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: A model-based adaptive control algorithm for fluid resuscitation was developed by leveraging a low-order lumped-parameter blood volume dynamics model, and...

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Bibliographic Details
Published inIEEE transactions on biomedical engineering Vol. 66; no. 7; pp. 1905 - 1914
Main Authors Jin, Xin, Bighamian, Ramin, Hahn, Jin-Oh
Format Journal Article
LanguageEnglish
Published United States IEEE 01.07.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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ISSN0018-9294
1558-2531
1558-2531
DOI10.1109/TBME.2018.2880927

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Summary:Objective: To develop and evaluate in silico, a model-based closed-loop fluid resuscitation control algorithm via blood volume feedback. Methods: A 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 algorithms may enable the 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.
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ISSN:0018-9294
1558-2531
1558-2531
DOI:10.1109/TBME.2018.2880927