Clinical trial NCT04448340: A Novel Machine Learning Algorithm to Predict the Lewy Body Dementias
Official title: A Novel Machine Learning Algorithm to Predict the Lewy Body Dementias Using Clinical and Neuropsychological Scores
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Dementia
Lead sponsor: National and Kapodistrian University of Athens
Sponsor class: OTHER
First posted: 2020-06-25
Enrollment: 200
Sex: ALL
Minimum age (years): 50
Maximum age (years): 90
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: 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.

Brief summary: 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: 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.

Eligibility criteria: 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

Primary outcomes:
measure: MMSE predictive for dlb or PDD
timeFrame: 1 year
measure: Parkinson's Disease - Cognitive Rating Scale (PD-CRS) predictive for DLB or PDD
timeFrame: 1 year
measure: Brief Visuospatial Memory Test (BVMT-TR) predictive for DLB or PDD
timeFrame: 1 year
measure: Symbol digit written predictive for DLB or PDD
timeFrame: 1 year
measure: Wechsler adult intelligence scale,predictive for DLB or PDD
timeFrame: 1 year
measure: trail making A and B predictive for DLB or PDD
timeFrame: 1 year
Secondary outcomes:
Other outcomes:
Locations:
city: Athens
country: Greece
facility: Anastasia Bougea
state: Attiki
Study officials:
affiliation: National and Kapodistrian University of Athens
name: ANASTASIA BOUGEA
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.