Evaluation of the performance and usability of a novel continuous glucose monitoring system

Yan, Li; Li, Qiang; Guan, Qingbo; Han, Mingsong; Yu, Zhiwen; Fang, Junfei; Zhao, Jiajun

doi:10.1007/s13410-022-01112-0

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…Clinical evaluations and user feedback have demonstrated excellent accuracy, with the GS1 CGM achieving a Mean Absolute Relative Difference (MARD) of 8.83%, a key measure of glucose monitor accuracy (lower MARD values indicate higher accuracy). In addition, the GS1 CGM has been tested in a variety of environments, including with over 1,600 hospitals, and has been used by over 600,000 users ( 9 , 10 ).…”

Section: Methodsmentioning

confidence: 99%

Prediction of personalised postprandial glycaemic response in type 1 diabetes mellitus

Xiong,

Xue,

Cai

et al. 2024

Front. Endocrinol.

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ObjectivesPatients with type 1 diabetes (T1D) face unique challenges in glycaemic control due to the complexity and uniqueness of the dietary structure in China, especially in terms of postprandial glycaemic response (PPGR). This study aimed to establish a personalized model for predicting PPGR in patients with T1D.Materials and methodsData provided by the First People’s Hospital of Yunnan Province, 13 patients with T1D, were recruited and provided with an intervention for at least two weeks. All patients were asked to wear a continuous glucose monitoring (CGM) device under free-living conditions during the study period. To tackle the challenge of incomplete data from wearable devices for CGM measurements, the GAIN method was used in this paper to achieve a more rational interpolation process. In this study, patients’ PPGRs were calculated, and a LightGBM prediction model was constructed based on a Bayesian hyperparameter optimisation algorithm and a random search algorithm, which integrated glucose measurement, insulin dose, dietary nutrient content, blood measurement and anthropometry as inputs.ResultsThe experimental outcomes revealed that the PPGR prediction model presented in this paper demonstrated superior accuracy (R=0.63) compared to both the carbohydrate content only model (R=0.14) and the baseline model emulating the standard of care for insulin administration (R=0.43). In addition, the interpretation of the model using the SHAP method showed that blood glucose levels at meals and blood glucose trends 30 minutes before meals were the most important features of the model.ConclusionThe proposed model offers a heightened precision in predicting PPGR in patients with T1D, so it can better guide the diet plan and insulin intake dose of patients with T1D.

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Section: Methodsmentioning

confidence: 99%

Prediction of personalised postprandial glycaemic response in type 1 diabetes mellitus

Xiong,

Xue,

Cai

et al. 2024

Front. Endocrinol.

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Importance of FDA-Integrated Continuous Glucose Monitors to Ensure Accuracy of Continuous Glucose Monitoring

Klonoff,

Gabbay,

Moon

et al. 2024

J Diabetes Sci Technol

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Continuous glucose monitoring (CGM) has been shown to improve glycemic control and self-monitoring, as well as to reduce the risk of hypoglycemia. Integrated CGM (iCGM) FDA-cleared systems with published performance data are established nonadjunctive and accurate CGM tools that can directly inform decision-making in the treatment of diabetes (i.e., insulin dosing). Studies have assessed accuracy and safety data of CGMs that were eventually cleared for iCGM by the FDA and that informed the recommendation for their nonadjunctive use. Subsequent robust clinical trials and real-world studies demonstrated clinical effectiveness with improvements in a range of patient outcomes. In recent years, a number of non-iCGM-approved CGM devices have entered the market outside the United States worldwide. Some of these non-iCGM-approved CGM devices require additional user verification of blood glucose levels to be performed for making treatment decisions, termed adjunctive. Moreover, in many non-iCGM-approved CGM devices, accuracy studies published in peer-reviewed journals are scarce or have many limitations. Consequently, non-iCGM-approved CGM devices cannot be automatically perceived as having the same performance or quality standards than those approved for iCGM by the FDA. As a result, although these devices tend to cost less than iCGMs that carry FDA clearance and could therefore be attractive from the point of view of a health care payer, it must be emphasized that evaluation of costs should not be limited to the device (such as the usability preference that patients have for nonadjunctive sensors compared to adjunctive sensors) but to the wider value of the total benefit that the product provides to the patient.

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Evaluating the precision and reliability of real-time continuous glucose monitoring systems in ambulatory settings: a systematic review

Dávila-Ruales,

Gilón,

Gómez

et al. 2024

Therapeutic Advances in Endocrinology

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Background: Continuous glucose monitoring (CGM) with minimally invasive devices plays a key role in the assessment of daily diabetes management by detecting and alerting to potentially dangerous trends in glucose levels, improving quality of life, and treatment adherence. However, there is still uncertainty as to whether CGMs are accurate enough to replace self-monitoring of blood glucose, especially in detecting episodes of hypoglycemia. Objectives: Evaluate clinical, numerical accuracy, sensitivity, and specificity of the CGM devices commercially available when compared to the reference standard of arterial or venous blood glucose. Data sources and methods: We searched the Cochrane Library, PubMed, EMBASE, and LILACS databases. The quality was assessed with the Quality Assessment Diagnostic Accuracy Studies (QUADAS-2) tool. Clinical and numerical accuracy data were extracted. Sensitivity and specificity were calculated using Review Manager software. Heterogeneity was assessed by visual examination of forest plot and summary receiver operating characteristic curves. Results: Twenty-two studies with a total of 2294 patients were included. The average mean absolute relative difference for overall diagnostic accuracy was 9.4%. None of the devices evaluated with ISO 15197:2013 criteria achieved values ⩾95% of measurements in the stipulated ranges in hypoglycemia (±15 mg/dL), but two devices did achieve it in hyperglycemia (±15%; Dexcom G6 and G7). Most of the devices evaluated with consensus error grids reached values above 99% in zones A and B only in overall accuracy and hyperglycemia. For hypoglycemia, the average sensitivity was 85.7% and specificity 95.33%, and for hyperglycemia was 97.45% and 96% respectively. Conclusion: Currently available CGM devices have adequate accuracy for euglycemia and hyperglycemia; however, it is still inadequate for hypoglycemia, although it has improved over time. Trial registration: Prospero registration ID CRD42023399767.

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Evaluation of the performance and usability of a novel continuous glucose monitoring system

Cited by 3 publications

References 28 publications

Prediction of personalised postprandial glycaemic response in type 1 diabetes mellitus

Prediction of personalised postprandial glycaemic response in type 1 diabetes mellitus

Importance of FDA-Integrated Continuous Glucose Monitors to Ensure Accuracy of Continuous Glucose Monitoring

Evaluating the precision and reliability of real-time continuous glucose monitoring systems in ambulatory settings: a systematic review

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