Blood glucose controller for neonatal intensive care: virtual trials development and first clinical trials

Abstract

Background: Premature neonates often experience hyperglycemia, which has been linked to worsened outcomes. Insulin therapy can assist in controlling blood glucose (BG) levels. However, a reliable, robust control protocol is required to avoid hypoglycemia and to ensure that clinically important nutrition goals are met.

Methods: This study presents an adaptive, model-based predictive controller designed to incorporate the unique metabolic state of the neonate. Controller performance was tested and refined in virtual trials on a 25-patient retrospective cohort. The effects of measurement frequency and BG sensor error were evaluated. A stochastic model of insulin sensitivity was used in control to provide a guaranteed maximum 4% risk of BG < 72 mg/dl to protect against hypoglycemia as well as account for patient variability over 1-3 h intervals when determining the intervention. The resulting controller is demonstrated in two 24 h clinical neonatal pilot trials at Christchurch Women's Hospital.

Results: Time in the 72-126 mg/dl BG band was increased by 103-161% compared to retrospective clinical control for virtual trials of the controller, with fewer hypoglycemic measurements. Controllers were robust to BG sensor errors. The model-based controller maintained glycemia to a tight target control range and accounted for interpatient variability in patient glycemic response despite using more insulin than the retrospective case, illustrating a further measure of controller robustness. Pilot clinical trials demonstrated initial safety and efficacy of the control method.

Conclusions: A controller was developed that made optimum use of the very limited available BG measurements in the neonatal intensive care unit and provided robustness against BG sensor error and longer BG measurement intervals. It used more insulin than typical sliding scale approaches or retrospective hospital control. The potential advantages of a model-based approach demonstrated in simulation were applied to initial clinical trials.

Figure 1.

Controller implementation schematic.

Figure 2.

Empirical CDFs of BG measurements for retrospective hospital control versus simulated trials of 1, 2, 3, and 4 h measurement and intervention frequency.

Figure 3.

Median and 5–95% interval of per-patient BG CDFs.

Figure 4.

Comparison of percentage of BG measurements within the 72–126 mg/dl BG range for retrospective and 2 h simulated control. Each circle represents one of the 25 patient profiles.

Figure 5.

Comparison of BG, insulin, nutrition, and SI profiles for patient 11 under retrospective control (dashed line) and simulated model-based control (solid line).

Figure 6.

Effect of simulated BG sensor error on BG control.

Figure 7.

Empirical CDFs of model-based controller results incorporating sensor error compared to retrospective hospital control. The main group of CDFs represent 2–20% sensor error for the model-based controller as summarized in Table 6.

Figure 8.

Blood glucose within target band compared to measurement frequency for constant and BG-concentration-based measurement frequency schemes and retrospective data.

Figure 9.

Neonatal intensive care unit clinical control trial 1. The top panel shows BG response with stochastic model forecasts (shaded area), the second panel shows insulin infusion rate as determined by the controller, and the bottom panel shows model-fitted insulin sensitivity. CI, confidence interval.

Figure 10.

Neonatal intensive care unit clinical control trial 2. The top panel shows BG response with stochastic model forecasts (shaded area), the second panel shows insulin infusion rate as determined by the controller, and the bottom panel shows model-fitted insulin sensitivity. CI, confidence interval.