Parvinian Bahram, Pathmanathan Pras, Daluwatte Chathuri, Yaghouby Farid, Gray Richard A, Weininger Sandy, Morrison Tina M, Scully Christopher G
Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, United States Food and Drug Administration, Silver Spring, MD, United States.
Front Physiol. 2019 Mar 26;10:220. doi: 10.3389/fphys.2019.00220. eCollection 2019.
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.
生理闭环控制的医疗设备会自动调整输送给患者的治疗，以调节测量到的生理变量。在重症监护场景中，这类设备可以实现例如液体复苏、药物输送、机械通气和/或麻醉与镇静的自动化。来自使用生理系统计算模型进行模拟的证据，在生理闭环控制设备的开发中可以发挥关键作用；但这一证据的效用将取决于所使用计算模型的可信度。生理系统的计算模型可能很复杂，存在众多非线性、时变特性和未知参数，这给模型评估带来了挑战。鉴于计算患者模型在生理闭环控制系统的设计和评估中有广泛的潜在用途，以及与不同用途相关的不同风险，具体的模型以及使模型在某个用例中可信所需的证据可能会有所不同。在本综述中，我们研究了计算患者模型在重症监护生理闭环控制系统（如血流动力学稳定性、机械通气、麻醉输送）的设计和评估中的各种用途，以及为支持该用途的模型而提供的证据类型（如验证、确认和不确定性量化活动）。然后，我们研究并讨论了医疗设备的可信度评估框架（美国机械工程师协会验证与确认小组委员会，医疗器械计算建模中的V&V 40验证与确认）如何应用于用于测试生理闭环控制系统的计算患者模型。
