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

Jin, Xin; Bighamian, Ramin; Hahn, Jin‐Oh

doi:10.1109/tbme.2018.2880927

Cited by 33 publications

(22 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Box 1 of Figure 3 highlights several COUs we identified that computational testing has been used for in the design and evaluation of PCLC devices. These include confirming the performance of supervisory systems and fallback modes in the presence of known disturbances (artin et al, 1992) and assessing the controller performance to maintain a physiological variable within a certain range under a range of patient conditions (Bighamian et al, 2016). These two COUs may not require the same CPM or the same levels of evidence demonstrating CPM performance, as discussed in our recent case study examining how the ASME V&V 40 framework could be applied to evaluating automated fluid resuscitation systems (Scully et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…Controllers used in hemodynamic stability systems adjust the infusion rate and/or time of delivery of fluids (e.g., colloids, crystalloids, or blood) and/or vasoactive drugs (e.g., SNP, phenylephrine). A variety of controller designs have been tested with CPMs including single input-single output adaptive and model predictive controllers (Malagutti et al, 2013; Silva et al, 2017), rule-based learning systems (Rinehart et al, 2011), PID controllers (Wassar et al, 2014; Bighamian et al, 2016), and multi-input–multi-output systems that control multiple drugs simultaneously (Held and Roy, 1995; Huang and Roy, 1998; Rao et al, 2000). System designs may include supervisory components that add a layer of safety by monitoring for known system limitations, such as noise/signal artifacts in the sensed physiological variables that could adversely impact the controller performance (artin et al, 1992).…”

Section: Closed-loop Systems For Hemodynamic Stabilitymentioning

confidence: 99%

“…The evidence from the simulation studies were used to initiate animal studies to further evaluate their controller designs. Bighamian et al (2016) developed 100 different configurations of a CPM representative of hypovolemia to assess their PID controller for a fluid resuscitation system against a range of autonomic, cardiac, and hemodynamic conditions by varying gain parameters in their model. Similarly, Rinehart et al (2013) assessed the robustness of their fluid resuscitation controller to varying patient weight and cardiac contractility.…”

Section: Closed-loop Systems For Hemodynamic Stabilitymentioning

confidence: 99%

“…While acknowledging the limitations of the model, the authors discuss the suitability for the questions being addressed in the study. Bighamian et al (2016) used a previously developed model (Ursino, 1998; Ursino and Magosso, 2003) to simulate 100 different hypovolemic patient conditions. An assessment of the distributions of hemodynamic variables was made to consider if the hemodynamic responses generated by the different parameter configurations were reasonable.…”

Section: Closed-loop Systems For Hemodynamic Stabilitymentioning

confidence: 99%

See 3 more Smart Citations

Credibility Evidence for Computational Patient Models Used in the Development of Physiological Closed-Loop Controlled Devices for Critical Care Medicine

et al. 2019

View full text Add to dashboard Cite

Physiological closed-loop controlled medical devices automatically adjust therapy delivered to a patient to adjust a measured physiological variable. In critical care scenarios, these types of devices could automate, for example, fluid resuscitation, drug delivery, mechanical ventilation, and/or anesthesia and sedation. Evidence from simulations using computational models of physiological systems can play a crucial role in the development of physiological closed-loop controlled devices; but the utility of this evidence will depend on the credibility of the computational model used. Computational models of physiological systems can be complex with numerous nonlinearities, time-varying properties, and unknown parameters, which leads to challenges in model assessment. Given the wide range of potential uses of computational patient models in the design and evaluation of physiological closed-loop controlled systems, and the varying risks associated with the diverse uses, the specific model as well as the necessary evidence to make a model credible for a use case may vary. In this review, we examine the various uses of computational patient models in the design and evaluation of critical care physiological closed-loop controlled systems (e.g., hemodynamic stability, mechanical ventilation, anesthetic delivery) as well as the types of evidence (e.g., verification, validation, and uncertainty quantification activities) presented to support the model for that use. We then examine and discuss how a credibility assessment framework (American Society of Mechanical Engineers Verification and Validation Subcommittee, V&V 40 Verification and Validation in Computational Modeling of Medical Devices) for medical devices can be applied to computational patient models used to test physiological closed-loop controlled systems.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Closed-loop Systems For Hemodynamic Stabilitymentioning

confidence: 99%

Section: Closed-loop Systems For Hemodynamic Stabilitymentioning

confidence: 99%

Section: Closed-loop Systems For Hemodynamic Stabilitymentioning

confidence: 99%

See 2 more Smart Citations

Credibility Evidence for Computational Patient Models Used in the Development of Physiological Closed-Loop Controlled Devices for Critical Care Medicine

