Closed-Loop Control of Anesthesia

Abstract

Feedback control is ubiquitous in nature and engineering and has revolutionized safety in fields from space travel to the automobile. In anesthesia, automated feedback control holds the promise of limiting the effects on performance of individual patient variability, optimizing the workload of the anesthesiologist, increasing the time spent in a more desirable clinical state, and ultimately improving the safety and quality of anesthesia care. The benefits of control systems will not be realized without widespread support from the health care team in close collaboration with industrial partners. In this review, we provide an introduction to the established field of control systems research for the everyday anesthesiologist. We introduce important concepts such as feedback and modeling specific to control problems and provide insight into design requirements for guaranteeing the safety and performance of feedback control systems. We focus our discussion on the optimization of anesthetic drug administration.

Regulatory Considerations for Physiological Closed-Loop Controlled Medical Devices Used for Automated Critical Care: Food and Drug Administration Workshop Discussion Topics

“…Another key advantage of PCLC devices is that, unlike manual therapy delivery, where the care provider may be prone to environmental distractions, PCLC devices are distraction-free because their sole function is to provide automated therapy to the patient. 12–14 This may reduce the incidence of human error by alleviating the workload of the caregiver and allowing him/her to focus on fewer more complex tasks in high-stress and workload environments.…”

“…These sources of risk can originate from (1) engineering aspects, such as lack of algorithm robustness 10,12,17,18 and necessary fail-safe mechanisms for hazardous scenarios previously corrected by clinicians 10,12 ; (2) from clinical aspects, such as sensor validity and reliability; and (3) from usability aspects, such as, complacency, loss of situational awareness (LSA), and skill degradation induced by automation in related applications. 4,19,20 Evaluation of the controller and algorithm interface design are central to assessing the risks associated with PCLC devices because PCLC devices can have additional complexities in their design and use as compared to manual care of similar therapies.…”

“…PCLC devices, in addition to proportional, integral, and derivative control, may include more advanced control algorithms including, but not limited to, adaptive, model-predictive, fuzzy control, and hybrid control system design are also used. 8,10,12 Identification of potential advantages and disadvantages of each algorithm type for its intended use remains a challenge. For example, the ability of the controller to address endogenous and exogenous disturbances and uncertainties arising from patient variabilities differs from 1 controller to another for a particular clinical application.…”

“…Combination of system complexity and lack of operational transparency may provide challenges for safety delivery of automated therapies. Absence of standardized terminology and lack of understanding of the medical device’s inner working and decision-making capabilities 10,12,36 may hamper the user’s ability to handle device failures promptly and effectively, potentially leading to patient harm.…”

Hybrid Approach for Automatic Drug Dispensing and Control

“…Although underdosing and over-dosing can lead to detrimental health consequences for the patient. The performance of open-loop system is highly dependent on the accuracy of the model it is based upon [6]. Van Halteren et al developed MobiHealth with the help of Body Area Network (BAN) to transmit patient data remotely MobiHealth does not support automated monitoring and patient feedback [14].…”