Quality of life and caregiver burden in familial frontotemporal lobar degeneration: Analyses of symptomatic and asymptomatic individuals within the LEFFTDS cohort.

OBJECTIVE: The Longitudinal Evaluation of Familial Frontotemporal Dementia Subjects evaluates familial frontotemporal lobar degeneration (FTLD) kindreds with MAPT, GRN, or C9orf72 mutations. Objectives were to examine whether health-related quality of life (HRQoL) correlates with clinical symptoms and caregiver burden, and whether self-rated and informant-rated HRQoL would correlate with each other. METHODS: Individuals were classified using the Clinical Dementia Rating (CDR® ) Scale plus National Alzheimer's Coordinating Center (NACC) FTLD. HRQoL was measured with DEMQOL and DEMQOL-proxy; caregiver burden with the Zarit Burden Interview (ZBI). For analysis, Pearson correlations and weighted kappa statistics were calculated. RESULTS: The cohort of 312 individuals included symptomatic and asymptomatic individuals. CDR® plus NACC FTLD was negatively correlated with DEMQOL (r = -0.20, P = .001), as were ZBI and DEMQOL (r = -0.22, P = .0009). There was fair agreement between subject and informant DEMQOL (κ = 0.36, P <.0001). CONCLUSION: Lower HRQoL was associated with higher cognitive/behavior impairment and higher caregiver burden. These findings demonstrate the negative impact of FTLD on individuals and caregivers.

Assessment of executive function declines in presymptomatic and mildly symptomatic familial frontotemporal dementia: NIH-EXAMINER as a potential clinical trial endpoint.

INTRODUCTION: Identifying clinical measures that track disease in the earliest stages of frontotemporal lobar degeneration (FTLD) is important for clinical trials. Familial FTLD provides a unique paradigm to study early FTLD. Executive dysfunction is a clinically relevant hallmark of FTLD and may be a marker of disease progression. METHODS: Ninety-three mutation carriers with no symptoms or minimal/questionable symptoms (MAPT, n = 31; GRN, n = 28; C9orf72, n = 34; Clinical Dementia Rating scale plus NACC FTLD Module < 1) and 78 noncarriers enrolled through Advancing Research and Treatment in Frontotemporal Lobar Degeneration/Longitudinal Evaluation of Familial Frontotemporal Dementia Subjects studies completed the Executive Abilities: Measures and Instruments for Neurobehavioral Evaluation and Research (NIH-EXAMINER) and the UDS neuropsychological battery. Linear mixed-effects models were used to identify group differences in cognition at baseline and longitudinally. We examined associations between cognition, clinical functioning, and magnetic resonance imaging volumes. RESULTS: NIH-EXAMINER scores detected baseline and differences in slopes between carriers and noncarriers, even in carriers with a baseline Clinical Dementia Rating scale plus NACC FTLD Module = 0. NIH-EXAMINER declines were associated with worsening clinical symptoms and brain volume loss. DISCUSSION: The NIH-EXAMINER is sensitive to cognitive changes in presymptomatic familial FTLD and is a promising surrogate endpoint.

The costs of dementia subtypes to California Medicare fee-for-service, 2015.

INTRODUCTION: Dementia is among the costliest of medical conditions, but it is not known how these costs vary by dementia subtype. METHODS: The effect of dementia diagnosis subtype on direct health care costs and utilization was estimated using 2015 California Medicare fee-for-service data. Potential drivers of increased costs in Lewy body dementia (LBD), in comparison to Alzheimer's disease, were tested. RESULTS: 3,001,987 Medicare beneficiaries were identified, of which 8.2% had a dementia diagnosis. Unspecified dementia was the most common diagnostic category (59.6%), followed by Alzheimer's disease (23.2%). LBD was the costliest subtype to Medicare, on average, followed by vascular dementia. The higher costs in LBD were explained in part by falls, urinary incontinence or infection, depression, anxiety, dehydration, and delirium. DISCUSSION: Dementia subtype is an important predictor of health care costs. Earlier identification and targeted treatment might mitigate the costs associated with co-occurring conditions in LBD.

The Rural Inpatient Mortality Study: Does Urban-Rural County Classification Predict Hospital Mortality in California?

