Glucose Risk Reporting for Hypoglycemia and Variability Patterns
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Solution Overview
Problem
Existing glucose monitoring systems struggle to effectively analyze large amounts of data to provide clinically meaningful insights for therapeutic decision-making, particularly in managing hypoglycemia and glucose variability, which is crucial for maintaining euglycemia.
Innovation Solution
A system and method that analyzes glucose data to determine central tendency and variability, presenting a glucose control assessment with visual indicators for likelihood of low glucose, median glucose, and variability, using a non-volatile memory and processor to generate a glucose report called the Insights report, which includes an Ambulatory Glucose Profile plot, Glucose Control Assessment, and indicators for high glucose variability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous glucose monitoring data is collected and analyzed, then glycemic control and hypoglycemia risk can be assessed, but the complexity of data presentation and clinical decision-making increases
Solution Approach 1:
The patent segments the comprehensive glucose data into distinct visual components: an Ambulatory Glucose Profile showing glucose trends over time, a Glucose Control Assessment providing summary statistics, and specific indicators for hypoglycemia risk. This segmentation allows complex data to be presented through multiple simplified visual elements that collectively provide complete information without overwhelming the user.
Solution Approach 2:
The patent introduces computerized algorithms as intermediaries that automatically process and interpret complex glucose data patterns. These algorithms analyze the CGM data, identify trends and anomalies, and generate the visual report elements, thereby mediating between the raw complex data and the clinician's decision-making process.
2Ease of operation
If detailed glucose data analysis is performed, then therapeutic decision-making can be guided, but the time required for data review increases
Solution Approach 1:
The patent extracts key information from the comprehensive CGM data and presents it in isolated, easily interpretable visual elements. The Glucose Control Assessment extracts summary statistics, the AGP extracts temporal patterns, and the hypoglycemia indicators extract risk information. This extraction allows clinicians to quickly grasp essential data points without reviewing every raw measurement.
Solution Approach 2:
The patent employs color-coded visual indicators to convey the severity and nature of glucose deviations. Different colors represent different glucose levels, trends, and risk categories, enabling rapid visual interpretation. For example, red indicators may signal hypoglycemic risk while green indicates safe ranges, allowing clinicians to immediately identify areas requiring attention.
3Productivity
If standardized glucose reports are implemented, then clinical action can be efficiently guided, but the flexibility for individual patient customization is reduced
Solution Approach 1:
The patent creates a dynamic reporting system where the standardized template can be adapted to individual patient needs. The visual elements can be configured to highlight different time periods, glucose ranges, or risk factors based on the patient's specific clinical situation. This allows the standardized structure to serve multiple patient-specific purposes without requiring completely custom reports for each patient.
Data Source
AI summary
A system and method provides a glucose report for determining glycemic risk based on an ambulatory glucose profile of glucose data over a time period, a glucose control assessment based on median and variability of glucose, and indicators of high glucose variability. Time of day periods are shown at which glucose levels can be seen. A median glucose goal and a low glucose line provide coupled with glucose variability provide a view into effects that raising or lowering the median goal would have. Likelihood of low glucose, median glucose compared to goal, and variability of glucose below median provide probabilities based on glucose data. Patterns can be seen and provide guidance for treatment.