Continuous Glucose Monitoring (CGM) Algorithms

The efficacy of Continuous Glucose Monitoring (CGM) relies heavily on sophisticated algorithms that process raw electrochemical signals into accurate glucose readings. The primary function of these algorithms is to overcome physiological lag (the 5–15 minute delay between blood and interstitial fluid glucose) using predictive mathematical models like Kalman Filters, which estimate current blood glucose based on past ISF trends and velocity.

A major technological leap has been the shift from Retrospective Calibration (requiring daily fingersticks to correct sensor drift) to Factory Calibration. By characterizing sensor batches during manufacturing and embedding drift-correction models directly into the firmware, devices like the FreeStyle Libre and Dexcom G6 achieved non-adjunctive status (FDA approval for insulin dosing without fingersticks). The industry standard for algorithmic accuracy is MARD (Mean Absolute Relative Difference), which has improved from >15% in early generations to <10% in modern systems, enabling the safe integration of CGMs into Automated Insulin Delivery (AID) loops.