Health technology assessment review: Computerized glucose regulation in the intensive care unit--how to create artificial control

Miriam Hoekstra, Mathijs Vogelzang, Evgeny Verbitskiy, Maarten W N Nijsten, Miriam Hoekstra, Mathijs Vogelzang, Evgeny Verbitskiy, Maarten W N Nijsten

Abstract

Current care guidelines recommend glucose control (GC) in critically ill patients. To achieve GC, many ICUs have implemented a (nurse-based) protocol on paper. However, such protocols are often complex, time-consuming, and can cause iatrogenic hypoglycemia. Computerized glucose regulation protocols may improve patient safety, efficiency, and nurse compliance. Such computerized clinical decision support systems (Cuss) use more complex logic to provide an insulin infusion rate based on previous blood glucose levels and other parameters. A computerized CDSS for glucose control has the potential to reduce overall workload, reduce the chance of human cognitive failure, and improve glucose control. Several computer-assisted glucose regulation programs have been published recently. In order of increasing complexity, the three main types of algorithms used are computerized flowcharts, Proportional-Integral-Derivative (PID), and Model Predictive Control (MPC). PID is essentially a closed-loop feedback system, whereas MPC models the behavior of glucose and insulin in ICU patients. Although the best approach has not yet been determined, it should be noted that PID controllers are generally thought to be more robust than MPC systems. The computerized Cuss that are most likely to emerge are those that are fully a part of the routine workflow, use patient-specific characteristics and apply variable sampling intervals.

Figures

Figure 1
Figure 1
Model Predictive Control (MPC) versus Proportional-Integrate-Derivative (PID) control. When using MPC control, the driver determines ('calculates') his driving strategy before departure after careful investigation of the road. When he uses the correct information (input variables), he stays on the road (yellow car), but small errors in input variables can lead the car in the wrong direction (red and blue cars). The drivers using PID control readjust their driving strategy often by frequently calculating the difference with the 'ideal' track.

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