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Clinical Trial Summary

Based on a retrospectively collected data analysis, a new tool, namely CoMDA (Cognition in Movement Disorders Assessment) is developed by merging each item of Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA) and Frontal Assessment Battery (FAB). A machine learning, able to classify the cognitive profile and predict patients' at risk of dementia, is created.


Clinical Trial Description

First, a prospectively data-base was setting up, collecting MMSE, FAB, MoCA and in-depht-neuropsychologocal-battery scores, obtained from the evaluation of 500 patients with parkinsonisms. Data were analyzed to compare the classification of patient cognition profile, obtained with MMSE, MoC, FAB, with that one obtained from in-depht neuropsychological evaluation. A very high percentage of false negative emerged, for each screening test (MMSE, MoCA and FAB). A further analyisis permitted to appreciate how, summarizing the scores, obtained in each abovementioned test, it was possible significantly reduce the rate of false negative. A new tool, namely "CoMDA" (Cognition in Movement Disorders Assessment), was composed, by merging each item of Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA) and Frontal Assessment Battery (FAB). Moreover, we created a machine learning, namely "Neural Net 91classification" able to classify the cognitive profile and predict patients' at risk of dementia, providing a prediction of the findings resulting from a in-depht neuropsychological evaluation. CoMDA and the related Neural Net 91classification represent a reliable, time-sparing screening instrument, which is much more powerful of other common, widely-adopted tools. ;


Study Design


Related Conditions & MeSH terms


NCT number NCT04858893
Study type Observational
Source Ospedale Generale Di Zona Moriggia-Pelascini
Contact
Status Completed
Phase
Start date January 1, 2017
Completion date August 31, 2020

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