal Based Monitoring System to link with brain image data and perform cross-comparison to find out possible pathological mechanisms of these CNS hypersomnias.(2) Use the Multi-Signal Based Monitoring System to link with brain image data and perform cross-comparison to further screen out these clinically significant biomarkers for CNS hypersomnias, and to find ideal and accurate physiological biomarkers that can monitor the course of the disease.(3) Utilize these precisely monitored biomarkers to track changes in the biomarkers and the long-term course of these CNS hypersomnias, and evaluate the treatment effect and prognosis.(4) Use computer machine learning and other algorithms to analyze and construct a variety of faster and more accurate prediction models for these CNS hypersomnias, thereby achieving the goal of preventive medicine.
Detailed description
Excessive daytime sleepiness (EDS) is a common symptom in the general population. The prevalence ranges from 5% to 30%. And daytime drowsiness often brings negative effects, and even the daily function and the quality of life is impaired due to these hypersomnias. In some severe cases, many accidents can occur and endanger life. The current third edition of the International Classification of Sleep Disorders (ICSD 3) specifically classified "Central nervous system disorders of hypersomnolence" as Narcolepsy type 1 and type 2 ; idiopathic hypersomnia(IH), and Kleine-Levin syndrome (KLS). However, so far, except for Narcolepsy type 1, which has a relatively clear pathological mechanism that is related to the reduced secretion of hypocretin, other hypersomnia disorders such as Narcolepsy type 2, IH and KLS, that is no clear neurophysiological diagnosis standard, and the mechanism of these diseases is still not clear. Therefore, the diagnosis can only rely on the clinical symptoms and the clinical experience physicians. That is why the diagnosis of these diseases still has great difficulties and challenges. Therefore, in order to make the diagnosis more accurate, the investigators have to find out the "Biologic and neurophysiologic biomarkers" for these diseases. And let patients receive the correct treatment quickly.
The purposes of this study are as follows:
- The main purpose is to use Multi-Signal Based Monitoring System to link with brain image data and perform cross-comparison to find out possible pathological mechanisms of these CNS hypersomnias.
- Use the Multi-Signal Based Monitoring System to link with brain image data and perform cross-comparison to further screen out these clinically significant biomarkers for CNS hypersomnias, and to find ideal and accurate physiological biomarkers that can monitor the course of the disease.
- Utilize these precisely monitored biomarkers to track changes in the biomarkers and the long-term course of these CNS hypersomnias, and evaluate the treatment effect and prognosis.
- Use computer machine learning and other algorithms to analyze and construct a variety of faster and more accurate prediction models for these CNS hypersomnias, thereby achieving the goal of preventive medicine.
Research method:
This is a retrospective and prospective cohort study. There are 600 subjects (age 9-45) will be collected. These subjects will be divided into the five groups: (1) experimental group (narcolepsy Type 1, 300 subjects); (2) experimental group (narcolepsy Type 2, 100 subjects); and (3) experimental group (KLS, 100 subjects); and (4) experimental group (IH,50 subjects); and (5) healthy control group (age and gender matched healthy subjects,50 subjects). The investigators will collect all the clinical data for each subject, including clinical characteristics, sleep examination data, actigraphy, HLA typing, and brain imaging data.
Data analysis method:
Use multiple physiological signals to generate real-time quantitative algorithms and find physiological biomarkers related to hypersomnias. Use the aforementioned data were categorized and grouped through data analysis based on computer machine learning, neural network, and other algorithms. Then the investigators will build a predictive model based on the results and write a medical report and publish it.
