A Novel Machine Learning Algorithm to Predict the Lewy Body Dementias
- Conditions
- Dementia
- Interventions
- Diagnostic Test: machine learning model
- Registration Number
- NCT04448340
- Lead Sponsor
- National and Kapodistrian University of Athens
- Brief Summary
Parkinson's disease dementia (PDD) and Dementia with lewy bodies (DLB) are dementia syndromes that overlap in many clinical features, making their diagnosis difficult in clinical practice, particularly in advanced stages. We propose a machine learning algorithm, based only on non-invasively and easily in-the-clinic collectable predictors, to identify these disorders with a high prognostic performance.
- Detailed Description
The algorithm will be develop using dataset from two specialized memory centers, employing a sample of PDD and DLB subjects whose diagnostic follow-up is available for at least 3 years after the baseline assessment. A restricted set of information regarding clinico- demographic characteristics, 6 neuropsychological tests (mini mental, PD Cognitive Rating Scale, Brief Visuospatial Memory test, Symbol digit written, Wechsler adult intelligence scale, trail making A and B) was used as predictors. Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), will be investigated for their ability to predict successfully whether patients suffered from PDD or DLB.
Recruitment & Eligibility
- Status
- UNKNOWN
- Sex
- All
- Target Recruitment
- 200
the PDD group comprised of patients fulfilling the Criteria for probable PDD of the Movement Disorders Society (b) the DLB group comprised of patients, according to the recent revised criteria for probable DLB .
- major psychiatrics disorders, depression
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description Dementia with Lewy Bodies machine learning model the DLB group comprised of 40 patients, according to the recent revised criteria for probable DLB Parkinson Disease Dementia machine learning model the PDD group comprised of 58 patients fulfilling the Criteria for probable PDD of the Movement Disorders Society
- Primary Outcome Measures
Name Time Method MMSE predictive for dlb or PDD 1 year Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), will combine these tests in order to investigate for their ability to predict successfully whether patients suffered from PDD or DLB.
Parkinson's Disease - Cognitive Rating Scale (PD-CRS) predictive for DLB or PDD 1 year Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), will combine these tests in order to investigate for their ability to predict successfully whether patients suffered from PDD or DLB.
Brief Visuospatial Memory Test (BVMT-TR) predictive for DLB or PDD 1 year Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), will combine these tests in order to investigate for their ability to predict successfully whether patients suffered from PDD or DLB.
Symbol digit written predictive for DLB or PDD 1 year Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), will combine these tests in order to investigate for their ability to predict successfully whether patients suffered from PDD or DLB.
Wechsler adult intelligence scale,predictive for DLB or PDD 1 year Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), will combine these tests in order to investigate for their ability to predict successfully whether patients suffered from PDD or DLB.
trail making A and B predictive for DLB or PDD 1 year Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), will combine these tests in order to investigate for their ability to predict successfully whether patients suffered from PDD or DLB.
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (1)
Anastasia Bougea
🇬🇷Athens, Attiki, Greece