Regulation Catches Up to Reality

Edelman, Steven V.

doi:10.1177/1932296816667749

Cited by 47 publications

(25 citation statements)

References 8 publications

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“…Furthermore, the T1DX REPLACE BG study provided evidence of the safety and effectiveness of non‐adjunctive sensor use and registry data indicates only 26% of participants using CGM always verified the sensor glucose by performing SMBG . Practical guidelines for non‐adjunctive use are being developed . However, research and clinical experience on non‐adjunctive use of CGM systems are limited in pediatric populations …”

Section: Continuous Glucose Monitorsmentioning

confidence: 99%

“…142 Practical guidelines for non-adjunctive use are being developed. [147][148][149][150][151][152][153][154] However, research and clinical experience on non-adjunctive use of CGM systems are limited in pediatric populations. 155 4.6 | Intermittently scanned/viewed CGM Recently introduced isCGM systems are factory calibrated, small in size, light weight, have good user acceptance and satisfactory accuracy with an overall MARD of 11% to 14%.…”

Section: Accuracy Of Cgmmentioning

confidence: 99%

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ISPAD Clinical Practice Consensus Guidelines 2018: Diabetes technologies

et al. 2018

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Section: Continuous Glucose Monitorsmentioning

confidence: 99%

Section: Accuracy Of Cgmmentioning

confidence: 99%

ISPAD Clinical Practice Consensus Guidelines 2018: Diabetes technologies

et al. 2018

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“…The FDA-approved simulator used in this work is a valid substitute for the preclinical testing of novel technologies in diabetes care (see, e.g., [ 43 ][ 44 ][ 45 ]). However, the use of in silico data is not meant as substitute to human trial; rather, it can be considered a good starting point for evaluating our GE method: in fact simulated data are complete, i.e.…”

Section: Discussionmentioning

confidence: 99%

Personalized blood glucose prediction: A hybrid approach using grammatical evolution and physiological models

et al. 2017

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The large patient variability in human physiology and the effects of variables such as exercise or meals challenge current prediction modeling techniques. Physiological models are very precise but they are typically complex and specific physiological knowledge is required. In contrast, data-based models allow the incorporation of additional inputs and accurately capture the relationship between these inputs and the outcome, but at the cost of losing the physiological meaning of the model. In this work, we designed a hybrid approach comprising physiological models for insulin and grammatical evolution, taking into account the clinical harm caused by deviations from the target blood glucose by using a penalizing fitness function based on the Clarke error grid. The prediction models were built using data obtained over 14 days for 100 virtual patients generated by the UVA/Padova T1D simulator. Midterm blood glucose was predicted for the 100 virtual patients using personalized models and different scenarios. The results obtained were promising; an average of 98.31% of the predictions fell in zones A and B of the Clarke error grid. Midterm predictions using personalized models are feasible when the configuration of grammatical evolution explored in this study is used. The study of new alternative models is important to move forward in the development of alarm-and-control applications for the management of type 1 diabetes and the customization of the patient’s treatments. The hybrid approach can be adapted to predict short-term blood glucose values to detect continuous glucose-monitoring sensor errors and to estimate blood glucose values when the continuous glucose-monitoring system fails to provide them.

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“…Conversely, in terms of accuracy and smart features incorporation, the Dexcom G5 Mobile CGM system is currently the leading product. With a 9% MARD, it is the first and only CGM system that can be used to make daily diabetes treatment decisions without finger pricking [57,58]. However, it still requires finger pricks for in vivo calibration, although some recent studies showed promising results towards a calibration-free scenario [59,60].…”

Section: Technological Trends and Challenges For The Next Generation mentioning

confidence: 99%

Wearable Continuous Glucose Monitoring Sensors: A Revolution in Diabetes Treatment

et al. 2017

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Worldwide, the number of people affected by diabetes is rapidly increasing due to aging populations and sedentary lifestyles, with the prospect of exceeding 500 million cases in 2030, resulting in one of the most challenging socio-health emergencies of the third millennium. Daily management of diabetes by patients relies on the capability of correctly measuring glucose concentration levels in the blood by using suitable sensors. In recent years, glucose monitoring has been revolutionized by the development of Continuous Glucose Monitoring (CGM) sensors, wearable non/minimally-invasive devices that measure glucose concentration by exploiting different physical principles, e.g., glucose-oxidase, fluorescence, or skin dielectric properties, and provide real-time measurements every 1-5 min. CGM opened new challenges in different disciplines, e.g., medicine, physics, electronics, chemistry, ergonomics, data/signal processing, and software development to mention but a few. This paper first makes an overview of wearable CGM sensor technologies, covering both commercial devices and research prototypes. Then, the role of CGM in the actual evolution of decision support systems for diabetes therapy is discussed. Finally, the paper presents new possible horizons for wearable CGM sensor applications and perspectives in terms of big data analytics for personalized and proactive medicine.

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Regulation Catches Up to Reality

Cited by 47 publications

References 8 publications

ISPAD Clinical Practice Consensus Guidelines 2018: Diabetes technologies

ISPAD Clinical Practice Consensus Guidelines 2018: Diabetes technologies

Personalized blood glucose prediction: A hybrid approach using grammatical evolution and physiological models

Wearable Continuous Glucose Monitoring Sensors: A Revolution in Diabetes Treatment

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