A Novel Machine Learning Algorithm to Predict the Lewy Body Dementias

Study Overview
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Brief Summary
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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.

Detailed Description
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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.

Official Title
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A Novel Machine Learning Algorithm to Predict the Lewy Body Dementias Using Clinical and Neuropsychological Scores

Conditions
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Dementia

Intervention / Treatment
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* Diagnostic Test: machine learning model


Participation Criteria
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Eligibility Criteria
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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: major psychiatrics disorders, depression

Ages Eligible for Study
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Minimum Age: 50 Years
Maximum Age: 90 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Parkinson Disease Dementia<br>the PDD group comprised of 58 patients fulfilling the Criteria for probable PDD of the Movement Disorders Society | Diagnostic Test: machine learning model<br>* 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.<br>|
| Dementia with Lewy Bodies<br>the DLB group comprised of 40 patients, according to the recent revised criteria for probable DLB | Diagnostic Test: machine learning model<br>* 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.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | 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 | 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 |