Clustering mild cognitive impairment by mini-mental state examination

Cognitive reserve, depressive symptoms, obesity, and change in employment status predict mental processing speed and executive function after COVID-19

The risk factors for post-COVID-19 cognitive impairment have been poorly described. This study aimed to identify the sociodemographic, clinical, and lifestyle characteristics that characterize a group of post-COVID-19 condition (PCC) participants with neuropsychological impairment. The study sample included 426 participants with PCC who underwent a neurobehavioral evaluation. We selected seven mental speed processing and executive function variables to obtain a data-driven partition. Clustering algorithms were applied, including K-means, bisecting K-means, and Gaussian mixture models. Different machine learning algorithms were then used to obtain a classifier able to separate the two clusters according to the demographic, clinical, emotional, and lifestyle variables, including logistic regression with least absolute shrinkage and selection operator (LASSO) (L1) and Ridge (L2) regularization, support vector machines (linear/quadratic/radial basis function kernels), and decision tree ensembles (random forest/gradient boosting trees). All clustering quality measures were in agreement in detecting only two clusters in the data based solely on cognitive performance. A model with four variables (cognitive reserve, depressive symptoms, obesity, and change in work situation) obtained with logistic regression with LASSO regularization was able to classify between good and poor cognitive performers with an accuracy and a weighted averaged precision of 72%, a recall of 73%, and an area under the curve of 0.72. PCC individuals with a lower cognitive reserve, more depressive symptoms, obesity, and a change in employment status were at greater risk for poor performance on tasks requiring mental processing speed and executive function. Study registration: www.ClinicalTrials.gov , identifier NCT05307575.

A Novel Approach for Cognitive Clustering of Parkinsonisms through Affinity Propagation

Cluster analysis is widely applied in the neuropsychological field for exploring patterns in cognitive profiles, but traditional hierarchical and non-hierarchical approaches could be often poorly effective or even inapplicable on certain type of data. Moreover, these traditional approaches need the initial specification of the number of clusters, based on a priori knowledge not always owned. For this reason, we proposed a novel method for cognitive clustering through the affinity propagation (AP) algorithm. In particular, we applied the AP clustering on the regression residuals of the Mini Mental State Examination scores—a commonly used screening tool for cognitive impairment—of a cohort of 49 Parkinson’s disease, 48 Progressive Supranuclear Palsy and 44 healthy control participants. We found four clusters, where two clusters (68 and 30 participants) showed almost intact cognitive performance, one cluster had a moderate cognitive impairment (34 participants), and the last cluster had a more extensive cognitive deficit (8 participants). The findings showed, for the first time, an intra- and inter-diagnostic heterogeneity in the cognitive profile of Parkinsonisms patients. Our novel method of unsupervised learning could represent a reliable tool for supporting the neuropsychologists in understanding the natural structure of the cognitive performance in the neurodegenerative diseases.

Discourse Measures to Differentiate Between Mild Cognitive Impairment and Healthy Aging

Although subtle discourse declines in people with mild cognitive impairment (MCI) have been reported, heterogeneous measures and tasks among the MCI discourse studies have yielded widely varying outcomes. The present study aimed to first, identify discourse measures that aid the differentiation among people with amnestic MCI (aMCI), people with non-amnestic MCI (naMCI), and cognitively healthy control (HC) participants, and second, delineate the cognitive functions related to such discourse measures. Three discourse tasks (an episodic narrative, a planning task, and a picture description) were performed by 30 aMCI, 22 naMCI, and 21 HC participants. Samples were analyzed using six categories of 15 measures, namely coherence, cohesion, proposition, grammaticality, lexicality, and fluency. The statistical analyses included (1) a multivariate analysis of variance for group comparison; (2) binary simple logistic regression and receiver operating characteristic curve analysis for differentiation between two groups; (3) binary multiple logistic regression for being diagnosed with naMCI or aMCI with the minimum number of predictors; and (4) Pearson correlation analysis for identifying the cognitive functions associated with the discourse measures. The proportion of cohesive words and propositional density in aMCI participants were worse than those in naMCI participants. Global coherence, the proportion of cohesive words, and the proportion of dysfluencies and pauses in naMCI participants were lower than those in the HC participants. Global and local coherence and the proportion of cohesive words, cohesive ties per utterances, propositional density, and dysfluencies and pauses in aMCI participants were worse than those in the HC participants. The aforementioned measures were demonstrated to be effective predictors for classifying groups by receiver operating characteristic curve analysis. In addition, the proportions of cohesive words and pauses were common discourse measures for differentiation between naMCI and HC participants or between aMCI and HC participants using binary multiple logistic regression. According to the correlation analysis, memory and executive functions are needed for coherent, cohesive, and efficient discourse productions in MCI. The detailed description of discourse performances in this study will aid the characterization of the declined language abilities of MCI participants and also the understanding of the cognitive functions involved in discourse performance in MCI.

