In-Silico Works Using an Improved Hovorka Equations Model and Clinical Works on the Control of Blood Glucose Levels in People With Type 1 Diabetes: Comparison Study

Background People with type 1 diabetes (T1D) depend on external insulin to regulate their blood glucose level (BGL) within the normoglycemic range between 4.0 to 7.0 mmol/L. Patients with T1D routinely conduct self-monitoring of blood glucose through finger pricks before insulin injections. An artificial pancreas is an innovative device that mimics the function of a healthy pancreas. Despite its recent advancement, the control algorithms used in an artificial pancreas are still lagging in delivering the proper insulin dosage to patients with T1D. Previous researchers attempted to improve the interrelation between parameters and variables in the original Hovorka equations model, later known as the improved Hovorka equations model; however, the improved equations model has not been tested in terms of its usability to regulate and control the BGL in a safe range for 2 or more people with T1D. Objective This study aimed to simulate the improved Hovorka equations model using actual patients’ data via MATLAB programming coupled with enhanced model-based predicted control and determine the optimum bolus insulin. The study then compares the performance results obtained from in-silico and clinical works. Methods Data from 3 patients were collected from clinic 1—Clinical Training Centre, Universiti Teknologi MARA Hospital, Sungai Buloh, Selangor—upon getting approval from the Universiti Teknologi MARA Ethics Committee. The inclusion criteria for participation were, namely having T1D, age 11-14 years, and highly dependent on insulin injection with four or more finger pricks or self-monitoring of blood glucose for BGL measurements per day. The patients with T1D typically received meals three times per day: breakfast, lunch, and dinner. A closed-loop algorithm (enhanced model-based predicted control) was used for the in-silico test, whereas an open-loop therapy was used for the clinical validation. As for the data analysis of patients, P values from a multiple linear regression were used to model the relationship between meal, insulin, and BGL. Results The optimum bolus insulins for patient 1 were 83.33, 33.33, and 16.67 mU/min; for patient 2 were 66.67, 50.01, and 33.33 mU/min; and for patient 3 were 100.02, 83.33, and 66.67 mU/min for breakfast, lunch, and dinner, respectively. As for the in-silico works, the percentages of time that the BGL was on target for patients 1, 2, and 3 were 79.59%, 87.76%, and 71.43%, respectively, as compared to the clinical works with <50%. A small P value (P<.001) indicated that the variables were significant. However, when compared to the BGL profile, both profiles were not comparable in simulating the BGL for patients with T1D. Conclusions The in-silico work was not comparable to the clinical work in simulating the BGL for patients with T1D due to the different methodologies used and the insufficient information that was reported to reproduce the calculation of the optimal bolus insulin.