Study design
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Number of arms
- 5
| Arm | Type | Receives |
|---|---|---|
| experimental group (narcolepsy Type 1) | – | –experimental group (narcolepsy Type 1, 300 subjects) |
| experimental group (narcolepsy Type 2) | – | –experimental group (narcolepsy Type 2, 100 subjects) |
| experimental group (KLS) | – | –experimental group (KLS, 100 subjects) |
| experimental group (IH) | – | –experimental group (IH,50 subjects) |
| healthy control group | – | –healthy control group (age and gender matched healthy subjects,50 subjects) |
Who may qualify
- Ages
- 9 to 45 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult
- Study population
- 9-45 years old subjects with narcolepsy , Kleine-Levin syndrome or Idiopathic Hypersomnia
Key inclusion criteria (3 listed)
- Patients with narcolepsy , Kleine-Levin syndrome(KLS) or Idiopathic Hypersomnia (IH) diagnosed by a physician who meet the ICSD-3 diagnostic criteria
- Age: 9-45 years old
- Those who agree to participate in this research and can sign the consent form.
Key exclusion criteria (10 listed)
- Patients with epilepsy, head trauma and severe organic brain disease.
- Patients with severe Obstructive Sleep Apnea (OSA) and severe Periodic Limb Movement Disorder (PLMD) who have not received treatment.
- People with narcolepsy due to other physical and brain diseases.
- Those who cannot cooperate with the brain imaging examination and neurocognitive function test.
- Exclude those who have had brain surgery for brain tumor hemangioma, or those who have cerebral blood vessel metal clips.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Polysomnography (PSG)
Time frame: Once a year until the study is completed (up to 3 years)
Change in sleep latency (SL, mins) based on PSG during the study.
Multiple sleep latency test (MSLT)
Time frame: Once a year until the study is completed (up to 3 years)
Change in Change in sleep latency (SL, mins) based on MSLT during the study.
HLA TYPING
Time frame: baseline
The investigators will use sequence-specific primer - polymerase chain reaction (SSP-PCR) to detect HLA-DQB1 and reverse sequence-specific oligonucleotide probes (SSOPs) to detect HLA-DQA1,and also use Sequencing Based Typing (SBT) and reverse sequence specific oligonucleotide (rSSO) to detect HLA-DRB and HLA-DQB in the lab.
Actigraphy
Time frame: Once a year until the study is completed (up to 3 years)
Change in sleep latency (mins) based on actigraphy during the study.
PET/MRI
Time frame: through study completion, an average of 1 year
Positron Emission Tomography is a fusion of PET and MRI imaging techniques that can show the spread of diseased cells in soft tissue. The PET/MRI system can scan various parts of the patient and collect PET and MRI images separately for early diagnosis.
13 secondary outcomes
Conners' Continuous Performance Test (CPT)
Time frame: Once a year until the study is completed (up to 3 years)
Wisconsin Card Sorting Test (WCST)
Time frame: Once a year until the study is completed (up to 3 years)
Epworth Sleepoiness Scale (ESS)
Time frame: Once a year until the study is completed (up to 3 years)
Pediatric Daytime Sleepiness Scale (PDSS)
Time frame: Once a year until the study is completed (up to 3 years)
Short Form-36 (SF-36)
Time frame: Once a year until the study is completed (up to 3 years)
Polysomnography (PSG)-SE
Time frame: Once a year until the study is completed (up to 3 years)
Polysomnography (PSG)-TST
Time frame: Once a year until the study is completed (up to 3 years)
Polysomnography (PSG)-WASO
Time frame: Once a year until the study is completed (up to 3 years)
Polysomnography (PSG)-REM
Time frame: Once a year until the study is completed (up to 3 years)
Polysomnography (PSG)-SWS
Time frame: Once a year until the study is completed (up to 3 years)
Actigraphy-TST
Time frame: Once a year until the study is completed (up to 3 years)
Actigraphy-SE
Time frame: Once a year until the study is completed (up to 3 years)
Actigraphy-WASO
Time frame: Once a year until the study is completed (up to 3 years)
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 5 Jul 2022.
- Results publication: PubMed 843779Abe K. Lithium prophylaxis of periodic hypersomnia. Br J Psychiatry. 1977 Mar;130:312-3. doi: 10.1192/bjp.130.3.312. No abstract available.
- Results publication: PubMed 17969461Anderson KN, Pilsworth S, Sharples LD, Smith IE, Shneerson JM. Idiopathic hypersomnia: a study of 77 cases. Sleep. 2007 Oct;30(10):1274-81. doi: 10.1093/sleep/30.10.1274.