The doubtful benefits of giving the benefit of the doubt: Lenient scoring of the spatial orientation items on the mini-Mental Status Exam increases false negative rates

Lenient scoring of spatial orientation errors (SOE) on the Mini-Mental State Exam (MMSE) is common practice, even though it deviates from standard protocol and may compromise its diagnostic power. This study was designed to empirically evaluate the effect of lenient scoring on the MMSE's classification accuracy. Participants were 113 community dwelling older adults recruited for a research study, representing a wide range of range of neurological status from cognitively healthy to Alzheimer's disease. Clinical classification was determined by expert assessors based on multiple sources of clinical evidence. Lenient scoring significantly inflated MMSE total scores (d = .88, large effect), and suppressed failure rates (from 26% to 14%). Standard scoring produced superior overall classification accuracy (75% vs. 67%) over lenient scoring and, more importantly, increased sensitivity from .33 to .53, with minimal loss in specificity (from 1.00 to .95). SOEs are empirical markers of cognitive decline and should not be adjusted based on clinical judgment. Results indicate that diminished sensitivity to cognitive impairment is an unintended consequence of lenient scoring and argue against this practice.

Cognitive screening among acute respiratory failure survivors: a cross-sectional evaluation of the Mini-Mental State Examination

Introduction

The Mini-Mental State Examination (MMSE) is a common cognitive screening test, but its utility in identifying impairments in survivors of acute respiratory failure is unclear. The purpose of this study was to evaluate MMSE performance versus a concurrently administered detailed neuropsychological test battery in survivors of acute respiratory failure.

Methods

This cross-sectional analysis used data from the ARDSNet Long Term Outcomes Study (ALTOS) and Awakening and Breathing Controlled Trial (ABC). Participants were 242 survivors of acute respiratory failure. The MMSE and detailed neuropsychological tests were administered at 6 and 12 months post-hospital discharge for the ALTOS study, and at hospital discharge, 3 and 12 months for the ABC study. Overall cognitive impairment identified by the MMSE (score <24) was compared to impairments identified by the neuropsychological tests. We also matched orientation, registration, attention, memory and language domains on the MMSE to the corresponding neuropsychological test. Pairwise correlations, sensitivity, specificity, positive and negative predictive values, and agreement were assessed.

Results

Agreement between MMSE and neuropsychological tests for overall cognitive impairment was fair (42 to 80%). Specificity was excellent (≥93%), but sensitivity was poor (19 to 37%). Correlations between MMSE domains and corresponding neuropsychological tests were weak to moderate (6 months: r = 0.11 to 0.28; 12 months: r = 0.09 to 0.34). The highest correlation between the MMSE and neuropsychological domains was for attention at 6 months (r = 0.28) and language at 12 months (r = 0.34).

Conclusions

In acute respiratory failure survivors, the MMSE has poor sensitivity in detecting cognitive impairment compared with concurrently administered detailed neuropsychological tests. MMSE results in this population should be interpreted with caution.