Stability of Diagnoses of Cognitive Impairment, Not Dementia in a Veterans Affairs Primary Care Population

Machine Learning Analysis of Digital Clock Drawing Test Performance for Differential Classification of Mild Cognitive Impairment Subtypes Versus Alzheimer's Disease

OBJECTIVE

To determine how well machine learning algorithms can classify mild cognitive impairment (MCI) subtypes and Alzheimer's disease (AD) using features obtained from the digital Clock Drawing Test (dCDT).

METHODS

dCDT protocols were administered to 163 patients diagnosed with AD(n = 59), amnestic MCI (aMCI; n = 26), combined mixed/dysexecutive MCI (mixed/dys MCI; n = 43), and patients without MCI (non-MCI; n = 35) using standard clock drawing command and copy procedures, that is, draw the face of the clock, put in all of the numbers, and set the hands for "10 after 11." A digital pen and custom software recorded patient's drawings. Three hundred and fifty features were evaluated for maximum information/minimum redundancy. The best subset of features was used to train classification models to determine diagnostic accuracy.

RESULTS

Neural network employing information theoretic feature selection approaches achieved the best 2-group classification results with 10-fold cross validation accuracies at or above 83%, that is, AD versus non-MCI = 91.42%; AD versus aMCI = 91.49%; AD versus mixed/dys MCI = 84.05%; aMCI versus mixed/dys MCI = 84.11%; aMCI versus non-MCI = 83.44%; and mixed/dys MCI versus non-MCI = 85.42%. A follow-up two-group non-MCI versus all MCI patients analysis yielded comparable results (83.69%). Two-group classification analyses were achieved with 25-125 dCDT features depending on group classification. Three- and four-group analyses yielded lower but still promising levels of classification accuracy.

CONCLUSION

Early identification of emergent neurodegenerative illness is criterial for better disease management. Applying machine learning to standard neuropsychological tests promises to be an effective first line screening method for classification of non-MCI and MCI subtypes.

Preoperative Cognitive Assessment Recommendations for the Older Adult

PURPOSE

The purpose of this project was to identify the need for and to improve the preoperative cognitive assessment of the older adult.

DESIGN

A retrospective chart review was used to explore the incidence of postoperative delirium (PD) and characteristics associated with it.

METHODS

A retrospective chart review was used to identify the incidence of PD in a community hospital. The data were analyzed using descriptive statistics for trends in demographic and physiological characteristics of older adults undergoing elective hip or knee surgery.

FINDINGS

The incidence of PD was found to be 11%. Older adults with PD had an increased mean age and comorbid conditions. PD was associated with a mean increase in hospital stay, postoperative complications, and 30-day readmission.

CONCLUSIONS

Preoperative cognitive assessment can identify high-risk patients, stratify care, medically optimize the older adult before surgery, and improve perioperative outcomes.

Diagnosing dementia and cognitive dysfunction in the elderly in primary health care: A systematic review

Dementia is a public health issue making the screening and diagnosing of dementia and its prodromal phases in all health settings imperative.

OBJECTIVE

using PRISMA, this systematic review aimed to identify how low-, middle-, and high-income countries establish dementia and cognitive dysfunction diagnoses in primary health care.

METHODS

studies from the past five years in English, Spanish, and Portuguese were retrieved from Scopus, PubMed, Embase, Lilacs, Scielo, and Web of Science. Of 1987 articles, 33 were selected for analysis.

RESULTS

only three articles were from middle-income countries and there were no studies from low-income countries. The most used instrument was the Mini-Mental State Examination (MMSE). Mild Cognitive Impairment (MCI) and dementia criteria were based on experts' recommendation as well as on the Diagnostic and Statistical Manual of Mental Disorders (DSM) and International Classification of Diseases (ICD-10), respectively.

CONCLUSION

differences between these criteria among high- and middle-income countries were observed.

Mild Cognitive Impairment Reversion and Progression: Rates and Predictors in Community-Living Older Persons in the Singapore Longitudinal Ageing Studies Cohort

Background

Studies report varying rates and predictors of mild cognitive impairment (MCI) progression and reversion.

Methods

We determined MCI reversion and progression among 473 community-living adults aged ≥55 years in the Singapore Longitudinal Ageing Study with an average of 6 years of follow-up and estimated association with baseline variables.

Results

A total of 208 MCI participants reverted to normal cognition (44.0%) and 19 progressed to dementia (4.0%). In a model adjusted for age, gender, education, ethnicity, cardiovascular risk factors/diseases, APOE ε4 status, depressive symptoms, leisure-time activities (LTA), and baseline Mini-Mental State Examination (MMSE), we found that LTA score (OR = 1.07, 95% CI 1.02-1.13), MMSE score (OR = 1.21, 95% CI 1.11-1.31), and subjective memory complaint (OR = 1.83, 95% CI 1.16-2.90) significantly predicted MCI reversion. Controlling for all variables, age (OR = 1.09, 95% CI 1.02-1.17), lower education (OR = 3.26, 95% CI 1.01-10.49), and the metabolic syndrome (OR = 3.13, 95% CI 1.12-8.77) significantly predicted MCI progression. Controlling for age, sex, ethnicity, and education, diabetes significantly predicted MCI progression (OR = 3.19, 95% CI 1.23-8.26), but the presence of other cardiometabolic factors reduced this association to an OR of 2.18 (95% CI 0.72-6.60).

Conclusion

In this relatively younger population, there were higher rates of MCI reversion and lower rates of MCI progression which were predicted by the positive effects of LTA and a higher MMSE score as well as by the deleterious effect of the metabolic syndrome and diabetes.