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NCT Number: NCT06629207

Artificial Intelligence in Molecular Imaging: Predicting Parkinson's Risk in REM Sleep Behavior Disorder

The study aims to systematically document the course of REM sleep behavior disorder (RBD) and investigate possible clinical and imaging biomarkers for disease progression and conversion risk to Parkinson's disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA). The study will use artificial intelligence to analyze imaging and develop a reliable method to predict and stratify patients approaching conversion to overt a-synucleinopathy. Participants will be clinically evaluated and 2 imaging procedures will be done.

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Key information

Conditions

Parkinson Disease Basal Ganglia Diseases Brain Diseases Central Nervous System Diseases Dementia Dementia, Lewy Body Lewy Body Disease Mental Disorders Movement Disorders Nervous System Diseases Neurocognitive Disorders Neurodegenerative Diseases Parasomnias Parkinsonian Disorders REM Sleep Behavior Disorder REM Sleep Parasomnias Sleep Wake Disorders Synucleinopathies

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Confirmed clinical iRBD diagnosis by movement disorder specialists according to the International Classification of Sleep Disorders
  • Written informed consent

Exclusion criteria

  • Known diagnosis of PD or other neurodegenerative disorder
  • Unequivocal signs of parkinsonism on examination
  • Narcolepsy or other known causes of RBD
  • Moderate to severe obstructive sleep apnea
  • Abnormal neurological or MRI examination

Treatment and study plan

PET/CT with 18-FDG

Device

FDG-PET scans will be acquired in a Siemens Biograph Vision Quadra PET/CT (Siemens, Germany) at 30-minute post-injection of approximately 80 MBq 18F-FDG. The duration of the acquisition is 20 minutes. The PET images will be reconstructed with the vendor's time of flight (TOF) point-spread-function (PSF) algorithm, following corrections for randoms, scatter, and decay. Attenuation correction will be performed first using low-dose CT.

SPECT : 123 I-FP-CIT (DATSCAN)

Device

DaT-Scans will be acquired in a GE Discovery NM/CT 670 Pro™. After injection of approximately 110 MBq 123I-FP-CIT, images will be acquired within 4 h post-injection. The duration of the acquisition is 35 minutes.

MRI

Device

MRI examination to exclude structural brain anomalies.

Primary outcomes

  1. Assessment of Deep Learning Model Accuracy in Predicting Neurodegenerative Conversion in isolated REM sleep behavior disorder (iRBD) through Early Biomarker Detection

    Time frame: From enrollment to end of follow-up period, expected to be 48 months

    The investigators aim to evaluate the predictive accuracy of a deep learning model in identifying patients with iRBD who will progress to a neurodegenerative disorder. The primary outcome will assess the model's sensitivity in detecting early imaging biomarkers linked to disease progression, with the goal of enabling earlier intervention and improving long-term outcomes.

Secondary outcomes

  1. Comparison of the Estimated versus Observed Annual Conversion Risk of Isolated Rapid Eye Movement Behavior Disorder (iRBD) to Neurodegenerative Disorders

    Time frame: From enrollment to end of follow-up period, expected to be 48 months

    The investigators aim to compare the estimated annual conversion risk of 6.3% in patients with iRBD to Parkinson's disease or another overt alpha-synucleinopathy with the conversion rates observed in the study.

  2. Evaluation of Deep Learning Model Accuracy in Predicting Conversion of Isolated REM Sleep Behavior Disorder (iRBD) to Parkinson's Disease

    Time frame: From enrollment to end of follow-up period, expected to be 48 months

    The investigators aim to evaluate the accuracy, receiver operating characteristic curves and area under the curve, specificity, and positive and negative predictive values of the applied deep learning method, predicting the conversion risk from iRBD to Parkinson's disease or another overt alpha-synucleinopathy.

Study contacts

Contact information is provided by the study sponsor or research team.

Axel Rominger, Prof. Dr. med.

CONTACT

[email protected]

+41 316322610

Franziska Strunz, PhD

CONTACT

[email protected]

+41 316643022

Sponsors and collaborators

Lead sponsor

Insel Gruppe AG, University Hospital Bern

Other

Registry information

Official study title

Artificial Intelligence on Molecular Imaging to Predict the Risks of Parkinson's Disease for Patients With Rapid Eye Movement Sleep Behavior Disorder

Acronym: NUK-RBD

Important dates

Study start
2024
Primary completion
2026
Study completion
2026
First posted
Oct 8, 2024
Registry last updated
Nov 8, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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