Development and In Silico Evaluation of a Model-Based Closed-Loop Fluid Resuscitation Control Algorithm.

OBJECTIVE

To develop and evaluate in silico a model-based closed-loop fluid resuscitation control algorithm via blood volume feedback.

METHODS

Model-based adaptive control algorithm for fluid resuscitation was developed by leveraging a low-order lumped-parameter blood volume dynamics model, and then in silico evaluated based on a detailed mechanistic model of circulatory physiology. The algorithm operates in two steps: (1) the blood volume dynamics model is individualized based on the patient's fractional blood volume response to an initial fluid bolus via system identification; and (2) an adaptive control law built on the individualized blood volume dynamics model regulates the blood volume of the patient.

RESULTS

The algorithm was able to track the blood volume set point as well as accurately estimate and monitor the patient's absolute blood volume level. The algorithm significantly outperformed a population-based proportional-integral-derivative control.

CONCLUSION

Model-based development of closed-loop fluid resuscitation control algorithm may enable regulation of blood volume and monitoring of absolute blood volume level.

SIGNIFICANCE

Model-based closed-loop fluid resuscitation algorithm may offer opportunities for standardized and patient-tailored therapy and reduction of clinician workload.