The Relation of Patient Dependence to Home Health Aide Use in Alzheimer's Disease

BACKGROUND: Although there has been much research devoted to understanding the predictors of nursing home placement (NHP) in Alzheimer's disease (AD) patients, there is currently a lack of research concerning the predictors of home health care. The objective of this study was to examine whether the Dependence Scale can predict home health aide (HHA) use. METHODS: The sample is drawn from the Predictors Study, a large, multicenter cohort of patients with probable AD, prospectively followed annually for up to 7 years in three university-based AD centers in the United States. Markov analyses (n=75) were used to calculate annual transition probabilities for the "new onset" of HHA use (instances where an HHA was absent at the previous visit, but present at the next visit) as a function of HHA presence at the preceding year's visit and dependence level at that preceding year's visit. RESULTS: The dependence level at the previous year's visit was a significant predictor of HHA use at the next year's visit. Three specific items of the Dependence Scale (needing household chores done for oneself, needing to be watched or kept company when awake, and needing to be escorted when outside) were significant predictors of the presence of an HHA. CONCLUSION: The Dependence Scale is a valuable tool for predicting HHA use in AD patients. Obtaining a better understanding of home health care in AD patients may help delay NHP and have a positive impact on the health and well-being of both the caregiver and the patient

Measuring cerebral atrophy and white matter hyperintensity burden to predict the rate of cognitive decline in Alzheimer disease

Objective: To determine if baseline measurements of cerebral atrophy and severity of white matter hyperintensity (WMH) predict the rate of future cognitive decline in patients with Alzheimer disease (AD). Design: Data were drawn from the Predictors Study, a longitudinal study that enrolls patients with mild AD and reassesses them every 6 months with use of the Columbia modified Mini-Mental State (mMMS) examination (score range, 0-57). Magnetic resonance images were analyzed to determine the severity of WMH, using the Scheltens scale, and the degree of atrophy, using the bicaudate ratio. Generalized estimating equations were used to determine whether severity of baseline magnetic resonance image measurements and their interaction predicted the rate of mMMS score decline at subsequent visits. Setting: Three university-based AD centers in the United States. Participants: At baseline, 84 patients with AD from the Predictors Study received structural magnetic resonance imaging and were selected for analysis. They had a mean of 6 follow-up evaluations. Main Outcome Measure: The mMMS score. Results: Generalized estimating equation models demonstrated that the degree of baseline atrophy (β = −0.316; P = .04), the severity of WMH (β = −0.173; P = .03), and their interaction (β = −6.061; P = .02) predicted the rate of decline in mMMS scores. Conclusions: Both degree of cerebral atrophy and severity of WMH are associated with the rapidity of cognitive decline in AD. Atrophy and WMH may have a synergistic effect on future decline in AD, such that patients with a high degree of both have a particularly precipitous cognitive course. These findings lend further support to the hypothesis that cerebrovascular pathological abnormalities contribute to the clinical syndrome of AD

Predicting Time to Nursing Home Care and Death in Individuals with Alzheimer Disease

Objective. —To develop and validate an approach that uses clinical features that can be determined in a standard patient visit to estimate the length of time before an individual patient with Alzheimer disease (AD) requires care equivalent to nursing home placement or dies. Design. —Prospective cohort study of 236 patients, followed up semiannually for up to 7 years. A second validation cohort of 105 patients was also followed. Setting. —Three AD research centers. Patients. —All patients met National Institute of Neurological and Communicative Disorders and Stroke—Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA) criteria for probable AD and had mild dementia at the initial visit. Intervention. —Predictive features, ascertained at the initial visit, were sex, duration of illness, age at onset, modified Mini-Mental State Examination (mMMS) score, and the presence or absence of extrapyramidal signs or psychotic features.Main Outcome Measures. —(1) Requiring the equivalent of nursing home placement and (2) death. Results. —Prediction algorithms were constructed for the 2 outcomes based on Cox proportional hazard models. For each algorithm, a predictor index is calculated based on the status of each predictive feature at the initial visit. A table that specifies the number of months in which 25%, 50%, and 75% of patients with any specific predictor index value are likely to reach the end point is then consulted.Survival curves for time to need for care equivalent to nursing home placement and for time to death derived from the algorithms for selected predictor indexes fell within the 95% confidence bands of actual survival curves for patients.When the predictor variables from the initial visit for the validation cohort patients were entered into the algorithm, the predicted survival curves for time to death fell within the 95% confidence bands of actual survival curves for the patients. Conclusions. —The prediction algorithms are a first but promising step toward providing specific prognoses to patients, families, and practitioners. This approach also has clear implications for the design and interpretation of clinical trials in patients with AD

Predicting Time to Nursing Home Care and Death in Individuals with Alzheimer Disease

Objective. —To develop and validate an approach that uses clinical features that can be determined in a standard patient visit to estimate the length of time before an individual patient with Alzheimer disease (AD) requires care equivalent to nursing home placement or dies. Design. —Prospective cohort study of 236 patients, followed up semiannually for up to 7 years. A second validation cohort of 105 patients was also followed. Setting. —Three AD research centers. Patients. —All patients met National Institute of Neurological and Communicative Disorders and Stroke—Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA) criteria for probable AD and had mild dementia at the initial visit. Intervention. —Predictive features, ascertained at the initial visit, were sex, duration of illness, age at onset, modified Mini-Mental State Examination (mMMS) score, and the presence or absence of extrapyramidal signs or psychotic features.Main Outcome Measures. —(1) Requiring the equivalent of nursing home placement and (2) death. Results. —Prediction algorithms were constructed for the 2 outcomes based on Cox proportional hazard models. For each algorithm, a predictor index is calculated based on the status of each predictive feature at the initial visit. A table that specifies the number of months in which 25%, 50%, and 75% of patients with any specific predictor index value are likely to reach the end point is then consulted.Survival curves for time to need for care equivalent to nursing home placement and for time to death derived from the algorithms for selected predictor indexes fell within the 95% confidence bands of actual survival curves for patients.When the predictor variables from the initial visit for the validation cohort patients were entered into the algorithm, the predicted survival curves for time to death fell within the 95% confidence bands of actual survival curves for the patients. Conclusions. —The prediction algorithms are a first but promising step toward providing specific prognoses to patients, families, and practitioners. This approach also has clear implications for the design and interpretation of clinical trials in patients with AD

