Skip to main content
Clinical Trials/NCT03872102
NCT03872102
Recruiting
Not Applicable

Quantitative Diagnostics of Parkinsonian Syndromes Using Multi-modal Neuroimaging and Deep Learning

University of Texas Southwestern Medical Center1 site in 1 country90 target enrollmentMarch 28, 2019

Overview

Phase
Not Applicable
Intervention
Aim 1: Develop a biomarker of PD disease progression rate
Conditions
Parkinson Disease
Sponsor
University of Texas Southwestern Medical Center
Enrollment
90
Locations
1
Primary Endpoint
Imaging biomarker that discriminates different neurodegenerative diseases
Status
Recruiting
Last Updated
3 months ago

Overview

Brief Summary

The goals of this study are: 1) to identify biomarkers using neuroimaging that are associated with progression rate using statistical methods, and 2) to identify biomarkers that are associated with the differential diagnosis of Parkinson's disease and atypical parkinsonism.

Detailed Description

Management of patients with parkinsonian symptoms has two critical gaps: (1) there are no clinically accepted biomarkers that may be used to inform disease progression rate in an individual with Parkinson disease (PD), and (2) no biomarkers exist to inform differential diagnosis of conditions that exhibit parkinsonian symptoms and signs. This 2-year study aims to develop a multi-modal neuroimaging biomarker that enables the prediction of disease progression rate in PD, and a biomarker that enables the differential diagnosis of PD, multiple systems atrophy (MSA), progressive supranuclear palsy (PSP), and healthy controls. This study consists of two parts; neuroimaging of a defined population of mid to late stage PD subjects currently followed at UT Southwestern Medical Center, and recruitment of new subjects with PD, MSA, and PSP who will be followed clinically over 2 years and who will undergo neuroimaging. Participants will be asked to undergo several types of neuroimaging which will be analyzed using machine learning techniques. At each study visit of the newly recruited cohorts, appropriate clinical scales will be performed based on their diagnosis and used to track and measure disease severity and progression.

Registry
clinicaltrials.gov
Start Date
March 28, 2019
End Date
December 31, 2026
Last Updated
3 months ago
Study Type
Observational
Sex
All

Investigators

Responsible Party
Principal Investigator
Principal Investigator

Padraig O'Suilleabhain

Professor

University of Texas Southwestern Medical Center

Eligibility Criteria

Inclusion Criteria

  • Diagnosis of Parkinson disease
  • Existence of sufficient clinical data from previous UTS Southwestern longitudinal study to determine progression rate (categorized as fast or slow)
  • Availability of suitable matched participant in the alternate progression group (fast or slow)
  • Willingness to participate in the imaging studies required for this study and to provide written informed consent
  • PD subjects will be recruited in accordance with the MDS Clinical Diagnostic Criteria for PD.
  • Duration of PD (since diagnosis) is \< 5 years
  • Willing to participate in imaging and clinical scoring visits, and provide written informed consent
  • Subject and investigator agree that it is highly likely subject will be able to participate throughout the 2-year study period (no plans to move)
  • MSA subjects will be recruited in accordance with the Second Consensus Statement on Diagnosis of Multiple System Atrophy.
  • Duration of MSA (since diagnosis) is \< 5 years

Exclusion Criteria

  • For Aims 1 and 2:
  • Any contraindications to undergoing the multimodal imaging program
  • All females of child-bearing potential, between the ages of 18-55, will be excluded from the study, unless they are confirmed to be not pregnant with a pregnancy test prior to scanning
  • This study will require constant clear communication throughout the duration of the study; therefore, non-English speakers will be excluded
  • Right-handed finger amputees
  • Cast on right hand or fingers at the time of enrollment
  • Has clinically significant liver, kidney, lung, metabolic or hormone disturbances which pose safety risk
  • Has a current clinically significant heart disease that poses a safety risk
  • Has a current clinically significant infectious disease or a medical comorbidity which poses a safety risk
  • Has a history of relevant severe drug allergy or hypersensitivity

Arms & Interventions

Aim 1: Develop a biomarker of PD disease progression rate

For Aim 1, we will enroll PD subjects spanning a range of progression rates that have been tracked at UT Southwestern Medical Center. Multimodal neuroimaging will be acquired from each subject. We will evaluate imaging data and known data on clinical progression using statistical techniques to determine a biomarker that associates with progression rate.

Aim 2: Develop a biomarker to distinguish between PD, PSP, MSA

For Aim 2, we will recruit subjects with PD, MSA, and PSP. We will also recruit healthy age/sex-matched controls. All subjects will complete a series of clinical assessments at three different time points, roughly 6-8 months apart: * Levodopa Equivalent Daily Dose * Parkinson disease questionnaire * Schwab and England ADL Scale * MDS-UPDRS (PD and healthy controls only) * UMSARS (MSA subjects only) * PSPRS (PSP subjects only) Multimodal neuroimaging will be acquired from each subject. We will evaluate imaging data from the participants along with prospectively collected information on clinical progression using statistical techniques to determine a biomarker that associates with the differentiation of PD, MSA, and PSP.

Outcomes

Primary Outcomes

Imaging biomarker that discriminates different neurodegenerative diseases

Time Frame: Baseline

The imaging biomarker consists of a machine learning model that differentiates the parkinsonian diseases: PD, PSP, and MSA using the neuroimaging data. The performance of the model will be assessed quantitatively using widely adopted performance metrics from the classification confusion matrix. The metrics will include disease sensitivity, disease specificity, and disease specific accuracy and overall accuracy.

Imaging biomarker of progression rate

Time Frame: Baseline

The imaging biomarker consists of a machine learning model that distinguishes fast and slow progressors using the neuroimaging data. The performance of the model will be assessed quantitatively using widely adopted performance metrics: sensitivity, specificity, and accuracy.

Secondary Outcomes

  • Change from baseline in Progressive Supranuclear Palsy Rating Scale (PSPRS) score(Baseline, 6-8 Month Visit, End of Study Visit (12-16 Month))
  • Change from baseline in MDS-UPDRS score(Baseline, 6-8 Month Visit, End of Study Visit (12-16 Month))
  • Change from baseline in Parkinson disease questionnaire(Baseline, 6-8 Month Visit, End of Study Visit (12-16 Month))
  • Change from baseline in Unified Multiple System Atrophy Rating Scale (UMSARS) score(Baseline, 6-8 Month Visit, End of Study Visit (12-16 Month))
  • Change from baseline in Schwab and England Activities of Daily Living Scale(Baseline, 6-8 Month Visit, End of Study Visit (12-16 Month))

Study Sites (1)

Loading locations...

Similar Trials