By integrating PiCCO-derived hemodynamic parameters with machine learning algorithms, this study aims to develop a predictive model for assessing heart failure-related clinical indicators—such as left ventricular ejection fraction (LVEF), N-terminal pro-brain natriuretic peptide (NT-proBNP) levels, and 30-day major adverse cardiovascular events (MACE) in patients with cardiogenic shock. This evidence concerns the gene NPPB and Shock.