A Novel Machine Learning Algorithm to Predict the Lewy Body Dementias

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

200 participants

Study Classification

OBSERVATIONAL

Study Start Date

2019-09-01

Study Completion Date

2021-03-01

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.

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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.

Conditions

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Dementia

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

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Parkinson Disease Dementia

the PDD group comprised of 58 patients fulfilling the Criteria for probable PDD of the Movement Disorders Society

machine learning model

Intervention Type 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.

Dementia with Lewy Bodies

the DLB group comprised of 40 patients, according to the recent revised criteria for probable DLB

machine learning model

Intervention Type 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.

Interventions

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machine 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.

Intervention Type DIAGNOSTIC_TEST

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 .