CONTEXT: Evidence suggests an association between rurality and decreased life expectancy. OBJECTIVE: To determine whether rural hospitals have higher hospital mortality, given that very sick patients may be transferred to regional hospitals. DESIGN: In this ecologic study, we combined Medicare hospital mortality ratings (N = 1267) with US census data, critical access hospital classification, and National Center for Health Statistics urban-rural county classifications. Ratings included mortality for coronary artery bypass grafting, stroke, chronic obstructive pulmonary disease, heart attack, heart failure, and pneumonia across 277 California hospitals between July 2011 and June 2014. We used generalized estimating equations to evaluate the association of urban-rural county classifications on mortality ratings. MAIN OUTCOME MEASURES: Unfavorable Medicare hospital mortality rating "worse than the national rate" compared with "better" or "same." RESULTS: Compared with large central "metro" (metropolitan) counties, hospitals in medium-sized metro counties had 6.4 times the odds of rating "worse than the national rate" for hospital mortality (95% confidence interval = 2.8-14.8, p < 0.001). For hospitals in small metro counties, the odds of having such a rating were 3.7 times greater (95% confidence interval = 0.7-23.4, p = 0.12), although not statistically significant. Few ratings were provided for rural counties, and analysis of rural counties was underpowered. CONCLUSION: Hospitals in medium-sized metro counties are associated with unfavorable Medicare mortality ratings, but current methods to assign mortality ratings may hinder fair comparisons. Patient transfers from rural locations to regional medical centers may contribute to these results, a potential factor that future research should examine.

Out-of-Network Emergency Department Use among Managed Medicaid Beneficiaries.

OBJECTIVE: Out-of-network emergency department (ED) use, or use that occurs outside the contracted network, may lead to increased care fragmentation and cost. We examined factors associated with out-of-network ED use among Medicaid beneficiaries. DATA SOURCES AND STUDY SETTING: Enrollment, claims, and encounter data for adult Medi-Cal health plan members with 1+ ED visits and complete Medicaid eligibility during the study period from 2013 to 2014. STUDY DESIGN: We analyzed the data to identify factors associated with out-of-network ED use classified by mode of arrival (ambulance vs. nonambulance). DATA EXTRACTION METHODS: We extracted encounter, ambulance, and ED census data and linked them together based on ED visit date. PRINCIPAL FINDINGS: Of 11,143 ED visits, 6,808 (61.1 percent) were out-of-network. The number of hours the study ED was on ambulance diversion increased the odds of out-of-network visits for the 3,365 (30.2 percent) ED visits arriving by ambulance. For all visit types, assignment to a primary care clinic at the in-network hospital and having had any primary care visit during the study period decreased the odds of out-of-network ED care. Individuals were more likely to go out-of-network for ED care if they lived in neighborhoods containing out-of-network EDs. CONCLUSIONS: There are a number of factors related to out-of-network ED use, including the proximity and density of out-of-network EDs, race and ethnicity, a prior history of out-of-network ED use, and individuals' connection to primary care. EDs that serve Medicaid beneficiaries may need to explore alternative sites and modalities of care as alternatives to the ED, and consider their ability to absorb large numbers of out-of-network visits given already limited capacity.

Variability and bias assessment in breast ADC measurement across multiple systems.

PURPOSE: To assess the ability of a recent, anatomically designed breast phantom incorporating T1 and diffusion elements to serve as a quality control device for quantitative comparison of apparent diffusion coefficient (ADC) measurements calculated from diffusion-weighted MRI (DWI) within and across MRI systems. MATERIALS AND METHODS: A bilateral breast phantom incorporating multiple T1 and diffusion tissue mimics and a geometric distortion array was imaged with DWI on 1.5 Tesla (T) and 3.0T scanners from two different manufacturers, using three different breast coils (three configurations total). Multiple measurements were acquired to assess the bias and variability of different diffusion weighted single-shot echo-planar imaging sequences on the scanner-coil systems. RESULTS: The repeatability of ADC measurements was mixed: the standard deviation relative to baseline across scanner-coil-sequences ranged from low variability (0.47, 95% confidence interval [CI]: 0.22-1.00) to high variability (1.69, 95% CI: 0.17-17.26), depending on material, with the lowest and highest variability from the same scanner-coil-sequence. Assessment of image distortion showed that right/left measurements of the geometric distortion array were 1 to 16% larger on the left coil side compared with the right coil side independent of scanner-coil systems, diffusion weighting, and phase-encoding direction. CONCLUSION: This breast phantom can be used to measure scanner-coil-sequence bias and variability for DWI. When establishing a multisystem study, this breast phantom may be used to minimize protocol differences (e.g., due to available sequences or shimming technique), to correct for bias that cannot be minimized, and to weigh results from each system depending on respective variability. J. Magn. Reson. Imaging 2016. J. MAGN. RESON. IMAGING 2016;44:846-855.

