Automated closed-loop resuscitation of multiple hemorrhages: a comparison between fuzzy logic and decision table controllers in a sheep model

AI algorithm for personalized resource allocation and treatment of hemorrhage casualties

A deep neural network-based artificial intelligence (AI) model was assessed for its utility in predicting vital signs of hemorrhage patients and optimizing the management of fluid resuscitation in mass casualties. With the use of a cardio-respiratory computational model to generate synthetic data of hemorrhage casualties, an application was created where a limited data stream (the initial 10 min of vital-sign monitoring) could be used to predict the outcomes of different fluid resuscitation allocations 60 min into the future. The predicted outcomes were then used to select the optimal resuscitation allocation for various simulated mass-casualty scenarios. This allowed the assessment of the potential benefits of using an allocation method based on personalized predictions of future vital signs versus a static population-based method that only uses currently available vital-sign information. The theoretical benefits of this approach included up to 46% additional casualties restored to healthy vital signs and a 119% increase in fluid-utilization efficiency. Although the study is not immune from limitations associated with synthetic data under specific assumptions, the work demonstrated the potential for incorporating neural network-based AI technologies in hemorrhage detection and treatment. The simulated injury and treatment scenarios used delineated possible benefits and opportunities available for using AI in pre-hospital trauma care. The greatest benefit of this technology lies in its ability to provide personalized interventions that optimize clinical outcomes under resource-limited conditions, such as in civilian or military mass-casualty events, involving moderate and severe hemorrhage.

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

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.

Development and validation of a mathematical model of heart rate response to fluid perturbation

Physiological closed-loop controlled (PCLC) medical devices monitor and automatically adjust the patient's condition by using physiological variables as feedback, ideally with minimal human intervention to achieve the target levels set by a clinician. PCLC devices present a challenge when it comes to evaluating their performance, where conducting large clinical trials can be expensive. Virtual physiological patients simulated by validated mathematical models can be utilized to obtain pre-clinical evidence of safety and assess the performance of the PCLC medical device during normal and worst-case conditions that are unlikely to happen in a limited clinical trial. A physiological variable that plays a major role during fluid resuscitation is heart rate (HR). For in silico assessment of PCLC medical devices regarding fluid perturbation, there is currently no mathematical model of HR validated in terms of its predictive capability performance. This paper develops and validates a mathematical model of HR response using data collected from sheep subjects undergoing hemorrhage and fluid infusion. The model proved to be accurate in estimating the HR response to fluid perturbation, where averaged between 21 calibration datasets, the fitting performance showed a normalized root mean square error (NRMSE) of [Formula: see text]. The model was also evaluated in terms of model predictive capability performance via a leave-one-out procedure (21 subjects) and an independent validation dataset (6 subjects). Two different virtual cohort generation tools were used in each validation analysis. The generated envelope of virtual subjects robustly met the defined acceptance criteria, in which [Formula: see text] of the testing datasets presented simulated HR patterns that were within a deviation of 50% from the observed data. In addition, out of 16000 and 18522 simulated subjects for the leave-one-out and independent datasets, the model was able to generate at least one virtual subject that was close to the real subject within an error margin of [Formula: see text] and [Formula: see text] NRMSE, respectively. In conclusion, the model can generate valid virtual HR physiological responses to fluid perturbation and be incorporated into future non-clinical simulated testing setups for assessing PCLC devices intended for fluid resuscitation.

Hardware-in-Loop Comparison of Physiological Closed-Loop Controllers for the Autonomous Management of Hypotension

Trauma and hemorrhage are leading causes of death and disability worldwide in both civilian and military contexts. The delivery of life-saving goal-directed fluid resuscitation can be difficult to provide in resource-constrained settings, such as in forward military positions or mass-casualty scenarios. Automated solutions for fluid resuscitation could bridge resource gaps in these austere settings. While multiple physiological closed-loop controllers for the management of hypotension have been proposed, to date there is no consensus on controller design. Here, we compare the performance of four controller types—decision table, single-input fuzzy logic, dual-input fuzzy logic, and proportional–integral–derivative using a previously developed hardware-in-loop test platform where a range of hemorrhage scenarios can be programmed. Controllers were compared using traditional controller performance metrics, but conclusions were difficult to draw due to inconsistencies across the metrics. Instead, we propose three aggregate metrics that reflect the target intensity, stability, and resource efficiency of a controller, with the goal of selecting controllers for further development. These aggregate metrics identify a dual-input, fuzzy-logic-based controller as the preferred combination of intensity, stability, and resource efficiency within this use case. Based on these results, the aggressively tuned dual-input fuzzy logic controller should be considered a priority for further development.

