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Performance of a computer-based assessment of cognitive function measures in two cohorts of seniors.

ABSTRACT: Computer-administered assessment of cognitive function is being increasingly incorporated in clinical trials; however, its performance in these settings has not been systematically evaluated.The Seniors Health and Activity Research Program pilot trial (N = 73) developed a computer-based tool for assessing memory performance and executive functioning. The Lifestyle Interventions and Independence for Elders investigators incorporated this battery in a full-scale multicenter clinical trial (N = 1635). We describe relationships that test scores have with those from interviewer-administered cognitive function tests and risk factors for cognitive deficits and describe performance measures (completeness, intraclass correlations [ICC]).Computer-based assessments of cognitive function had consistent relationships across the pilot and full-scale trial cohorts with interviewer-administered assessments of cognitive function, age, and a measure of physical function. In the Lifestyle Interventions and Independence for Elders cohort, their external validity was further demonstrated by associations with other risk factors for cognitive dysfunction: education, hypertension, diabetes, and physical function. Acceptable levels of data completeness (>83%) were achieved on all computer-based measures; however, rates of missing data were higher among older participants (odds ratio = 1.06 for each additional year; p < 0.001) and those who reported no current computer use (odds ratio = 2.71; p < 0.001). ICCs among clinics were at least as low (ICC < 0.013) as for interviewer measures (ICC < 0.023), reflecting good standardization. All cognitive measures loaded onto the first principal component (global cognitive function), which accounted for 40% of the overall variance.Our results support the use of computer-based tools for assessing cognitive function in multicenter clinical trials of older individuals.

SUBMITTER: Espeland MA

PROVIDER: S-EPMC3775886 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature

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Performance of a computer-based assessment of cognitive function measures in two cohorts of seniors.

Espeland Mark A MA Katula Jeffrey A JA Rushing Julia J Kramer Arthur F AF Jennings Janine M JM Sink Kaycee M KM Nadkarni Neelesh K NK Reid Kieran F KF Castro Cynthia M CM Church Timothy T Kerwin Diana R DR Williamson Jeff D JD Marottoli Richard A RA Rushing Scott S Marsiske Michael M Rapp Stephen R SR

International journal of geriatric psychiatry 20130416 12

<h4>Background</h4>Computer-administered assessment of cognitive function is being increasingly incorporated in clinical trials; however, its performance in these settings has not been systematically evaluated.<h4>Design</h4>The Seniors Health and Activity Research Program pilot trial (N = 73) developed a computer-based tool for assessing memory performance and executive functioning. The Lifestyle Interventions and Independence for Elders investigators incorporated this battery in a full-scale m ...[more]

PMID: 23589390

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Project description:BackgroundCurrent strategies for cardiovascular disease (CVD) risk assessment among adults without known CVD are limited by suboptimal performance and a narrow focus on only atherosclerotic CVD (ASCVD). We hypothesized that a strategy combining promising biomarkers across multiple different testing modalities would improve global and atherosclerotic CVD risk assessment among individuals without known CVD.MethodsWe included participants from MESA (Multi-Ethnic Study of Atherosclerosis) (n=6621) and the Dallas Heart Study (n=2202) who were free from CVD and underwent measurement of left ventricular hypertrophy by ECG, coronary artery calcium, N-terminal pro B-type natriuretic peptide, high-sensitivity cardiac troponin T, and high-sensitivity C-reactive protein. Associations of test results with the global composite CVD outcome (CVD death, myocardial infarction, stroke, coronary or peripheral revascularization, incident heart failure, or atrial fibrillation) and ASCVD (fatal or nonfatal myocardial infarction or stroke) were assessed over >10 years of follow-up. Multivariable analyses for the primary global CVD end point adjusted for traditional risk factors plus statin use and creatinine (base model).ResultsEach test result was independently associated with global composite CVD events in MESA after adjustment for the components of the base model and the other test results (P<0.05 for each). When the 5 tests were added to the base model, the c-statistic improved from 0.74 to 0.79 (P=0.001), significant integrated discrimination improvement (0.07, 95% confidence interval [CI] 0.06-0.08, P<0.001) and category free net reclassification improvement (0.47; 95% CI, 0.38-0.56; P=0.003) were observed, and the model was well calibrated (χ2=12.2, P=0.20). Using a simple integer score counting the number of abnormal tests, compared with those with a score of 0, global CVD risk was increased among participants with a score of 1 (adjusted hazard ratio, 1.9; 95% CI, 1.4-2.6), 2 (hazard ratio, 3.2; 95% CI, 2.3-4.4), 3 (hazard ratio, 4.7; 95% CI, 3.4-6.5), and ≥4 (hazard ratio, 7.5; 95% CI, 5.2-10.6). Findings replicated in the Dallas Health Study were similar for the ASCVD outcome.ConclusionsAmong adults without known CVD, a novel multimodality testing strategy using left ventricular hypertrophy by ECG, coronary artery calcium, N-terminal pro B-type natriuretic peptide, high-sensitivity cardiac troponin T, and high-sensitivity C-reactive protein significantly improved global CVD and ASCVD risk assessment.

Project description:BackgroundCognitive decline remains highly underdiagnosed despite efforts to find novel cognitive biomarkers. Electroencephalography (EEG) features based on machine-learning (ML) may offer a non-invasive, low-cost approach for identifying cognitive decline. However, most studies use cumbersome multi-electrode systems. This study aims to evaluate the ability to assess cognitive states using machine learning (ML)-based EEG features extracted from a single-channel EEG with an auditory cognitive assessment.MethodsThis study included data collected from senior participants in different cognitive states (60) and healthy controls (22), performing an auditory cognitive assessment while being recorded with a single-channel EEG. Mini-Mental State Examination (MMSE) scores were used to designate groups, with cutoff scores of 24 and 27. EEG data processing included wavelet-packet decomposition and ML to extract EEG features. Data analysis included Pearson correlations and generalized linear mixed-models on several EEG variables: Delta and Theta frequency-bands and three ML-based EEG features: VC9, ST4, and A0, previously extracted from a different dataset and showed association with cognitive load.ResultsMMSE scores significantly correlated with reaction times and EEG features A0 and ST4. The features also showed significant separation between study groups: A0 separated between the MMSE < 24 and MMSE ≥ 28 groups, in addition to separating between young participants and senior groups. ST4 differentiated between the MMSE < 24 group and all other groups (MMSE 24-27, MMSE ≥ 28 and healthy young groups), showing sensitivity to subtle changes in cognitive states. EEG features Theta, Delta, A0, and VC9 showed increased activity with higher cognitive load levels, present only in the healthy young group, indicating different activity patterns between young and senior participants in different cognitive states. Consisted with previous reports, this association was most prominent for VC9 which significantly separated between all level of cognitive load.DiscussionThis study successfully demonstrated the ability to assess cognitive states with an easy-to-use single-channel EEG using an auditory cognitive assessment. The short set-up time and novel ML features enable objective and easy assessment of cognitive states. Future studies should explore the potential usefulness of this tool for characterizing changes in EEG patterns of cognitive decline over time, for detection of cognitive decline on a large scale in every clinic to potentially allow early intervention.Trial registrationNIH Clinical Trials Registry [https://clinicaltrials.gov/ct2/show/results/NCT04386902], identifier [NCT04386902]; Israeli Ministry of Health registry [https://my.health.gov.il/CliniTrials/Pages/MOH_2019-10-07_007352.aspx], identifier [007352].

Dataset Information

Performance of a computer-based assessment of cognitive function measures in two cohorts of seniors.

Publications

Performance of a computer-based assessment of cognitive function measures in two cohorts of seniors.

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