Hypersomnia
Conditions
Keywords
Sleep
Brief 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 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: 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. 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.
Interventions
None listed
Sponsors
Chang Gung Memorial Hospital
Study design
Observational model
COHORT
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
9 Years to 45 Years
Healthy volunteers
Yes
Inclusion criteria
1. Patients with narcolepsy , Kleine-Levin syndrome(KLS) or Idiopathic Hypersomnia (IH) diagnosed by a physician who meet the ICSD-3 diagnostic criteria 2. Age: 9-45 years old 3. Those who agree to participate in this research and can sign the consent form.
Exclusion criteria
1. Patients with epilepsy, head trauma and severe organic brain disease. 2. Patients with severe Obstructive Sleep Apnea (OSA) and severe Periodic Limb Movement Disorder (PLMD) who have not received treatment. 3. People with narcolepsy due to other physical and brain diseases. 4. Those who cannot cooperate with the brain imaging examination and neurocognitive function test. 5. Exclude those who have had brain surgery for brain tumor hemangioma, or those who have cerebral blood vessel metal clips. 6. Exclude current pacemakers. 7. Excluded those who had implanted artificial heart metal valve. 8. Those who underwent surgery within the last 3 months were excluded. 9. rule out claustrophobia 10. Those who are unwilling to participate in this research or are unwilling to fill in the consent form.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Polysomnography (PSG) | 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) | 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 | 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 | 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 | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Polysomnography (PSG)-SE | Once a year until the study is completed (up to 3 years) | Change in sleep efficiency (SE, %)based on PSG during the study. |
| Polysomnography (PSG)-TST | Once a year until the study is completed (up to 3 years) | Change in total sleep time (TST, mins) based on PSG during the study. |
| Polysomnography (PSG)-WASO | Once a year until the study is completed (up to 3 years) | Change in slow wave sleep (SWS, %) based on PSG during the study. |
| Polysomnography (PSG)-REM | Once a year until the study is completed (up to 3 years) | Change in REM sleep (%) based on PSG during the study. |
| Conners' Continuous Performance Test (CPT) | Once a year until the study is completed (up to 3 years) | 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. |
| Actigraphy-TST | Once a year until the study is completed (up to 3 years) | Total sleep time (TST, mins) based on actigraphy during the study. |
| Actigraphy-SE | Once a year until the study is completed (up to 3 years) | Sleep efficiency (SE, %) based on actigraphy during the study. |
| Actigraphy-WASO | Once a year until the study is completed (up to 3 years) | Wake after sleep onset (WASO) based on actigraphy during the study. |
| Polysomnography (PSG)-SWS | Once a year until the study is completed (up to 3 years) | Change in slow wave sleep (SWS, %) based on PSG during the study. |
| Wisconsin Card Sorting Test (WCST) | Once a year until the study is completed (up to 3 years) | 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. |
| Epworth Sleepoiness Scale (ESS) | Once a year until the study is completed (up to 3 years) | 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. |
| Pediatric Daytime Sleepiness Scale (PDSS) | Once a year until the study is completed (up to 3 years) | 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. |
| Short Form-36 (SF-36) | Once a year until the study is completed (up to 3 years) | 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. |
Countries
Taiwan
Contacts
Primary ContactYu-Shu Huang, PhD
Outcome results
None listed