Autonomous systems in anesthesia : where do we stand in 2020? A narrative review

As most of us are aware, almost every facet of our society is becoming, for better or worse, progressively more technology-dependent. Technological advancement has made autonomous systems, also known as robots, an integral part of our life in several fields, including medicine. The application of robots in anesthesia could be classified into 3 types of robots. The first ones are pharmacological robots. These robots are based on closed-loop systems that allow better-individualized anesthetic drug titration for optimal homeostasis during general anesthesia and sedation. Recent evidence also demonstrates that autonomous systems could control hemodynamic parameters proficiently outperforming manual control in the operating room. The second type of robot is mechanical. They enable automated motorized reproduction of tasks requiring high manual dexterity level. Such robots have been advocated to be more accurate than humans and, thus, could be safer for the patient. The third type is a cognitive robot also known as decision support system. This type of robot is able to recognize crucial clinical situation that requires human intervention. When these events occur, the system notifies the attending clinician, describes relevant related clinical observations, proposes pertinent therapeutic options and, when allowed by the attending clinician, may even administer treatment. It seems that cognitive robots could increase patients' safety. Robots in anesthesia offer not only the possibility to free the attending clinicians from repetitive tasks but can also reduce mental workload allowing them to focus on tasks that require human intelligence such as analytical and clinical approach, lifesaving decision-making capacity, and interpersonal interaction. Nevertheless, further studies have yet to be done to test the combination of these 3 types of robots to maintain simultaneously the homeostasis of multiple biological variables and to test the safety of such combination on a large-scale population

DEVELOPMENT AND IN-SILICO EVALUATION OF A CLOSED-LOOP FLUID RESUSCITATION CONTROL ALGORITHM WITH MEAN ARTERIAL PRESSURE FEEDBACK

In this thesis, a model-based closed-loop fluid resuscitation controller using mean arterial pressure (MAP) feedback is designed and later evaluated on an in-silico testbed. The controller is based on a subject specific model of blood volume and MAP response to fluid infusion. This simple hemodynamic model is described using five parameters only. The model was able to reproduce blood volume and blood pressure response to fluid infusion using an experimental dataset collected from 23 sheep and is therefore suitable to use for control design purposes. A model-reference adaptive control scheme was chosen to account for inter-subject variability captured in the parametric uncertainties of the underlying physiological model. Three versions of the control algorithm were studied under different measurement availability scenarios. In-silico evaluation of the three controllers was done using a comprehensive cardiovascular physiology model on a cohort of 100 virtually generated patients. Results clearly show that a tradeoff exists between tracking and estimation performance depending on measurement availability

Model-Free Reinforcement Learning for Automated Fluid Administration in Critical Care

Fluid administration, also called fluid resuscitation, is a medical treatment to restore the lost blood volume and optimize cardiac functions in critical care scenarios such as burn, hemorrhage, and septic shock. Automated fluid administration systems (AFAS), a potential means to improve the treatment, employ computational control algorithms to automatically adjust optimal fluid infusion dosages by targeting physiological variables (e.g., blood volume or blood pressure). Most of the existing AFAS control algorithms are model-based approaches, and their performance is highly dependent on the model accuracy, making them less desirable in real-world care of critically ill patients due to complexity and variability of modeling patients physiological states. This work presents a novel model-free reinforcement learning (RL) approach for the control of fluid infusion dosages in AFAS systems. The proposed RL agent learns to adjust the blood volume to a desired value by choosing the optimal infusion dosages using a Q-learning algorithm. The RL agent learns the optimal actions by interacting with the environment (without having the knowledge of system dynamics). The proposed methodology (i) overcomes the need for a precise mathematical model in AFAS systems and (ii) provides a robust performance in rejecting clinical noises and reaching desired hemodynamic states, as will be shown by simulation results

A Control-Theoretic Model of Hemodynamic Responses to Blood Volume Perturbation

This thesis presents a mathematical model to reproduce hemodynamic responses of different endpoints to the blood volume perturbation in circulation system. The proposed model includes three sub-models, which are a control-theoretic model relating blood volume response to blood volume perturbation, a physiologic-based model relating cardiac output response to blood volume perturbation, and a control-theoretic model relating mean arterial pressure response to cardiac output perturbation. Two unique characteristics of this hemodynamic model are that the model can reproduce responses accurately even with its simplicity, and can be easily understood by control engineers because of its physiological transparency. With these two advantages, closed-loop resuscitation controller evaluation can be performed in model-based approach instead of evaluating results from animal studies, which are relatively costly and time-consuming. In this thesis, the hemodynamic model was examined and evaluated by using experimental dataset collected from 11 animals. The results of system identification analysis, in-silico evaluation and parametric sensitivity analysis showed that the hemodynamic model may faithfully serve as a evaluation basis for the closed-loop resuscitation controllers

