Continuous glucose monitoring systems for type 1 diabetes mellitus.

BACKGROUND: Self-monitoring of blood glucose is essential to optimise glycaemic control in type 1 diabetes mellitus. Continuous glucose monitoring (CGM) systems measure interstitial fluid glucose levels to provide semi-continuous information about glucose levels, which identifies fluctuations that would not have been identified with conventional self-monitoring. Two types of CGM systems can be defined: retrospective systems and real-time systems. Real-time systems continuously provide the actual glucose concentration on a display. Currently, the use of CGM is not common practice and its reimbursement status is a point of debate in many countries. OBJECTIVES: To assess the effects of CGM systems compared to conventional self-monitoring of blood glucose (SMBG) in patients with diabetes mellitus type 1. SEARCH METHODS: We searched The Cochrane Library, MEDLINE, EMBASE and CINAHL for the identification of studies. Last search date was June 8, 2011. SELECTION CRITERIA: Randomised controlled trials (RCTs) comparing retrospective or real-time CGM with conventional self-monitoring of blood glucose levels or with another type of CGM system in patients with type 1 diabetes mellitus. Primary outcomes were glycaemic control, e.g. level of glycosylated haemoglobin A1c (HbA1c) and health-related quality of life. Secondary outcomes were adverse events and complications, CGM derived glycaemic control, death and costs. DATA COLLECTION AND ANALYSIS: Two authors independently selected the studies, assessed the risk of bias and performed data-extraction. Although there was clinical and methodological heterogeneity between studies an exploratory meta-analysis was performed on those outcomes the authors felt could be pooled without losing clinical merit. MAIN RESULTS: The search identified 1366 references. Twenty-two RCTs meeting the inclusion criteria of this review were identified. The results of the meta-analyses (across all age groups) indicate benefit of CGM for patients starting on CGM sensor augmented insulin pump therapy compared to patients using multiple daily injections of insulin (MDI) and standard monitoring blood glucose (SMBG). After six months there was a significant larger decline in HbA1c level for real-time CGM users starting insulin pump therapy compared to patients using MDI and SMBG (mean difference (MD) in change in HbA1c level -0.7%, 95% confidence interval (CI) -0.8% to -0.5%, 2 RCTs, 562 patients, I(2)=84%). The risk of hypoglycaemia was increased for CGM users, but CIs were wide and included unity (4/43 versus 1/35; RR 3.26, 95% CI 0.38 to 27.82 and 21/247 versus 17/248; RR 1.24, 95% CI 0.67 to 2.29). One study reported the occurrence of ketoacidosis from baseline to six months; there was however only one event. Both RCTs were in patients with poorly controlled diabetes.For patients starting with CGM only, the average decline in HbA1c level six months after baseline was also statistically significantly larger for CGM users compared to SMBG users, but much smaller than for patients starting using an insulin pump and CGM at the same time (MD change in HbA1c level -0.2%, 95% CI -0.4% to -0.1%, 6 RCTs, 963 patients, I(2)=55%). On average, there was no significant difference in risk of severe hypoglycaemia or ketoacidosis between CGM and SMBG users. The confidence interval however, was wide and included a decreased as well as an increased risk for CGM users compared to the control group (severe hypoglycaemia: 36/411 versus 33/407; RR 1.02, 95% CI 0.65 to 1.62, 4 RCTs, I(2)=0% and ketoacidosis: 8/411 versus 8/407; RR 0.94, 95% CI 0.36 to 2.40, 4 RCTs, I(2)=0%).Health-related quality of life was reported in five of the 22 studies. In none of these studies a significant difference between CGM and SMBG was found. Diabetes complications, death and costs were not measured.There were no studies in pregnant women with diabetes type 1 and in patients with hypoglycaemia unawareness. AUTHORS' CONCLUSIONS: There is limited evidence for the effectiveness of real-time continuous glucose monitoring (CGM) use in children, adults and patients with poorly controlled diabetes. The largest improvements in glycaemic control were seen for sensor-augmented insulin pump therapy in patients with poorly controlled diabetes who had not used an insulin pump before. The risk of severe hypoglycaemia or ketoacidosis was not significantly increased for CGM users, but as these events occurred infrequent these results have to be interpreted cautiously.There are indications that higher compliance of wearing the CGM device improves glycosylated haemoglobin A1c level (HbA1c) to a larger extent. 

