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Background and aims: Onboarding of the FreeStyle Libre, an intermittently scanned continuous glucose monitoring (isCGM) device, was pre-dominantly conducted in-person prior to the Covid-19 pandemic. However, onboarding rapidly become virtual due to enforced social distancing restrictions. This audit aimed to determine if onboarding method impacted on glycaemic outcomes and engagement statistics in people living with diabetes (pwD). Method(s): PwD who started FreeStyle Libre between January 2019 and March 2022, had their mode of onboarding recorded and had >=70% data were identified and included within the audit. Glycaemic indices and engagement statistics (previous 90 day averages) were obtained from LibreView (Abbott, USA) three months after the last person was onboarded, and compared using linear models, adjusting for FreeStyle Libre duration, %active (where appropriate), age and sex. Result(s): From 1007 eligible participants (in-person 44% [n = 445];virtual 56% [n = 562]), FreeStyle Libre usage duration was greater for those onboarded in-person vs. virtually (974[891,1101) vs. 420[280,564] days [p < 0.001]). There were no significant differences in glycaemic or engagement indices between in-person and virtual onboarding methods: average glucose (10[9,11]) vs. 10[9,11])mmol/l), %time very-low (<3.0mmol/l, 0[0,1]) vs. 0[0,1]%), %time low (3.0-3.8mmol/ l, 2[1,4] vs. 2[1,4]), %time in range (3.9-10.0mmol/ l, 54[+/-17] vs. 53[+/-19]%), %time high (10.1-13.9mmol/ l, 27[21,31]) vs. 26[21,31]%), %time very-high (>13.9mmol/l, 14[6,24] vs. 15[7,26]%), %active (96[90,100] vs. 94[87,99]%) or scans/day (11[8,15] vs. 10[7,14]). Conclusion(s): There were no differences in glycaemic outcomes or engagement indices between pwD between onboarding methods. Virtual onboarding using online videos for isCGM is as equally effective as face to face.
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Objectives: Insulin optimisation requires review of glucose monitoring;Covid-19 posed challenges to this. We evaluated DBm -a remote monitoring platform utilising a glucometer and smartphone app. Method(s): Evaluation was from January to November 2021. Inclusion criteria was insulin treated diabetes with HbA1c greater than 68mmol/mol. HbA1c, demographics, frequency of CBG uploads and interactions with clinicians were collected. Result(s): 97 patients were offered DBm. 48.5% used the app. There were no statistically significant differences in gender (p = 0.05), age (p = 0.36), type of diabetes (p = 0.13) or deprivation index (p = 0.96) between users and non-users. Patients of white ethnicity were less likely to use the platform (p = 0.01). Amongst users, 70% had a reduction of HbA1c of at least 5mmol/mol over six months, with a mean reduction of 25.6mmol/mol (p = 0.01). There was no difference in age (p = 0.64), gender (p = 0.4), and type of diabetes (p = 0.23) between responders and non-responders. There was also no difference in number of call back requests generated by patients (p = 0.32) or number of CBG uploads (p = 0.899) between responders and non-responders. Conclusion(s): Uptake of the remote monitoring solution was just under 50%, with no evidence of digital exclusion, although the finding that white ethnicity patients were less likely to use the system needs further exploration. Most users had improved glucose control, but there was no association with numbers of tests or call back requests. This study demonstrates that insulin optimisation can effectively be delivered using a remote glucose monitoring system. Future work will explore patient experience and patient satisfaction.
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Aims: Continuous glucose monitoring (CGM) is widely used in pregnant women with pre-gestational diabetes, but optimal targets have not been defined in gestational diabetes. Previous work identified mild hyperglycaemia in pregnant women without gestational diabetes, but with risk factors such as obesity. We aimed to examine CGM metrics and patterns of glycaemia in women with gestational diabetes compared to healthy pregnant women with comparable risk factors. Method(s): We recruited 73 healthy women with >1 risk factor (gestational diabetes excluded using Covid-19 criteria, OGTT) and 200 women with gestational diabetes (NICE and interim-Covid- 19 criteria) from antenatal clinics at 28 weeks' gestation. A Dexcom G6 CGM device was cited on the non-dominant upper arm. Result(s): Women with gestational diabetes had significantly higher weight (mean +/- SEM 95.7 kg +/- 1.3 Vs 85.4 kg +/- 2.2) and BMI (36.0 +/- 0.5 Vs 31.3 +/- 0.7) compared to healthy pregnant women (p < 0.01). Women with gestational diabetes had significantly higher mean CGM-glucose (mean +/- SEM 5.6 +/- 0.01 Vs 5.4 +/- 0.01mmol/l;p < 0.01), significantly altered time-below- range (median(IQR);1.0% (0.2-2.9) vs 2.5% (0.7-5.5);p < 0.05) and time-in- range (95.0% (91.1-97.9) vs 94.5% (87.9-96.2);p < 0.05) but comparable time-above- range to healthy women with risk factors. Diurnal glucose profiles in women with gestational diabetes were comparable to healthy women between 14:00 and 18:00, but demonstrated significant increases in glucose at all other time points during the 24-h cycle (p < 0.01). Conclusion(s): Mean CGM glucose is the most reliable CGM metric to distinguish women with gestational diabetes from healthy pregnant women with risk factors.
