Dementia, Lewy Body, Parkinson Disease, REM Sleep Behavior Disorder
Conditions
Keywords
Parkinson Disease, Artificial Intelligence, REM Sleep Behavior Disorder
Brief summary
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.
Interventions
DEVICEPET/CT with 18-FDG
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.
DEVICESPECT : 123 I-FP-CIT (DATSCAN)
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.
DEVICEMRI
MRI examination to exclude structural brain anomalies.
Sponsors
Insel Gruppe AG, University Hospital Bern
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
1. Confirmed clinical iRBD diagnosis by movement disorder specialists according to the International Classification of Sleep Disorders 2. Written informed consent
Exclusion criteria
1. Known diagnosis of PD or other neurodegenerative disorder 2. Unequivocal signs of parkinsonism on examination 3. Narcolepsy or other known causes of RBD 4. Moderate to severe obstructive sleep apnea 5. Abnormal neurological or MRI examination
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Assessment of Deep Learning Model Accuracy in Predicting Neurodegenerative Conversion in isolated REM sleep behavior disorder (iRBD) through Early Biomarker Detection | 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
| Measure | Time frame | Description |
|---|---|---|
| Comparison of the Estimated versus Observed Annual Conversion Risk of Isolated Rapid Eye Movement Behavior Disorder (iRBD) to Neurodegenerative Disorders | 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. |
| Evaluation of Deep Learning Model Accuracy in Predicting Conversion of Isolated REM Sleep Behavior Disorder (iRBD) to Parkinson's Disease | 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. |
Countries
Switzerland
Contacts
Primary ContactAxel Rominger, Prof. Dr. med.
Backup ContactFranziska Strunz, PhD
Outcome results
None listed