ArticleJournal of diabetes science and technology2017
Analysis of the Accuracy and Performance of a Continuous Glucose Monitoring Sensor Prototype: An In-Silico Study Using the UVA/PADOVA Type 1 Diabetes Simulator.
Article in Journal of diabetes science and technology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Developing the UVA/Padova Type 1 Diabetes Simulator: Modeling, Validation, Refinements, and Utility.Journal of diabetes science and technology · 2023Review
- Mathematical Models of Meal Amount and Timing Variability With Implementation in the Type-1 Diabetes Patient Decision Simulator.Journal of diabetes science and technology · 2021Article
- Toward a Framework for Outcome-Based Analytical Performance Specifications: A Methodology Review of Indirect Methods for Evaluating the Impact of Measurement Uncertainty on Clinical Outcomes.Clinical chemistry · 2019Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundComputer simulation has been shown over the past decade to be a powerful tool to study the impact of medical devices characteristics on clinical outcomes. Specifically, in type 1 diabetes (T1D), computer simulation platforms have all but replaced preclinical studies and are commonly used to study the impact of measurement errors on glycemia.
methodWe use complex mathematical models to represent the characteristics of 3 continuous glucose monitoring systems using previously acquired data. Leveraging these models within the framework of the UVa/Padova T1D simulator, we study the impact of CGM errors in 6 simulation scenarios designed to generate a wide variety of glycemic conditions. Assessment of the simulated accuracy of each different CGM systems is performed using mean absolute relative deviation (MARD) and precision absolute relative deviation (PARD). We also quantify the capacity of each system to detect hypoglycemic events.
resultsThe simulated Roche CGM sensor prototype (RCGM) outperformed the 2 alternate systems (CGM-1 & CGM-2) in accuracy (MARD = 8% vs 11.4% vs 18%) and precision (PARD = 6.4% vs 9.4% vs 14.1%). These results held for all studied glucose and rate of change ranges. Moreover, it detected more than 90% of hypoglycemia, with a mean time lag less than 4 minutes (CGM-1: 86%/15 min, CGM-2: 57%/24 min).
conclusionThe RCGM system model led to strong performances in these simulation studies, with higher accuracy and precision than alternate systems. Its characteristics placed it firmly as a strong candidate for CGM based therapy, and should be confirmed in large clinical studies.
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Registered trials
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