On adaptive control and particle filtering in the automatic administration of medicinal drugs

Automatic feedback methodologies for the administration of medicinal drugs offer undisputed potential benefits in terms of cost reduction and improved clinical outcomes. However, despite several decades of research, the ultimate safety of many--it would be fair to say most--closed-loop drug delivery approaches remains under question and manual methods based on clinicians' expertise are still dominant in clinical practice. Key challenges to the design of control systems for these applications include uncertainty in pharmacological models, as well as intra- and interpatient variability in the response to drug administration. Pharmacological systems may feature nonlinearities, time delays, time-varying parameters and non-Gaussian stochastic processes. This dissertation investigates a novel multi-controller adaptive control strategy capable of delivering safe control for closed-loop drug delivery applications without impairing clinicians' ability to make an expert assessment of a clinical situation. Our new feedback control approach, which we have named Robust Adaptive Control with Particle Filtering (RAC-PF), estimates a patient's individual response characteristic in real-time through particle filtering and uses the Bayesian inference result to select the most suitable controller for closed-loop operation from a bank of candidate controllers designed using the robust methodology of mu-synthesis. The work is presented as four distinct pieces of research. We first apply the existing approach of Robust Multiple-Model Adaptive Control (RMMAC), which features robust controllers and Kalman filter estimators, to the case-study of administration of the vasodepressor drug sodium nitroprusside and examine benefits and drawbacks. We then consider particle filtering as an alternative to Kalman filter-based methods for the real-time estimation of pharmacological dose-response, and apply this to the nonlinear pharmacokinetic-pharmacodynamic model of the anaesthetic drug propofol. We ultimately combine particle filters and robust controllers to create RAC-PF, and test our novel approach first in a proof-of-concept design and finally in the case of sodium nitroprusside. The results presented in the dissertation are based on computational studies, including extensive Monte-Carlo simulation campaigns. Our findings of improved parameter estimates from noisy observations support the use of particle filtering as a viable tool for real-time Bayesian inference in pharmacological system identification. The potential of the RAC-PF approach as an extension of RMMAC for closed-loop control of a broader class of systems is also clearly highlighted, with the proposed new approach delivering safe control of acute hypertension through sodium nitroprusside infusion when applied to a very general population response model. All approaches presented are generalisable and may be readily adapted to other drug delivery instances

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

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

Improving survival in out of hospital cardiac arrest a prospective synthesis of best practice

Cardiac arrest is the leading cause of death in the United States. By reviewing and analyzing the successes and failures of resuscitation efforts, it has been possible to identify critical components which have come to be known as the “Chain of Survival:” Early Recognition, Early CPR, Early Defibrillation, Early ALS, and Early Post Resuscitative Care. A failure in any one of the five links will result in a failed resuscitation. Early Recognition is the beginning of the resuscitation effort and includes a number of related components. Witnessed cardiac arrests, those that are seen or heard to occur, have a significantly higher chance of survival than those which are unwitnessed. Properly identifying agonal gasps: irregular, forceful, reflexive breaths which can occur during cardiac arrest, is key to recognition of arrest and activation of the emergency response system. Emergency dispatchers trained to recognize cardiac arrest, as well as to initiate Early CPR via telephonic instruction, have been identified as key personnel in the resuscitation effort. Once professional rescuers have been dispatched, response delays due to distance and traffic can be costly. The use of new technologies like GPS and traffic signal preemption (as well as the use of Police, Fire and EMS in conjunction) has been shown to make it possible to get qualified persons to the scene of a cardiac arrest more safely and more quickly. Once on scene, early, high quality CPR has been shown to dramatically improve survival. After just 8 minutes without assistance, a victim of cardiac arrest has a near zero percent chance of survival. CPR of high quality has been shown to help maintain survivability until more definitive care can be obtained. Early Defibrillation is another key component to survival in many cardiac arrests. While CPR can sustain organ function briefly, cardiac arrest is rarely reversed without defibrillation. Increasingly widespread prevalence of public automated external defibrillators (AEDs) has made Early Defibrillation easier. Furthermore, increased use of AEDs by lay and professional rescuers has called into question the value of more traditional, higher risk interventions like intubation and medication administration. Early ALS interventions have been a staple of resuscitation for decades, but there is little data to support the use of these interventions during cardiac arrest. Early Post-Resuscitative Care, however, has been shown to be an area where invasive ALS interventions can and do make a difference in improved survival. By looking at the body of research for links in the Chain of Survival, opportunities for improvement of resuscitation were identified. Persons who spend significant time around an individual at high risk for heart disease should be educated on possible precipitating symptoms of a myocardial infarct or other early signs of potential cardiac arrest. Persons likely to encounter a cardiac arrest should likewise be trained not only in how to recognize cardiac arrest (through the combination of unresponsiveness and abnormal breathing) but also to initiate basic care via compressions-only CPR. Emergency dispatchers should be increasingly trained to recognize cardiac arrest, as well how to effectively provide dispatcher assisted CPR. The focus of these efforts should be high quality CPR and the early deployment of defibrillation. The use of AEDs by bystanders should be encouraged whenever possible. The emphasis on CPR and use of an AED should be paramount, with invasive ALS interventions eschewed for the simpler and more effective therapies. Once ROSC has been obtained, the use of ALS interventions in unstable patients has been shown not only to prevent death due to transient hemodynamic instability, but also to improve the likelihood of survival with little to no neurological deficit. By embracing the chain of survival, and identifying the critical areas in need of research and improvement, it is possible to provide recommendations that may lead to improved survival from cardiac arrest

