BackgroundCognitive impairment is frequent among people living with Parkinson’s disease: up to 40% of patients exhibit symptoms of mild cognitive impairment and 25% meet the criteria for dementia. Parkinson’s Disease Cognitive Rating Scale (PD-CRS) is one of the recommended scales by the Movement Disorders Society Task Force for level 1 screening of dementia. However, its psychometric properties have not been studied in the Colombian population.MethodsA cross-sectional study was conducted on 100 patients with Parkinson’s disease diagnosed by a movement disorders neurologist. Patients were evaluated with PD-CRS and MoCA. Principal component analysis was conducted, and then confirmatory factor analysis was implemented through the maximum-likelihood method. Internal consistency was evaluated using Cronbach α. Convergent and divergent validity were also calculated and concurrent validity with the MoCA was assessed.Results62% were males. Their median age was 68 years (IQR 57–74) and the median disease duration was 4 years (IQR 2–9). 77% were classified in early stages (Hoehn and Yahr stage ≤ 2), while the MDS-UPDRS part III score was 25 (IQR 15.5–38). In the principal component factor analysis, the pattern matrix unveiled a mnesic and a non-mnesic domain. Confirmatory factor analysis showed similar explanatory capacity (λ ≥ 0.50) for items other than naming (λ = 0.34). Cronbach’s α for the full 9-items instrument was 0.74. MoCA and PD-CRS total scores were correlated (ρ = 0.71, p = 0.000). Assuming a cut-off score of 62 points, there is an agreement of 89% with the definition of dementia by MoCA for Colombia (κ = 0.59; p = 0.000).ConclusionPD-CRS has acceptable psychometric properties for the Colombian population and has significant correlation and agreement with a validated scale (MoCA).

IntroductionThe assessments of the motor symptoms in Parkinson’s disease (PD) are usually limited to clinical rating scales (MDS UPDRS III), and it depends on the clinician’s experience. This study aims to propose a machine learning technique algorithm using the variables from upper and lower limbs, to classify people with PD from healthy people, using data from a portable low-cost device (RGB-D camera). And can be used to support the diagnosis and follow-up of patients in developing countries and remote areas.MethodsWe used Kinect®eMotion system to capture the spatiotemporal gait data from 30 patients with PD and 30 healthy age-matched controls in three walking trials. First, a correlation matrix was made using the variables of upper and lower limbs. After this, we applied a backward feature selection model using R and Python to determine the most relevant variables. Three further analyses were done using variables selected from backward feature selection model (Dataset A), movement disorders specialist (Dataset B), and all the variables from the dataset (Dataset C). We ran seven machine learning models for each model. Dataset was divided 80% for algorithm training and 20% for evaluation. Finally, a causal inference model (CIM) using the DoWhy library was performed on Dataset B due to its accuracy and simplicity.ResultsThe Random Forest model is the most accurate for all three variable Datasets (Dataset A: 81.8%; Dataset B: 83.6%; Dataset C: 84.5%) followed by the support vector machine. The CIM shows a relation between leg variables and the arms swing asymmetry (ASA) and a proportional relationship between ASA and the diagnosis of PD with a robust estimator (1,537).ConclusionsMachine learning techniques based on objective measures using portable low-cost devices (Kinect®eMotion) are useful and accurate to classify patients with Parkinson’s disease. This method can be used to evaluate patients remotely and help clinicians make decisions regarding follow-up and treatment.