Poor agreement of computerized calculators for mean amplitude of glycemic excursions.

BACKGROUND: Glucose variability has been identified as a predictor of hypoglycemia and has been associated with mortality in critically ill patients without diabetes. A popular metric to quantify glucose variability is the mean amplitude of glycemic excursions (MAGE). The "ruler and pencil" approach to calculate MAGE is operator-dependent and time-consuming for analysis of continuous glucose monitoring data. Therefore, several computer software programs have been developed for the automated calculation of MAGE. The aim of our study was to evaluate the agreement of currently available MAGE calculators when applied to the same set of continuous glucose monitoring (CGM) traces. MATERIALS AND METHODS: Four software programs for calculation of MAGE were identified and used to calculate MAGE of 21 CGM traces from seven patients with type 1 diabetes. Subsequently, the median MAGE per calculator was calculated. The correlation between the MAGE calculators was evaluated by Spearman's correlation analysis. Between-group comparison was performed using analysis of variance. RESULTS: The median MAGE (interquartile range) per calculator was 8.7 (7.1-10.7), 6.7 (5.5-8.6), 6.7 (5.2-8.6), and 5.8 (4.3-7.1), which was statistically different overall (P<0.001). The correlation coefficients between the calculators ranged from 0.787 to 0.999. CONCLUSIONS: Available computer programs developed to calculate MAGE show varying agreement. Although software programs for the calculation of MAGE would seem attractive to assess glucose variability, their use has limitations by different outcomes, in the absence of a gold standard.

Feasibility of a portable bihormonal closed-loop system to control glucose excursions at home under free-living conditions for 48 hours.

BACKGROUND: This study assessed the feasibility of a portable bihormonal closed-loop system at home. SUBJECTS AND METHODS: Sixteen pump-treated patients with type 1 diabetes received 48 h of closed-loop therapy with a telemonitored insulin- and glucagon-delivering closed-loop system and 48 h of patient-managed open-loop therapy. RESULTS: Owing to technical problems in five cases, only 11 patients could be analyzed. Whereas median (interquartile range) glucose levels were not significantly different during Day 1 of open-loop control (OL1) from closed-loop control (CL1) (8.27 [0.83] mmol/L vs. 8.84 [1.47] mmol/L; P=0.206), they were significantly lower during Day 2 of closed-loop control (CL2) versus open-loop control (OL2) (7.70 [2.29] mmol/L vs. 8.84 [0.87] mmol/L; P=0.027). Time spent in euglycemia (3.9-10 mmol/L) was comparable with 67.2% (38.5%) in OL1 versus 79.2% (16.9%) in CL1 (P=0.189) and 66.0% (29.8%) in OL2 versus 76.5% (23.9%) in CL2 (P=0.162). Time spent in hypoglycemia (<3.9 mmol/L) was comparable on Day 1 of control (OL1, 0.68% [8.68%]; CL1, 2.08% [7.61%]; P=0.593) but significantly higher during Day 2 of control (OL2, 0.00% [11.07%]; CL2, 2.8% [9.8%]; P=0.0172) (P=0.017). CONCLUSIONS: Bihormonal closed-loop control is feasible at home, with comparable time in euglycemia to open-loop control and significantly lower median glucose levels on Day 2 of control at the expense of more time in hypoglycemia, albeit still at a very low percentage of time.

Real-time improvement of continuous glucose monitoring accuracy: the smart sensor concept.

OBJECTIVE: Reliability 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 METHODS: The 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. RESULTS: The 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. CONCLUSIONS: We 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.

Accuracy and reliability of continuous glucose monitoring systems: a head-to-head comparison.