A FRAMEWORK FOR CREDIBILITY ASSESSMENT OF SUBJECT-SPECIFIC PHYSIOLOGICAL MODELS

Physiological closed-loop controllers and decision support systems are medical devices that enable some degree of automation to meet the needs of patients in resource-limited environments such as critical care and surgical units. Traditional methods of safety and effectiveness evidence generation such as pre-clinical animal and human clinical studies are cost prohibitive and may not fully capture different performance attributes of such complex safety-criticalsystems primarily due to subject variability. In silico studies using subject-specific physiological models (SSPMs) may provide a versatile platform to generate pre-clinical and clinical safety evidence for medical devices and help reduce the size and scope of animal studies and/or clinical trials. To achieve such a goal, the credibility of the SSPMs must be established for the purpose it is intended to serve. While in the past decades significant research has been dedicated towards development oftools and methods for development and evaluation of SSPMs, adoption of such models remains limited, partly due to lack of trust in SSPMs for safety-critical applications. This may be due to a lack of a cohesive and disciplined credibility assessment framework for SSPMs. In this dissertation a novel framework is proposed for credibility assessment of SSPMs. The framework combines various credibility activities in a unified manner to avoid or reduce resource intensive steps, effectively identify model or data limitations, provide direction as to how to address potential model weaknesses, and provide much needed transparency in the model evaluation process to the decision-makers. To identify various credibility activities, the framework is informed by an extensive literature review of more mature modeling spaces focusing on non- SSPMs as well as a literature review identifying gaps in the published work related to SSPMs. The utility of the proposed framework is successfully demonstrated by its application towards credibility assessment of a CO2 ventilatory gas exchange model intended to predict physiological parameters, and a blood volume kinetic model intended to predict changes in blood volume inresponse to fluid resuscitation and hemorrhage. The proposed framework facilitates development of more reliable SSPMs and will result in increased adoption of such models to be used for evaluation of safety-critical medical devices such as Clinical Decision Support (CDS) and Physiological Closed-Loop Controlled (PCLC) systems

Perioperative goal-directed therapy - What is the evidence?

Perioperative goal-directed therapy aims at optimizing global hemodynamics during the perioperative period by titrating fluids, vasopressors, and/or inotropes to predefined hemodynamic goals. There is evidence on the benefit of perioperative goal-directed therapy, but its adoption into clinical practice is slow and incomprehensive. Current evidence indicates that treating patients according to perioperative goal-directed therapy protocols reduces morbidity and mortality, particularly in patients having high-risk surgery. Perioperative goal-directed therapy protocols need to be started early, should include vasoactive agents in addition to fluids, and should target blood flow related variables. Future promising developments in the field of perioperative goal-directed therapy include personalized hemodynamic management and closed-loop system management. (C) 2019 Elsevier Ltd. All rights reserved

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

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 non-linearities, 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

SYSTEMS-LEVEL MODELING AND VALIDATION OF CARDIOVASCULAR SYSTEM RESPONSES TO FLUID AND VASOPRESSOR INFUSION FOR AUTOMATED CRITICAL CARE SYSTEMS

Effective treatment of critically ill patients requires adequate administration of drugs to resuscitate and stabilize the patient by maintaining the volume of blood against bleeding and preserving the blood circulation to the body tissues. In today’s clinical practice, drug dose is adjusted by human clinicians. Therefore, treatment is often subjective and ad-hoc depending on the style and experience of the clinician. Thus, in theory, it is anticipated that well-designed automated critical care systems can help clinicians make superior adjustments to drug doses while they are always vigilant and never distracted by other obligations. Yet, automated critical care systems developed by researchers are ad-hoc, because they determine the control law, i.e., drug infusion rate, using input-output observations rather than the insights on the patient’s physiological states gained from rigorous data-based analysis of mathematical models. Thus, it is worth developing model-based automated systems relating the fluid and vasopressor dose input to the underlying physiological states. This necessitates dose-response mathematical models capable of reproducing realistic physiological and dose-mediated states with reasonable computational load. However, most of existing models are too simplistic to reflect physiological reality, while others are too complicated with thousands of parameters to tune. To address these challenges, we believe that a hybrid physiologic-phenomenological modeling paradigm is effective in developing mathematical models for automated systems: low-order phenomenological models with adaptive personalization capability are suited to develop control algorithms, while physiological models can provide high-fidelity patterns with physiological transparency suited to interpret the underlying physiological states. In this study, hybrid physiologic-phenomenological models of blood volume and cardiovascular responses to fluid and vasopressor infusion are successfully developed and validated using experimental data. It is shown that the models can adequately reproduce the underlying physiological states and endpoints to fluid and vasopressor infusion. The main contributions of this research are lined in the following three folds. First, the models are robust against inter-individual variability, in which they can be adapted to each patient with a small number of tunable parameters. Second, they are physiologically transparent where the underlying physiological states not measured in the standard clinical setting can be interpreted and streamlined during an intervention. And eventually the interpreted underlying states can be employed as direct endpoints to monitor the patient and guide the treatment in a closed-loop or decision-support platform