Human-in-the-Loop Insulin Dosing

Fault Tolerant Strategies for Automated Insulin Delivery Considering the Human Component: Current and Future Perspectives

Automated Insulin Delivery (AID) are systems developed for daily use by people with type 1 diabetes (T1D). To ensure the safety of users, it is essential to consider how the human factor affects the performance and safety of these devices. While there are numerous publications on hardware-related failures of AID systems, there are few studies on the human component of the system. From a control point of view, people with T1D using AID systems are at the same time the plant to be controlled and the plant operator. Therefore, users may induce faults in the controller, sensors, actuators, and the plant itself. Strategies to cope with the human interaction in AID systems are needed for further development of the technology. In this paper, we present an analysis of potential faults introduced by AID users when the system is under normal operation. This is followed by a review of current fault tolerant control (FTC) approaches to identify missing areas of research. The paper concludes with a discussion on future directions for the new generation of FTC AID systems.

A Hybrid Automata Approach for Monitoring the Patient in the Loop in Artificial Pancreas Systems

The use of automated insulin delivery systems has become a reality for people with type 1 diabetes (T1D), with several hybrid systems already on the market. One of the particularities of this technology is that the patient is in the loop. People with T1D are the plant to control and also a plant operator, because they may have to provide information to the control loop. The most immediate information provided by patients that affects performance and safety are the announcement of meals and exercise. Therefore, to ensure safety and performance, the human factor impact needs to be addressed by designing fault monitoring strategies. In this paper, a monitoring system is developed to diagnose potential patient modes and faults. The monitoring system is based on the residual generation of a bank of observers. To that aim, a linear parameter varying (LPV) polytopic representation of the system is adopted and a bank of Kalman filters is designed using linear matrix inequalities (LMI). The system uncertainty is propagated using a zonotopic-set representation, which allows determining confidence bounds for each of the observer outputs and residuals. For the detection of modes, a hybrid automaton model is generated and diagnosis is performed by interpreting the events and transitions within the automaton. The developed system is tested in simulation, showing the potential benefits of using the proposed approach for artificial pancreas systems.

Efficacy, safety, and user experience of DIY or open-source artificial pancreas systems: a systematic review

The do-it-yourself artificial pancreas system (DIYAPS) is a patient-driven initiative with the potential to revolutionise diabetes management, automating insulin delivery with existing pumps and CGM combined with open-source algorithms. Given the considerable interest in this topic within the diabetes community, we have conducted a systematic review of DIYAPS efficacy, safety, and user experience. Following recognised procedures and reporting standards, we identified 10 eligible publications of 730 participants within the peer-reviewed literature. Overall, studies reported improvements in time in range, HbA1c (glycated haemoglobin), reduced hypoglycaemia, and improved quality of life with DIYAPS use. While results were positive, the identified studies were small, and the majority were observational and at high risk of bias. Further research including well-designed randomised trials comparing DIYAPS with appropriate comparators is recommended.

When a Ventilator Takes Autonomous Decisions without Seeking Approbation nor Warning Clinicians: A Case Series

Background

Complexity and functions of automated medical devices used to support life (eg, ventilators, dialysis machines, monitors, insulin pump with continuous blood glucose monitoring system, etc.) increase over time. Until recently, devices were partially automated by very simple feedback loops, with no or few software dependence (such as the simplest home thermostat). For the last two decades, devices have been increasingly driven by complex algorithms devoted to improve patient's treatment and monitoring as well as users experience.

Methods

We report the unexpected and inappropriate operation of two recent ventilators, associated to potential harmful consequences. We provide both a description of the clinical situations (five ICU patients, archetypal situations) and a test bench analysis.

Results

While set in volume mode, these ventilators activated an algorithm dedicated to limit airway pressure when an increase in airway resistance occurred. In such situations, a pressure-like mode was activated (with decelerating inspiratory flow and set pressure, with target of volume). The main consequences observed were that the tidal volume was no longer guaranteed or delivered and that the pressure limitation operated by the algorithm prevented the airway pressure from reaching the high-pressure alarm threshold.

Conclusion

This led to the silent takeover of commands by the ventilator without clinicians or nurses being aware of it and without any warnings or alarms emission adapted to the severity of the event. Generally speaking, such an algorithm questions the place of automation and its limit when users are not aware of its presence as well as the need for regulation and additional tests before its implementation. Intensivists and respiratory care specialists should remain vigilant regarding the risk of rare but critical events related to unexpected functioning or insufficiently tested equipment during the pre-clinical development phases. They should not neglect misunderstood critical events without having performed sufficient investigations.