Control-oriented physiological modeling of hemodynamic responses to blood volume perturbation

Bighamian, Ramin; Parvinian, Bahram; Scully, Christopher G.; Kramer, George C.; Hahn, Jin‐Oh

doi:10.1016/j.conengprac.2018.01.008

Cited by 27 publications

(46 citation statements)

References 55 publications

(59 reference statements)

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“…For closed-loop devices that are applied while patient physiological and clinical conditions may be changing, the loading conditions (disturbances) of the model and simulation need to be considered in relation to the dynamics of the system. For example, if system identification techniques are used to develop a data-driven model from experiments with a known input (e.g., a fixed hemorrhage followed by constant infusion of fluids), the performance of the model under different input conditions may not be captured (Bighamian et al, 2018). This could result in the need to compare the model to a variety of experimental conditions that are expected in the clinical scenario.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2019

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“…This distribution mechanism is modeled through a feedback controller that adjusts the rate of fluid shift between the two compartments to achieve a desired steady-state BV response (see Fig 1A and 1B ). Since the inter-compartmental fluid shift behaves differently under the gain or loss situation due to the different compositions of the involved fluids, the model assumes distinct distribution ratios for infusion versus loss rate (see Fig 1C , left picture) [ 20 ]. For a given rate of infusion U and loss V at each time t , the desired steady-state change in BV, r BV , is defined as: where α u and α v denote the fluid distribution ratio between intravascular and interstitial compartments under gain and loss situations.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, as a case study, we examine the adequacy of lumped-parameter mathematical models of patient physiology developed for evaluating PCLC fluid resuscitation devices. We build a refined physiological model of blood volume (BV) response by expanding an original model we developed in our prior research [ 19 , 20 ]. We use the experimental data collected from sheep subjects undergoing hemorrhage and fluid resuscitation.…”

Section: Introductionmentioning

confidence: 99%

Accuracy assessment methods for physiological model selection toward evaluation of closed-loop controlled medical devices

et al. 2021

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Physiological closed-loop controlled (PCLC) medical devices are complex systems integrating one or more medical devices with a patient’s physiology through closed-loop control algorithms; introducing many failure modes and parameters that impact performance. These control algorithms should be tested through safety and efficacy trials to compare their performance to the standard of care and determine whether there is sufficient evidence of safety for their use in real care setting. With this aim, credible mathematical models have been constructed and used throughout the development and evaluation phases of a PCLC medical device to support the engineering design and improve safety aspects. Uncertainties about the fidelity of these models and ambiguities about the choice of measures for modeling performance need to be addressed before a reliable PCLC evaluation can be achieved. This research develops tools for evaluating the accuracy of physiological models and establishes fundamental measures for predictive capability assessment across different physiological models. As a case study, we built a refined physiological model of blood volume (BV) response by expanding an original model we developed in our prior work. Using experimental data collected from 16 sheep undergoing hemorrhage and fluid resuscitation, first, we compared the calibration performance of the two candidate physiological models, i.e., original and refined, using root-mean-squared error (RMSE), Akiake information criterion (AIC), and a new multi-dimensional approach utilizing normalized features extracted from the fitting error. Compared to the original model, the refined model demonstrated a significant improvement in calibration performance in terms of RMSE (9%, P = 0.03) and multi-dimensional measure (48%, P = 0.02), while a comparable AIC between the two models verified that the enhanced calibration performance in the refined model is not due to data over-fitting. Second, we compared the physiological predictive capability of the two models under three different scenarios: prediction of subject-specific steady-state BV response, subject-specific transient BV response to hemorrhage perturbation, and leave-one-out inter-subject BV response. Results indicated enhanced accuracy and predictive capability for the refined physiological model with significantly larger proportion of measurements that were within the prediction envelope in the transient and leave-one-out prediction scenarios (P < 0.02). All together, this study helps to identify and merge new methods for credibility assessment and physiological model selection, leading to a more efficient process for PCLC medical device evaluation.

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“…Computational models of anatomy or physiology include improved drug delivery in the cornea with ultrasound energy ( 24 ); physiological models of heart cells ( 25 ), renal circulation ( 26 ), hemodynamic responses to blood volume perturbations ( 27 ), left bundle branch block ( 28 ), gas dynamics in the retina ( 29 ), coupled electrical and mechanical activity in the heart ( 30 ); energy absorption in patients with deep-brain stimulators ( 31 – 34 ), breast tissue expanders ( 35 ), in pregnant women and fetus during MRI exams ( 36 ); subthalamic nucleus ( 37 ), the breast ( 38 ), cancellous bone ( 39 ), the head ( 40 ) and whole body models ( 41 , 42 ).…”

Section: Computational Modeling Researchmentioning

confidence: 99%

“…Additional efforts are pushing the state of the art of simulation for medical devices , including fluid-structure-interaction of deformable blood clots, computational human phantoms for active implants ( 37 ), lesion insertion and image reconstruction ( 55 ), computational patient models for closed-loop control devices ( 27 ), whole-heart modeling for electrophysiology devices ( 30 ), computational modeling for determining hemolysis levels in patients supported by blood-circulating medical devices ( 56 ), evaluating exposure risk from nickel leaching devices ( 57 ) and risk assessment for framing policy and deciding on the stockpile of personal protective equipment for wide-spread outbreaks or virus epidemics ( 58 ).…”

Section: Computational Modeling Researchmentioning

confidence: 99%

Advancing Regulatory Science With Computational Modeling for Medical Devices at the FDA's Office of Science and Engineering Laboratories

et al. 2018

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Protecting and promoting public health is the mission of the U.S. Food and Drug Administration (FDA). FDA's Center for Devices and Radiological Health (CDRH), which regulates medical devices marketed in the U.S., envisions itself as the world's leader in medical device innovation and regulatory science–the development of new methods, standards, and approaches to assess the safety, efficacy, quality, and performance of medical devices. Traditionally, bench testing, animal studies, and clinical trials have been the main sources of evidence for getting medical devices on the market in the U.S. In recent years, however, computational modeling has become an increasingly powerful tool for evaluating medical devices, complementing bench, animal and clinical methods. Moreover, computational modeling methods are increasingly being used within software platforms, serving as clinical decision support tools, and are being embedded in medical devices. Because of its reach and huge potential, computational modeling has been identified as a priority by CDRH, and indeed by FDA's leadership. Therefore, the Office of Science and Engineering Laboratories (OSEL)—the research arm of CDRH—has committed significant resources to transforming computational modeling from a valuable scientific tool to a valuable regulatory tool, and developing mechanisms to rely more on digital evidence in place of other evidence. This article introduces the role of computational modeling for medical devices, describes OSEL's ongoing research, and overviews how evidence from computational modeling (i.e., digital evidence) has been used in regulatory submissions by industry to CDRH in recent years. It concludes by discussing the potential future role for computational modeling and digital evidence in medical devices.

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Control-oriented physiological modeling of hemodynamic responses to blood volume perturbation

Cited by 27 publications

References 55 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

Accuracy assessment methods for physiological model selection toward evaluation of closed-loop controlled medical devices

Advancing Regulatory Science With Computational Modeling for Medical Devices at the FDA's Office of Science and Engineering Laboratories

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