Comparison of Cgmanalysis, a Free Open-Source Continuous Glucose Monitoring Data Management and Analysis Software, with Commercially Available CGM Platforms: Data Standardization for Diabetes Technology Research

Assessing the feasibility of time in tight range (TITR) targets with advanced hybrid closed loop (AHCL) use in children and adolescents: A single-centre real-world study

AIMS

Time in Tight Range (TITR) is a novel glycaemic metric in monitoring type 1 diabetes (T1D) management. The aim of this study was to assess the attainability of the TITR target in children and adolescents using the advanced hybrid closed loop (AHCL).

METHODS

The 2128-day CGM data from 56 children and adolescents with T1D using AHCL (Minimed-780G) were analysed. Time in Range (TIR) (3.9-10 mmol/L), TITR (3.9-7.7 mmol/L), and other glycaemic parameters were separately analysed in terms of whole day, daytime (06.00-23:59), and nighttime (00.00-05.59) results. The participants were divided into two groups by autocorrection rate where Group 1 had a rate of <30% and Group 2 had a rate of ≥30.

RESULTS

All glycaemic parameters indicated a better glycaemic outcome in the nighttime with higher TIR and TITR values compared with daytime (for TIR 87.5 ± 9.5% vs. 78.8 ± 8%, p < 0.001, and TITR 68.2 ± 13.5% vs. 57.5 ± 8.8%, p < 0.001). The rates of TITR >50% and >60% were 87% and 52%, respectively. When those with TITR >60% (n: 29) and those without (n: 27) were evaluated in terms of hypoglycaemia, no statistically significant difference was found in time below range (TBR) 3-3.9 mmol/L (0.3% vs. 2.1%, p: 0.084) and TBR < 3 mmol/L (0.47% vs. 0.3%, p: 0.298). Group 1 had a significantly higher TIR and TITR compared to Group 2 (82.6 ± 6.1% vs. 75.6 ± 8.6%, p: 0.008 and 62.1 ± 7.5% vs. 53.8 ± 7.5%, p: 0.002, respectively).

CONCLUSIONS

Most children and adolescents on AHCL achieved the 50% target for TITR whereas more than half achieved the >60% target. A target of >50% for TITR seems realistic in children with T1D using AHCL.

An Update on the iglu Software Package for Interpreting Continuous Glucose Monitoring Data

Background: Continuous glucose monitors (CGMs) are increasingly used to provide detailed quantification of glycemic control and glucose variability. An open-source R package iglu has been developed to assist with automatic CGM metrics computation and data visualization, providing a comprehensive list of implemented CGM metrics. Motivated by the recent international consensus statement on CGM metrics and recommendations from recent reviews of available CGM software, we present an updated version of iglu with improved accessibility and expanded functionality. Methods: The functionality was expanded to include automated computation of hypo- and hyperglycemia episodes with corresponding visualizations, composite metrics of glycemic control (glycemia risk index and personal glycemic state), and glycemic metrics associated with postprandial excursions. The algorithm for mean amplitude of glycemic excursions has been updated for improved accuracy, and the corresponding visualization has been added. Automated hierarchical clustering capabilities have been added to facilitate statistical analysis. Accessibility was improved by providing support for the automatic processing of common data formats, expanding the graphical user interface, and providing mirrored functionality in Python. Results: The updated version of iglu has been released to the Comprehensive R Archive Network (CRAN) as version 4. The corresponding Python wrapper has been released to the Python Package Index (PyPI) as version 1. The new functionality has been demonstrated using CGM data from 19 subjects with prediabetes and type 2 diabetes. Conclusions: An updated version of iglu provides comprehensive and accessible software for analyses of CGM data that meets the needs of researchers with varying levels of programming experience. It is freely available on CRAN and on GitHub at https://github.com/irinagain/iglu.