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Aims: We have shown previously in 93 individuals with type 1 diabetes using the FreeStyle Libre flash glucose monitor that the week after their first Covid-19 vaccination, the percent 'time in target range 3.9-10mmol/ l' (%TTR) average went from 55.2%-> 52.4% (effect size -5.1%) with 58% of people recording a fall. 47 (50%) people with HbA1c < 56mmol/mol %TTR went from 69.3-> 63.5 (-8.3%) and 24 (25%) people using insulin+oral treatment 56.7%-> 50.7% (-10.1%). We have now repeated the exercise after the most recent Covid-19 vaccination. Method(s): FreeStyle Libre data and medical records of the same patients from the previous study were examined for the week before and week after their most recent Covid-19 vaccination. () in the results section show change in %TTR as % of the prior value to show effect size. TTR% results from 2 weeks before and after were also considered. Result(s): Median time between vaccines was 38 weeks IQR (37-40). After the latest vaccination average %TTR average went from 51.1%-> 49.8% (-2.5%) with a reduction found in 54% of patients. Impact on the 39 patients with HbA1c < 56mmol/mol -% TTR from 66.2%-> 61.8% (-6.5%) and the 20 (25%) patients using insulin+oral %TTR from 48.2%-> 47.1% (-2.2%). 65% of the patients whose %TTR fell previously, fell again after this vaccination. Fortnight average %TTR 53.5%-> 52.1% (-2.7%) whereas in the previous study across fortnight %TTR 55.4%-> 54.0% (-2.4%). Conclusion(s): The perturbation effect on blood glucose with 1st Covid-19 vaccination was seen again in the latest vaccination but reduced in magnitude, confirming that a significant group of type 1 diabetes individuals' glycaemic control is still being impacted by the Covid-19 vaccination.
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Background: My Diabetes My Way (MDMW) is NHS Scotland's interactive website, offering education, structured eLearning and online records access for people living with diabetes. We aimed to analyse user activity during the last 12 months. Method(s): Data were collected during the period from November 2021 to October 2022. Registration and user audit logs were analysed, observing activity across all website content and features. Result(s): An average of 62,853 pages were accessed on the public website each month. Significant activity increases were observed in December 2021 (n = 81,237). There were increased views in September 2022 (n = 76,502) and October (n = 73,039) The top five pages accessed were;Coronavirus: advice for people living with diabetes (n = 12,478), FreeStyle Libre (n = 4325), Emergency advice (n = 1576), Blood pressure-reducing your risks of complications (n = 1559) and Blood glucose monitoring and HbA1c targets (n = 1485). eLearning: During this period, 382 individuals completed one of 11 QISMET-accredited structured eLearning courses. eLearning course usage increased in relation to patient awareness activity. Social Media: There are currently 3919 Facebook and 3600 Twitter followers. Records Access: 67,655 patients had registered to access their data and 35,157 had actively accessed their records by the end of October 2022. Patient feedback remains highly positive. Conclusion(s): MDMW is a consistent and reliable resource for people with diabetes and their families to access at any time online. User statistics continue to rise, while latest development plans include the addition of new Patient Reported Outcome Measures, risk prediction features, and enhanced sharing of data with the healthcare team.
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In situations where it is difficult for patients to visit hospitals, such as the coronavirus disease pandemic, it is important to more detailly predict hemoglobin A1C (HbA1c) from flash glucose monitor (FGM) data. CGM data over 14 days can be obtained from a FGM sensor;therefore, there are many options for extracting the duration from which glucose levels are derived. Thus, the extracted durations were closely studied to determine which mean glucose levels can predict HbA1c more accurately. Seventy-three outpatients with type 2 diabetes mellitus underwent HbA1c testing, wore a FGM (FreeStyle Libre Pro), and did not change diabetic treatments, on a hospital visit. FGM data over 24 h 13 days (from 00:00 on day 2 to 24:00 on day 14 [FGM attachment: day 1]) were analyzed. The mean glucose levels were calculated corresponding to the following durations: 1 day: day 2 ~ day 14 (n=13), 2 days: days 2-3 ~ days 13-14 (n=12) 12 days: days 2-13 ~ days 3-14 (n=2), 13 days: days 2-14 (n=1) [total 91 durations] (extracted mean glucose levels). Data were analyzed in all patients (n=73), in patients with hypoglycemia in the 13 days (Hypo) group (n=40), and in patients without hypoglycemia in the 13 days (Nonhypo) group (n=33). In all patients, HbA1c was correlated to all 91 extracted mean glucose levels (r=0.76-0.86, p<0.001). HbA1c was the most significantly correlated to the mean glucose levels over 13 days (days 2-14). "Correlation coefficients between HbA1c and extracted mean glucose levels" ("r, HbA1c, EMGL") were also correlated to number of extracted days for the extracted mean glucose levels (r=0.80, p<0.001 [n=91]). In the Hypo group, HbA1c was correlated to all 91 extracted mean glucose levels (r=0.55-0.73, p<0.001). The mean glucose levels over 13 days (days 2-14) were the most significantly correlated to HbA1c. "r, HbA1c, EMGL" correlated to the number of extracted days for the extracted mean glucose levels (r=0.68, p<0.001;Fig. 2). In the Nonhypo group, HbA1c was correlated to all 91 extracted mean glucose levels (r=0.73-0.87, p<0.001). The mean glucose levels over 12 days (days 2-13) were the most significantly correlated to HbA1c. "r, HbA1c, EMGL" correlated to the number of extracted days for the extracted mean glucose levels (r=0.61, p<0.001). The results of the present study are consistent with that of a previous study reporting that the minimum duration needed to estimate time in range over 90 days is 14 days. In the prediction of HbA1c using data from one FGM sensor, prolonged measurement can make the glucose management indicator more accurate. Especially for patients with hypoglycemia, the importance of prolonged measurement may be applicable.
