Insulin Delivery Controller Meal Bolus Correction
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Solution Overview
Problem
Conventional closed loop insulin delivery systems often apply multiple negative corrections when glucose levels are low, increasing the likelihood of postprandial hyperglycemia due to their design of reducing basal insulin delivery and calculating meal boluses, which can lead to imperfect glucose control in diabetes management.
Innovation Solution
A system and method for calculating medication boluses in a closed loop system that includes a controller communicating with a medication delivery device, using control logic to calculate a meal bolus and a meal bolus correction based on glucose levels, insulin-on-board (IOB) levels, and insulin sensitivity, to adjust insulin delivery and prevent hyperglycemia by applying appropriate bolus corrections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional closed loop systems reduce basal insulin delivery and calculate meal boluses when glucose levels are low, then glucose control is improved, but multiple negative corrections are applied which increase the likelihood of postprandialhyperglycemia
Solution Approach 1:
The system applies preliminary anti-action by calculating meal bolus corrections that anticipate and counteract the potential for over-correction. When glucose levels are low, the system pre-calculates appropriate bolus adjustments based on insulin-on-board levels, preventing the sequence of multiple negative corrections that would otherwise lead to postprandialhyperglycemia. This proactive approach resolves the contradiction by acting before the harmful effect can occur.
Solution Approach 2:
The system employs feedback mechanisms by continuously monitoring glucose levels, insulin delivery history, and insulin-on-board calculations. The control algorithm uses this feedback to dynamically adjust meal bolus corrections, ensuring that corrections are applied appropriately without causing over-correction. This closed-loop feedback resolves the contradiction by adapting the system's response based on actual physiological conditions and previous insulin delivery patterns.
2Extent of automation
If multiple negative corrections are applied to low glucose levels, then glucose control algorithm operates as designed, but the likelihood of postprandialhyperglycemia increases
Solution Approach 1:
The system applies parameter changes by modifying the meal bolus calculation parameters based on insulin-on-board levels and glucose trends. Instead of applying fixed negative corrections, the system dynamically adjusts correction parameters considering the current insulin state, thereby maintaining automation while improving the reliability of glycemic control. This resolves the contradiction by making the automated system's parameters adaptive rather than static.
3Measurement precision
If conventional approaches use repeated finger sticks and vigilant control, then glucose measurements are obtained, but the approach is burdensome to the patient
Solution Approach 1:
The system applies self-service by implementing automatic meal bolus correction calculations that eliminate the need for manual finger sticks and vigilant patient control. The glucose sensor continuously provides measurements, and the control algorithm automatically calculates and delivers appropriate insulin corrections without requiring patient intervention. This resolves the contradiction by making the system self-sufficient, thereby maintaining measurement precision while dramatically reducing patient burden.
Data Source
AI summary
A system includes a controller that is in communication with a medication delivery device and that includes control logic. The control logic is operative to: calculate a meal bolus; calculate a meal bolus correction that is based, at least in part, on a glucose level and also whether the glucose level is above or below a threshold; and calculate a corrected meal bolus based, at least in part, on the meal bolus and the meal bolus correction.