Graphical and Numerical Evaluation of Continuous Glucose Sensing Time Lag

Abstract

Aim: This study introduces a new method for graphical and numerical evaluation of time lags typically associated with subcutaneous glucose sensing, based on Poincaré-type plot and a maximum statistical agreement criterion. Methods:The proposed method is illustrated by retrospective analysis of 56 continuous glucose monitor (CGM) time series collected by the FreeStyle Navigator™ (Abbott Diabetes Care, Alameda, CA) from 28 patients with type 1 diabetes mellitus, each wearing simultaneously two sensors (on arm and abdomen) and parallel reference blood glucose (BG) collected with a reference YSI (Yellow Springs, OH) analyzer every 15 min. The average duration of a time series was 111 h; there were approximately 10,000 sensor-reference data pairs. Results: When sliding in time CGM readings versus BG, the point of minimal spread of a Poincaré-type plot marks visually the time of CGM delay. The same point is numerically estimated by minimizing the distance between BG and CGM readings. The average observed time lag between reference BG and CGM was 12.5 min. Stratified by BG rate of change, the time lag was longer (16.8 min) when BG was falling, compared to steady or rising BG (11.7 min and 9.9 min, respectively) (P Ͻ 0.005). The time lags at the two sensor locations were not significantly different: 12.4 min on the arm, 12.6 min on the abdomen. Conclusions: In this data set, substantial blood-to-sensor time delays were observed, possibly because of both blood-to-interstitial glucose transport and instrumental delay. Analysis of BG-CGM co-dynamics that is free from mathematical approximation of glucose fluctuations resulted in convenient visualization and numerical estimation of these delays. 139

Evaluating the Clinical Accuracy of GlucoMen®Day: A Novel Microdialysis-Based Continuous Glucose Monitor

“…2 Delays ranging from 0 to 30 minutes were applied. For each applied delay, the "agreement criterion" (i.e., the Pearson correlation coefficient) between the resulting CGM/reference data pairs was then calculated.…”

“…2,21,29 Point and trend accuracy of GlucoMen ® Day were thus calculated taking into account such time lag (i.e., for each patient, the full set of reference values was shifted 11 minutes forward in time with respect to the corresponding CGM signal).…”

“…1 Continuous glucose monitoring is actually recognized as the only technique with the ability to provide complete diurnal/nocturnal glucose patterns and supply trend information. 1,2 Several clinical studies have indicated that utilization of CGM data, both retrospectively and in real time, can help adjust insulin therapies, prevent (or minimize the occurrence of) extreme glycemic excursions, 3,4 reduce glycated hemoglobin values, 5,6,7 and achieve a tight glycemic control in both intensive care 8,9 and surgical units. 10 Despite these clear advantages, utilization of CGM techniques is still limited in the current clinical practice, mainly because of concerns about their accuracy, reliability, robustness, and patient tolerability.…”

Peritoneal dialysis assists residual renal function to maintain glucose tolerance: a prospective observational study

“…The recently developed continuous glucose monitoring system (CGMS) allows precise assessment of daily blood glucose fluctuations, hypoglycemia, and hyperglycemia [15][16][17][18][19][20]. The present study was performed to determine the blood glucose profiles of patients with ESRD before and after the initiation of peritoneal dialysis (PD) in order to evaluate the effects of this dialysis modality.…”

Minimizing the Impact of Time Lag Variability on Accuracy Evaluation of Continuous Glucose Monitoring Systems

“…27 This method entails the progressive sliding in time of the CGM data versus the corresponding BG reference values. The delay that provides the maximum statistical agreement between CGM and BG data is considered as the average time lag.…”