Association of Self-Reported Frailty with Falls and Fractures among Patients New to Dialysis.

BACKGROUND: Although frailty has been linked to higher risk of falls and fracture in the general population, only few studies have examined the extent to which frailty is associated with these outcomes among patients with end-stage renal disease, who are at particularly high risk for these events. METHODS: A total of 1,646 patients who were beginning maintenance hemodialysis in 297 dialysis units throughout the United States from September 2005 to June 2007 were enrolled in the Comprehensive Dialysis Study, and 1,053 Medicare beneficiaries were included in this study. Self-reported frailty was defined by the patients endorsing 2 or more of the following: poor physical functioning, exhaustion or low physical activity. Falls and fractures requiring medical attention were identified through Medicare claims data. We examined the association between frailty and the time to first fall or fracture using the Fine-Gray modification of Cox proportional hazards regression, adjusted for demographics, Quételet's body mass index, diabetes mellitus, heart failure and atherosclerosis. RESULTS: Seventy-seven percent of patients were frail by self-report. The median length of follow-up was 2.5 (1.0-3.9) years. Crude rates of first medically urgent falls or fractures were 66 and 126 per 1,000 person-years in non-frail and self-reported frail participants, respectively. After accounting for demographic factors, comorbidities and the competing risk of death, self-reported frailty was associated with a higher risk of falls or fractures requiring medical attention (hazards ratio 1.60, 95% CI 1.16-2.20). CONCLUSION: Participants reporting frailty experienced nearly twice the risk of medically urgent falls or fractures compared to those who did not report frailty.

Bayesian decision analysis for choosing between diagnostic/prognostic prediction procedures.

New diagnostic procedures and prognostic markers are continually being developed for a wide range of medical complaints. Medical institutions are therefore regularly faced with the decision as to whether to replace an existing procedure with a new one. The decision to adopt a new method is primarily based on diagnostic/predictive accuracy and cost-effectiveness, but this trade-off is not usually considered in a formal decision-theoretic way. The decision process for diagnostic procedures is complicated by the fact that diagnostic decisions are typically based on thresholding one or more continuous variables. Therefore, a formal decision process should account for uncertainty in the optimal threshold value for each diagnostic procedure. We here propose a Bayesian decision approach based on maximizing expected utility (incorporating accuracy and costs) with respect to diagnostic procedure and threshold level simultaneously. The Bayesian decision approach is illustrated via an application comparing the utility of different bone mineral density (BMD) measurements for determining the need for preventative treatment of osteoporotic hip fracture in elderly patients.

K-Bayes reconstruction for perfusion MRI II: modeling and technical development.

Despite the continued spread of magnetic resonance imaging (MRI) methods in scientific studies and clinical diagnosis, MRI applications are mostly restricted to high-resolution modalities such as structural MRI. While perfusion MRI gives complementary information on blood flow in the brain, its reduced resolution limits its power for detecting specific disease effects on perfusion patterns. This reduced resolution is compounded by artifacts such as partial volume effects, Gibbs ringing, and aliasing, which are caused by necessarily limited k-space sampling and the subsequent use of discrete Fourier transform (DFT) reconstruction. Here, a Bayesian modeling procedure (K-Bayes) is developed for the reconstruction of perfusion MRI. The K-Bayes approach combines a process model for the MRI signal in k-space with a Markov random field prior distribution that incorporates high-resolution segmented structural MRI information. A simulation study, described in Part I (Concepts and Applications), was performed to determine qualitative and quantitative improvements in K-Bayes reconstructed images compared with those obtained via DFT. The improvements were validated using in vivo perfusion MRI data of the human brain. The K-Bayes reconstructed images were demonstrated to provide reduced bias, increased precision, greater effect sizes, and higher resolution than those obtained using DFT.