- Results publication: PubMed 22995695Arnulf I, Rico TJ, Mignot E. Diagnosis, disease course, and management of patients with Kleine-Levin syndrome. Lancet Neurol. 2012 Oct;11(10):918-28. doi: 10.1016/S1474-4422(12)70187-4.
- Results publication: PubMed 20465024Ali M, Auger RR, Slocumb NL, Morgenthaler TI. Idiopathic hypersomnia: clinical features and response to treatment. J Clin Sleep Med. 2009 Dec 15;5(6):562-8.
- Results publication: PubMed 9278632Bassetti C, Aldrich MS. Idiopathic hypersomnia. A series of 42 patients. Brain. 1997 Aug;120 ( Pt 8):1423-35. doi: 10.1093/brain/120.8.1423.
- Results publication: PubMed 20123089Brankack J, Kukushka VI, Vyssotski AL, Draguhn A. EEG gamma frequency and sleep-wake scoring in mice: comparing two types of supervised classifiers. Brain Res. 2010 Mar 31;1322:59-71. doi: 10.1016/j.brainres.2010.01.069. Epub 2010 Feb 1.
- Results publication: PubMed 14023898CRITCHLEY M. Periodic hypersomnia and megaphagia in adolescent males. Brain. 1962 Dec;85:627-56. doi: 10.1093/brain/85.4.627. No abstract available.
- Results publication: PubMed 25009530Engstrom M, Hallbook T, Szakacs A, Karlsson T, Landtblom AM. Functional magnetic resonance imaging in narcolepsy and the kleine-levin syndrome. Front Neurol. 2014 Jun 25;5:105. doi: 10.3389/fneur.2014.00105. eCollection 2014.
- Results publication: PubMed 22178068Fraiwan L, Lweesy K, Khasawneh N, Wenz H, Dickhaus H. Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier. Comput Methods Programs Biomed. 2012 Oct;108(1):10-9. doi: 10.1016/j.cmpb.2011.11.005. Epub 2011 Dec 16.
- Results publication: PubMed 19742411Frenette E, Kushida CA. Primary hypersomnias of central origin. Semin Neurol. 2009 Sep;29(4):354-67. doi: 10.1055/s-0029-1237114. Epub 2009 Sep 9.
- Results publication: PubMed 20181520Grimaldi D, Pierangeli G, Barletta G, Terlizzi R, Plazzi G, Cevoli S, Franceschini C, Montagna P, Cortelli P. Spectral analysis of heart rate variability reveals an enhanced sympathetic activity in narcolepsy with cataplexy. Clin Neurophysiol. 2010 Jul;121(7):1142-7. doi: 10.1016/j.clinph.2010.01.028. Epub 2010 Feb 23.
- Results publication: PubMed 19423426Grosse-Wentrup M, Liefhold C, Gramann K, Buss M. Beamforming in noninvasive brain-computer interfaces. IEEE Trans Biomed Eng. 2009 Apr;56(4):1209-19. doi: 10.1109/TBME.2008.2009768.
- Individual participant dataSharing planned: Undecided
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A Long-Term Study of ALKS 2680 in Subjects With Narcolepsy and Idiopathic Hypersomnia
Narcolepsy Type 1, Narcolepsy Type 2
RecruitingPhase 3
A Study Evaluating the Efficacy and Safety of Xywav Expanded Dosing vs Placebo in Participants With Narcolepsy or IH
Narcolepsy, Idiopathic Hypersomnia
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Yu-Shu Huang, PhD study director
Principal Investigator
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Hypersomnia, run by Chang Gung Memorial Hospital at 2 sites in Taiwan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 10 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 5 Jul 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in Taiwan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Yu-Shu Huang, PhD (+886 3 3281200 ext. 3836, yushuhuang1212@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Polysomnography (PSG) (time frame: Once a year until the study is completed (up to 3 years)). The study lists 5 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05443373, which also lists 17 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 5 Jul 2022.