ABSTRACT
The assessment of cognitive deficits is pivotal for diagnosis and management in patients with parkinsonisms. Low levels of correspondence are observed between evaluations assessed with screening cognitive tests in comparison with those assessed with in-depth neuropsychological batteries. A new tool, we named CoMDA (Cognition in Movement Disorders Assessment), was composed by merging Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Frontal Assessment Battery (FAB). In total, 500 patients (400 with Parkinson's disease, 41 with vascular parkinsonism, 31 with progressive supranuclear palsy, and 28 with multiple system atrophy) underwent CoMDA (level 1-L1) and in-depth neuropsychological battery (level 2-L2). Machine learning was developed to classify the CoMDA score and obtain an accurate prediction of the cognitive profile along three different classes: normal cognition (NC), mild cognitive impairment (MCI), and impaired cognition (IC). The classification accuracy of CoMDA, assessed by ROC analysis, was compared with MMSE, MoCA, and FAB. The area under the curve (AUC) of CoMDA was significantly higher than that of MMSE, MoCA and FAB (p < 0.0001, p = 0.028 and p = 0.0007, respectively). Among 15 different algorithmic methods, the Quadratic Discriminant Analysis algorithm (CoMDA-ML) showed higher overall-metrics performance levels in predictive performance. Considering L2 as a 3-level continuous feature, CoMDA-ML produces accurate and generalizable classifications: micro-average ROC curve, AUC = 0.81; and AUC = 0.85 for NC, 0.67 for MCI, and 0.83 for IC. CoMDA and COMDA-ML are reliable and time-sparing tools, accurate in classifying cognitive profile in parkinsonisms.This study has been registered on ClinicalTrials.gov (NCT04858893).
ABSTRACT
There is a strong and growing interest in using the large amount of high-quality operational data available within an airline. One reason for this is the push by regulators to use data to demonstrate safety performance by monitoring the outputs of Safety Performance Indicators relative to targeted goals. However, the current exceedance-based approaches alone do not provide sufficient operational risk information to support managers and operators making proximate real-time data-driven decisions. The purpose of this study was to develop and test a set of metrics which can complement the current exceedance-based methods. The approach was to develop two construct variables that were designed with the aim to: (1) create an aggregate construct variable that can differentiate between normal and abnormal landings (row_mean); and (2) determine if temporal sequence patterns can be detected within the data set that can differentiate between the two landing groups (row_sequence). To assess the differentiation ability of the aggregate constructs, a set of both statistical and visual tests were run in order to detect quantitative and qualitative differences between the data series representing two landing groups prior to touchdown. The result, verified with a time series k-means cluster analysis, show that the composite constructs seem to differentiate normal and abnormal landings by capturing time-varying importance of individual variables in the final 300 seconds before touchdown. Together the approaches discussed in this article present an interesting and complementary way forward that should be further pursued.
