Comparative Simulation Study of Glucose Control Methods Designed for Use in the Intensive Care Unit Setting via a Novel Controller Scoring Metric

Abstract

Background: Effective glucose control in the intensive care unit (ICU) setting has the potential to decrease morbidity and mortality rates and thereby decrease health care expenditures. To evaluate what constitutes effective glucose control, typically several metrics are reported, including time in range, time in mild and severe hypoglycemia, coefficient of variation, and others. To date, there is no one metric that combines all of these individual metrics to give a number indicative of overall performance. We proposed a composite metric that combines 5 commonly reported metrics, and we used this composite metric to compare 6 glucose controllers.

Methods: We evaluated the following controllers: Ideal Medical Technologies (IMT) artificial-intelligence-based controller, Yale protocol, Glucommander, Wintergerst et al PID controller, GRIP, and NICE-SUGAR. We evaluated each controller across 80 simulated patients, 4 clinically relevant exogenous dextrose infusions, and one nonclinical infusion as a test of the controller's ability to handle difficult situations. This gave a total of 2400 5-day simulations, and 585 604 individual glucose values for analysis. We used a random walk sensor error model that gave a 10% MARD. For each controller, we calculated severe hypoglycemia (<40 mg/dL), mild hypoglycemia (40-69 mg/dL), normoglycemia (70-140 mg/dL), hyperglycemia (>140 mg/dL), and coefficient of variation (CV), as well as our novel controller metric.

Results: For the controllers tested, we achieved the following median values for our novel controller scoring metric: IMT: 88.1, YALE: 46.7, GLUC: 47.2, PID: 50, GRIP: 48.2, NICE: 46.4.

Conclusion: The novel scoring metric employed in this study shows promise as a means for evaluating new and existing ICU-based glucose controllers, and it could be used in the future to compare results of glucose control studies in critical care. The IMT AI-based glucose controller demonstrated the most consistent performance results based on this new metric.

Keywords: artificial intelligence; closed loop control; glucometrics; glucose; intensive care unit; knowledge-based system.

Conflict of interest statement

Declaration of Conflicting Interests: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: LD and JD are stock holders in Ideal Medical Technologies Inc.

Figures

Figure 1.

The point at (0, 1) is a fixed point in the fit we used. The rational coefficient cubic is obtained by taking the coefficients of the best fit and rounding them to the nearest hundredth, for ease of implementation. The rational coefficient cubic is the one we chose, seen as h(p) in Table 1.

Figure 2.

Class interfaces.

Figure 3.

Relative difference profile of a sensor with a large drift shows frequent barrier reflections (depicted in red), with all relative differences remaining inside the expected range.

Figure 4.

Over time, the random walk method allows the relative difference to wander within the allowed range.

Figure 5.

The memoryless uniform exhibits a much noisier relative difference profile.

Figure 6.

Native response with time-variant sensitivity (TVS) and without (CS). CS, constant sensitivity. Time 0 glucose = 200 mg/dL. Continuous dextrose infusion of 5 mg/kg/min.

Figure 7.

Native response with time-variant half-life (TVHL) and without (CHL). CHL, constant half-life. Time 0 glucose = 200 mg/dL. Continuous dextrose infusion of 5 mg/kg/min.

Figure 8.

Native response with time-variant volume of distribution (TVVD) and without (CVD). CVD, constant volume of distribution. Time 0 glucose = 200 mg/dL. Continuous dextrose infusion of 5 mg/kg/min.

Figure 9.

Overall GSM scores by controller. Results are median (25-75).

Figure 10.

Mild hypoglycemia (40-69 mg/dL) scores by controller. Results are median (25-75).

Figure 11.

Normoglycemia (70-140 mg/dL) scores by controller. Results are median (25-75).

Figure 12.

Hyperglycemia (>140 mg/dL) scores by controller. Results are median (25-75).

Figure 13.

Coefficient of variation scores by controller. Results are median (25-75).

Figure 14.

Individual glucose traces for one test scenario with starting glucose 200 mg/dL. All controllers controlled to a range of 100-140 mg/dL, except NICE, which controlled to a range of 81-108 mg/dL. CRMin, control range minimum = 100 mg/dL; CRMax, control range maximum = 140 mg/dL.