Hypoglycemia Detection With Adaptive Carbohydrate Dosing
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Solution Overview
Problem
Existing methods for detecting and treating hypoglycemia in diabetics are inadequate as they fail to account for delays between blood plasma and tissue glucose concentration changes, do not consider recent carbohydrate consumption, and lack follow-up measurements to verify treatment efficacy.
Innovation Solution
A method using a computing device to detect hypoglycemia based on patient symptoms and blood glucose measurements, compute a recommended carbohydrate amount, and include surveillance phases to ensure effective treatment, adjusting amounts based on recent consumption and glucose dynamics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spot monitoring by measuring blood glucose concentration is used to detect hypoglycemia, then hypoglycemia can be detected, but the detection may be delayed due to the time lag between blood plasma glucose concentration changes and tissue glucose concentration changes
Solution Approach 1:
The system performs preliminary action by detecting hypoglycemia based on patient symptoms before the blood glucose measurement confirms it, and before the tissue glucose concentration actually drops. This allows the system to initiate treatment recommendations earlier, compensating for the inherent delay in glucose concentration measurements between blood plasma and tissue.
2Ease of operation
If a fixed carbohydrate amount is prescribed to treat hypoglycemia, then treatment can be provided quickly, but the treatment may be inappropriate for the patient's specific condition and recent carbohydrate consumption
Solution Approach 1:
The system applies dynamics by computing a personalized carbohydrate amount that adapts to the patient's specific condition. The computation considers the current blood glucose measurement, patient symptoms, and recent carbohydrate consumption, dynamically adjusting the recommended amount rather than using a fixed prescription. This ensures treatment appropriateness while maintaining relatively quick provision through automated computation.
Solution Approach 2:
The system implements feedback by considering the patient's recent carbohydrate consumption and current glucose levels to compute the appropriate treatment amount. This feedback loop ensures that the prescribed carbohydrate amount is tailored to the patient's specific metabolic state, preventing both under-treatment and over-treatment while maintaining treatment speed through automated calculation.
3Reliability
If follow-up assessments are performed to verify hypoglycemia treatment efficacy, then treatment effectiveness can be confirmed, but the assessment process becomes more complex and time-consuming
Solution Approach 1:
The system uses feedback by performing follow-up blood glucose measurements to verify that the prescribed carbohydrate amount effectively treated the hypoglycemia. This closed-loop approach confirms treatment efficacy by comparing pre- and post-treatment glucose levels, ensuring reliable verification while keeping the process manageable through systematic automated assessment.
Data Source
AI summary
A method is described herein to detect and treat hypoglycemia. An example of this method involves the detection of hypoglycemia in a patient with a computing device, computation of a recommended carbohydrate amount to ingest by the patient with the computing device in response to said detecting the hypoglycemia, output of the recommended carbohydrate amount with the computing device, and the performance of a hypoglycemia surveillance with the computing device to determine whether the recommended carbohydrate amount remedied the hypoglycemia.