Extensive assessment of blood glucose monitoring during postprandial period and its impact on closed-loop performance

[EN] Background: Closed-loop (CL) systems aims to outperform usual treatments in blood glucose control and continuous glucose monitors (CGM) are a key component in such systems. Meals represents one of the main disturbances in blood glucose control, and postprandial period (PP) is a challenging situation for both CL system and CGM accuracy. Methods: We performed an extensive analysis of sensor¿s performance by numerical accuracy and precision during PP, as well as its influence in blood glucose control under CL therapy. Results: During PP the mean absolute relative difference (MARD) for both sensors presented lower accuracy in the hypoglycemic range (19.4 ± 12.8%) than in other ranges (12.2 ± 8.6% in euglycemic range and 9.3 ± 9.3% in hyperglycemic range). The overall MARD was 12.1 ± 8.2%. We have also observed lower MARD for rates of change between 0 and 2 mg/dl. In CL therapy, the 10 trials with the best sensor spent less time in hypoglycemia (PG < 70 mg/dl) than the 10 trials with the worst sensors (2 ± 7 minutes vs 32 ± 38 minutes, respectively). Conclusions: In terms of accuracy, our results resemble to previously reported. Furthermore, our results showed that sensors with the lowest MARD spent less time in hypoglycemic range, indicating that the performance of CL algorithm to control PP was related to sensor accuracy.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project has been partially supported by the Spanish Government through Grants DPI 2013-46982-C2-1-R, DPI 2016-78831-C2-1-R, DPI 2013-46982-C2-2-R, and DPI 2016-78831-C2-2-R, the National Council of Technological and Scientific Development, CNPq Brazil through Grants 202050/2015-7 and 207688/2014-1.Biagi, L.; Hirata-Bertachi, A.; Conget, I.; Quirós, C.; Giménez, M.; Ampudia-Blasco, F.; Rossetti, P.... (2017). Extensive assessment of blood glucose monitoring during postprandial period and its impact on closed-loop performance. Journal of Diabetes Science and Technology. 11(6):1089-1095. https://doi.org/10.1177/1932296817714272S10891095116Doyle, F. J., Huyett, L. M., Lee, J. B., Zisser, H. C., & Dassau, E. (2014). Closed-Loop Artificial Pancreas Systems: Engineering the Algorithms. Diabetes Care, 37(5), 1191-1197. doi:10.2337/dc13-2108Cengiz, E., & Tamborlane, W. V. (2009). A Tale of Two Compartments: Interstitial Versus Blood Glucose Monitoring. Diabetes Technology & Therapeutics, 11(S1), S-11-S-16. doi:10.1089/dia.2009.0002Cobelli, C., Schiavon, M., Dalla Man, C., Basu, A., & Basu, R. (2016). Interstitial Fluid Glucose Is Not Just a Shifted-in-Time but a Distorted Mirror of Blood Glucose: Insight from an In Silico Study. Diabetes Technology & Therapeutics, 18(8), 505-511. doi:10.1089/dia.2016.0112Castle, J. R., & Ward, W. K. (2010). Amperometric Glucose Sensors: Sources of Error and Potential Benefit of Redundancy. Journal of Diabetes Science and Technology, 4(1), 221-225. doi:10.1177/193229681000400127Basu, A., Dube, S., Veettil, S., Slama, M., Kudva, Y. C., Peyser, T., … Basu, R. (2014). Time Lag of Glucose From Intravascular to Interstitial Compartment in Type 1 Diabetes. Journal of Diabetes Science and Technology, 9(1), 63-68. doi:10.1177/1932296814554797Keenan, D. B., Grosman, B., Clark, H. W., Roy, A., Weinzimer, S. A., Shah, R. V., & Mastrototaro, J. J. (2011). Continuous Glucose Monitoring Considerations for the Development of a Closed-Loop Artificial Pancreas System. Journal of Diabetes Science and Technology, 5(6), 