Characterizing insulin dosing behaviour and glycaemic excursions: Development of metrics using connected insulin pen and continuous glucose monitoring

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Abstract
Aims: Connected insulin pens (CIPs) provide insulin dosing data that can be leveraged to drive improvements in glycaemic control. To realize this potential in routine care, insulin data need to be distilled into actionable insights for clinicians. We describe two algorithms for detecting glucose excursions using continuous glucose monitoring (CGM) data and then link excursions to CIP data to derive “insulin metrics” characterizing suboptimal bolus dosing practices. Materials and Methods: This post hoc analysis used CGM and CIP data from a 12‐week observational study ( clinicaltrials.gov : NCT03368807) of 64 adults with type 1 or type 2 diabetes receiving multiple daily injection insulin therapy and glycated haemoglobin ≥8%. Two updated algorithms were applied to analyse glucose excursions associated with pre‐meal boluses, missed bolus doses (MBDs), delayed boluses and correction boluses. Results: Glycaemic metrics obtained using both algorithms were similar. Time in range (%TIR) was lower, and time above range (%TAR) and glycaemic variability (%GV) were higher, on days with MBDs. Compared with pre‐meal boluses, delayed and correction boluses were followed by glucose excursions with larger change in glucose, longer duration with glucose >180 mg/dL, lower post‐excursion %TIR and higher post‐excursion %TAR; excursions following MBDs showed lower %TIR, higher %TAR and lower percent recovery. Glucose excursions were larger and longer when CGM was masked than when unmasked. Conclusions: This analysis demonstrates “insulin metrics”—characterizations of insulin dosing behaviour derived from integrated CGM and CIP data—and provides a foundation for future work that will expand the understanding of an individual's insulin self‐administration practices and improve clinical decision‐making.
Funding Information
  • Eli Lilly and Company

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