An Automated Hardware-in-Loop Testbed for Evaluating Hemorrhagic Shock Resuscitation Controllers

Hemorrhage remains a leading cause of death, with early goal-directed fluid resuscitation being a pillar of mortality prevention. While closed-loop resuscitation can potentially benefit this effort, development of these systems is resource-intensive, making it a challenge to compare infusion controllers and respective hardware within a range of physiologically relevant hemorrhage scenarios. Here, we present a hardware-in-loop automated testbed for resuscitation controllers (HATRC) that provides a simple yet robust methodology to evaluate controllers. HATRC is a flow-loop benchtop system comprised of multiple PhysioVessels which mimic pressure-volume responsiveness for different resuscitation infusates. Subject variability and infusate switching were integrated for more complex testing. Further, HATRC can modulate fluidic resistance to mimic arterial resistance changes after vasopressor administration. Finally, all outflow rates are computer-controlled, with rules to dictate hemorrhage, clotting, and urine rates. Using HATRC, we evaluated a decision-table controller at two sampling rates with different hemorrhage scenarios. HATRC allows quantification of twelve performance metrics for each controller configuration and scenario, producing heterogeneous results and highlighting the need for controller evaluation with multiple hemorrhage scenarios. In conclusion, HATRC can be used to evaluate closed-loop controllers through user-defined hemorrhage scenarios while rating their performance. Extensive controller troubleshooting using HATRC can accelerate product development and subsequent translation.

Closed-Loop Controlled Fluid Administration Systems: A Comprehensive Scoping Review

Physiological Closed-Loop Controlled systems continue to take a growing part in clinical practice, offering possibilities of providing more accurate, goal-directed care while reducing clinicians’ cognitive and task load. These systems also provide a standardized approach for the clinical management of the patient, leading to a reduction in care variability across multiple dimensions. For fluid management and administration, the advantages of closed-loop technology are clear, especially in conditions that require precise care to improve outcomes, such as peri-operative care, trauma, and acute burn care. Controller design varies from simplistic to complex designs, based on detailed physiological models and adaptive properties that account for inter-patient and intra-patient variability; their maturity level ranges from theoretical models tested in silico to commercially available, FDA-approved products. This comprehensive scoping review was conducted in order to assess the current technological landscape of this field, describe the systems currently available or under development, and suggest further advancements that may unfold in the coming years. Ten distinct systems were identified and discussed.

Evaluation of a Proportional-Integral-Derivative Controller for Hemorrhage Resuscitation Using a Hardware-in-Loop Test Platform

Hemorrhage is a leading cause of preventable death in trauma, which can often be avoided with proper fluid resuscitation. Fluid administration can be cognitive-demanding for medical personnel as the rates and volumes must be personalized to the trauma due to variations in injury severity and overall fluid responsiveness. Thus, automated fluid administration systems are ideal to simplify hemorrhagic shock resuscitation if properly designed for a wide range of hemorrhage scenarios. Here, we highlight the development of a proportional–integral–derivative (PID) controller using a hardware-in-loop test platform. The controller relies only on an input data stream of arterial pressure and a target pressure; the PID controller then outputs infusion rates to stabilize the subject. To evaluate PID controller performance with more than 10 controller metrics, the hardware-in-loop platform allowed for 11 different trauma-relevant hemorrhage scenarios for the controller to resuscitate against. Overall, the two controller configurations performed uniquely for the scenarios, with one reaching the target quicker but often overshooting, while the other rarely overshot the target but failed to reach the target during severe hemorrhage. In conclusion, PID controllers have the potential to simplify hemorrhage resuscitation if properly designed and evaluated, which can be accomplished with the test platform shown here.

Collective Variational Inference for Personalized and Generative Physiological Modeling: A Case Study on Hemorrhage Resuscitation

OBJECTIVE

Individual physiological experiments typically provide useful but incomplete information about a studied physiological process. As a result, inferring the unknown parameters of a physiological model from experimental data is often challenging. The objective of this paper is to propose and illustrate the efficacy of a collective variational inference (C-VI) method, intended to reconcile low-information and heterogeneous data from a collection of experiments to produce robust personalized and generative physiological models.

METHODS

To derive the C-VI method, we utilize a probabilistic graphical model to impose structure on the available physiological data, and algorithmically characterize the graphical model using variational Bayesian inference techniques. To illustrate the efficacy of the C-VI method, we apply it to a case study on the mathematical modeling of hemorrhage resuscitation.

RESULTS

In the context of hemorrhage resuscitation modeling, the C-VI method could reconcile heterogeneous combinations of hematocrit, cardiac output, and blood pressure data across multiple experiments to obtain (i) robust personalized models along with associated measures of uncertainty and signal quality, and (ii) a generative model capable of reproducing the physiological behavior of the population.

CONCLUSION

The C-VI method facilitates the personalized and generative modeling of physiological processes in the presence of low-information and heterogeneous data.

