17
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      Continuous Glucose Monitoring: A Review of Recent Studies Demonstrating Improved Glycemic Outcomes

      1
      Diabetes Technology & Therapeutics
      Mary Ann Liebert Inc

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Continuous Glucose Monitoring (CGM) has been demonstrated to be clinically valuable, reducing risks of hypoglycemia and hyperglycemia, glycemic variability (GV), and improving patient quality of life for a wide range of patient populations and clinical indications. Use of CGM can help reduce HbA1c and mean glucose. One CGM device, with accuracy (%MARD) of approximately 10%, has recently been approved for self-adjustment of insulin dosages (nonadjuvant use) and approved for reimbursement for therapeutic use in the United States. CGM had previously been used off-label for that purpose. CGM has been demonstrated to be clinically useful in both type 1 and type 2 diabetes for patients receiving a wide variety of treatment regimens. CGM is beneficial for people using either multiple daily injections (MDI) or continuous subcutaneous insulin infusion (CSII). CGM is used both in retrospective (professional, masked) and real-time (personal, unmasked) modes: both approaches can be beneficial. When CGM is used to suspend insulin infusion when hypoglycemia is detected until glucose returns to a safe level (low-glucose suspend), there are benefits beyond sensor-augmented pump (SAP), with greater reduction in the risk of hypoglycemia. Predictive low-glucose suspend provides greater benefits in this regard. Closed-loop control with insulin provides further improvement in quality of glycemic control. A hybrid closed-loop system has recently been approved by the U.S. FDA. Closed-loop control using both insulin and glucagon can reduce risk of hypoglycemia even more. CGM facilitates rigorous evaluation of new forms of therapy, characterizing pharmacodynamics, assessing frequency and severity of hypo- and hyperglycemia, and characterizing several aspects of GV.

          Related collections

          Most cited references104

          • Record: found
          • Abstract: found
          • Article: not found

          Flash Glucose-Sensing Technology as a Replacement for Blood Glucose Monitoring for the Management of Insulin-Treated Type 2 Diabetes: a Multicenter, Open-Label Randomized Controlled Trial

          Introduction Glycemic control in participants with insulin-treated diabetes remains challenging. We assessed safety and efficacy of new flash glucose-sensing technology to replace self-monitoring of blood glucose (SMBG). Methods This open-label randomized controlled study (ClinicalTrials.gov, NCT02082184) enrolled adults with type 2 diabetes on intensive insulin therapy from 26 European diabetes centers. Following 2 weeks of blinded sensor wear, 2:1 (intervention/control) randomization (centrally, using biased-coin minimization dependant on study center and insulin administration) was to control (SMBG) or intervention (glucose-sensing technology). Participants and investigators were not masked to group allocation. Primary outcome was difference in HbA1c at 6 months in the full analysis set. Prespecified secondary outcomes included time in hypoglycemia, effect of age, and patient satisfaction. Results Participants (n = 224) were randomized (149 intervention, 75 controls). At 6 months, there was no difference in the change in HbA1c between intervention and controls: −3.1 ± 0.75 mmol/mol, [−0.29 ± 0.07% (mean ± SE)] and −3.4 ± 1.04 mmol/mol (−0.31 ± 0.09%) respectively; p = 0.8222. A difference was detected in participants aged <65 years [−5.7 ± 0.96 mmol/mol (−0.53 ± 0.09%) and −2.2 ± 1.31 mmol/mol (−0.20 ± 0.12%), respectively; p = 0.0301]. Time in hypoglycemia <3.9 mmol/L (70 mg/dL) reduced by 0.47 ± 0.13 h/day [mean ± SE (p = 0.0006)], and <3.1 mmol/L (55 mg/dL) reduced by 0.22 ± 0.07 h/day (p = 0.0014) for intervention participants compared with controls; reductions of 43% and 53%, respectively. SMBG frequency, similar at baseline, decreased in intervention participants from 3.8 ± 1.4 tests/day (mean ± SD) to 0.3 ± 0.7, remaining unchanged in controls. Treatment satisfaction was higher in intervention compared with controls (DTSQ 13.1 ± 0.50 (mean ± SE) and 9.0 ± 0.72, respectively; p < 0.0001). No serious adverse events or severe hypoglycemic events were reported related to sensor data use. Forty-two serious events [16 (10.7%) intervention participants, 12 (16.0%) controls] were not device-related. Six intervention participants reported nine adverse events for sensor-wear reactions (two severe, six moderate, one mild). Conclusion Flash glucose-sensing technology use in type 2 diabetes with intensive insulin therapy results in no difference in HbA1c change and reduced hypoglycemia, thus offering a safe, effective replacement for SMBG. Trial registration ClinicalTrials.gov identifier: NCT02082184. Funding Abbott Diabetes Care. Electronic supplementary material The online version of this article (doi:10.1007/s13300-016-0223-6) contains supplementary material, which is available to authorized users.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Outpatient glycemic control with a bionic pancreas in type 1 diabetes.

            The safety and effectiveness of automated glycemic management have not been tested in multiday studies under unrestricted outpatient conditions.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found

              Normal Reference Range for Mean Tissue Glucose and Glycemic Variability Derived from Continuous Glucose Monitoring for Subjects Without Diabetes in Different Ethnic Groups

              Glycemic variability has been proposed as a contributing factor in the development of diabetes complications. Multiple measures exist to calculate the magnitude of glycemic variability, but normative ranges for subjects without diabetes have not been described. For treatment targets and clinical research we present normative ranges for published measures of glycemic variability. Seventy-eight subjects without diabetes having a fasting plasma glucose of <120 mg/dL (6.7 mmol/L) underwent up to 72 h of continuous glucose monitoring (CGM) with a Medtronic Minimed (Northridge, CA) CGMS(®) Gold device. Glycemic variability was calculated using EasyGV(©) software (available free for non-commercial use at www.easygv.co.uk ), a custom program that calculates the SD, M-value, mean amplitude of glycemic excursions (MAGE), average daily risk ratio (ADRR), Lability Index (LI), J-Index, Low Blood Glucose Index (LBGI), High Blood Glucose Index (HBGI), continuous overlapping net glycemic action (CONGA), mean of daily differences (MODD), Glycemic Risk Assessment in Diabetes Equation (GRADE), and mean absolute glucose (MAG). Eight CGM traces were excluded because there were inadequate data. From the remaining 70 traces, normative reference ranges (mean±2 SD) for glycemic variability were calculated: SD, 0-3.0; CONGA, 3.6-5.5; LI, 0.0-4.7; J-Index, 4.7-23.6; LBGI, 0.0-6.9; HBGI, 0.0-7.7; GRADE, 0.0-4.7; MODD, 0.0-3.5; MAGE-CGM, 0.0-2.8; ADDR, 0.0-8.7; M-value, 0.0-12.5; and MAG, 0.5-2.2. We present normative ranges for measures of glycemic variability in adult subjects without diabetes for use in clinical care and academic research.
                Bookmark

                Author and article information

                Journal
                Diabetes Technology & Therapeutics
                Diabetes Technology & Therapeutics
                Mary Ann Liebert Inc
                1520-9156
                1557-8593
                June 2017
                June 2017
                : 19
                : S3
                : S-25-S-37
                Affiliations
                [1 ]Biomedical Informatics Consultants LLC, Potomac, Maryland.
                Article
                10.1089/dia.2017.0035
                5467105
                28585879
                9263b028-f97f-4107-932d-3e88d913454d
                © 2017

                http://www.liebertpub.com/nv/resources-tools/text-and-data-mining-policy/121/

                History

                Comments

                Comment on this article