Accuracy and robustness of dynamical tracking of average glycemia (A1c) to provide real-time estimation of hemoglobin A1c using routine self-monitored blood glucose data.

BACKGROUND

Laboratory hemoglobin A1c (HbA1c) assays are typically done only every few months. However, self-monitored blood glucose (SMBG) readings offer the possibility for real-time estimation of HbA1c. We present a new dynamical method tracking changes in average glycemia to provide real-time estimation of A1c (eA1c).

MATERIALS AND METHODS

A new two-step algorithm was constructed that includes: (1) tracking fasting glycemia to compute base eA1c updated with every fasting SMBG data point and (2) calibration of the base eA1c trace with monthly seven-point SMBG profiles to capture the principal components of blood glucose variability and produce eA1c. A training data set (n=379 subjects) was used to estimate model parameters. The model was then fixed and applied to an independent test data set (n=375 subjects). Accuracy was evaluated in the test data set by computing mean absolute deviation (MAD) and mean absolute relative deviation (MARD) of eA1c from reference HbA1c, as well as eA1c-HbA1c correlation.

RESULTS

MAD was 0.50, MARD was 6.7%, and correlation between eA1c and reference HbA1c was r=0.76. Using an HbA1c error grid plot, 77.5% of all eA1c fell within 10% from reference HbA1c, and 97.9% fell within 20% from reference.

CONCLUSIONS

A dynamical estimation model was developed that achieved accurate tracking of average glycemia over time. The model is capable of working with infrequent SMBG data typical for type 2 diabetes, thereby providing a new tool for HbA1c estimation at the patient level. The computational demands of the procedure are low; thus it is readily implementable into home SMBG meters. Real-time HbA1c estimation could increase patients' motivation to improve diabetes control.