In Silico Validation of Personalized Safe Intervals for Carbohydrate Counting Errors

Area of Science:

• Endocrinology
• Metabolic Diseases
• Biomedical Engineering

Background:

• Accurate carbohydrate counting (CC) is vital for blood glucose regulation in Type 1 diabetes mellitus (T1DM).
• Common CC errors can lead to incorrect prandial insulin dosing, impacting glycemic control.
• Individual variability in CC error tolerance necessitates personalized management strategies.

Purpose of the Study:

• To demonstrate that personalized limits for CC errors can be computed using patient-specific data.
• To validate a method for calculating these personalized CC error limits.
• To investigate the impact of CC errors, within and outside personalized limits, on postprandial blood glucose levels.

Main Methods:

• Utilized the T1DM Simulator to create virtual subjects for a clinical trial.
• Simulated 450 meals over 90 days with a consistent daily meal plan.
• Introduced varying intervals of CC errors (near, below, and above personalized limits) to assess their effects on glycemic control.

Main Results:

• CC errors within personalized limits resulted in acceptable postprandial glycemic fluctuations.
• Significant degradation of time in range was observed when CC errors exceeded personalized limits in 50% and 97.5% of meals.
• The proposed method effectively identified safe intervals for CC errors.

Conclusions:

• Personalized limits for CC errors are a valuable tool for assessing patient capabilities in carbohydrate counting.
• This method can guide tailored patient education to improve T1DM management.
• Implementing personalized CC error limits can enhance blood glucose regulation and overall diabetes care.