SIGNIFICANCE

The resulting models provide a solid basis for the development and testing of interpretable physiological monitoring, decision-support, and closed-loop control algorithms.

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

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.

Automated Blood Pressure Control

Arterial pressure management is a crucial task in the operating room and intensive care unit. In high-risk surgical and in critically ill patients, sustained hypotension is managed with continuous infusion of vasopressor agents, which most commonly have direct α agonist activity like phenylephrine or norepinephrine. The current standard of care to guide vasopressor infusion is manual titration to an arterial pressure target range. This approach may be improved by using automated systems that titrate vasopressor infusions to maintain a target pressure. In this article, we review the evidence behind blood pressure management in the operating room and intensive care unit and discuss current and potential future applications of automated blood pressure control.

Performance of closed-loop resuscitation in a pig model of haemorrhagic shock with fluid alone or in combination with norepinephrine, a pilot study

We evaluated the performance of a new device to control the administration of fluid alone or co-administration of fluid and norepinephrine in a pig model of haemorrhagic shock in two sets of experiments. In the first one, resuscitation was guided using continuous arterial pressure measurements (three groups: resuscitation with fluid by a physician, CL resuscitation with fluid, and CL resuscitation with fluid and norepinephrine). In the second one, resuscitation was guided using discontinuous arterial pressure measurements (three groups: CL resuscitation with fluid alone, CL resuscitation with fluid and moderate dose norepinephrine, and CL resuscitation with fluid and a high dose of norepinephrine). Pigs were resuscitated for 1 h. In the first set of experiments, proportion of time spent in the target area of 78-88 mmHg of systolic arterial pressure was not statistically different between the three groups: manual, 71.2% (39.1-80.1); CL with fluid, 87.8% (68.3-97.4); and CL with fluid and norepinephrine, 78.1% (59.2-83.6), p = 0.151. In the second set of experiments, performance of CL resuscitation with fluid or with combination of fluid and high or moderate dose of norepinephrine was not significantly different (p = 0.543 for time in target). Pigs resuscitated with norepinephrine required less fluid and had less haemodilution than pigs resuscitated with fluid alone. Performance of CL resuscitation using continuous arterial pressure measurement was not significantly different than optimised manual treatment by a dedicated physician. Performance of CL resuscitation was reduced with discontinuous arterial pressure measurements in comparison with continuous arterial pressure measurements.

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

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 patient-specific 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 population-average 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.

Evaluation of Fluid Resuscitation Control Algorithms via a Hardware-in-the-Loop Test Bed

OBJECTIVE

This paper presents a hardware-in-the-loop (HIL) testing platform for evaluating the performance of fluid resuscitation control algorithms. The proposed platform is a cyber-physical system that integrates physical devices with computational models and computer-based algorithms.

METHODS

The HIL test bed is evaluated against in silico and in vivo data to ensure the hemodynamic variables are appropriately predicted in the proposed platform. The test bed is then used to investigate the performance of two fluid resuscitation control algorithms: a decision table (rule-based) and a proportional-integral-derivative (PID) controller.

RESULTS

The statistical evaluation of test bed indicates that similar results are observed in the HIL test bed, in silico implementation, and the in vivo data, verifying that the HIL test bed can adequately predict the hemodynamic responses. Comparison of the two fluid resuscitation controllers reveals that both controllers stabilized hemodynamic variables over time and had similar speed to efficiently achieve the target level of the hemodynamic endpoint. However, the accuracy of the PID controller was higher than the rule-based for the scenarios tested in the HIL platform.

CONCLUSION

The results demonstrate the potential of the HIL test bed for realistic testing of physiologic controllers by incorporating physical devices with computational models of physiology and disturbances.

SIGNIFICANCE

This type of testing enables relatively fast evaluation of physiologic closed-loop control systems to aid in iterative design processes and offers complementary means to existing techniques (e.g., in silico, in vivo, and clinical studies) for testing of such systems against a wide range of disturbances and scenarios.

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

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 algorithm may enable 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.

Performance of closed-loop resuscitation of haemorrhagic shock with fluid alone or in combination with norepinephrine: an experimental study

Background

Closed-loop resuscitation can improve personalization of care, decrease workload and bring expert knowledge in isolated areas. We have developed a new device to control the administration of fluid or simultaneous co-administration of fluid and norepinephrine using arterial pressure.

Method

We evaluated the performance of our prototype in a rodent model of haemorrhagic shock. After haemorrhagic shock, rats were randomized to five experimental groups: three were resuscitated with fluid and two with co-administration of fluid and norepinephrine. Among groups resuscitated with fluid, one was resuscitated by a physician and two were resuscitated according to two different closed-loop algorithms. Among groups resuscitated with fluid and norepinephrine, one was resuscitated by a physician and the other one by the closed-loop device. The precision of arterial pressure during the resuscitation period was assessed using rising time, time passed in the target area and performance error calculations.

