Graphical and numerical evaluation of continuous glucose sensing time lag.

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 < 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.

[1]  G M Steil,et al.  Can interstitial glucose assessment replace blood glucose measurements? , 2000, Diabetes technology & therapeutics.

[2]  G. Steil,et al.  Subcutaneous glucose predicts plasma glucose independent of insulin: implications for continuous monitoring. , 1999, The American journal of physiology.

[3]  William L Clarke,et al.  Modeling of Calibration Effectiveness and Blood-to-Interstitial Glucose Dynamics as Potential Confounders of the Accuracy of Continuous Glucose Sensors during Hyperinsulinemic Clamp , 2007, Journal of diabetes science and technology.

[4]  Jessica R Castle,et al.  Continuous glucose monitoring in subjects with type 1 diabetes: improvement in accuracy by correcting for background current. , 2010, Diabetes technology & therapeutics.

[5]  R. A. Thuraisingham,et al.  Enhancing Poincare plot information via sampling rates , 2007, Appl. Math. Comput..

[6]  Joost B L Hoekstra,et al.  Relationship between interstitial and blood glucose in type 1 diabetes patients: delay and the push-pull phenomenon revisited. , 2007, Diabetes technology & therapeutics.

[7]  G. S. Wilson,et al.  Interstitial glucose concentration and glycemia: implications for continuous subcutaneous glucose monitoring. , 2000, American journal of physiology. Endocrinology and metabolism.

[8]  Larry S. Liebovitch,et al.  Two lessons from fractals and chaos , 2000, Complex..

[9]  Marimuthu Palaniswami,et al.  Do existing measures of Poincare plot geometry reflect nonlinear features of heart rate variability? , 2001, IEEE Transactions on Biomedical Engineering.

[10]  A J Schoonen,et al.  Determination of Time Delay between Blood and Interstitial Adipose Tissue Glucose Concentration Change by Microdialysis in Healthy Volunteers , 2001, The International journal of artificial organs.

[11]  Ronald Brazg,et al.  A continuous glucose sensor based on wired enzyme technology -- results from a 3-day trial in patients with type 1 diabetes. , 2003, Diabetes technology & therapeutics.

[12]  G. Diamond,et al.  Fascinating rhythm: a primer on chaos theory and its application to cardiology. , 1990, American heart journal.

[13]  F. Hariri,et al.  Interstitial fluid glucose dynamics during insulin-induced hypoglycaemia , 2005, Diabetologia.

[14]  Timothy L. Routh,et al.  Function of an Implanted Tissue Glucose Sensor for More than 1 Year in Animals , 2010, Science Translational Medicine.

[15]  William L Clarke,et al.  Quantifying temporal glucose variability in diabetes via continuous glucose monitoring: mathematical methods and clinical application. , 2005, Diabetes technology & therapeutics.

[16]  C. Saudek,et al.  Timing of changes in interstitial and venous blood glucose measured with a continuous subcutaneous glucose sensor. , 2003, Diabetes.

[17]  Peter Adolfsson,et al.  Accuracy and reliability of continuous glucose monitoring in individuals with type 1 diabetes during recreational diving. , 2009, Diabetes technology & therapeutics.

[18]  D. Cox,et al.  Symmetrization of the Blood Glucose Measurement Scale and Its Applications , 1997, Diabetes Care.

[19]  J. Brauker,et al.  Analysis of time lags and other sources of error of the DexCom SEVEN continuous glucose monitor. , 2009, Diabetes technology & therapeutics.

[20]  Boris Kovatchev,et al.  Peculiarities of the Continuous Glucose Monitoring Data Stream and Their Impact on Developing Closed-Loop Control Technology , 2008, Journal of diabetes science and technology.

[21]  Philip J Stout,et al.  A novel approach to mitigating the physiological lag between blood and interstitial fluid glucose measurements. , 2004, Diabetes technology & therapeutics.

[22]  Marimuthu Palaniswami,et al.  Poincaré plot interpretation using a physiological model of HRV based on a network of oscillators. , 2002, American journal of physiology. Heart and circulatory physiology.

[23]  F. Takens.  Detecting strange attractors in turbulence , 1981 .

[24]  Michal Javorka,et al.  Heart rate variability in young patients with diabetes mellitus and healthy subjects explored by Poincaré and sequence plots , 2005, Clinical physiology and functional imaging.

[25]  A. Tonkin,et al.  Poincaré plot of heart rate variability allows quantitative display of parasympathetic nervous activity in humans. , 1996, Clinical science.

[26]  Josep Vehí,et al.  Estimating Plasma Glucose from Interstitial Glucose: The Issue of Calibration Algorithms in Commercial Continuous Glucose Monitoring Devices , 2010, Sensors.

[27]  Scott M. Pappada,et al.  Neural network-based real-time prediction of glucose in patients with insulin-dependent diabetes. , 2011, Diabetes technology & therapeutics.

[28]  R. Potts,et al.  Physiological differences between interstitial glucose and blood glucose measured in human subjects. , 2003, Diabetes care.