1327-1336. doi:10.1177/193229681100500603Van Bon, A. C., Jonker, L. D., Koebrugge, R., Koops, R., Hoekstra, J. B. L., & DeVries, J. H. (2012). Feasibility of a Bihormonal Closed-Loop System to Control Postexercise and Postprandial Glucose Excursions. Journal of Diabetes Science and Technology, 6(5), 1114-1122. doi:10.1177/193229681200600516Rossetti, P., Quirós, C., Moscardó, V., Comas, A., Giménez, M., Ampudia-Blasco, F. J., … Vehí, J. (2017). Closed-Loop Control of Postprandial Glycemia Using an Insulin-on-Board Limitation Through Continuous Action on Glucose Target. Diabetes Technology & Therapeutics, 19(6), 355-362. doi:10.1089/dia.2016.0443Bailey, T., Zisser, H., & Chang, A. (2009). New Features and Performance of a Next-Generation SEVEN-Day Continuous Glucose Monitoring System with Short Lag Time. Diabetes Technology & Therapeutics, 11(12), 749-755. doi:10.1089/dia.2009.0075Zschornack, E., Schmid, C., Pleus, S., Link, M., Klötzer, H.-M., Obermaier, K., … Freckmann, G. (2013). Evaluation of the Performance of a Novel System for Continuous Glucose Monitoring. Journal of Diabetes Science and Technology, 7(4), 815-823. doi:10.1177/193229681300700403Pleus, S., Schmid, C., Link, M., Zschornack, E., Klötzer, H.-M., Haug, C., & Freckmann, G. (2013). Performance Evaluation of a Continuous Glucose Monitoring System under Conditions Similar to Daily Life. Journal of Diabetes Science and Technology, 7(4), 833-841. doi:10.1177/193229681300700405Zisser, H. C., Bailey, T. S., Schwartz, S., Ratner, R. E., & Wise, J. (2009). Accuracy of the SEVEN® Continuous Glucose Monitoring System: Comparison with Frequently Sampled Venous Glucose Measurements. Journal of Diabetes Science and Technology, 3(5), 1146-1154. doi:10.1177/193229680900300519Obermaier, K., Schmelzeisen-Redeker, G., Schoemaker, M., Klötzer, H.-M., Kirchsteiger, H., Eikmeier, H., & del Re, L. (2013). Performance Evaluations of Continuous Glucose Monitoring Systems: Precision Absolute Relative Deviation is Part of the Assessment. Journal of Diabetes Science and Technology, 7(4), 824-832. doi:10.1177/193229681300700404Clarke, W. L., Cox, D., Gonder-Frederick, L. A., Carter, W., & Pohl, S. L. (1987). Evaluating Clinical Accuracy of Systems for Self-Monitoring of Blood Glucose. Diabetes Care, 10(5), 622-628. doi:10.2337/diacare.10.5.622Martin Bland, J., & Altman, D. (1986). STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT. The Lancet, 327(8476), 307-310. doi:10.1016/s0140-6736(86)90837-8Breton, M., & Kovatchev, B. (2008). Analysis, Modeling, and Simulation of the Accuracy of Continuous Glucose Sensors. Journal of Diabetes Science and Technology, 2(5), 853-862. doi:10.1177/193229680800200517Kropff, J., Bruttomesso, D., Doll, W., Farret, A., Galasso, S., Luijf, Y. M., … DeVries, J. H. (2014). Accuracy of two continuous glucose monitoring systems: a head‐to‐head comparison under clinical research centre and daily life conditions. Diabetes, Obesity and Metabolism, 17(4), 343-349. doi:10.1111/dom.12378Reddy, M., Herrero, P., Sharkawy, M. E., Pesl, P., Jugnee, N., Pavitt, D., … Oliver, N. S. (2015). Metabolic Control With the Bio-inspired Artificial Pancreas in Adults With Type 1 Diabetes. Journal of Diabetes Science and Technology, 10(2), 405-413. doi:10.1177/1932296815616134Pleus, S., Schoemaker, M., Morgenstern, K., Schmelzeisen-Redeker, G., Haug, C., Link, M., … Freckmann, G. (2015). Rate-of-Change Dependence of the Performance of Two CGM Systems During Induced Glucose Swings. Journal of Diabetes Science and Technology, 9(4), 801-807. doi:10.1177/193229681557871