{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,6]],"date-time":"2026-09-06T09:10:22Z","timestamp":1788685822488,"version":"build-2803163510"},"reference-count":50,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T00:00:00Z","timestamp":1760486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>This study used the Extreme Gradient Boosting (XGBoost) machine learning model to conduct an in-depth analysis of the potential relationship between pulse index continuous cardiac output (PiCCO) and multiple clinical prognostic indicators, including 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. The aim of this study was to investigate the predictive ability of PiCCO hemodynamic parameters and the relative contribution features based on the XGBoost model.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Multi-class receiver operating characteristic (ROC) curves explored that the XGBoost prediction model performed extremely well about LVEF and NT-proBNP. Further SHapley Additive explanation (SHAP) value analysis revealed the contributions of different PiCCO hemodynamic parameters.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Features such as CI (cardiac index), CPI (cardiac power index), and SVRI (systemic vascular resistance index) showed significant positive effects on the prediction of LVEF and NT-proBNP. In terms of MACE, dPmax (index of the left ventricular contractility), CFI (cardiac function index), and GEDVI (global end-diastolic volume index) showed significant predictive value.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>Overall, XGBoost machine learning model based on PiCCO hemodynamic parameters provide evidence that effectively predict key clinical prognostic indicators in the patients with cardiogenic shock. These results provide important theoretical basis for further individualized clinical decision-making in cardiogenic shock patients.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fmed.2025.1683425","type":"journal-article","created":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T05:42:19Z","timestamp":1760506939000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["PiCCO hemodynamic parameters in cardiogenic shock: prediction of LVEF, NT-proBNP and MACE based on XGBoost machine learning 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