Mathematical Models for Optimisation of Drug Administration in Intensive Care Units

Clinical status of critically ill patients is often extreme and rapidly evolving. Hence, pharmacological therapies must be tailored to patients' characteristics and adapted according to the evolution of their clinical pictures. To identify optimal personalized treatments, possible scenarios produced by different therapeutic choices must be predicted and compared. This process requires complex analyses involving the development of appropriate mathematical models. In this Thesis, I focused on two important aspects of the pharmacological treatment of critically ill patients: the administration of antimicrobial drugs and the control of their glycaemic level. Although these problems are clinically very different, the modelling of their pathophysiological mechanisms can be addressed with similar tools. I performed analyses based on retrospective clinical data collected with MargheritaTre, an electronic health record developed by GiViTI. The software to synchronize databases from hospitals to our laboratory and to preprocess data for analyses was written for the purpose of this Thesis. Starting from the study of the physiological mechanisms at the basis of vancomycin pharmacokinetics I constructed a model to describe the evolution of the plasma concentration of this drug in critically ill patients. Compartment models were fitted on a sample of 141 patients, testing about 30 patient covariates and several functional dependencies for each variable. Glucose dynamics were described through a system of delay differential equations reproducing intake, uptake and endogenous production of glucose, and organ-organ interactions mediated by hormones. Existing models, describing only the dynamics of glucose and insulin, fail to reproduce the correct evolution when glucose concentrations vary too rapidly. I improved these models, by introducing an equation describing glucagon dynamics and taking into account its effect on glucose metabolism. I investigated the dynamical properties of my model with analytical analyses, numerical simulations and fitting it to observed data

Automatic Control Strategies of Mean Arterial Pressure and Cardiac Output. MIMO controllers, PID, internal model control, adaptive model reference, and neural nets are developed to regulate mean arterial pressure and cardiac output using the drugs sodium Nitroprusside and dopamine

