Use of continuous glucose monitoring to improve diabetes mellitus management.
- SourceAvailable from: Chunhui Zhao
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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  , 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. "
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 above mentioned 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 which can be empirically identified from any subject or constructed by 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 auto-regressive models with exogenous inputs (ARX) based on thirty 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 modelling data.IEEE transactions on bio-medical engineering 05/2015; 62(5). DOI:10.1109/TBME.2014.2387293 · 2.23 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  and glucose variability . "
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 · 2.76 Impact Factor
Article: Role of emerging new technologiesDiabetes Technology & Therapeutics 11/2008; 10(5):413-4. DOI:10.1089/dia.2008.0069 · 2.29 Impact Factor