Predicting fluid-response, the heart of hemodynamic management: A model-based solution

Fluid responsiveness; Fluid resuscitation; Hemodynamic monitoring; Intensive care unit; Mathematical modelling; Forecasting; Mammals; Parameter estimation; Resuscitation; Areas under the curves; Fluid response; Fluid therapy; Mathematical modeling; Model-based OPC; Polar plot; Stroke volumes; Intensive care units; animal experiment; animal model; aortic flow; area under the curve; article; calibration; control system; fluid resuscitation; heart function; heart stroke volume; hemodynamic monitoring; human; intensive care unit; limit of agreement; nonhuman; pig; prediction; receiver operating characteristic; animal; fluid therapy; heart; hemodynamics; Animals; Area Under Curve; Fluid Therapy; Heart; Hemodynamics; Humans; Stroke Volume; Swine

Abstract :

[en] Background: Intravenous fluid infusions are an important therapy for patients with circulatory shock. However, it is challenging to predict how patients’ cardiac stroke volume (SV) will respond, and thus identify how much fluids should be delivered, if any. Model-predicted SV time-profiles of response to fluid infusions could potentially be used to guide fluid therapy. Method: A clinically applicable model-based method predicts SV changes in response to fluid-infusions for a pig trial (N = 6). Validation/calibration SV, SVmea, is from an aortic flow probe. Model parameters are identified in 3 ways: fitting to SVmea from the entire infusion, SVflfit, from the first 200 ml, SVfl200, or from the first 100 ml, SVfl100. RMSE compares error of model-based SV time-profiles for each parameter identification method, and polar plot analysis assesses trending ability. Receiver-operating characteristic (ROC) analysis evaluates ability of model-predicted SVs, SVfl200 and SVfl100, to distinguish non-responsive and responsive infusions, using area-under the curve (AUC), and balanced accuracy as a measure of performance. Results: RMSE for SVflFit, SVfl200, and SVfl100 was 1.8, 3.2, and 6.5 ml, respectively, and polar plot angular limit of agreement from was 11.6, 28.0, and 68.8°, respectively. For predicting responsive and non-responsive interventions SVfl200, and SVfl100 had ROC AUC of 0.64 and 0.69, respectively, and balanced accuracy was 0.75 in both cases. Conclusions: The model-predicted SV time-profiles matched measured SV trends well for SVflFit, SVfl200, but not SVfl100. Thus, the model can fit the observed SV dynamics, and can deliver good SV prediction given a sufficient parameter identification period. This trial is limited by small numbers and provides proof-of-method, with further experimental and clinical investigation needed. Potentially, this method could deliver model-predicted SV time-profiles to guide fluid therapy decisions, or as part of a closed-loop fluid control system. © 2021 Elsevier Ltd

Disciplines :

Anesthesia & intensive care

Author, co-author :

Smith, R.; Department of Mechanical Engineering, University of Canterbury, New Zealand

Pretty, C.G.; Department of Mechanical Engineering, University of Canterbury, New Zealand

Shaw, G.M.; Christchurch Hospital Intensive Care Unit, New Zealand

Desaive, Thomas ; Université de Liège - ULiège > Département d'astrophysique, géophysique et océanographie (AGO) > Thermodynamique des phénomènes irréversibles

Chase, J.G.; Department of Mechanical Engineering, University of Canterbury, New Zealand

Language :

English

Title :

Predicting fluid-response, the heart of hemodynamic management: A model-based solution

Publication date :

2021

Journal title :

Computers in Biology and Medicine

ISSN :

0010-4825

eISSN :

1879-0534

Publisher :

Elsevier Ltd

Volume :

139

Peer reviewed :

Peer Reviewed verified by ORBi

Funding text :

This work was supported with funding from the University of Canterbury Doctoral Scholarship, MedTech CoRE , Royal Society of New Zealand Engineering Technology-based Innovation in Medicine consortium grant, and EU FP7 International Research Staff Exchange Scheme. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Available on ORBi :

since 05 December 2023

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