Development, psychometric testing, and revision of the atlanta heart failure knowledge test

BACKGROUND AND RESEARCH OBJECTIVE: Several heart failure (HF) knowledge tools have been developed and tested over the past decade; however, they vary in content, format, psychometric properties, and availability. This article details the development, psychometric testing, and revision of the Atlanta Heart Failure Knowledge Test (A-HFKT) as a standardized instrument for both the research and clinical settings. PARTICIPANTS AND METHODS: Development and psychometric testing of the A-HFKT were undertaken with 116 New York Heart Association (NYHA) class II and III community-dwelling HF patients and their family members (FMs) participating in a family intervention study. Internal consistency, reliability, and content validity were examined. Construct validity was assessed by correlating education level, literacy, dietary sodium ingestion, medication adherence, and healthcare utilization with knowledge. RESULTS: Content validity ratings on relevance and clarity ranged from 0.55 to 1.0, with 81% of the items rated from 0.88 to 1.0. Cronbach α values were .84 for patients, .75 for FMs, and .73 for combined results. Construct validity testing revealed a small but significant correlation between higher patient and FM knowledge on sodium restriction questions and lower ingested sodium, r = −0.17, P = .05 and r = −0.19, P = .04, respectively, and between patient knowledge and number of days that medications were taken correctly (diuretics: r = 0.173, P < .05, and angiotensin-converting enzyme: r = 0.223, P = .01). Finally, patients seeking emergency care or requiring hospitalization in the 4 months before study entry were found to have significantly lower FM knowledge using both t test and logistic regression modeling. CONCLUSIONS: The A-HFKT was revised using the content and construct validity data and is available for use with HF patients and FMs. The construct validity testing indicates that patient knowledge has a significant relationship to aspects of self-care. Furthermore, family knowledge may influence patient adherence with sodium restriction and healthcare utilization behavior

The Grizzly, December 7, 1979

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Using A Pharmacy-Based Intervention To Improve Antipsychotic Adherence Among Patients With Serious Mental Illness

Background: Similar to patients with other chronic disorders, patients with serious mental illness (SMI) are often poorly adherent with prescribed medications. Objective: We conducted a randomized controlled trial examining the effectiveness of a pharmacy-based intervention (Meds-Help) in increasing antipsychotic medication adherence among Department of Veterans Affairs (VA) patients with SMI. We also examined the impact of Meds-Help on psychiatric symptoms, quality of life, and satisfaction with care. Methods: We enrolled 118 patients from 4 VA facilities with schizophrenia, schizoaffective, or bipolar disorder who were on long-term antipsychotics but had antipsychotic medication possession ratios (MPRs) Results: Prior to enrollment, Meds-Help and UC patients had mean antipsychotic MPRs of 0.54 and 0.55, respectively. At 6 months, mean MPRs were 0.91 for Meds-Help and 0.64 for UC patients; at 12 months, they were 0.86 for Meds-Help and 0.62 for UC patients. In multivariate analyses adjusting for patient factors, Meds-Help patients had significantly higher MPRs at 6 and 12 months (P \u3c .0001). There were no significant differences between groups in PANSS, QWB, or CSQ-8 scores, but power to detect small effects was limited. Conclusions: Congruent with prior studies of patients with other disorders, a practical pharmacy-based intervention increased antipsychotic adherence among patients with SMI. However, SMI patients may require additional care management components to improve outcomes

Exploiting Large Neuroimaging Datasets to Create Connectome-Constrained Approaches for more Robust, Efficient, and Adaptable Artificial Intelligence

Despite the progress in deep learning networks, efficient learning at the edge (enabling adaptable, low-complexity machine learning solutions) remains a critical need for defense and commercial applications. We envision a pipeline to utilize large neuroimaging datasets, including maps of the brain which capture neuron and synapse connectivity, to improve machine learning approaches. We have pursued different approaches within this pipeline structure. First, as a demonstration of data-driven discovery, the team has developed a technique for discovery of repeated subcircuits, or motifs. These were incorporated into a neural architecture search approach to evolve network architectures. Second, we have conducted analysis of the heading direction circuit in the fruit fly, which performs fusion of visual and angular velocity features, to explore augmenting existing computational models with new insight. Our team discovered a novel pattern of connectivity, implemented a new model, and demonstrated sensor fusion on a robotic platform. Third, the team analyzed circuitry for memory formation in the fruit fly connectome, enabling the design of a novel generative replay approach. Finally, the team has begun analysis of connectivity in mammalian cortex to explore potential improvements to transformer networks. These constraints increased network robustness on the most challenging examples in the CIFAR-10-C computer vision robustness benchmark task, while reducing learnable attention parameters by over an order of magnitude. Taken together, these results demonstrate multiple potential approaches to utilize insight from neural systems for developing robust and efficient machine learning techniques.Comment: 11 pages, 4 figure