Accuracy of an Off-Label Transmitter and Data Manager Paired With an Intermittent Scanned Continuous Glucose Monitor in Adults With Type 1 Diabetes

BACKGROUND

This work evaluates the accuracy and agreement between the FreeStyle Libre sensor (FSL) and an off-label converted real-time continuous glucose monitor (c-rtCGM) device consisting of the MiaoMiao transmitter and the xDrip+ application which can be coupled to the FSL.

METHODS

Four weeks of glucose data were collected from 21 participants with type 1 diabetes using the c-rtCGM and FSL: two weeks with a single initial calibration (uncalibrated) and two weeks with a daily calibration (calibrated). Accuracy and agreement evaluation included mean absolute relative difference (MARD), the %20/20 rule, Bland-Altman plots, and the Consensus Error Grid analysis.

RESULTS

Values reported by the c-rtCGM system compared with the FSL resulted in an overall MARD of 12.06% and 84.71% of the results falling within Consensus Error Grid Zone A when the device is calibrated. For uncalibrated devices, an overall MARD of 17.49% was obtained. Decreased accuracy was shown in the hypoglycemic range and for rates of change greater than 2 mg/dL/min. The between-device bias also incremented with increasing glucose values.

CONCLUSION

Measurements recorded by the c-rtCGM were found to be accurate when compared with FSL data only when performing daily c-rtCGM device calibrations. High drops in accuracy and agreement between devices occurred when the c-rtCGM was not calibrated.

Changes in the glycaemia risk index and its association with other continuous glucose monitoring metrics after initiation of an automated insulin delivery system in adults with type 1 diabetes

AIM

To evaluate the glycaemia risk index (GRI) and its association with other continuous glucose monitoring (CGM) metrics after initiation of an automated insulin delivery (AID) system in patients with type 1 diabetes (T1D).

MATERIALS AND METHODS

Up to 90 days of CGM data before and after initiation of an AID system from 185 CGM users with T1D were collected. GRI and other CGM metrics were calculated using cgmanalysis R software and were analysed for 24 hours, for both night-time and daytime. GRI values were assigned to five GRI zones: zone A (0-20), B (21-40), C (41-60), D (61-80) and E (81-100).

RESULTS

Compared with baseline, GRI and its components decreased significantly after AID initiation (GRI: 48.7 ± 21.8 vs. 29 ± 13; hypoglycaemia component: 2.7 ± 2.8 vs. 1.6 ± 1.7; hyperglycaemia component: 25.3 ± 14.5 vs. 15 ± 8.5; P < .001 for all). The GRI was inversely correlated with time in range before (r = -0.962) and after (r = -0.961) AID initiation (P < .001 for both). GRI was correlated with time above range (before: r = 0.906; after = 0.910; P < .001 for both), but not with time below range (P > .05). All CGM metrics improved after AID initiation during 24 hours, for both daytime and night-time (P < .001 for all). Metrics improved significantly more during night-time than daytime (P < .01).

CONCLUSIONS

GRI was highly correlated with various CGM metrics above, but not below target range, both before and after AID initiation.

Minimum Sampling Duration for Continuous Glucose Monitoring Metrics to Achieve Representative Glycemic Outcomes in Suboptimal Continuous Glucose Monitor Use

BACKGROUND

Two weeks of continuous glucose monitoring (CGM) sampling with >70% CGM use is recommended to accurately reflect 90 days of glycemic metrics. However, minimum sampling duration for CGM use <70% is not well studied. We investigated the minimum duration of CGM sampling required for each CGM metric to achieve representative glycemic outcomes for <70% CGM use over 90 days.

METHODS

Ninety days of CGM data were collected in 336 real-life CGM users with type 1 diabetes. CGM data were grouped in 5% increments of CGM use (45%-95%) over 90 days. For each CGM metric and each CGM use category, the correlation between the summary statistic calculated using each sampling period and all 90 days of data was determined using the squared value of the Spearmen correlation coefficient (R2).

RESULTS

For CGM use 45% to 95% over 90 days, minimum sampling period is 14 days for mean glucose, time in range (70-180 mg/dL), time >180 mg/dL, and time >250 mg/dL; 28 days for coefficient of variation, and 35 days for time <54 mg/dL. For time <70 mg/dL, 28 days is sufficient between 45 and 80% CGM use, while 21 days is required >80% CGM use.

