Continuous glucose monitoring systems for type 1 diabetes mellitus.

AimsTo assess the accuracy and reliability of the two most widely used continuous glucose monitoring (CGM) systems.MethodsWe studied the Dexcom®G4 Platinum (DG4P; Dexcom, San Diego, CA, USA) and Medtronic Paradigm Veo Enlite system (ENL; Medtronic, Northridge, CA, USA) CGM systems, in 24 patients with type 1 diabetes. The CGM systems were tested during 6-day home use and a nested 6-h clinical research centre (CRC) visit. During the CRC visit, frequent venous blood glucose samples were used as reference while patients received a meal with an increased insulin bolus to induce an aggravated postprandial glucose nadir. At home, patients performed at least six reference capillary blood measurements per day. A Wilcoxon signed-rank test was performed using all data points ≥15 min apart.ResultsThe overall mean absolute relative difference (MARD) value [standard deviation (s.d.)] measured at the CRC was 13.6 (11.0)% for the DG4P and 16.6 (13.5)% for the ENL [p < 0.0002, confidence interval of difference (CI Δ) 1.7–4.3%, n = 530]. The overall MARD assessed at home was 12.2 (12.0)% for the DG4P and 19.9 (20.5)% for the ENL (p < 0.0001, CI Δ = 5.8–8.7%, n = 839). During the CRC visit, the MARD in the hypoglycaemic range [≤3.9 mmol/l (70 mg/dl)], was 17.6 (12.2)% for the DG4P and 24.6 (18.8)% for the ENL (p = 0.005, CI Δ 3.1–10.7%, n = 117). Both sensors showed higher MARD values during hypoglycaemia than during euglycaemia [3.9–10 mmol/l (70–180 mg/dl)]: for the DG4P 17.6 versus 13.0% and for the ENL 24.6 versus 14.2%.ConclusionsDuring circumstances of intended use, including both a CRC and home phase, the ENL was noticeably less accurate than the DG4P sensor. Both sensors showed lower accuracy in the hypoglycaemic range. The DG4P was less affected by this negative effect of hypoglycaemia on sensor accuracy than was the ENL.

OBJECTIVEReliability of continuous glucose monitoring (CGM) sensors is key in several applications. In this work we demonstrate that real-time algorithms can render CGM sensors smarter by reducing their uncertainty and inaccuracy and improving their ability to alert for hypo- and hyperglycemic events.RESEARCH DESIGN AND METHODSThe smart CGM (sCGM) sensor concept consists of a commercial CGM sensor whose output enters three software modules, able to work in real time, for denoising, enhancement, and prediction. These three software modules were recently presented in the CGM literature, and here we apply them to the Dexcom SEVEN Plus continuous glucose monitor. We assessed the performance of the sCGM on data collected in two trials, each containing 12 patients with type 1 diabetes.RESULTSThe denoising module improves the smoothness of the CGM time series by an average of ∼57%, the enhancement module reduces the mean absolute relative difference from 15.1 to 10.3%, increases by 12.6% the pairs of values falling in the A-zone of the Clarke error grid, and finally, the prediction module forecasts hypo- and hyperglycemic events an average of 14 min ahead of time.CONCLUSIONSWe have introduced and implemented the sCGM sensor concept. Analysis of data from 24 patients demonstrates that incorporation of suitable real-time signal processing algorithms for denoising, enhancement, and prediction can significantly improve the performance of CGM applications. This can be of great clinical impact for hypo- and hyperglycemic alert generation as well in artificial pancreas devices.

OBJECTIVETo assess the effect of three premeal timings of rapid-acting insulin on postprandial glucose excursions in type 1 diabetes.RESEARCH DESIGN AND METHODSTen subjects participated in a three-way randomized crossover trial. Mean ± SD age was 45.5 ± 12.1 years, A1C was 8.55 ± 1.50%, duration of diabetes was 23.8 ± 7.8 years, and duration of continuous subcutaneous insulin infusion therapy was 8.5 ± 6.1 years. Insulin aspart was administered at 30, 15, or 0 min before mealtime.RESULTSArea under the curve was lower in the −15 stratum (0.41 ± 0.51 mmol/l/min) than that in the −30 stratum (1.89 ± 0.72 mmol/l/min, P = 0.029) and 0 stratum (2.11 ± 0.66 mmol/l/min, P = 0.030). Maximum glucose excursion was lower in the −15 stratum (4.77 ± 0.52 mmol/l) than that in the −30 (6.48 ± 0.76 mmol/l, P = 0.025) and 0 stratum (6.93 ± 0.76 mmol/l, P = 0.022). Peak glucose level was lower in the −15 stratum (9.26 ± 0.72 mmol/l) than that in the −30 stratum (11.74 ± 0.80 mmol/l, P = 0.007) and the 0 stratum (12.29 ± 0.93, P = 0.009). Time spent in the 3.5–10 mmol/l range was higher in the −15 stratum (224.5 ± 25.0 min) than that in the 0 stratum (90.5 ± 23.2 min, P = 0.001). There was no significant difference in occurrence of glucose levels <3.5 mmol/l between strata (P = 0.901).CONCLUSIONSAdministration of rapid-acting insulin analogs 15 min before mealtime results in lower postprandial glucose excursions and more time spent in the 3.5–10.0 mmol/l range, without increased risk of hypoglycemia.