1,025 research outputs found

    Requirements for Modeling and Simulation for Space Medicine Operations: Preliminary Considerations

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    The NASA Space Medicine program is now developing plans for more extensive use of high-fidelity medical Simulation systems. The use of simulation is seen as means to more effectively use the limited time available for astronaut medical training. Training systems should be adaptable for use in a variety of training environments, including classrooms or laboratories, space vehicle mockups, analog environments, and in microgravity. Modeling and simulation can also provide the space medicine development program a mechanism for evaluation of other medical technologies under operationally realistic conditions. Systems and procedures need preflight verification with ground-based testing. Traditionally, component testing has been accomplished, but practical means for "human in the loop" verification of patient care systems have been lacking. Medical modeling and simulation technology offer potential means to accomplish such validation work. Initial considerations in the development of functional requirements and design standards for simulation systems for space medicine are discussed

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

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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 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

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    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

    A FRAMEWORK FOR CREDIBILITY ASSESSMENT OF SUBJECT-SPECIFIC PHYSIOLOGICAL MODELS

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    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

    Activity Report 2020 : Automatic Control Lund University

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    A review of human circulatory system simulation: Bridging the gap between engineering and medicine

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    (1) Background: Simulation-based training (SBT) is the practice of using hands-on training to immerse learners in a risk-free and high-fidelity environment. SBT is used in various fields due to its risk-free benefits from a safety and an economic perspective. In addition, SBT provides immersive training unmatched by traditional teaching the interactive visualization needed in particular scenarios. Medical SBT is a prevalent practice as it allows for a platform for learners to learn in a risk-free and cost-effective environment, especially in critical care, as mistakes could easily cause fatalities. An essential category of care is human circulatory system care (HCSC), which includes essential-to-simulate complications such as cardiac arrest. (2) Methods: In this paper, a deeper look onto existing human circulatory system medical SBT is presented to assess and highlight the important features that should be present with a focus on extracorporeal membrane oxygenation cannulation (ECMO) simulators and cardiac catheterization. (3) Results: A list of features is also suggested for an ideal simulator to bridge the gap between medical studies and simulator engineering, followed by a case study of an ECMO SBT system design. (4) Conclusions: A collection and discussion of existing work for HCSC SBT are portrayed as a guide for researchers and practitioners to compare existing SBT and recreating them effectively. 2021 by the authors.Acknowledgments: This publication was made possible by an Award (GSRA6-2-0418-19015) from the Qatar National Research Fund (a member of the Qatar Foundation). The contents herein are solely the responsibility of the authors. This publication was also supported by Qatar University Internal Grant No. M-CTP-CENG-2020-1. The findings achieved herein are solely the responsibility of the authors.Scopu

    Refinement and automation using algorithmic control of BreathForce, a respiratory training system for patients with spinal cord Injuries.

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    Spinal cord injuries (SCI) can lead to impaired respiratory and cardiovascular function and a general decrease in lung compliance. This can complicate breathing as well as impair the ability to sigh, cough, and clear secretions, leading to increased risk of respiratory infections. Respiratory training has been shown to combat these effects. BreathForce is under active development to create a user-centric inspiratory-expiratory device that is an affordable option for at-home training. This study reports on the refinement of valve design and automation incorporated into BreathForce to enhance and enforce clinical practices and processes as part of the respiratory training protocol used with SCI patients. The system establishes resistance to flow using a custom designed (SolidWorks Flow Sim 2020) proportional valve driven by a 180-degree servo motor (Towerpro MG996R). Computational Fluid Dynamics (CFD) methods were used to evaluate the downstream to upstream pressure differential as each modified valve design was rotated from completely closed to completely open. Boundary conditions were set at the inlet and outlet of the device to imitate the peak volumetric flow rate of a healthy adult male weighing 70 kg (0.167 L/sec). The static pressure at the inlet and outlet of the device as well as the pressure differential were output parameters for each incremental position of the proportional valve. A microprocessor (Feather M0, Adafruit) was used to automate respiratory training. The original system calculated target expiratory and inspiratory training pressures but required the clinicians to manually set the valve position. An algorithm was developed to automatically set the target valve position for training based on a measurement of the maximum inhalation and exhalation pressures. The pressure drop generated by the user was measured during normal breathing as the servo motor incrementally moved the valve from open to closed. Once the generated pressure was within ninety percent of the target pressure (~ 15% of max capacity), the servo motor was stopped, and that valve position was stored. Healthy volunteers were used to validate system operation. Data was saved to an included SD card and real time clock (Adalogger FeatherWing, Adafruit) to record maximum and minimum pressures generated, as well as session training data at approximately 20 Hz. The simulation goal was to develop a valve geometry that maintained resistance to flow over the widest range of valve body rotation (0 to approximately 180 degrees). Seventeen design iterations were created and tested via CFD. The algorithm successfully located the optimal valve position for both expiration and inspiration training based on individual users maximum expiratory and inspiratory pressures measured on system startup. Additionally, a simple feedback algorithm was included to adjust the valve position in small increments during training based upon the percentage of target pressure the user was generating. Since pressure drop is related to volumetric flow, if a user generated an artificially high pressure (hyperventilation, coughing) during training, continuous adjustment of the valve position aided users in reaching appropriate target pressures. Flow simulations set the stage for continued refinement of the custom valve designs which are currently 3D printed. The inherent print resolution limitations of this manufacturing method are acceptable only for prototyping, and as the product moves towards manufacturability, the valve structure will be injection molded. Each training session begins with a measurement of maximum and minimum pressures, so the target training pressure the user experiences automatically increases as the user gains in their respiratory capacity. Building in automation proved successful in enforcing clinical protocols developed at the Frazier Rehabilitation Institute and refinements will continue as the system moves to the clinic for evaluation with patients under IRB approval

    Large space structures and systems in the space station era: A bibliography with indexes (supplement 04)

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    Bibliographies and abstracts are listed for 1211 reports, articles, and other documents introduced into the NASA scientific and technical information system between 1 Jul. and 30 Dec. 1991. Its purpose is to provide helpful information to the researcher, manager, and designer in technology development and mission design according to system, interactive analysis and design, structural concepts and control systems, electronics, advanced materials, assembly concepts, propulsion, and solar power satellite systems

    Medical Device Interoperability With Provable Safety Properties

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    Applications that can communicate with and control multiple medical devices have the potential to radically improve patient safety and the effectiveness of medical treatment. Medical device interoperability requires devices to have an open, standards-based interface that allows communication with any other device that implements the same interface. This will enable applications and functionality that can improve patient safety and outcomes. To build interoperable systems, we need to match up the capabilities of the medical devices with the needs of the application. An application that requires heart rate as an input and provides a control signal to an infusion pump requires a source of heart rate and a pump that will accept the control signal. We present means for devices to describe their capabilities and a methodology for automatically checking an application’s device requirements against the device capabilities. If such applications are going to be used for patient care, there needs to be convincing proof of their safety. The safety of a medical device is closely tied to its intended use and use environment. Medical device manufacturers create a hazard analysis of their device, where they explore the hazards associated with its intended use. We describe hazard analysis for interoperable devices and how to create system safety properties from these hazard analyses. The use environment of the application includes the application, connected devices, patient, and clinical workflow. The patient model is specific to each application and represents the patient’s response to treatment. We introduce Clinical Application Modeling Language (CAML), based on Extended Finite State Machines, and use model checking to test safety properties from the hazard analysis against the parallel composition of the application, patient model, clinical workflow, and the device models of connected devices
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