Effect of Continuous Education on Readmission Rates for CHF Patients

Aim: To evaluate if continuing the education of Congest Heart Failure patients post-discharge will decrease the amount of readmissions within 6 months of discharge. Background: Causes for decreased readmission rates in Congestive Heart Failure patients have been evaluated in multiple studies. The evaluation of the current research showed having discharge education and post- discharge follow-ups decreased the rate of readmission within 6 months. There is a sufficient amount of evidence supporting the implementation of education upon discharge and follow-ups of Congestive Heart Failure patients. Data Source: Databases and search engines used included: PubMed, OneSearch, CINAHL, DogPile, and Google. Of 25 articles read, 10 articles were included in the review of literature. Results: Three specific forms of patient education were reviewed. These included a telephone follow up program, six months of continued patient education, and a plan tailored to the individual needs of the patient. All three interventions were effective in showing a decrease in readmission rates. Conclusion: Increased time of continued education is believed to be effective in decreasing the readmission of Congestive Heart Failure patients within 30 days of discharge

The partitioned LASSO-patternsearch algorithm with application to gene expression data

In systems biology, the task of reverse engineering gene pathways from data has been limited not just by the curse of dimensionality (the interaction space is huge) but also by systematic error in the data. The gene expression barcode reduces spurious association driven by batch effects and probe effects. The binary nature of the resulting expression calls lends itself perfectly to modern regularization approaches that thrive in high-dimensional settings. The Partitioned LASSO-Patternsearch algorithm is proposed to identify patterns of multiple dichotomous risk factors for outcomes of interest in genomic studies. A partitioning scheme is used to identify promising patterns by solving many LASSO-Patternsearch subproblems in parallel. All variables that survive this stage proceed to an aggregation stage where the most significant patterns are identified by solving a reduced LASSO-Patternsearch problem in just these variables. This approach was applied to genetic data sets with expression levels dichotomized by gene expression bar code. Most of the genes and second-order interactions thus selected and are known to be related to the outcomes. We demonstrate with simulations and data analyses that the proposed method not only selects variables and patterns more accurately, but also provides smaller models with better prediction accuracy, in comparison to several alternative methodologies.https://doi.org/10.1186/1471-2105-13-9

Canagliflozin and renal outcomes in type 2 diabetes and nephropathy

BACKGROUND Type 2 diabetes mellitus is the leading cause of kidney failure worldwide, but few effective long-term treatments are available. In cardiovascular trials of inhibitors of sodium–glucose cotransporter 2 (SGLT2), exploratory results have suggested that such drugs may improve renal outcomes in patients with type 2 diabetes. METHODS In this double-blind, randomized trial, we assigned patients with type 2 diabetes and albuminuric chronic kidney disease to receive canagliflozin, an oral SGLT2 inhibitor, at a dose of 100 mg daily or placebo. All the patients had an estimated glomerular filtration rate (GFR) of 30 to <90 ml per minute per 1.73 m2 of body-surface area and albuminuria (ratio of albumin [mg] to creatinine [g], >300 to 5000) and were treated with renin–angiotensin system blockade. The primary outcome was a composite of end-stage kidney disease (dialysis, transplantation, or a sustained estimated GFR of <15 ml per minute per 1.73 m2), a doubling of the serum creatinine level, or death from renal or cardiovascular causes. Prespecified secondary outcomes were tested hierarchically. RESULTS The trial was stopped early after a planned interim analysis on the recommendation of the data and safety monitoring committee. At that time, 4401 patients had undergone randomization, with a median follow-up of 2.62 years. The relative risk of the primary outcome was 30% lower in the canagliflozin group than in the placebo group, with event rates of 43.2 and 61.2 per 1000 patient-years, respectively (hazard ratio, 0.70; 95% confidence interval [CI], 0.59 to 0.82; P=0.00001). The relative risk of the renal-specific composite of end-stage kidney disease, a doubling of the creatinine level, or death from renal causes was lower by 34% (hazard ratio, 0.66; 95% CI, 0.53 to 0.81; P<0.001), and the relative risk of end-stage kidney disease was lower by 32% (hazard ratio, 0.68; 95% CI, 0.54 to 0.86; P=0.002). The canagliflozin group also had a lower risk of cardiovascular death, myocardial infarction, or stroke (hazard ratio, 0.80; 95% CI, 0.67 to 0.95; P=0.01) and hospitalization for heart failure (hazard ratio, 0.61; 95% CI, 0.47 to 0.80; P<0.001). There were no significant differences in rates of amputation or fracture. CONCLUSIONS In patients with type 2 diabetes and kidney disease, the risk of kidney failure and cardiovascular events was lower in the canagliflozin group than in the placebo group at a median follow-up of 2.62 years

Magnetic Resonance Image Segmentation with Thin Plate Spline Thresholding ‡

2 We propose a new method for the T1-weighted magnetic resonance image (MRI) segmen-tation. Thin plate splines are fitted to overlapping blocks of an image slice and thresholds are found. The knots and the smoothing parameters of the splines are chosen by a mod-ified version of the generalized cross validation criterion. Each block is associated with a weighting function, which serves to blend the splines together as well as the thresholds in a smooth fashion. The blended image is then thresholded to get the boundaries between gray matter, white matter, cerebrospinal fluid, and others. We tested the method on MGH CMA 20 normal data. The results show that our method achieves good segmentation compared to human segmentation and SPM segmentation. Also our method generates subpixel result

Using distance covariance for improved variable selection

Using distance covariance for improved variable selectio