To assess the influence of cognitive, functional and behavioral factors, co-morbidities as well as caregiver characteristics on driving cessation in dementia patients.
The study cohort consists of ...those 240 dementia cases of the ongoing prospective registry on dementia in Austria (PRODEM) who were former or current car-drivers (mean age 74.2 (±8.8) years, 39.6% females, 80.8% Alzheimer's disease). Reasons for driving cessation were assessed with the patients' caregivers. Standardized questionnaires were used to evaluate patient- and caregiver characteristics. Cognitive functioning was determined by Mini-Mental State Examination (MMSE), the CERAD neuropsychological test battery and Clinical Dementia Rating (CDR), activities of daily living (ADL) by the Disability Assessment for Dementia, behavior by the Neuropsychiatric Inventory (NPI) and caregiver burden by the Zarit burden scale.
Among subjects who had ceased driving, 136 (93.8%) did so because of "Unacceptable risk" according to caregiver's judgment. Car accidents and revocation of the driving license were responsible in 8 (5.5%) and 1(0.7%) participant, respectively. Female gender (OR 5.057; 95%CI 1.803-14.180; p = 0.002), constructional abilities (OR 0.611; 95%CI 0.445-0.839; p = 0.002) and impairment in Activities of Daily Living (OR 0.941; 95%CI 0.911-0.973; p<0.001) were the only significant and independent associates of driving cessation. In multivariate analysis none of the currently proposed screening tools for assessment of fitness to drive in elderly subjects including the MMSE and CDR were significantly associated with driving cessation.
The risk-estimate of caregivers, but not car accidents or revocation of the driving license determines if dementia patients cease driving. Female gender and increasing impairment in constructional abilities and ADL raise the probability for driving cessation. If any of these factors also relates to undesired traffic situations needs to be determined before recommendations for their inclusion into practice parameters for the assessment of driving abilities in the elderly can be derived from our data.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Background: Functional (un-)coupling (task-related change of functional connectivity) between different sites of the brain is a mechanism of general importance for cognitive processes. In Alzheimer's ...disease (AD), prior research identified diminished cortical connectivity as a hallmark of the disease. However, little is known about the relation between the amount of functional (un-)coupling and cognitive performance and decline in AD. Method: Cognitive performance (based on CERAD-Plus scores) and electroencephalogram (EEG)-based functional (un-)coupling measures (connectivity changes from rest to a Face-Name-Encoding task) were assessed in 135 AD patients (age: M = 73.8 years; SD = 9.0). Of these, 68 patients (M = 73.9 years; SD = 8.9) participated in a follow-up assessment of their cognitive performance 1.5 years later. Results: The amounts of functional (un-)coupling in left anterior-posterior and homotopic interhemispheric connections in beta1-band were related to cognitive performance at baseline (β = .340; p < .001; β = .274; P = .001, respectively). For both markers, a higher amount of functional coupling was associated with better cognitive performance. Both markers also were significant predictors for cognitive decline. However, while patients with greater functional coupling in left anterior-posterior connections declined less in cognitive performance (β = .329; P = .035) those with greater functional coupling in interhemispheric connections declined more (β = −.402; P = .010). Conclusion: These findings suggest an important role of functional coupling mechanisms in left anterior–posterior and interhemispheric connections in AD. Especially the complex relationship with cognitive decline in AD patients might be an interesting aspect for future studies.
Highlights • Largest clinical study of quantitative EEG markers for slowing, synchrony and complexity versus AD severity including 118 patients. • Advanced metrics for quantitative EEG in resting ...state and during a face–name encoding task. • MMSE scores explaining up to 51% of the variations in QEEG markers.
Few studies have investigated in detail which factors influence activities of daily (ADL) in Alzheimer's disease (AD).
To assess the influence of cognitive, gender, and other factors on ADL in ...patients with mild to moderate AD.
This study is part of the Prospective Registry on Dementia in Austria (PRODEM) project, a multicenter dementia research project. A cohort of 221 AD patients (130 females; means: age 76 years, disease duration 34.4 months, MMSE 22.3) was included in a cross-sectional analysis. Everyday abilities were assessed with the Disability Assessment for Dementia scale, and cognitive functions with the CERAD plus neuropsychological test battery. Two models of multiple linear regressions were performed to find factors predicting functional decline, one entering demographical and disease related factors, and a joint model combining demographical and disease variables with neuropsychological scores.
Non-cognitive factors explained 18%, whereas the adding of neuropsychological variables explained 39% of variance. Poor figural and verbal memory, constructional abilities, old age, longer disease duration, depression, and male gender were independent risk factors for reduced ADL. Instrumental and basic ADL were predicted by similar factors, except gender (predicting only instrumental ADL) and phonological fluency (predictor of basic ADL).
In addition to demographical factors, disease duration, and depression, neuropsychological variables are valuable predictors of the functional status in AD in an early disease stage.