High blood pressure, also called hypertension is one of the most common worldwide diseases afflicting humans and is a major risk factor for stroke, myocardial infarction, vascular disease, and chronic kidney disease. If blood pressure is controlled and oscillations in the hemodynamic variables are reduced, patients experience fewer complications after surgery. In clinical practice, this is usually achieved using manual drug delivery. Given that different patients have different sensitivity and reaction time to drugs, determining manually the right drug infusion rates may be difficult. This is a problem where automatic drug delivery can provide a solution, especially if it is designed to adapt to variations in the patient’s conditions. This research work presents an investigation into the development of abnormal blood pressure (hypertension) controllers for postoperative patients. Control of the drugs infusion rates is used to simultaneously regulate the hemodynamic variables such as the Mean Arterial Pressure (MAP) and the Cardiac Output (CO) at the desired level. The implementation of optimal control system is very essential to improve the quality of patient care and also to reduce the workload of healthcare staff and costs. Many researchers have conducted studies earlier on modelling and/or control of abnormal blood pressure for postoperative patients. However, there are still many concerns about smooth transition of blood pressure without any side effect. The blood pressure is classified in two categories: high blood pressure (Hypertension) and low blood pressure (Hypotension). The hypertension often occurred after cardiac surgery, and the hypotension occurred during cardiac surgery. To achieve the optimal control solution for these abnormal blood pressures, many methods are proposed, one of the common methods is infusing the drug related to blood pressure to maintain it at the desired level. There are several kinds of vasodilating drugs such as Sodium Nitroprusside (SNP), Dopamine (DPM), Nitro-glycerine (NTG), and so on, which can be used to treat postoperative patients, also used for hypertensive emergencies to keep the blood pressure at safety level. A comparative performance of two types of algorithms has been presented in chapter four. These include the Internal Model Control (IMC), and Proportional-Integral-Derivative (PID) controller. The resulting controllers are implemented, tested and verified for three sensitivity patient response. SNP is used for all three patients’ situation in order to reduce the pressure smoothly and maintain it at the desire level. A Genetic Algorithms (GAs) optimization technique has been implemented to optimise the controllers’ parameters. A set of experiments are presented to demonstrate the merits and capabilities of the control algorithms. The simulation results in chapter four have demonstrated that the performance criteria are satisfied with the IMC, and PID controllers. On the other hand, the settling time for the PID control of all three patients’ response is shorter than the settling time with IMC controller. Using multiple interacting drugs to control both the MAP and CO of patients with different sensitivity to drugs is a challenging task. A Multivariable Model Reference Adaptive Control (MMRAC) algorithm is developed using a two-input, two-output patient model. Because of the difference in patient’s sensitivity to the drug, and in order to cover the wide ranges of patients, Model Reference Adaptive Control (MRAC) has been implemented to obtain the optimal infusion rates of DPM and SNP. This is developed in chapters five and six. Computer simulations were carried out to investigate the performance of this controller. The results show that the proposed adaptive scheme is robust with respect to disturbances and variations in model parameters, the simulation results have demonstrated that this algorithm cannot cover the wide range of patient’s sensitivity to drugs, due to that shortcoming, a PID controller using a Neural Network that tunes the controller parameters was designed and implemented. The parameters of the PID controller were optimised offline using Matlab genetic algorithm. The proposed Neuro-PID controller has been tested and validated to demonstrate its merits and capabilities compared to the existing approaches to cover wide range of patients.Libyan Ministry of Higher Education scholarshi

A Quotient Basis Kernel for the prediction of mortality in severe sepsis patients

In this paper, we describe a novel kernel for multinomial distributions, namely the Quotient Basis Kernel (QBK), which is based on a suitable reparametrization of the input space through algebraic geometry and statistics. The QBK is used here for data transformation prior to classification in a medical problem concerning the prediction of mortality in patients suffering severe sepsis. This is a common clinical syndrome, often treated at the Intensive Care Unit (ICU) in a time-critical context. Mortality prediction results with Support Vector Machines using QBK compare favorably with those obtained using alternative kernels and standard clinical procedures.Postprint (published version

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

Maternal Hemodynamic Effects of Medical Gases and Uterotonics in Obstetrics

Aim of study: To elucidate the hemodynamic effects of pharmaceutical and medical interventions during pregnancy and childbirth on the mother.Introduction: Oxytocin, oxygen, and nitrous oxide are pharmaceuticals very commonly used in labor and delivery. These pharmaceuticals have known cardiovascular adverse effects. Some of these effects might be detrimental for the mother in case of major blood loss or preexisting cardiovascular disease, but the full extent of these effects is not known. The newer uterotonic carbetocin may have another adverse effect profile.Study population: Pregnant women during elective cesarean section; first trimester pregnant women during scheduled surgery for suction curettage; and pregnant and nonpregnant women during the third trimester.Methods: Cardiovascular effects are measured through ECG, blood pressure, oxygen saturation, and photoplethysmographic pulse wave analysis. By measuring the light absorption of infrared light through the finger, a waveform is obtained, from which it is possible to calculate indices of vascular stiffness and cardiac performance.Results: Oxytocin and carbetocin both have similar effects of vasodilation and blood pressure decrease. Pregnant women experienced more profound subjective side effects from nitrous oxide inhalations than nonpregnant controls. Oxygen alone and in a mix with nitrous oxide have vasoconstrictive and possible negative inotropic effects. These effects were more profound in pregnant women than in nonpregnant controls.Conclusion: The abovementioned medical interventions have cardiovascular effects that are sometimes quite profound. These effects can be shown with a simple and pain-free methodology. Carbetocin seems to have similar cardiovascular adverse effects compared to Oxytocin. Prudence should be taken when administering these drugs to compromised mothers. Both nitrous oxide and oxygen have vasoconstrictive and possible negative inotropic effects that were more prominent in pregnant women than in nonpregnant controls. Some of the effects seen from nitrous oxide might be due to the oxygen fraction in the gas mixture. Awareness of cardiovascular effects is important when treatment of the mother with oxytocin receptor agonists as well as with nitrous oxide and oxygen is considered. Oxygen treatment should not be used without a precise indication