Dementia
Conditions
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.
Interventions
DIAGNOSTIC_TESTmachine learning model
Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), were investigated for their ability to predict successfully whether patients suffered from PDD or DLB.
Sponsors
National and Kapodistrian University of Athens
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
50 Years to 90 Years
Healthy volunteers
No
Inclusion criteria
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 .
Exclusion criteria
* major psychiatrics disorders, depression
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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. |
Countries
Greece
Contacts
Primary ContactANASTASIA BOUGEA, DR
Backup ContactANASTASIA BOUGEA
Outcome results
None listed