We analyzed the relation of several synchrony markers in the electroencephalogram (EEG) and Alzheimer’s disease (AD) severity as measured by Mini-Mental State Examination (MMSE) scores. The study ...sample consisted of 79 subjects diagnosed with probable AD. All subjects were participants in the PRODEM-Austria study. Following a homogeneous protocol, the EEG was recorded both in resting state and during a cognitive task. We employed quadratic least squares regression to describe the relation between MMSE and the EEG markers. Factor analysis was used for estimating a potentially lower number of unobserved synchrony factors. These common factors were then related to MMSE scores as well. Most markers displayed an initial increase of EEG synchrony with MMSE scores from 26 to 21 or 20, and a decrease below. This effect was most prominent during the cognitive task and may be owed to cerebral compensatory mechanisms. Factor analysis provided interesting insights in the synchrony structures and the first common factors were related to MMSE scores with coefficients of determination up to 0.433. We conclude that several of the proposed EEG markers are related to AD severity for the overall sample with a wide dispersion for individual subjects. Part of these fluctuations may be owed to fluctuations and day-to-day variability associated with MMSE measurements. Our study provides a systematic analysis of EEG synchrony based on a large and homogeneous sample. The results indicate that the individual markers capture different aspects of EEG synchrony and may reflect cerebral compensatory mechanisms in the early stages of AD.
Quantitative electroencephalogram (qEEG) recorded during cognitive tasks has been shown to differentiate between patients with Alzheimer's disease (AD) and healthy individuals. However, the ...association between various qEEG markers recorded during mnestic paradigms and clinical measures of AD has not been studied in detail.
To evaluate if ‘cognitive’ qEEG is a useful diagnostic option, particularly if memory paradigms are used as cognitive stimulators.
This study is part of the Prospective Registry on Dementia in Austria (PRODEM), a multicenter dementia research project. A cohort of 79 probable AD patients was included in a cross-sectional analysis. qEEG recordings performed in resting states were compared with recordings during cognitively active states. Cognition was evoked with a face–name paradigm and a paired-associate word list task, respectively. Relative band powers, coherence and auto-mutual information were computed as functions of MMSE scores for the memory paradigms and during rest. Analyses were adjusted for the co-variables age, sex, duration of dementia and educational level.
MMSE scores explained 36–51% of the variances of qEEG-markers. Face–name encoding with eyes open was superior to resting state with eyes closed in relative theta and beta1 power as well as coherence, whereas relative alpha power and auto-mutual information yielded more significant results during resting state with eyes closed. The face–name task yielded stronger correlations with MMSE scores than the verbal memory task.
qEEG alterations recorded during mnestic activity, particularly face–name encoding showed the highest association with the MMSE and may serve as a clinically valuable marker for disease severity.
•MMSE scores explained 36–51% of the variances of qEEG-markers.•Face–name encoding was superior to resting state in relative theta and beta1 power.•Relative alpha power and auto-mutual information were more significant in resting state.•Face–name task yielded stronger correlations with MMSE than verbal memory task.
Patient dependence has rarely been studied in mild-to-moderate Alzheimer's disease (AD).
To identify factors which predict patient dependence in mild-to-moderate AD.
We studied 398 ...non-institutionalized AD patients (234 females) of the ongoing Prospective Registry on Dementia (PRODEM) in Austria. The Dependence Scale (DS) was used to assess patient dependence. Patient assessment comprised functional abilities, neuropsychiatric symptoms and cognitive functions. A multiple linear regression analysis was performed to identify predictors of patient dependence.
AD patients were mildly-to-moderately impaired (mean scores and SDs were: CDR 0.84 ± 0.43; DAD 74.4 ± 23.3, MMSE = 22.5 ± 3.6). Psychopathology and caregiver burden were in the low range (mean NPI score 13.2, range 0 to 98; mean ZBI score 18, range 0-64). Seventy five percent of patients were classified as having a mild level of patient dependence (DS sum score 0 to 6). Patient dependence correlated significantly and positively with age, functional measures, psychopathology and depression, disease duration, and caregiver burden. Significant negative, but low correlations were found between patient dependence, cognitive variables, and global cognition. Activities of daily living, patient age, and disease severity accounted for 63% of variance in patient dependence, whereas cognitive variables accounted for only 11%.
Dependence in this cohort was mainly related to age and functional impairment, and less so to cognitive and neuropsychiatric variables. This differs from studies investigating patients in more advanced disease stages which found abnormal behavior and impairments of cognition as main predictors of patient dependence.
We investigated the correlation of Alzheimer's disease (AD) severity as measured by the Mini-Mental State Examination (MMSE) to the signal complexity measures auto-mutual information, Shannon entropy ...and Tsallis entropy in 79 patients with probable AD from the multi-centric Prospective Dementia Database Austria (PRODEM). Using quadratic (linear) regressions, auto-mutual information explained up to 48% (43%), Shannon entropy up to 48% (37%) and Tsallis entropy up to 49% (35%) of the variations in MMSE scores, all at left temporal (T7) electrode site. The steepest slope of the linear regression was found for auto-mutual information (Δy/Δx = 36). For Shannon and Tsallis entropy, slopes were less steep. Comparing to traditional slowing measures, complexity measures yielded higher coefficients of determination. We conclude that auto-mutual information is well suited to characterize disease severity in mild to moderate AD.
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