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Research Papers

Closed-Loop Fluid Resuscitation Control Via Blood Volume Estimation

[+] Author and Article Information
Ramin Bighamian

Department of Mechanical Engineering,
University of Maryland,
2181 Glenn L. Martin Hall,
College Park, MD 20742
e-mail: rbighami@umd.edu

Chang-Sei Kim

Department of Mechanical Engineering,
University of Maryland,
2181 Glenn L. Martin Hall,
College Park, MD 20742
e-mail: cskim75@umd.edu

Andrew T. Reisner

Department of Emergency Medicine,
Massachusetts General Hospital,
55 Fruit Street,
Boston, MA 02114
e-mail: AREISNER@mgh.harvard.edu

Jin-Oh Hahn

Mem. ASME
Department of Mechanical Engineering,
University of Maryland,
2181 Glenn L. Martin Hall,
College Park, MD 20742
e-mail: Jhahn12@umd.edu

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received October 7, 2015; final manuscript received April 27, 2016; published online July 13, 2016. Assoc. Editor: Xiaopeng Zhao.

J. Dyn. Sys., Meas., Control 138(11), 111005 (Jul 13, 2016) (8 pages) Paper No: DS-15-1492; doi: 10.1115/1.4033833 History: Received October 07, 2015; Revised April 27, 2016

This paper presents a closed-loop control of fluid resuscitation to overcome hypovolemia based on model-based estimation of relative changes in blood volume (BV). In this approach, the control system consists of a model-based relative BV (RBV) estimator and a feedback controller. The former predicts relative changes in the BV response to augmented fluid by analyzing an arterial blood pressure (BP) waveform and the electrocardiogram (ECG). Then, the latter determines the amount of fluid to be augmented by comparing target versus predicted relative changes in BV. In this way, unlike many previous methods for fluid resuscitation based on controlled variable(s) nonlinearly correlated with the changes in BV, fluid resuscitation can be guided by a controlled variable linearly correlated with the changes in BV. This paper reports initial design of the closed-loop fluid resuscitation system and its in silico evaluation in a wide range of hypovolemic scenarios. The results suggest that closed-loop fluid resuscitation guided by a controlled variable linearly correlated with the changes in BV can be effective in overcoming hypovolemia: across 100 randomly produced hypovolemia cases, it resulted in the BV regulation error of 7.98 ± 171.6 ml, amounting to 0.18 ± 3.04% of the underlying BV. When guided by pulse pressure (PP), a classical controlled variable nonlinearly correlated with the changes in BV; the same closed-loop fluid resuscitation system resulted in persistent under-resuscitation with the BV regulation error of −779.1 ± 147.4 ml, amounting to −13.9 ± 2.65% of the underlying BV.

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Figures

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Fig. 1

Closed-loop fluid resuscitation via model-based estimation of relative changes in left-ventricular (LV) end-diastolic volume (EDV) as linear surrogate of relative changes in BV

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Fig. 2

Model-based RBV estimation: (a) LV pressure–volume (P–V) loop and (b)–(d) Estimation of LV P–V loop based on BP and ECG

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Fig. 3

Model-based smoothing of RBV. The RBV predicted by the model-based RBV estimator at the end of fifth fluid resuscitation run (δV¯(5)  = 14.8% < 15% target) was not accurate, resulting in premature fluid resuscitation that was stopped after fifth run. The model-based smoothing predicted δV¯¯(5)  = 16.1% > 15% and the fluid resuscitation continued up to eighth run, preventing premature fluid resuscitation.

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Fig. 4

Distributions of the initial and target BV, CO, and BP

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Fig. 5

True versus predicted EDV and SV: (a) Model-based RBV estimator and (b) PP

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Fig. 6

True versus predicted EF

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Fig. 7

Efficacy of run-to-run closed-loop fluid resuscitation based on predicted RBV versus PP. (a) The relative (percent) change in true EDV at the first and the last runs. The control objective was to regulate the change in the last run at 15% (green horizontal line). (b) Percent BV regulation error.

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