Results

Groups resuscitated with fluid had similar performances and passed as much time in the target area of 80–90 mmHg as the manual group [manual: 76.8% (67.9–78.2), closed-loop: 64.6% (45.7–72.9) and 80.9% (59.1–85.3)]. Rats resuscitated with fluid and norepinephrine using closed-loop passed similar time in target area than manual group [closed-loop: 74.4% (58.4–84.5) vs. manual: 60.1% (46.1–72.4)] but had shorter rising time to reach target area [160 s (106–187) vs. 434 s (254–1081)] than those resuscitated by a physician. Rats resuscitated with co-administration of fluid and norepinephrine required less fluid and had less hemodilution than rats resuscitated with fluid alone. Lactate decrease was similar between groups resuscitated with fluid alone and fluid with norepinephrine.

Conclusions

This study assessed extensively the performances of several algorithms for closed-loop resuscitation of haemorrhagic shock with fluid alone and with co-administration of fluid and norepinephrine. The performance of the closed-loop algorithms tested was similar to physician-guided treatment with considerable saving of work for the caregiver. Arterial pressure closed-loop guided algorithms can be extended to combined administration of fluid and norepinephrine.

Electronic supplementary material

The online version of this article (10.1186/s13613-018-0436-0) contains supplementary material, which is available to authorized users.

A pilot study evaluating adaptive closed-loop fluid resuscitation during states of absolute and relative hypovolemia in dogs

OBJECTIVE

To evaluate and determine the performance of a partially automated as well as a fully automated closed-loop fluid resuscitation system during states of absolute and relative hypovolemia.

DESIGN

Prospective experimental trial.

SETTING

Research laboratory.

ANIMALS

Five adult Beagle dogs.

METHODS

Isoflurane anesthetized mechanically ventilated dogs were subjected to absolute hypovolemia (controlled: 2 trials; uncontrolled: 3 trials), relative hypovolemia (2 trials), and the combination of relative and absolute controlled hypovolemia (2 trials). Controlled and uncontrolled hypovolemia were produced by withdrawing blood from the carotid or femoral artery. Relative hypovolemia was produced by increasing the isoflurane concentration (1 trial) or by infusion of intravenous sodium nitroprusside (1 trial). Relative hypovolemia combined with controlled absolute hypovolemia was produced by increasing the isoflurane concentration (1 trial) and infusion of IV sodium nitroprusside (1 trial). Hemodynamic parameters including stroke volume variation (SVV) were continuously monitored and recorded in all dogs. A proprietary closed-loop fluid administration system based on fluid distribution and compartmental dynamical systems administered a continuous infusion of lactated Ringers solution in order to restore and maintain SVV to a predetermined target value.

MEASUREMENTS AND MAIN RESULTS

A total of 9 experiments were performed on 5 dogs. Hemodynamic parameters deteriorated and SVV increased during controlled or uncontrolled hypovolemia, relative hypovolemia, and during relative hypovolemia combined with controlled hypovolemia. Stroke volume variation was restored to baseline values during closed-loop fluid infusion.

CONCLUSIONS

Closed-loop fluid administration based on IV fluid distribution and compartmental dynamical systems can be used to provide goal directed fluid therapy during absolute or relative hypovolemia in mechanically ventilated isoflurane anesthetized dogs.

The Science and Translation of Lactate Shuttle Theory

Once thought to be a waste product of anaerobic metabolism, lactate is now known to form continuously under aerobic conditions. Shuttling between producer and consumer cells fulfills at least three purposes for lactate: (1) a major energy source, (2) the major gluconeogenic precursor, and (3) a signaling molecule. "Lactate shuttle" (LS) concepts describe the roles of lactate in delivery of oxidative and gluconeogenic substrates as well as in cell signaling. In medicine, it has long been recognized that the elevation of blood lactate correlates with illness or injury severity. However, with lactate shuttle theory in mind, some clinicians are now appreciating lactatemia as a "strain" and not a "stress" biomarker. In fact, clinical studies are utilizing lactate to treat pro-inflammatory conditions and to deliver optimal fuel for working muscles in sports medicine. The above, as well as historic and recent studies of lactate metabolism and shuttling, are discussed in the following review.

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

This paper presents a physiological model to reproduce hemodynamic responses to blood volume perturbation. The model consists of three sub-models: a control-theoretic model relating blood volume response to blood volume perturbation; a simple physics-based model relating blood volume to stroke volume and cardiac output; and a phenomenological model relating cardiac output to blood pressure. A unique characteristic of this model is its balance for simplicity and physiological transparency. Initial validity of the model was examined using experimental data collected from 11 animals. The model may serve as a viable basis for the design and evaluation of closed-loop fluid resuscitation controllers.