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Clinical care automation has been a domain of interest for a few decades by virtue of its potential for error-free and vigilant performance of routine and low-level patient monitoring and treatment tasks (Hemmerling et al, 2010;Salinas et al, 2011;Dussaussoy et al, 2014;Rinehart et al, 2015;Brogi et al, 2017;Hundeshagen et al, 2017;Pasin et al, 2017), yet realizing this potential is contingent upon establishing the safety and the performance characteristics of clinical care automation. Patient physiology models built upon physical principles (hereafter called physiological models) can facilitate the development (Jin-Oh et al, 2012;Bighamian et al, 2014;Jin et al, 2018) and the testing (Kovatchev et al, 2009;Ortiz et al, 2010;Brown et al, 2015) of clinical care automation capabilities. However, individualizing physiological models (which can enable the systematic development and the testing of patient-specific clinical care automation) presents formidable challenge due to the conflict between model complexity and data scarcity.…”

Section: Introductionmentioning

confidence: 99%

Practical Use of Regularization in Individualizing a Mathematical Model of Cardiovascular Hemodynamics Using Scarce Data

Tivay

Jin

et al. 2020

Front. Physiol.

Self Cite

View full text Add to dashboard Cite

Individualizing physiological models to a patient can enable patient-specific monitoring and treatment in critical care environments. However, this task often presents a unique "practical identifiability" challenge due to the conflict between model complexity and data scarcity. Regularization provides an established framework to cope with this conflict by compensating for data scarcity with prior knowledge. However, regularization has not been widely pursued in individualizing physiological models to facilitate patientspecific critical care. Thus, the goal of this work is to garner potentially generalizable insight into the practical use of regularization in individualizing a complex physiological model using scarce data by investigating its effect in a clinically significant critical care case study of blood volume kinetics and cardiovascular hemodynamics in hemorrhage and circulatory resuscitation. We construct a population-average model as prior knowledge and individualize the physiological model via regularization to illustrate that regularization can be effective in individualizing a physiological model to learn salient individual-specific characteristics (resulting in the goodness of fit to individual-specific data) while restricting unnecessary deviations from the populationaverage model (achieving practical identifiability). We also illustrate that regularization yields parsimonious individualization of only sensitive parameters as well as adequate physiological plausibility and relevance in predicting internal physiological states.

show abstract

Development and validation of a mathematical model to simulate human cardiovascular and respiratory responses to battlefield trauma

Jin

Laxminarayan

Nagaraja

et al. 2022

Numer Methods Biomed Eng

Self Cite

View full text Add to dashboard Cite

Mathematical models of human cardiovascular and respiratory systems provide a viable alternative to generate synthetic data to train artificial intelligence (AI) clinical decision‐support systems and assess closed‐loop control technologies, for military medical applications. However, existing models are either complex, standalone systems that lack the interface to other applications or fail to capture the essential features of the physiological responses to the major causes of battlefield trauma (i.e., hemorrhage and airway compromise). To address these limitations, we developed the cardio‐respiratory (CR) model by expanding and integrating two previously published models of the cardiovascular and respiratory systems. We compared the vital signs predicted by the CR model with those from three models, using experimental data from 27 subjects in five studies, involving hemorrhage, fluid resuscitation, and respiratory perturbations. Overall, the CR model yielded relatively small root mean square errors (RMSEs) for mean arterial pressure (MAP; 20.88 mm Hg), end‐tidal CO2 (ETCO2; 3.50 mm Hg), O2 saturation (SpO2; 3.40%), and arterial O2 pressure (PaO2; 10.06 mm Hg), but a relatively large RMSE for heart rate (HR; 70.23 beats/min). In addition, the RMSEs for the CR model were 3% to 10% smaller than the three other models for HR, 11% to 15% for ETCO2, 0% to 33% for SpO2, and 10% to 64% for PaO2, while they were similar for MAP. In conclusion, the CR model balances simplicity and accuracy, while qualitatively and quantitatively capturing human physiological responses to battlefield trauma, supporting its use to train and assess emerging AI and control systems.

show abstract

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

Cited by 33 publications

References 28 publications

Credibility Evidence for Computational Patient Models Used in the Development of Physiological Closed-Loop Controlled Devices for Critical Care Medicine

Credibility Evidence for Computational Patient Models Used in the Development of Physiological Closed-Loop Controlled Devices for Critical Care Medicine

Practical Use of Regularization in Individualizing a Mathematical Model of Cardiovascular Hemodynamics Using Scarce Data

Development and validation of a mathematical model to simulate human cardiovascular and respiratory responses to battlefield trauma

Contact Info

Product

Resources

About