Matthew Richard

Precipitating and Predisposing Events and Symptoms For Admission to Assisted Living or Nursing Home Care

Background In Canada, the rise of private-pay assisted living facilities is changing the long-term care landscape. Even so, few clinical implications of having these facilities in the spectrum of care have been studied. Our objective was to compare events and symptoms that might predispose and precipitate a move of older adults to assisted living or to a nursing home. Methods Cross-sectional, descriptive Nova Scotia survey of residents and family members on admission. Health-care use and dementia diagnosis were recorded from the admission record. Dementia was staged using the Global Deterioration Scale and the Dependence Scale. The SymptomGuide, a standardized dementia symptom inventory, was used to assay which symptoms were most influential in the decision to seek long term care. Caregiver stress was elicited by a self-report questionnaire. Results Of 353 people admitted during the enrolment period, 174 (49%) took part in the survey. Most (97; 55.7%) were involved in a move from the community to a nursing home, 54 (31.0%) from the community to assisted living, and 23 (13.2%) from assisted living to a nursing home. In each setting, dementia was the commonest predisposing factor (seen in >90%) with a precipitating event seen in 120 (69%) people. The precipitating events included a medical illness (n = 97; 55%) or caregiver illness, death or move (33; 19%). Dependence was associated with place of care, with more severely impaired people more commonly represented in people who moved to nursing homes. Conclusions People move from the community chiefly due to dementia, and often with a precipitant. Compared with a move to assisted living, moving to nursing homes generally indicates greater dependence, and typically worse dementia severity. Even so, assisted-living facilities are not just for the “worried well”, but are used by people with dementia, caregiver stress, and recent hospitalization.

Neuropsychiatric symptom clusters targeted for treatment at earlier versus later stages of dementia.

To characterize clusters of neuropsychiatric symptoms targeted for tracking the disease course in people with dementia, in relation to stage.

Staging Dementia From Symptom Profiles on a Care Partner Website

Background The World Wide Web allows access to patient/care partner perspectives on the lived experience of dementia. We were interested in how symptoms that care partners target for tracking relate to dementia stage, and whether dementia could be staged using only these online profiles of targeted symptoms. Objectives To use clinical data where the dementia stage is known to develop a model that classifies an individual’s stage of dementia based on their symptom profile and to apply this model to classify dementia stages for subjects from a Web-based dataset. Methods An Artificial Neural Network (ANN) was used to identify the relationships between the dementia stages and individualized profiles of people with dementia obtained from the 60-item SymptomGuide (SG). The clinic-based training dataset (n=320), with known dementia stages, was used to create an ANN model for classifying stages in Web-based users (n=1930). Results The ANN model was trained in 66% of the 320 Memory Clinic patients, with the remaining 34% used to test its accuracy in classification. Training and testing staging distributions were not significantly different. In the 1930 Web-based profiles, 309 people (16%) were classified as having mild cognitive impairment, 36% as mild dementia, 29% as moderate, and 19% as severe. In both the clinical and Web-based symptom profiles, most symptoms became more common as the stage of dementia worsened (eg, mean 5.6 SD 5.9 symptoms in the MCI group versus 11.9 SD 11.3 in the severe). Overall, Web profiles recorded more symptoms (mean 7.1 SD 8.0) than did clinic ones (mean 5.5 SD 1.8). Even so, symptom profiles were relatively similar between the Web-based and clinical datasets. Conclusion Symptoms targeted for online tracking by care partners of people with dementia can be used to stage dementia. Even so, caution is needed to assure the validity of data collected online as the current staging algorithm should be seen as an initial step.

Network visualization to discern patterns of relationships between symptoms in dementia

The multidimensional characterization of complex biomedical systems usually demands a large number of cases in order to obtain reliable inferences. Even so, the number of participants in many studies is relatively small as, for example, in typical clinical trials. Here we suggest an approach based on network visualization, combined with resampling, to discern the patterns of relationships among variables. We illustrate how this can be applied to analyze changes in multiple outcomes in people with dementia. The relationships between several dozens of variables were represented by connectivity graphs, drawn by calculating the relative risk of observing a pair of symptoms in an individual to their co-occurrence by chance only. The statistical significance of the relationships was calculated by generating a bootstrap sample. If the null hypothesis (e.g., the relative risks = 1 or equivalently, the pointwise mutual information = 0) was rejected, the vertices on the graph representing the variables were connected by an edge. The number of edges (the degree of connectivity) reflects the stage of the cognitive impairment, with worse dementia indicated by lower connectivity. Arranging symptoms consistently allows characteristic profiles to be displayed; this in turn can allow patterns of treatment effects to be discerned, with at-a-glance pattern recognition.

Detection of distinct neuropsychiatric symptom clusters at early- versus late-stages of Alzheimer's disease

Alzheimers disease (AD) in general is characterized by common cognitive and behavioral dysfunction that evolve as the disease progresses. The complexity of AD is reflected in patient heterogeneity and emergence of different symptoms as the disease progresses. We employed a network visualization approach to display significant symptom associations of 60 symptoms by the severity of dementia. Background • NPS clustering patterns were similar between the MCI/Mild and Moderate/Severe severity groups, with a distinct depressive cluster and a distinct psychotic cluster.