CONCLUSION

We defined minimum sampling durations for all CGM metrics in suboptimal CGM use. CGM sampling of at least 14 days is required for >45% CGM use over 90 days to sufficiently reflect most of the CGM metrics. Assessment of hypoglycemia and coefficient of variation require a longer sampling period regardless of CGM use duration.

Improving the management of patients with chronic cardiac and respiratory diseases by extending pulse-oximeter uses: the dynamic pulse-oximetry

Respiratory and cardio-vascular chronic diseases are among the most common noncommunicable diseases (NCDs) worldwide, accounting for a large portion of health-care costs in terms of mortality and disability. Their prevalence is expected to rise further in the coming years as the population ages. The current model of care for diagnosing and monitoring NCDs is out of date because it results in late medical interventions and/or an unfavourable cost-effectiveness balance based on reported symptoms and subsequent inpatient tests and treatments. Health projects and programs are being implemented in an attempt to move the time of an NCD's diagnosis, as well as its monitoring and follow up, out of hospital settings and as close to real life as possible, with the goal of benefiting both patients' quality of life and health system budgets. Following the SARS-CoV-2 pandemic, this implementation received additional impetus. Pulseoximeters (POs) are currently used in a variety of clinical settings, but they can also aid in the telemonitoring of certain patients. POs that can measure activities as well as pulse rate and oxygen saturation as proxies of cardio-vascular and respiratory function are now being introduced to the market. To obtain these data, the devices must be absolutely reliable, that is, accurate and precise, and capable of recording for a long enough period of time to allow for diagnosis. This paper is a review of current pulse-oximetry (POy) use, with the goal of investigating how its current use can be expanded to manage not only cardio-respiratory NCDs, but also acute emergencies with telemonitoring when hospitalization is not required but the patients' situation is debatable. Newly designed devices, both "consumer" and "professional," will be scrutinized, particularly those capable of continuously recording vital parameters on a 24-hour basis and coupling them with daily activities, a practice known as dynamic pulse-oximetry.

Software Packages and Tools for the Analysis of Continuous Glucose Monitoring Data

The advancement of technology in the field of glycemic control has led to the widespread use of continuous glucose monitoring (CGM), which can be nowadays obtained from wearable devices equipped with a minimally invasive sensor, that is, transcutaneous needle type or implantable, and a transmitter that sends information to a receiver or smart device for data storage and display. This work aims to review the currently available software packages and tools for the analysis of CGM data. Based on the purposes of this work, 12 software packages have been identified from the literature, published until December 2021, namely: GlyCulator, EasyGV (Easy Glycemic Variability), CGM-GUIDE^© (Continuous Glucose Monitoring Graphical User Interface for Diabetes Evaluation), GVAP (Glycemic Variability Analyzer Program), Tidepool, CGManalyzer, cgmanalysis, GLU, CGMStatsAnalyser, iglu, rGV, and cgmquantify. Comparison of available software packages and tools has been done in terms of main characteristics (i.e., publication year, presence of a graphical user interface, availability, open-source code, number of citations, programming language, supported devices, supported data format and organization of the data structure, documentation, presence of a toy example, video tutorial, data upload and download, measurement-units conversion), preprocessing procedures, data display options, and computed metrics; also, each of the computed metrics has been analyzed in terms of its adherence to the American Diabetes Association (ADA) 2017 international consensus on CGM data analysis and the ADA 2019 international consensus on time in range. Eventually, the agreement between metrics computed by different software and tools has been investigated. Based on such comparison, usability and complexity of data management, as well as the possibility to perform customized or patients-group analyses, have been discussed by highlighting limitations and strengths, also in relation to possible different user categories (i.e., patients, clinicians, researchers). The information provided could be useful to researchers interested in working in the diabetic research field as to clinicians and endocrinologists who need tools capable of handling CGM data effectively.