OBJECTIVE: This study assessed the accuracy and reliability of three continuous glucose monitoring (CGM) systems. RESEARCH DESIGN AND METHODS: We studied the Animas® (West Chester, PA) Vibe™ with Dexcom® (San Diego, CA) G4™ version A sensor (G4A), the Abbott Diabetes Care (Alameda, CA) Freestyle® Navigator I (NAV), and the Medtronic (Northridge, CA) Paradigm® with Enlite™ sensor (ENL) in 20 patients with type 1 diabetes mellitus. All systems were investigated both in a clinical research center (CRC) and at home. In the CRC, patients received a meal with a delayed and increased insulin dose to induce a postprandial glucose peak and nadir. Hereafter, randomization determined which two of the three systems would be worn at home until the end of functioning, attempting use beyond manufacturer-specified lifetime. Patients performed at least five reference finger sticks per day. An analysis of variance was performed on all data points ≥15 min apart. RESULTS: Overall average mean absolute relative difference (MARD) (SD) measured at the CRC was 16.5% (14.3%) for NAV and 16.4% (15.6%) for ENL, outperforming G4A at 20.5% (18.2%) (P<0.001). Overall MARD when assessed at home was 14.5% (16.7%) for NAV and 16.5 (18.8%) for G4A, outperforming ENL at 18.9% (23.6%) (P=0.006). Median time until end of functioning was similar: 10.0 (1.0) days for G4A, 8.0 (3.5) days for NAV, and 8.0 (1.5) days for ENL (P=0.119). CONCLUSIONS: In the CRC, G4A was less accurate than NAV and ENL sensors, which seemed comparable. However, at home, ENL was less accurate than NAV and G4A. Moreover, CGM systems often show sufficient accuracy to be used beyond manufacturer-specified lifetime.

Patch pump versus conventional pump: postprandial glycemic excursions and the influence of wear time.

BACKGROUND AND AIMS: The aim of this study was to compare blood glucose and plasma insulin profiles after bolus insulin infusion by a patch pump (PP) versus a conventional pump (CP), directly after placement and after Day 3 of use. PATIENTS AND METHODS: Twenty patients with type 1 diabetes came in for two blocks of visits: one block of two visits while wearing the OmniPod® (Insulet Corp., Bedford, MA) insulin pump (PP) and one block of two visits while wearing the Medtronic Diabetes (Northridge, CA) Paradigm® pump (CP). Patients administered an identical mealtime insulin bolus of at least 6 IU. RESULTS: For PP, maximum glucose levels were 28.7% lower on Day 3 (P=0.020), when maximum insulin levels were 30.3% higher (P=0.002). For CP, maximum glucose levels were 26.5% lower on Day 3 (P=0.015), when maximum insulin levels were 46.4% higher (P=0.003). Glucose levels (mean [interquartile range]) were significantly lower on Day 3 for PP (168.2 [145.8] mg/dL vs. 139.4 [77.8] mg/dL; P=0.013), but not significantly so for CP (159.0 [66.1] mg/dL vs. 139.5 [57.9] mg/dL; P=0.084). Mean insulin levels were significantly higher on Day 3 for CP (195 [120] pmol/L vs. 230 [90] pmol/L; P=0.01), but not significantly so for PP (178 [106] pmol/L vs. 194 [120] pmol/L; P=0.099). There were no significant differences between the two catheter lengths. CONCLUSIONS: Postprandial glycemic excursions were lower on Day 3 of catheter wear time, but there were no differences between PPs and CPs. These findings support the proposal that catheter wear time plays an important role in insulin absorption.

Day and night closed-loop control in adults with type 1 diabetes: a comparison of two closed-loop algorithms driving continuous subcutaneous insulin infusion versus patient self-management.

