A Multi-Signal Based Monitoring System for CNS Hypersomnias

Hypersomnia Clinical Trial

Official title:

A Multi-Signal Based Monitoring System for CNS Hypersomnias : A 10-year Longitudinal Study

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05443373
• Other study ID #: 201902163A3
• Status: Recruiting
• Start date: June 4, 2020
• Est. completion date: July 31, 2023

Study information

• Verified date: May 2022
• Source: Chang Gung Memorial Hospital
• Contact: Yu-Shu Huang, PhD
• Phone: +886 3 3281200
• Email: yushuhuang1212[@]gmail.com
• Is FDA regulated: No
• Study type: Observational

Clinical 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 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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 600
• Est. completion date: July 31, 2023
• Est. primary completion date: July 31, 2023
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 9 Years to 45 Years

Eligibility

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.

Related Conditions & MeSH terms

• Disorders of Excessive Somnolence
• Hypersomnia

Locations

Country	Name	City	State
Taiwan	Chang Gung Memorial Hospital	Taoyuan
Taiwan	Chang Gung Memorial Hospital, Linkou	Taoyuan City

Sponsors (1)

Lead Sponsor	Collaborator
Chang Gung Memorial Hospital

Country where clinical trial is conducted

Taiwan, 

References & Publications (17)

Abe K. Lithium prophylaxis of periodic hypersomnia. Br J Psychiatry. 1977 Mar;130:312-3. — View Citation

Ali 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. — View Citation

Anderson KN, Pilsworth S, Sharples LD, Smith IE, Shneerson JM. Idiopathic hypersomnia: a study of 77 cases. Sleep. 2007 Oct;30(10):1274-81. — View Citation

Arnulf 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. Review. — View Citation

Bassetti C, Aldrich MS. Idiopathic hypersomnia. A series of 42 patients. Brain. 1997 Aug;120 ( Pt 8):1423-35. — View Citation

Brankack 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. — View Citation

CRITCHLEY M. Periodic hypersomnia and megaphagia in adolescent males. Brain. 1962 Dec;85:627-56. — View Citation

Engström M, Hallböök 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. Review. — View Citation

Fraiwan 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.c — View Citation

Frenette 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. Review. — View Citation

Grimaldi 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;12 — View Citation

Grosse-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. — View Citation

Guilleminault 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. — View Citation

Hadjiyannakis 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. — View Citation

Jaussent 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. — View Citation

Kanbayashi T, Kodama T, Kondo H, Satoh S, Inoue Y, Chiba S, Shimizu T, Nishino S. CSF histamine contents in narcolepsy, idiopathic hypersomnia and obstructive sleep apnea syndrome. Sleep. 2009 Feb;32(2):181-7. — View Citation

Pike M, Stores G. Kleine-Levin syndrome: a cause of diagnostic confusion. Arch Dis Child. 1994 Oct;71(4):355-7. — View Citation

* Note: There are 17 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Polysomnography (PSG)	Change in sleep latency (SL, mins) based on PSG during the study.	Once a year until the study is completed (up to 3 years)
Primary	Multiple sleep latency test (MSLT)	Change in Change in sleep latency (SL, mins) based on MSLT during the study.	Once a year until the study is completed (up to 3 years)
Primary	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.	baseline
Primary	Actigraphy	Change in sleep latency (mins) based on actigraphy during the study.	Once a year until the study is completed (up to 3 years)
Primary	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.	through study completion, an average of 1 year
Secondary	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.	Once a year until the study is completed (up to 3 years)
Secondary	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.	Once a year until the study is completed (up to 3 years)
Secondary	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.	Once a year until the study is completed (up to 3 years)
Secondary	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.	Once a year until the study is completed (up to 3 years)
Secondary	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.	Once a year until the study is completed (up to 3 years)
Secondary	Polysomnography (PSG)-SE	Change in sleep efficiency (SE, %)based on PSG during the study.	Once a year until the study is completed (up to 3 years)
Secondary	Polysomnography (PSG)-TST	Change in total sleep time (TST, mins) based on PSG during the study.	Once a year until the study is completed (up to 3 years)
Secondary	Polysomnography (PSG)-WASO	Change in slow wave sleep (SWS, %) based on PSG during the study.	Once a year until the study is completed (up to 3 years)
Secondary	Polysomnography (PSG)-REM	Change in REM sleep (%) based on PSG during the study.	Once a year until the study is completed (up to 3 years)
Secondary	Polysomnography (PSG)-SWS	Change in slow wave sleep (SWS, %) based on PSG during the study.	Once a year until the study is completed (up to 3 years)
Secondary	Actigraphy-TST	Total sleep time (TST, mins) based on actigraphy during the study.	Once a year until the study is completed (up to 3 years)
Secondary	Actigraphy-SE	Sleep efficiency (SE, %) based on actigraphy during the study.	Once a year until the study is completed (up to 3 years)
Secondary	Actigraphy-WASO	Wake after sleep onset (WASO) based on actigraphy during the study.	Once a year until the study is completed (up to 3 years)