Use of continuous glucose monitoring to improve diabetes mellitus management

Barbara Davis Center for Childhood Diabetes, University of Colorado Health Sciences Center, Aurora, Colorado, USA.
Endocrinology & Metabolism Clinics of North America (Impact Factor: 3.4). 12/2007; 36 Suppl 2:46-68. DOI: 10.1016/S0889-8529(07)80011-9
Source: PubMed
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    • "However, it requires a finger-stick to draw blood several times a day which may not provide adequate information. A newer alternative approach is continuous glucose monitoring (CGM) system[6] [7] [8], which determines glucose levels on a continuous basis (every few minutes) and allows a more thorough metabolic control. Voluminous glucose time-series data are measured online and displayed, which provide maximal information about shifting blood glucose levels throughout the day and facilitate the making of optimal treatment decisions for the diabetes subjects. "
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    ABSTRACT: Goal: For conventional modeling methods, the work of model identification has to be repeated with sufficient data for each subject because different subjects may have different response to exogenous inputs. This may cause repetitive cost and burden for patients and clinicians and require a lot of modeling efforts. Here, to overcome the aforementioned problems, a rapid model development strategy for new subjects is proposed using the idea of model migration for online glucose prediction. Methods: First, a base model is obtained that can be empirically identified from any subject or constructed by a priori knowledge. Then, parameters of inputs in the base model are properly revised based on a small amount of new data from new subjects so that the updated models can reflect the specific glucose dynamics excited by inputs for new subjects. These problems are investigated by developing autoregressive models with exogenous inputs (ARX) based on 30 in silico subjects using UVA/Padova metabolic simulator. Results: The prediction accuracy of the rapid modeling method is comparable to that for subject-dependent modeling method for some cases. Also, it can present better generalization ability. Conclusion: The proposed method can be regarded as an effective and economic modeling method instead of repetitive subject-dependent modeling method especially for lack of modeling data.
    IEEE transactions on bio-medical engineering 05/2015; 62(5). DOI:10.1109/TBME.2014.2387293 · 2.35 Impact Factor
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    • "New promising devices for the treatment of diabetes are also the Continuous Glucose Monitoring (CGM) sensors, since they provide more information on daytime and night-time glucose pattern as compared to spot measurements. Their use in patients with type 1 diabetes has shown positive effects in reduction of the HbA1c [7] and glucose variability [8]. "
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    ABSTRACT: Patients with diabetes are recommended to self-monitor their blood glucose levels also at home. Accuracy of a hand-held glucometer and a Continuous Glucose Monitoring (CGM) device were comparatively evaluated. Venous blood samples (for reference laboratory determinations; n=428) were collected from 18 type 1 patients (35-65 years old), immediately followed by capillary measurement (Bayer ContourLink meter) and CGM readings (Medtronic Paradigm). Laboratory values did not differ statistically from ContourLink and CGM readings, mean difference (±SD) being -0.05±1.06 mmol/L and 0.10±1.84 mmol/L glucose, respectively. A bias ((value-reference)/reference×100) ≥15% was observed in 27.7% and 54.9% of cases, respectively. Notably, below 3.9 mmol/L glucose (hypoglycemic threshold), an absolute error>0.8 mmol/L was found in 78.9% and 94.1% of cases. The absolute errors of the CGM device were inversely related to the rate of glucose change (r=0.598, p<0.001). A very large error was observed at the extreme glycemic values, which may lead to erroneous therapy. Consequently, performance of future portable glucometers should be focused in particular under hypo- and hyper-glycemia. Moreover, integrated CGM devices should not disregard the effect of the rate of blood glucose change on the sensor readings.
    Clinica chimica acta; international journal of clinical chemistry 01/2012; 413(1-2):312-8. DOI:10.1016/j.cca.2011.10.012 · 2.82 Impact Factor
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    ABSTRACT: The prevalence of type 1 diabetes continues to increase worldwide at a rate higher than previously projected, while the number of patients achieving American Diabetes Association (ADA) glycated hemoglobin (A1c) goals remains suboptimal. There are numerous barriers to patients achieving A1c targets including increased frequency of severe hypoglycemia associated with lowering plasma glucose as measured by lower A1c values. Continuous glucose monitoring (CGM) was first approved for retrospective analysis and now has advanced to the next step in diabetes management with the approval of real-time glucose sensing. Real-time CGM, in short term studies, has been shown to decrease A1c values, improve glucose variability (GV), and minimize the time and number of hypoglycemic events in patients with type 1 diabetes. These products are approved for adjunctive use to self-monitoring of blood glucose (SMBG), but future long-term studies are needed to document their safety, efficacy, ability to replace SMBG as a tool of monitoring, and ultimately utility into closed-loop insulin delivery systems. New algorithms will need to be developed that account for rapid changes in the glucose values, so that accuracy of the sensor data can be maintained. In addition, for better clinical care and usage, algorithms also need to be developed for both patients and the providers to guide them for their ongoing diabetes care.
    Current diabetes reviews 09/2008; 4(3):207-17. DOI:10.2174/157339908785294370
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