OBJECTIVE: To compare two validated closed-loop (CL) algorithms versus patient self-control with CSII in terms of glycemic control. RESEARCH DESIGN AND METHODS: This study was a multicenter, randomized, three-way crossover, open-label trial in 48 patients with type 1 diabetes mellitus for at least 6 months, treated with continuous subcutaneous insulin infusion. Blood glucose was controlled for 23 h by the algorithm of the Universities of Pavia and Padova with a Safety Supervision Module developed at the Universities of Virginia and California at Santa Barbara (international artificial pancreas [iAP]), by the algorithm of University of Cambridge (CAM), or by patients themselves in open loop (OL) during three hospital admissions including meals and exercise. The main analysis was on an intention-to-treat basis. Main outcome measures included time spent in target (glucose levels between 3.9 and 8.0 mmol/L or between 3.9 and 10.0 mmol/L after meals). RESULTS: Time spent in the target range was similar in CL and OL: 62.6% for OL, 59.2% for iAP, and 58.3% for CAM. While mean glucose level was significantly lower in OL (7.19, 8.15, and 8.26 mmol/L, respectively) (overall P = 0.001), percentage of time spent in hypoglycemia (<3.9 mmol/L) was almost threefold reduced during CL (6.4%, 2.1%, and 2.0%) (overall P = 0.001) with less time ≤2.8 mmol/L (overall P = 0.038). There were no significant differences in outcomes between algorithms. CONCLUSIONS: Both CAM and iAP algorithms provide safe glycemic control.

Continuous glucose monitoring accuracy results vary between assessment at home and assessment at the clinical research center.

BACKGROUND: Continuous glucose monitoring system (CGMS) accuracy is of critical importance both in delivering therapeutic value and as a component of a closed-loop system. This study aims at assessing the differences between accuracy assessments of CGMS at home and at the clinical research center (CRC). METHODS: Twelve patients with type 1 diabetes used the Dexcom® SEVEN® PLUS (DexCom, Inc.) CGMS for 7 days. Patients performed ≥6 finger pricks [self-measurement of blood glucose (SMBG)] per day while at home. Reference blood glucose measurements were taken during a 24 h CRC admission (YSI 2300 STAT Plus™). Continuous glucose monitoring system data were compared with YSI and SMBG values. Outcome measures included mean absolute relative difference (MARD) and Clarke error grid analysis (CEGA). RESULTS: During CRC admission, the MARD of CGMS vs YSI glucose values was 19.2% (n = 509)--significantly higher than 16.8% at home (n = 611) (p = .004). In the hypoglycemic range, MARD was 23.9% at CRC (n = 26)--not significantly different from 41.6% at home (n = 39) (p = .269). In the hyperglycemic range, CRC MARD at 20.3% (n = 115) was significantly higher than home MARD at 11.2% (n = 118) (p = .001). Clarke error grid analysis showed no significant difference in distribution of data pairs (overall p = .317). CONCLUSIONS: This study illustrates the importance of the setting used when assessing CGMS accuracy. Continuous glucose monitoring system accuracy at home appeared better than at the CRC. This is probably due to the higher sampling rate of reference measurements, feasible only in the CRC. Testing CGMS accuracy in the CRC provides valuable information over and above home testing.

The added value of oral glucose tolerance testing in pre-diabetes.

With the increased acceptance of glycated hemoglobin measurement as the test of choice for the diagnosis and detection of diabetes, doubts which surround the use of the oral glucose tolerance test (OGTT) in detecting disturbances in glucose levels have become even more apparent. Metabolically, there are still arguments to use the OGTT. Epidemiological studies though, have not always supported the efficacy of the OGTT when used for screening in obese patients. In our opinion, current evidence suggests an additive value of the OGTT, its main advantage being the ability to detect stages of pre-diabetes more accurately than HbA1c and the ability to investigate postprandial glucose levels in a physiological way.

Premeal injection of rapid-acting insulin reduces postprandial glycemic excursions in type 1 diabetes.

OBJECTIVE: To assess the effect of three premeal timings of rapid-acting insulin on postprandial glucose excursions in type 1 diabetes. RESEARCH DESIGN AND METHODS: Ten 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. RESULTS: Area 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). CONCLUSIONS: Administration 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.