Lessons for Medicare Part D in the hemodialysis community

BACKGROUND: Medicare beneficiaries without prescription drug coverage consistently fill fewer prescriptions than beneficiaries with some form of drug coverage due to cost. ESRD patients, who are disproportionately poor and typically use multiple oral medications, would likely benefit substantially from any form of prescription drug coverage. Because most hemodialysis patients are Medicare-eligible, they as well as their providers would be expected to be well informed of changes in Medicare prescription drug coverage. By examining the level of understanding and use of the temporary Medicare Prescription Drug Discount Card Program in the hemodialysis population, we can gain a better understanding of the potential long-term utilization for Medicare Part D. METHODS: We surveyed English-speaking adult hemodialysis patients with Medicare coverage from two urban hemodialysis centers affiliated with the University of California San Francisco (UCSF) during July and August 2005 (n = 70). We also surveyed University- and community-based nephrologists and non-physician dialysis health care professionals over the same time frame (n = 70). RESULTS: Fifty-nine percent of patients received prescription drug coverage through Medi-Cal, 20% through another insurance program, and 21% had no prescription drug coverage. Forty percent of patients with no prescription drug coverage reported "sometimes" or "rarely" being able to obtain medications vs. 22% of patients with some form of drug coverage. None of the patients surveyed actually had a Medicare-approved prescription drug card, and of those who intended to apply, only 10% reported knowing how to do so. Only 11% health care professionals knew the eligibility requirements of the drug discount cards. CONCLUSION: Despite a significant need, hemodialysis patients and providers were poorly educated about the Medicare Prescription Drug Discount Cards. This has broad implications for the dissemination of information about Medicare Part D

DeepBrain: Functional Representation of Neural In-Situ Hybridization Images for Gene Ontology Classification Using Deep Convolutional Autoencoders

This paper presents a novel deep learning-based method for learning a functional representation of mammalian neural images. The method uses a deep convolutional denoising autoencoder (CDAE) for generating an invariant, compact representation of in situ hybridization (ISH) images. While most existing methods for bio-imaging analysis were not developed to handle images with highly complex anatomical structures, the results presented in this paper show that functional representation extracted by CDAE can help learn features of functional gene ontology categories for their classification in a highly accurate manner. Using this CDAE representation, our method outperforms the previous state-of-the-art classification rate, by improving the average AUC from 0.92 to 0.98, i.e., achieving 75% reduction in error. The method operates on input images that were downsampled significantly with respect to the original ones to make it computationally feasible

The Solar Neighborhood XXIII CCD Photometric Distance Estimates of SCR Targets -- 77 M Dwarf Systems within 25 Parsecs

We present CCD photometric distance estimates of 100 SCR (SuperCOSMOS RECONS) systems with $\mu$ $\geq$ 0\farcs18/yr, 28 of which are new discoveries previously unpublished in this series of papers. These distances are estimated using a combination of new $VRI$ photometry acquired at CTIO and $JHK$ magnitudes extracted from 2MASS. The estimates are improvements over those determined using photographic plate $BRI$ magnitudes from SuperCOSMOS plus $JHK$, as presented in the original discovery papers. In total, 77 of the 100 systems investigated are predicted to be within 25 pc. If all 77 systems are confirmed to have $\pi$$_{trig}$ $\ge$ 40 milliarcseconds, this sample would represent a 23% increase in M dwarf systems nearer than 25 pc in the southern sky.Comment: 34 pages, 8 figure

The Trail, 1963-11-06

https://soundideas.pugetsound.edu/thetrail_all/1878/thumbnail.jp

Genetic risk prediction of atrial fibrillation

Background—Atrial fibrillation (AF) has a substantial genetic basis. Identification of individuals at greatest AF risk could minimize the incidence of cardioembolic stroke. Methods—To determine whether genetic data can stratify risk for development of AF, we examined associations between AF genetic risk scores and incident AF in five prospective studies comprising 18,919 individuals of European ancestry. We examined associations between AF genetic risk scores and ischemic stroke in a separate study of 509 ischemic stroke cases (202 cardioembolic [40%]) and 3,028 referents. Scores were based on 11 to 719 common variants (≥5%) associated with AF at P-values ranging from <1x10-3 to <1x10-8 in a prior independent genetic association study. Results—Incident AF occurred in 1,032 (5.5%) individuals. AF genetic risk scores were associated with new-onset AF after adjusting for clinical risk factors. The pooled hazard ratio for incident AF for the highest versus lowest quartile of genetic risk scores ranged from 1.28 (719 variants; 95%CI, 1.13-1.46; P=1.5x10-4) to 1.67 (25 variants; 95%CI, 1.47-1.90; P=9.3x10-15). Discrimination of combined clinical and genetic risk scores varied across studies and scores (maximum C statistic, 0.629-0.811; maximum ΔC statistic from clinical score alone, 0.009-0.017). AF genetic risk was associated with stroke in age- and sex-adjusted models. For example, individuals in the highest versus lowest quartile of a 127-variant score had a 2.49-fold increased odds of cardioembolic stroke (95%CI, 1.39-4.58; P=2.7x10-3). The effect persisted after excluding individuals (n=70) with known AF (odds ratio, 2.25; 95%CI, 1.20-4.40; P=0.01). Conclusions—Comprehensive AF genetic risk scores were associated with incident AF beyond associations for clinical AF risk factors, though offered small improvements in discrimination. AF genetic risk was also associated with cardioembolic stroke in age- and sex-adjusted analyses. Efforts are warranted to determine whether AF genetic risk may improve identification of subclinical AF or help distinguish between stroke mechanisms