Systems and methods for use of insulin information for meal indication

Inventors

Budiman, Erwin Satrya • Doniger, Kenneth J. • Dunn, Timothy C. • Crouther, Nathan C. • Berman, Glenn • Wolpert, Howard A.

Assignees

Abbott Diabetes Care Inc

Abstract

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.

Core Innovation

The invention provides continuous glucose monitoring analytics that generate an “Insights” glucose report including an Ambulatory Glucose Profile plot with hourly percentiles, a Glucose Control Assessment using glucose median and glucose variability, and indicators for high variability. The framework incorporates likelihood of low glucose concepts such as low glucose allowance and uses AU70-based High Risk Curves and Control Grid zones to standardize hypoglycemia risk categorization under uncertainty, including a certainty bubble / uncertainty bubble.

The invention defines Treatment Recommendation Point and Margin To Treat to guide therapy adjustments while accounting for uncertainty in the analytics. It also describes a broader control-grid methodology using alternative metrics such as time-in and time-out-of-target and links to longer-term risk, including retinopathy risk via HbA1c and DKA risk via estimated β-hydroxybutyrate (β-OHB). In an expedited computation approach, the therapy management system uses approximated figure of merit for rapid processing.

In addition to the general CGM analytics framework, the invention extends to episode-detection algorithms that identify threshold-based and change-based Low/High/Rise/Fall episodes and forms episode chains mapped to diabetes self-care behavior. The system further includes meal-marker and glucose-pattern driven treatment/reminder logic, where meal identification can be explicit/implicit/event-based and glucose patterns trigger reminders, including a missed meal bolus. The disclosed architecture includes a processor, memory, a CGM sensor, and display/print/remote connectivity, along with rule-based mapping and lookup tables for recommendations.

Claims Coverage

The partial document evidences three independent claims that all cover a meal-related, pattern-based missed insulin bolus reminder workflow, grounded in CGM glucose data, meal occurrence determination, and insulin-information absence. Across the independent claims, the inventive core consists of detecting a meal-related rapid-rise glucose pattern and issuing a missed-bolus warning when no insulin information is received; dependent claims further refine meal identification, insulin-information sourcing, and warning/optional recommendation conditions.

The independent claim set consistently centers on a conditional, meal-temporal, pattern-based logic: identify/detect a glucose pattern temporally related to a determined meal, and issue a warning for a missed insulin bolus when the pattern indicates a rapid rise and insulin information is not received. The independent claims differ mainly in the claim type (method, system, and computing device) while retaining the same inventive decision logic foundation.

Stated Advantages

Documented Applications