PiCCO hemodynamic parameters in cardiogenic shock: prediction of LVEF, NT-proBNP and MACE based on XGBoost machine learning model
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.
Authors
- Jieyun You
- Jing Huang (ORCID: https://orcid.org/0000-0002-0638-4805)
- Wei Guo (ORCID: https://orcid.org/0000-0002-2646-6517)
- Qi Zhang (ORCID: https://orcid.org/0000-0001-7041-643X)
- Yue Yu (ORCID: https://orcid.org/0000-0002-7960-3968)
- Yuxiao Sun (ORCID: https://orcid.org/0000-0002-8472-2582)
- Tianwen Wei (ORCID: https://orcid.org/0009-0005-0995-0795)
Institutions
- Shanghai East Hospital (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-10-15
- DOI
- https://doi.org/10.3389/fmed.2025.1683425
- Primary Topic
- Mechanical Circulatory Support Devices
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Shanghai Pudong New Area Health Commission