Mortality Risk Prediction Dynamics After Heart Failure Treatment Optimization: Repeat Risk Assessment Using Online Risk Calculators.

Objectives: Heart failure (HF) management has significantly improved over the past two decades, leading to better survival. This study aimed to assess changes in predicted mortality risk after 12 months of management in a multidisciplinary HF clinic.

Materials And Methods: Out of 1,032 consecutive HF outpatients admitted from March-2012 to November-2018, 357 completed the 12-months follow-up and had N-terminal pro-B-type natriuretic peptide (NTproBNP), high sensitivity troponin T (hs-TnT), and interleukin-1 receptor-like-1 (known as ST2) measurements available both at baseline and follow-up.

Head-to-head comparison of contemporary heart failure risk scores.

Aims: Several heart failure (HF) web-based risk scores are currently used in clinical practice. Currently, we lack head-to-head comparison of the accuracy of risk scores. This study aimed to assess correlation and mortality prediction performance of Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC-HF) risk score, which includes clinical variables + medications; Seattle Heart Failure Model (SHFM), which includes clinical variables + treatments + analytes; PARADIGM Risk of Events and Death in the Contemporary Treatment of Heart Failure (PREDICT-HF) and Barcelona Bio-Heart Failure (BCN-Bio-HF) risk calculator, which also include biomarkers, like N-terminal pro B-type natriuretic peptide (NT-proBNP).

Weighted contrastive divergence.

Learning algorithms for energy based Boltzmann architectures that rely on gradient descent are in general computationally prohibitive, typically due to the exponential number of terms involved in computing the partition function. In this way one has to resort to approximation schemes for the evaluation of the gradient. This is the case of Restricted Boltzmann Machines (RBM) and its learning algorithm Contrastive Divergence (CD).