Part of the mission of the Center for Devices and Radiological Health (CDRH) at the US Food and Drug Administration is to facilitate medical device innovation. Therefore, CDRH plays an important role in helping its stakeholders such as manufacturers, health care professionals, patients, patient advocates, academia, and other government agencies navigate the regulatory landscape for medical devices. This is particularly important for innovative physiological closed-loop controlled (PCLC) devices used in critical care environments, such as intensive care units, emergency settings, and battlefield environments. CDRH’s current working definition of a PCLC medical device is a medical device that incorporates physiological sensor(s) for automatic manipulation of a physiological variable through actuation of therapy that is conventionally made by a clinician. These emerging devices enable automatic therapy delivery and may have the potential to revolutionize the standard of care by ensuring adequate and timely therapy delivery with improved performance in high workload and high-stress environments. For emergency response and military applications, automatic PCLC devices may play an important role in reducing cognitive overload, minimizing human error, and enhancing medical care during surge scenarios (ie, events that exceed the capability of the normal medical infrastructure). CDRH held an open public workshop on October 13 and 14, 2015 with the aim of fostering an open discussion on design, implementation, and evaluation considerations associated with PCLC devices used in critical care environments. CDRH is currently developing regulatory recommendations and guidelines that will facilitate innovation for PCLC devices. This article highlights the contents of the white paper that was central to the workshop and focuses on the ensuing discussions regarding the engineering, clinical, and human factors considerations. (Anesth Analg 2018;126:1916–25)

Medical devices historically have been monolithic units -developed, validated, and approved by regulatory authorities as stand-alone entities. Modern medical devices increasingly incorporate connectivity mechanisms that offer the potential to stream device data into electronic health records, integrate information from multiple devices into single customizable displays, and coordinate the actions of groups of cooperating devices to realize "closed loop" scenarios and automate clinical workflows.In this paper, we describe a publish-subscribe architecture for medical device integration based on the Java Messaging Service. We overview of a model-based development environment that we have built for rapidly programming device coordination scenarios. We assess the extent to which this framework is capable of supporting and complementing the Integrated Clinical Environment that has been proposed by the Medical Device Plug and Play Interoperability Project The implementation of this framework is free available and open source. One of the primary goals of the framework is to provide researchers in acadaemia, industry, and government with an open test bed for exploring development, quality assurance, and regulatory issues related to medical device coordination.

Knowledge of the fetal oxygen saturation is not associated with a reduction in the rate of cesarean delivery or with improvement in the condition of the newborn. (ClinicalTrials.gov number, NCT00098709 [ClinicalTrials.gov].).

The concept of "system of systems" architecture is increasingly prevalent in many critical domains. Such systems allow information to be pulled from a variety of sources, analyzed to discover correlations and trends, stored to enable realtime and post-hoc assessment, mined to better inform decisionmaking, and leveraged to automate control of system units. In contrast, medical devices typically have been developed as monolithic stand-alone units. However, a vision is emerging of a notion of a medical application platform (MAP) that would provide device and health information systems (HIS) interoperability, safety critical network middleware, and an execution environment for clinical applications ("apps") that offer numerous advantages for safety and effectiveness in health care delivery.In this paper, we present the clinical safety/effectiveness and economic motivations for MAPs, and describe key characteristics of MAPs that are guiding the search for appropriate technology, regulatory, and ecosystem solutions. We give an overview of the Integrated Clinical Environment (ICE) -one particular achitecture for MAPs, and the Medical Device Coordination Framework -a prototype implementation of the ICE architecture. Abstract-The concept of "system of systems" architecture is increasingly prevalent in many critical domains. Such systems allow information to be pulled from a variety of sources, analyzed to discover correlations and trends, stored to enable realtime and post-hoc assessment, mined to better inform decisionmaking, and leveraged to automate control of system units. In contrast, medical devices typically have been developed as monolithic stand-alone units. However, a vision is emerging of a notion of a medical application platform (MAP) that would provide device and health information systems (HIS) interoperability, safety critical network middleware, and an execution environment for clinical applications ("apps") that offer numerous advantages for safety and effectiveness in health care delivery.In this paper, we present the clinical safety/effectiveness and economic motivations for MAPs, and describe key characteristics of MAPs that are guiding the search for appropriate technology, regulatory, and ecosystem solutions. We give an overview of the Integrated Clinical Environment (ICE) -one particular achitecture for MAPs, and the Medical Device Coordination Framework -a prototype implementation of the ICE architecture.

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.

Clinical studies have demonstrated that epidermal pigmentation level can affect cerebral oximetry measurements. To evaluate the robustness of these devices, we have developed a phantom-based test method that includes an epidermis-simulating layer with several melanin concentrations and a 3D-printed cerebrovascular module. Measurements were performed with neonatal, pediatric and adult sensors from two commercial oximeters, where neonatal probes had shorter source-detector separation distances. Referenced blood oxygenation levels ranged from 30 to 90%. Cerebral oximeter outputs exhibited a consistent decrease in saturation level with simulated melanin content; this effect was greatest at low saturation levels, producing a change of up to 15%. Dependence on pigmentation was strongest in a neonatal sensor, possibly due to its high reflectivity. Overall, our findings indicate that a modular channel-array phantom approach can provide a practical tool for assessing the impact of skin pigmentation on cerebral oximeter performance and that modifications to algorithms and/or instrumentation may be needed to mitigate pigmentation bias.

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Cerebral oximetry based on near-infrared spectroscopy represents a unique noninvasive tool for real-time surgical monitoring, yet studies have shown a significant discrepancy in accuracy among commercial systems. Towards the establishment of a standardized method for performance testing, we have studied a solid phantom approachbased on a 3D-printed cerebrovascular module (CVM) incorporating an array of 148 cylindrical channels-that has several advantages over liquid phantoms. Development and characterization of a CVM prototype are described, including high-resolution imaging and spectrophotometry measurements. The CVM was filled with whole bovine blood tuned over an oxygen saturation range of 30-90% and molded-silicone layers simulating extracerebral tissues were used to evaluate penetration depth. Saturation measurement accuracy was assessed in two commercially-available clinical cerebral oximeters. For one oximeter, both neonatal and pediatric sensors showed a high degree of precision, whereas accuracy was strongly dependent on saturation level and extracerebral geometry. The second oximeter showed worse precision, yet greater robustness to variations in extracerebral layers. These results indicate that 3D-printed channel array phantoms represent a promising new approach for standardized testing of clinical oximeters.