Presenter of 1 Presentation
COGNITIVE SCREENING IN PATIENTS WITH PARKINSONISM: PROPOSAL FOR A NEW, MACHINE LEARNING-BASED DIAGNOSTIC TOOL
Abstract
Aims
The assessment of cognitive deficits is crucial in the field of parkinsonisms diagnosis and management. To evaluate cognitive profile, screening tests are generally preferred, since in-depth neuropsychological batteries are reliable but time-consuming. Otherwise, low-level of correspondence is observed, when the two evaluations are compared.
A new tool, we named CoMDA (Cognition in Movement Disorders Assessment), was composed by merging all items, without repetition, of Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA) and Frontal Assessment Battery (FAB). Moreover, a machine learning was developed to classify the CoMDA score and to reach accurate prediction of risk of dementia.
Methods
500 patients (400 with Parkinson’s disease, 41 with vascular parkinsonism, 31 with progressive supranuclear palsy, 28 with multisystem atrophy) underwent CoMDA (Level 1) and in-depth neuropsychological battery (Level 2), considered as the gold standard for classification of cognition
Results
Referring on Level 2, the rate of false negatives was 55,7% for MMSE, 45,6% for FAB and 57,4% for MoCA. Otherwise, the classification obtained with CoMDA dropped to 31,7% the false negative rate. Considering Level 2 as a 3-level continuous feature, a Machine Learning model was developed to classify L2 feature accurately and with adequate cross-generalization capacity. CoMDA-ML produce the accurate and generalizable L2 predictions (micro average ROC curve, AUC = .81; and AUC=.85, .67 and .83 for L2 individual classes respectively).
Conclusions
CoMDA and COMDA-ML are reliable and time-sparing instruments, accurate in distinguishing cognitively impaired patients from those with normal cognition.
This study has been registered on ClinicalTrials.gov (NCT04858893).