Background The ability to predict risk allows healthcare providers to propose which patients
might benefit most from certain therapies, and is relevant to payers' demands to justify …

Background Cardiovascular diseases kill approximately 17 million people globally every
year, and they mainly exhibit as myocardial infarctions and heart failures. Heart failure (HF) …

The most challenging feature of heart failure (HF) still remains the evaluation of congestion.
Residual congestion at discharge and the difficulties in perfectly dosing therapies in order to …

Background Predicting mortality is important in patients with heart failure (HF). However,
current strategies for predicting risk are only modestly successful, likely because they are …

Background: Myocardial fibrosis is an important pathophysiological mechanism underlying
the development of heart failure (HF). Given the biochemical targets of sacubitril/valsartan …

Renal dysfunction (RD) often characterizes the worse course of patients with advanced
heart failure (AHF). Many prognosis assessments are hindered by researcher biases …

Heart failure (HF) and frailty are two distinct yet commonly associated conditions. The
interplay between the two conditions is complex, due to overlaps in underlying mechanisms …

Objectives: The purpose of this study was to compare outcomes (and the effect of
sacubitril/valsartan) according to etiology in the PARADIGM-HF (Prospective comparison of …

Aims Our aim was to develop a machine learning (ML)-based risk stratification system to
predict 1-, 2-, 3-, 4-, and 5-year all-cause mortality from pre-implant parameters of patients …

Aims Risk stratification of heart failure (HF) patients with preserved ejection fraction (HFpEF)
can promote a more personalized treatment. We tested the prognostic value of a multi …