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    A new model-based algorithm for fluid resuscitation was developed and tested in silico. This closed-loop system accurately regulates blood volume and outperforms traditional methods, potentially improving patient care.

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    Area of Science:

    • Biomedical Engineering
    • Physiological Modeling
    • Control Systems

    Background:

    • Fluid resuscitation is critical in managing hypovolemia.
    • Current fluid resuscitation strategies often lack precise blood volume control.
    • Automated control systems may enhance therapeutic precision.

    Purpose of the Study:

    • To develop and evaluate an in silico model-based closed-loop fluid resuscitation control algorithm.
    • To utilize blood volume feedback for precise fluid management.
    • To assess the algorithm's performance against existing control methods.

    Main Methods:

    • Developed a model-based adaptive control algorithm using a lumped-parameter blood volume dynamics model.
    • Individualized the model via system identification based on patient response to fluid bolus.
    • Evaluated the algorithm in silico using a detailed mechanistic model of circulatory physiology.

    Main Results:

    • The algorithm successfully tracked blood volume set points.
    • Accurate estimation and monitoring of absolute blood volume levels were achieved.
    • Demonstrated significant performance improvement over population-based proportional-integral-derivative control.

    Conclusions:

    • Model-based closed-loop control offers a potential method for regulating blood volume.
    • The algorithm may enable precise monitoring of absolute blood volume.
    • This approach could lead to standardized, patient-tailored fluid therapy and reduced clinician workload.