Patient Dependence and Longitudinal Changes in Costs of Care in Alzheimer's Disease

BACKGROUND/AIMS: To examine the incremental effect of patients' dependence on others, on cost of medical and nonmedical care, and on informal caregiving hours over time. METHODS: Data are obtained from 172 patients from the Predictors Study, a large, multicenter cohort of patients with probable Alzheimer disease (AD) followed annually for 4 years in 3 University-based AD centers in the USA. Enrollment required a modified Mini-Mental State Examination score >or=30. We examined the effects of patient dependence (measured by the Dependence Scale, DS) and function (measured by the Blessed Dementia Rating Scale, BDRS) on medical care cost, nonmedical care cost, and informal caregiving time using random effects regression models. RESULTS: A one-point increase in DS score was associated with a 5.7% increase in medical cost, a 10.5% increase in nonmedical cost, and a 4.1% increase in caregiving time. A one-point increase in BDRS score was associated with a 7.6% increase in medical cost, a 3.9% increase in nonmedical cost and an 8.7% increase in caregiving time. CONCLUSIONS: Both functional impairment and patient dependence were associated with higher costs of care and caregiving time. Measures of functional impairment and patient dependence provide unique and incremental information on the overall impact of AD on patients and their caregivers

Seizures in Alzheimer Disease. Who, When, and How Common?

Background: Transient symptoms in Alzheimer disease (AD) are frequent and include seizures, syncope, and episodes of inattention or confusion. The incidence of seizures in AD and predictors of which patients with AD might be more predisposed to them is based primarily on retrospective studies and is not well established. Objective: To determine the incidence and predictors of new-onset unprovoked seizures. Design: Prospective cohort study. Setting: Three academic centers. Patients: Four hundred fifty-three patients with probable AD observed prospectively from mild disease stages since 1992. Main Outcome Measure: Informant interviews every 6 months included questions about whether the patient had a seizure (convulsion, fainting, or “funny” spell) and whether diagnosis or treatment for epilepsy or seizure was made. Two epileptologists independently retrospectively reviewed all available medical records for 52 patients with positive responses to either of these questions, and using a specific checklist form, events were diagnosed as to whether they were unprovoked seizures (intrarater concordance, κ = 0.67). Diagnosis of unprovoked seizures constituted the event in survival analyses. Potential predictors included sex, age, race/ethnicity, educational achievement, duration of illness, baseline cognition and function, depression, medical comorbidities, and time-dependent use of cholinesterase inhibitors and neuroleptic agents, apolipoprotein E genotype, and previous electroencephalographic findings. Results: Over the course of 3518 visit-assessments (per patient: mean, 7.8; maximum, 27), 7 patients (1.5%) developed seizures. Younger age was associated with higher risk (hazard ratio, 1.23; 95% confidence interval, 1.08-1.41; P = .003 for each additional year of age) of seizure incidence. No other predictor was significant. The overall incidence of seizures was low (418 per 100 000 person-years of observation) although significantly higher than expected for idiopathic unprovoked seizures in similar age ranges of the general population (hazard ratio, 8.06; 95% confidence interval, 3.23-16.61). Conclusions: Unprovoked seizures are uncommon in AD, but they do occur more frequently than in the general population. Younger age is a risk factor for seizures in AD

Hearing Impairment and Cognitive Decline: A Pilot Study Conducted Within the Atherosclerosis Risk in Communities Neurocognitive Study

Hearing impairment (HI) is prevalent, is modifiable, and has been associated with cognitive decline. We tested the hypothesis that audiometric HI measured in 2013 is associated with poorer cognitive function in 253 men and women from Washington County, Maryland (mean age = 76.9 years) in a pilot study carried out within the Atherosclerosis Risk in Communities Neurocognitive Study. Three cognitive tests were administered in 1990–1992, 1996–1998, and 2013, and a full neuropsychological battery was administered in 2013. Multivariable-adjusted differences in standardized cognitive scores (cross-sectional analysis) and trajectories of 20-year change (longitudinal analysis) were modeled using linear regression and generalized estimating equations, respectively. Hearing thresholds for pure tone frequencies of 0.5–4 kHz were averaged to obtain a pure tone average in the better-hearing ear. Hearing was categorized as follows: ≤25 dB, no HI; 26–40 dB, mild HI; and >40 dB, moderate/severe HI. Comparing participants with moderate/severe HI to participants with no HI, 20-year rates of decline in memory and global function differed by −0.47 standard deviations (P = 0.02) and −0.29 standard deviations (P = 0.02), respectively. Estimated declines were greatest in participants who did not wear a hearing aid. These findings add to the limited literature on cognitive impairments associated with HI, and they support future research on whether HI treatment may reduce risk of cognitive decline

Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and SNP data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-driven neuroimaging phenotypes

Application of Latent Variable Methods to the Study of Cognitive Decline When Tests Change over Time

The way a construct is measured can differ across cohort study visits, complicating longitudinal comparisons. We demonstrated the use of factor analysis to link differing cognitive test batteries over visits to common metrics representing general cognitive performance, memory, executive functioning, and language