Quantifying the Influence of Combined Lung and Kidney Support Using a Cardiovascular Model and Sensitivity Analysis-Informed Parameter Identification
<|reference_start|>Quantifying the Influence of Combined Lung and Kidney Support Using a Cardiovascular Model and Sensitivity Analysis-Informed Parameter Identification: Combined extracorporeal membrane oxygenation (ECMO) and continuous renal replacement therapy (CRRT) pose complex hemodynamic challenges in intensive care. In this study, a comprehensive lumped parameter model (LPM) is developed to simulate the cardiovascular system, incorporating ECMO and CRRT circuit dynamics. The model is used to analyze nine CRRT-ECMO connection schemes under varying flow conditions. Using a robust parameter identification framework based on global sensitivity analysis (GSA) and multi-start gradient-based optimization, we calibrated the model on 30 clinical data points from eight veno-arterial ECMO patients.   Our results indicate that CRRT has a significant impact on the cardiovascular system, with changes in pulmonary artery pressure of up to 202.5 %, highly dependent on ECMO flow. The GSA proved to be a powerful tool to improve the parameter estimation process. The established parameter estimation framework is fast and robust without the need for hyperparameter tuning and improves the parameter estimation process with an R^2>0.98 between simulation and experimental data. It uses modeling methods that could pave the way for real-time applications in intensive care.   This open-source framework provides a valuable tool for the systematic evaluation of combined ECMO and CRRT, which can be used to develop standardized treatment protocols and improve patient outcomes in critical care. In addition, as a digital twin, this model also provides a good basis for addressing research questions related to mechanical circulatory and respiratory support.<|reference_end|>
@article{thiel2024quantifying,
    title={Quantifying the Influence of Combined Lung and Kidney Support Using a
  Cardiovascular Model and Sensitivity Analysis-Informed Parameter
  Identification},
    author={Jan-Niklas Thiel, Ana Martins Costa, Bettina Wiegmann, Jutta Arens,
  Ulrich Steinseifer, Michael Neidlin},
    journal={arXiv preprint arXiv:2407.08871},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.08871},
    primaryClass={physics.med-ph cs.NA math.NA}
}