Predictive Value of Machine Learning for the Risk of In-Hospital Death in Patients With Heart Failure: A Systematic Review and Meta-Analysis.

Background: The efficiency of machine learning (ML) based predictive models in predicting in-hospital mortality for heart failure (HF) patients is a topic of debate. In this context, this study's objective is to conduct a meta-analysis to compare and assess existing prognostic models designed for predicting in-hospital mortality in HF patients.

Methods: A systematic search of databases was conducted, including PubMed, Embase, Web of Science, and Cochrane Library up to January 2023.

The Bioinformatical Identification of Potential Biomarkers in Heart Failure Diagnosis and Treatment.

Background: Heart failure (HF) is defined as the inability of the heart's systolic and diastolic function to properly discharge blood flow from the veins to the heart. The goal of our research is to look into the possible mechanism that causes HF.

Methods: The GSE5406 database was used for screening the differentially expressed genes (DEGs).

[Defibrillation efficacy of a newly developed automated external defibrillator in a swine ventricular tachycardia/fibrillation model].

Objective: The aim of this study was to evaluate the recognition and defibrillation efficiency of a newly developed automated external defibrillator (AED).

Method: Ventricular tachycardia (VT)/ventricular fibrillation (VF) was induced by alternating current (50 Hz) through an electrode placed on apex of right ventricle in 23 anesthetized swine and recorded, recognized and defibrillated by a newly developed AED.

Results: A total of 96 VF was induced and 145 defibrillations were recorded.