Dynamic Insulin Sensitivity Factor Estimation
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Solution Overview
Problem
Current methods for estimating insulin sensitivity factors (ISF) and carb-to-insulin ratios (CIR) in diabetic patients are imprecise, leading to suboptimal insulin dosing and frequent episodes of hyper- and hypoglycemia, especially during periods of physiological change such as illness, stress, or increased activity, due to reliance on outdated parameters and infrequent updates.
Innovation Solution
A system and method that uses continuous glucose monitoring data and insulin pen data to estimate ISF and CIR by calculating basal and bolus insulin sensitivity factors based on recent glucose measurements, allowing for more frequent updates and robust estimation through the use of wearable devices to account for changes in physiological state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods use fixed ISF and CIR parameters calculated from limited data points, then the dosing calculation process is simple, but the precision of insulin dosing deteriorates during periods of physiological change
Solution Approach 1:
The system transitions from static ISF and CIR parameters to dynamic parameters that are continuously updated based on recent glucose measurements and physiological state changes. The estimation process adapts to changing conditions by incorporating time-varying data from glucose monitors and insulin pens, allowing parameters to evolve with the patient's physiological state rather than remaining fixed.
Solution Approach 2:
The system implements feedback loops where glucose measurement data and insulin administration data are continuously fed back into the estimation algorithm. This feedback mechanism allows the system to automatically adjust ISF and CIR parameters based on observed glucose responses to insulin, creating a closed-loop system that improves dosing precision over time without requiring manual intervention.
2Reliability
If ISF parameters are updated frequently to capture physiological changes, then dosing accuracy improves, but the amount of data processing and computational work increases
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing glucose measurement data and insulin administration data in advance. This pre-processing of data ensures that when parameter updates are needed, the computational work is already partially complete, reducing the real-time processing burden while maintaining the ability to frequently update parameters when physiological changes occur.
Solution Approach 2:
The system changes the parameters being estimated from fixed values to time-varying parameters that adapt to physiological state. By estimating ISF and CIR as dynamic parameters based on recent data windows rather than fixed historical values, the system achieves higher reliability in changing conditions without requiring processing of entire historical datasets, thus improving efficiency.
3Measurement precision
If extensive data collection is required to accurately estimate ISF, then dosing precision improves, but the time and convenience for patients and health care professionals deteriorates
Solution Approach 1:
The system implements self-service by automatically collecting glucose measurement data and insulin administration data without requiring manual patient input. The estimation algorithm autonomously processes this data to calculate ISF and CIR parameters, eliminating the burden of manual data collection from patients and reducing the time required for health care professionals to gather and process information.
Solution Approach 2:
The system uses multi-functional data collection that serves multiple purposes simultaneously. The same glucose and insulin data used for monitoring also serves as the basis for parameter estimation, eliminating the need for separate data collection processes and reducing overall time and effort required while maintaining precision.
4Loss of time
If parameter estimation relies on health care professional judgment from limited data points, then the process is quick and simple, but the accuracy of ISF determination deteriorates
Solution Approach 1:
The system replaces the mechanical process of manual professional judgment with an automated computational estimation algorithm. The algorithm processes glucose and insulin data using mathematical models to calculate ISF parameters, substituting human cognitive processes with systematic computational methods that can quickly process data while maintaining or improving accuracy through consistent application of estimation principles.
Data Source
AI summary
A subject is prescribed short and long acting insulin medicament regimens. When a qualified fasting event occurs, the basal insulin sensitivity estimate of the subject is updated using (i) an expected fasting blood glucose level based upon the long acting insulin medicament dosing specified by the long acting regimen during the fasting event, (ii) glucose measurements contemporaneous with the fasting event and (iii) a prior insulin sensitivity factor. A basal insulin sensitivity factor curve is calculated from the updated basal insulin sensitivity estimate. A bolus insulin sensitivity estimate of the subject is updated upon occurrence of a correction bolus with a short acting insulin medicament using (i) an expected blood glucose level based upon the correction bolus, (ii) glucose measurements after occurrence of the correction bolus, and (iii) a prior insulin sensitivity factor. A bolus insulin sensitivity factor curve is calculated from the updated bolus insulin sensitivity estimate.