Current methods for assessing glycemic control use averaged measures (such as HbA1C) and provide little information on the glycemic pathology of patients. Visual tools and mathematical formulas can allow improved characterization of glycemic behavior for achieving better glycemic control.
We developed new algorithms and visualizations that quantify and describe glycemic behavior, using device-agnostic data from previously published high frequency SMBG data. We plotted the glucose datasets as temporal glucose histograms, and calculated the glycemic burden (GB, weighted glucose over time) and glycemic severity index (GSI, a composite score of four glycemic variability measures).
Patients with different problems in their glycemic control had histograms with different shapes, GB and GSI. Using these tools, patients who had the same HbA1c level were shown to have significantly different glycemic pathologies. The temporal evolution of glycemic control could be analyzed using intervals as short as 2 weeks, including diurnal variations.
We propose a paradigm change from current diagnosis and treatment methods. We propose to classify patients and their severity not by their HbA1C or single glucose measurements, but rather according to their actual glycemic pathology as determined from the glucose histogram, GB and GSI. By analyzing these parameters, a treatment can be devised that is geared towards the normalization of the glycemic behavior, as exemplified by the glucose histogram. This approach provides insights into the glycemic derangements, enabling the clinician to design a personalized treatment in addition, or as a replacement, to the HbA1C and standard algorithms.