Meal-Specific Residual Carb Modeling for Correction Bolus Accuracy
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Solution Overview
Problem
Existing insulin calculators often fail to accurately determine bolus doses due to variations in meal composition and individual factors, leading to undesirable glucose level fluctuations.
Innovation Solution
A physiological model fitted for a specific meal is used to predict residual carbohydrates and adjust correction bolus doses, considering factors like meal composition, insulin sensitivity, and patient condition, to maintain glucose levels within a desired range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional insulin calculators are used to determine bolus doses, then the calculation process is simple and quick, but the accuracy of glucose level control deteriorates due to variations in meal composition and individual factors
Solution Approach 1:
The system performs preliminary actions by fitting a physiological model to the patient's historical glucose data before the meal carbohydrate input is known. This pre-fitting establishes baseline parameters that are then used to predict residual carbohydrates and adjust the bolus dose, improving accuracy without requiring complex real-time calculations during the meal event
Solution Approach 2:
The system implements feedback by continuously monitoring actual glucose levels after meal bolus delivery and using this information to refine the physiological model parameters. This feedback loop enables the system to learn from past performance and improve future bolus calculations, resolving the contradiction between simple calculation and accurate control
2Stability of the object's composition
If correction boluses are delivered reactively based on current glucose levels, then the response is simple, but glucose level stability deteriorates due to overshooting and fluctuations
Solution Approach 1:
The system performs preliminary prediction of residual carbohydrates that will be absorbed from the meal before the full glucose impact occurs. By calculating the expected glucose rise and delivering a proactive correction bolus based on this prediction, the system prevents glucose spikes rather than reacting to them, thereby improving stability without excessive time loss
Solution Approach 2:
The system applies preliminary anti-action by delivering a correction bolus that counteracts the expected glucose rise from residual carbohydrates before the hyperglycemia fully develops. This anticipatory correction prevents the harmful glucose spike rather than responding to it after occurrence, improving both stability and response time
3Measurement precision
If meal-specific physiological modeling is implemented to predict residual carbohydrates, then bolus dose accuracy improves, but the complexity of data processing and model fitting increases
Solution Approach 1:
The system applies partial action by fitting the physiological model to only the most relevant portion of glucose data (e.g., postprandial glucose excursion after a meal bolus) rather than requiring complete daily data. This selective data fitting reduces computational complexity while maintaining sufficient accuracy for residual carbohydrate prediction
Solution Approach 2:
The system creates a disposable, meal-specific physiological model that is fitted temporarily for each meal event and then discarded, rather than maintaining a complex permanent model. This approach reduces the difficulty of model fitting by limiting the data scope and computational requirements to what is necessary for each individual meal prediction
Data Source
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AI summary
A processor-implemented method comprises obtaining measured glucose values of a person, fitting a physiological model to a portion of the measured glucose values within a time window after a start of a meal to determine meal-specific values of parameters of the physiological model that characterizes the person's glycemic response to the meal, estimating an amount of residual glucose or residual carbohydrate of the meal that would not be counteracted by insulin already delivered to the person using the physiological model and the meal-specific values of the parameters of the physiological model, and determining a dose of a correction bolus to be delivered to the person based on the amount of residual glucose or residual carbohydrate of the meal.