A large scale hearing loss screen reveals an extensive unexplored genetic landscape for auditory dysfunction

The developmental and physiological complexity of the auditory system is likely reflected in the underlying set of genes involved in auditory function. In humans, over 150 non-syndromic loci have been identified, and there are more than 400 human genetic syndromes with a hearing loss component. Over 100 non-syndromic hearing loss genes have been identified in mouse and human, but we remain ignorant of the full extent of the genetic landscape involved in auditory dysfunction. As part of the International Mouse Phenotyping Consortium, we undertook a hearing loss screen in a cohort of 3006 mouse knockout strains. In total, we identify 67 candidate hearing loss genes. We detect known hearing loss genes, but the vast majority, 52, of the candidate genes were novel. Our analysis reveals a large and unexplored genetic landscape involved with auditory function

Monitoring of oxidative and metabolic stress during cardiac surgery by means of breath biomarkers: an observational study

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The impact of carotid plaque presence and morphology on mortality outcome in cardiological patients

BACKGROUND: Carotid plaque severity and morphology can affect cardiovascular prognosis. We evaluate both the importance of echographically assessed carotid artery plaque geometry and morphology as predictors of death in hospitalised cardiological patients. METHODS: 541 hospitalised patients admitted in a cardiological division (age = 66 ± 11 years, 411 men), have been studied through ultrasound Duplex carotid scan and successively followed-up for a median of 34 months. Echo evaluation assessed plaque severity and morphology (presence of heterogeneity and profile). RESULTS: 361 patients showed carotid stenosis (67% with <50% stenosis, 18% with 50–69% stenosis, 9% with >70% stenosis, 4% with near occlusion and 2% with total occlusion). During the follow-up period, there were 83 all-cause deaths (15% of the total population). Using Cox's proportional hazard model, age (RR 1.06, 95% CI 1.03–1.09, p = 0.000), ejection fraction > 50% (RR = 0.62, 95% CI 0.4–0.96, p = 0.03), treatment with statins (RR = 0.52, 95% CI 0.29–0.95, p = 0.34) and the presence of a heterogeneous plaque (RR 1.6; 95% CI, 1.2 to 2.14, p = 0.002) were independent predictors of death. Kaplan – Meier survival estimates have shown the best outcome in patients without plaque, intermediate in patients with homogeneous plaques and the worst outcome in patients with heterogeneous plaques (90% vs 79% vs 73%, p = 0.0001). CONCLUSION: In hospitalised cardiological patients, carotid plaque presence and morphology assessed by ultrasound are independent predictors of death

Concept, Design and Implementation of a Cardiovascular Gene-Centric 50 K SNP Array for Large-Scale Genomic Association Studies

A wealth of genetic associations for cardiovascular and metabolic phenotypes in humans has been accumulating over the last decade, in particular a large number of loci derived from recent genome wide association studies (GWAS). True complex disease-associated loci often exert modest effects, so their delineation currently requires integration of diverse phenotypic data from large studies to ensure robust meta-analyses. We have designed a gene-centric 50 K single nucleotide polymorphism (SNP) array to assess potentially relevant loci across a range of cardiovascular, metabolic and inflammatory syndromes. The array utilizes a “cosmopolitan” tagging approach to capture the genetic diversity across ∼2,000 loci in populations represented in the HapMap and SeattleSNPs projects. The array content is informed by GWAS of vascular and inflammatory disease, expression quantitative trait loci implicated in atherosclerosis, pathway based approaches and comprehensive literature searching. The custom flexibility of the array platform facilitated interrogation of loci at differing stringencies, according to a gene prioritization strategy that allows saturation of high priority loci with a greater density of markers than the existing GWAS tools, particularly in African HapMap samples. We also demonstrate that the IBC array can be used to complement GWAS, increasing coverage in high priority CVD-related loci across all major HapMap populations. DNA from over 200,000 extensively phenotyped individuals will be genotyped with this array with a significant portion of the generated data being released into the academic domain facilitating in silico replication attempts, analyses of rare variants and cross-cohort meta-analyses in diverse populations. These datasets will also facilitate more robust secondary analyses, such as explorations with alternative genetic models, epistasis and gene-environment interactions