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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Parkinson Disease Dementia | the PDD group comprised of 58 patients fulfilling the Criteria for probable PDD of the Movement Disorders Society |
| |
| Dementia with Lewy Bodies | the DLB group comprised of 40 patients, according to the recent revised criteria for probable DLB |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| machine learning model | Diagnostic Test | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| MMSE predictive for dlb or PDD | 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. | 1 year |
| Parkinson's Disease - Cognitive Rating Scale (PD-CRS) predictive for DLB or PDD | 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. | 1 year |
| Brief Visuospatial Memory Test (BVMT-TR) predictive for DLB or PDD | 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. | 1 year |
| Symbol digit written predictive for DLB or PDD | 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. | 1 year |
| Wechsler adult intelligence scale,predictive for DLB or PDD | 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. | 1 year |
| trail making A and B predictive for DLB or PDD |
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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:
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the PDD group comprised of patients fulfilling the Criteria for probable PDD and the DLB group.Patients will be enrolled from the register-based database of two clinics. The following data were collected: gender, age, education, hand dominance, Disease duration (years) and levodopa equivalent daily dose (LEDD). The burden of disease will be assess by the Movement Disorders Society-United Parkinson's Disease Rating Scale (MDS-UPDRS) part III in the Off medication state and the following six cognitive/behavioral tests: Mini-Mental State Examination (MMSE), PD- Cognitive Rating Scale (PD-CRS), Brief Visuospatial Memory test (BVMT-TR), Symbol digit written (SDMT), Trail making test (TMT A,B), Wechsler adultintelligence scale (WAIS-V). All patients will undergo brain MRI and blood test to exclude secondarycauses of dementia.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| ANASTASIA BOUGEA, DR | Contact | +306930481046 | annita139@yahoo.gr | |
| ANASTASIA BOUGEA | Contact | +306930481046 | annita139@yahoo.gr |
| Name | Affiliation | Role |
|---|---|---|
| ANASTASIA BOUGEA | National and Kapodistrian University of Athens | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Anastasia Bougea | Recruiting | Athens | Attica | 16674 | Greece |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 33550890 | Derived | Bougea A, Efthymiopoulou E, Spanou I, Zikos P. A Novel Machine Learning Algorithm Predicts Dementia With Lewy Bodies Versus Parkinson's Disease Dementia Based on Clinical and Neuropsychological Scores. J Geriatr Psychiatry Neurol. 2022 May;35(3):317-320. doi: 10.1177/0891988721993556. Epub 2021 Feb 8. |
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| ID | Term |
|---|---|
| D003704 | Dementia |
| ID | Term |
|---|---|
| D001927 | Brain Diseases |
| D002493 | Central Nervous System Diseases |
| D009422 | Nervous System Diseases |
| D019965 | Neurocognitive Disorders |
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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.
| 1 year |
| D001523 | Mental Disorders |