Resistance to Data Loss of Glycemic Variability Measurements in Long-Term Continuous Glucose Monitoring

Kucharski, Przemysław; Pagacz, Konrad; Szadkowska, Agnieszka; Młynarski, Wojciech; Romanowski, Andrzej; Fendler, Wojciech

doi:10.1089/dia.2018.0247

Cited by 6 publications

(5 citation statements)

References 20 publications

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“…Our findings are comparable to another study that collected data using an Enlite Sensor (Medtronic, Dublin, USA) that found glucose variability measurements were robust to data loss in Type 1 diabetics (Kucharski et al 2018). Absolute errors were similar, with MAPE values consistently below 5% and MAGE being the most "vulnerable to missing data" (Kucharski et al 2018).…”

Section: Main Findingssupporting

confidence: 83%

“…Previous research investigating up to 80% of missing glucose data in a sample of adults living with type 1 diabetes reported glucose measurements to be robust to data loss, with calculated mean absolute percentages errors (MAPE) remaining below 5% (Kucharski et al 2018). This analysis was conducted on data collected using the Medtronic Enlite Sensor which passively transmits data automatically.…”

Section: Introductionmentioning

confidence: 99%

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Resistance to data loss from the Freestyle Libre: impact on glucose variability indices and recommendations for data analysis

Kingsnorth

Whelan

Orme

et al. 2021

Appl. Physiol. Nutr. Metab.

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Like many wearables, flash glucose monitoring relies on user compliance and is subject to missing data. As recent research is beginning to utilise glucose technologies as behaviour change tools, it is important to understand whether missing data is tolerable. Complete Freestyle Libre data files were amputed to remove 1-6 hours of data both at random and over mealtimes (breakfast, lunch and dinner). Absolute percent errors (MAPE) and intraclass correlation coefficients (ICC) were calculated to evaluate agreement and reliability. Thirty-two (91%) participants provided at least one complete day (24-hours) of data (age: 44.8±8.6 years, female: 18 (56%); mean fasting glucose: 5.0±0.6 mmol/L). Mean and CONGA (60 minutes) were robust to data loss (MAPE ≤3%). Larger errors were calculated for standard deviation, coefficient of variation (CV) and MAGE at increasing missingness (MAPE 2-10%, 2-9% and 4-18%, respectively). ICC decreased as missing data increased, with most indicating excellent reliability (>0.9) apart from certain MAGE ICC, which indicated good reliability (0.84-0.9). Researchers and clinicians should be aware of the potential for larger errors when reporting standard deviation, CV and MAGE at higher rates of data loss in nondiabetic populations. But where mean and CONGA are of interest, data loss is less of a concern. Novelty:  As research now utilises flash glucose monitoring as behavioural change tools in nondiabetic populations, it is important to consider the influence of missing data.  Glycaemic variability indices of mean and CONGA are robust to data loss, but standard deviation, CV and MAGE are influenced at higher rates of missingness.

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Section: Main Findingssupporting

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Resistance to data loss from the Freestyle Libre: impact on glucose variability indices and recommendations for data analysis

Kingsnorth

Whelan

Orme

et al. 2021

Appl. Physiol. Nutr. Metab.

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“…While application of a linear interpolation approach to repair data gaps has been proposed previously, 26 we did not replace missing data with substituted values. In this regard, previous studies showed that some measures of glucose variability are comparatively resistant to data loss caused by random factors in patients with T1D 27 and in populations free of diabetes 28 . However, further studies are needed to establish the most efficient way of dealing with missing CGM data in patients with T2D.…”

Section: Discussionmentioning

confidence: 99%

Relationship between blood glucose variability in ambulatory glucose profile and standardized continuous glucose monitoring metrics: Subanalysis of a prospective cohort study

Tokutsu

Okada

Mita

et al. 2021

Diabetes Obesity Metabolism

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Aim: To clarify the relationship between ambulatory glucose profile (AGP) indexes and standardized continuous glucose monitoring (CGM) metrics in patients with type 2 diabetes (T2D).Methods: This is an exploratory, cross-sectional analysis of baseline data collected from a prospective, multicentre, 5-year follow-up observational study conducted and published previously by our group. The study participants were 999 outpatients with T2D who used CGM at baseline, and had no apparent history of cardiovascular disease. We investigated the relationship between average interquartile range (IQR) and time in range (TIR). We also calculated, for the first time, the cutoff values to achieve the TIR target values.Results: In both the TIR more than 70% and TIR more than 90% achievement groups, the average IQR was notably small compared with the non-achievement groups.Particularly in comparison of the TIR quartiles, the average IQR became significantly smaller as the TIR became larger. The average IQR correlated negatively with TIR, and the cutoff values for TIR of more than 70% achievement and TIR of more than 90% achievement were an average IQR (>70%/>90%) of 2.13/1.85 mmol/L. Yosuke Okada and Tomoya Mita contributed equally to this study.

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“…The results of research will enable computer systems to make quick decisions in the field of monitoring and control. Furthermore, the computational environment may be suited to personal-medical applications, such as the continuous glucose monitoring, resulting in better diagnosis, safety and hence better life comfort [26].…”

Section: Discussion and Future Workmentioning

confidence: 99%

Towards Big Data Solutions for Industrial Tomography Data Processing

Kowalska

Łuczak

Sielski

et al. 2019

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

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This paper presents an overview of what Big Data can bring to the modern industry. Through following the history of contemporary Big Data frameworks the authors observe that the tools available have reached sufficient maturity so as to be usable in an industrial setting. The authors propose the concept of a system for collecting, organising, processing and analysing experimental data obtained from measurements with process tomography. Process tomography is used for noninvasive flow monitoring and data acquisition. The measurement data is collected, stored and processed to identify process regimes and process threats. Further general examples of solutions that aim to take advantage of the existence of such tools are presented as proof of viability of such approach. As the first step in the process of creating the proposed system, a scalable, distributed, containerisation-based cluster has been constructed, with consumer-grade hardware.

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Resistance to Data Loss of Glycemic Variability Measurements in Long-Term Continuous Glucose Monitoring

Cited by 6 publications

References 20 publications

Resistance to data loss from the Freestyle Libre: impact on glucose variability indices and recommendations for data analysis

Resistance to data loss from the Freestyle Libre: impact on glucose variability indices and recommendations for data analysis

Relationship between blood glucose variability in ambulatory glucose profile and standardized continuous glucose monitoring metrics: Subanalysis of a prospective cohort study

Towards Big Data Solutions for Industrial Tomography Data Processing

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