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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| Published in | IEEE transactions on biomedical engineering Vol. 66; no. 7; pp. 1905 - 1914 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
01.07.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0018-9294 1558-2531 1558-2531 |
| DOI | 10.1109/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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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ISSN: | 0018-9294 1558-2531 1558-2531 |
| DOI: | 10.1109/TBME.2018.2880927 |