Diagnostic and prognostic value of circulating micro RNAs in heart failure with preserved and reduced ejection fraction

micro RNAs(mi RNAs) are powerful regulators of posttranscriptional gene expression and play an important role in pathophysiological processes. Circulating mi RNAs can be quantified in body liquids and are promising biomarkers in numerous diseases. In cardiovascular disease mi RNAs have been proven to be reliable diagnostic biomarkers for different disease entities. In cardiac fibrosis(CF) and heart failure(HF) dysregulated circulating mi RNAs have been identified,indicating their promising applicability as diagnostic biomarkers. Some mi RNAs were successfully tested in risk stratification of HF implementing their potential use as prognostic biomarkers. In this respect mi RNAs might soon be implemented in diagnostic clinical routine. In the young field of mi RNA based research advances have been made in identifying mi RNAs as potential targets for the treatment of experimental CF and HF. Promising study results suggest their potential future application as therapeutic agents in treatment of cardiovascular disease. This article summarizes the current state of the various aspects of mi RNA research in the field of CF and HF with reduced ejection fraction as well as preserved ejection fraction. The review provides an overview of the application of circulating mi RNAs as biomarkers in CF and HF and current approaches to therapeutically utilize mi RNAs in this field of cardiovascular disease.

Comparison of Cox Model Methods in A Low-dimensional Setting with Few Events

Prognostic models based on survival data frequently make use of the Cox proportional hazards model. Developing reliable Cox models with few events relative to the number of predictors can be challenging, even in low-dimensional datasets, with a much larger number of observations than variables. In such a setting we examined the performance of methods used to estimate a Cox model, including （i） full model using all available predictors and estimated by standard techniques, （ii） backward elimination （BE）, （iii） ridge regression, （iv） least absolute shrinkage and selection operator （lasso）, and （v） elastic net. Based on a prospective cohort of patients with manifest coronary artery disease （CAD）, we performed a simulation study to compare the predictive accuracy, calibration, and discrimination of these approaches, Candidate predictors for incident cardiovascular events we used included clinical variables, biomarkers, and a selection of genetic variants associated with CAD. The penalized methods, i.e., ridge, lasso, and elastic net, showed a comparable performance, in terms of predictive accuracy, calibration, and discrimination, and outperformed BE and the full model. Excessive shrinkage was observed in some cases for the penalized methods, mostly on the simulation scenarios having the lowest ratio of a number of events to the number of variables. We conclude that in similar settings, these three penalized methods can be used interchangeably. The full model and backward elimination are not recommended in rare event scenarios.