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Objective: The primary objective was to explore indications for inpatient glucose telemetry. Method(s): The inpatient glucose telemetry (IGT) has been instituted at the peak of COVID cases at an urban academic medical center. Besides remote glucose monitoring due to infection isolation, feasibility, reliability and indications for IGT were investigated in hospitalized patients. Result(s): IGT was used in n=75 patients in critical and non-critical care inpatient settings. In addition to remote glucose monitoring due to infection isolation, feasibility, reliability and indications for IGT were investigated in cases requiring hypoglycemia prevention, multimorbidity, fingertip bruising, transplant, cancer, intensive insulin management, brain/psychiatric disease/injury, and inpatient rehabilitation.IGT was used in patients hospitalized with personal home continuous glucose monitoring system and patients with recurrent diabetes hospitalizations. Conclusion(s): Inpatient glucose telemetry indications must be expanded beyond hypoglycemia prevention.
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There is a consensus that fee-for-service reimbursement does too little to encourage the provision of high-value care. Our Enterprise, an integrated payer-provider based in Pittsburgh, created an alternative compensation model for endocrinologists. Our plan introduces a gradual shift in the role of endocrinologists from clinical duties to a more collaborative role with their primary care colleagues. Considering that most patients with diabetes are managed under primary care, this shift allows endocrinologists to support primary care physicians (PCPs) in managing patients with diabetes and other endocrine-related illnesses while decreasing the number of traditional in-office referrals to endocrinology. Despite the unexpected changes brought on by COVID, in first 9 months of the compensation model, we observed its impact on care delivery as well as the relationship between participating specialists and PCPs. Practice- and provider-level quality data has shown improvement in diabetes-specific quality metrics. In one year, 16 out of 54 target practices earned NCQA recognition for diabetes management. A total of 88% of participating PCPs reported a satisfaction score > 90% with the new plan. Ultimately, our model shows promise as a replacement for fee-for-service compensation, with a likelihood of lowering costs and improved quality of care.Copyright © 2022 The Author(s)
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Introduction: COVID-19 pandemic and eventual lockdown gave a major boost to online heavy action gaming like PUBG. Adolescents are finding gaming the best avenue to connect and socialize with friends addressing their needs of human interaction and coping with the pandemic. Objective(s): This pilot study aimed to evaluate changes in glycemic control and the role of online gaming during the exceptional time of COVID-19 complete lockdown in a cohort of children with T1DM and 2 years after. Method(s): We evaluated children with T1DM on basal bolus regimen who were monitored using the FreeStyle Libre glucose monitoring system. Analysis were extracted from downloads and compared to timing after lockdown. Result(s): A 47 patients (39 males) with mean age 13.6 +/- 2.8 years and duration of diabetes (5.2 +/- 1.3 years) were followed up. The glycemic control was worse during than after lockdown mean glucose management indicator of 8.3% versus 7.2% (p = 0.001). There was higher time above range of 44%, lower time in range of 39% and time below range of 6% during quarantine than after (p < 0.001 for all). A significantly higher coefficient of variation (CV) indicating an increased glucose variability in the lockdown period compared to postlockdown was observed (42.6% vs. 37.2%, p = 0.011).Time spent playing online games occupied>=8 h daily in 73% together with decreased sleep duration. They had more snacking leading to increase insulin bolus by 32% (p = 0.002) and weight gain by 12% (p = 0.006). Conclusion(s): Glycemic controlin T1DM adolescents addicted to online gaming worsened during restrictions of COVID-19 pandemics. Maintaining regular physical activity in a safe home environment with dietary and insulin dose recommendations is an essential strategy for young individuals to better control glycemic excursusions. As we mark 2 years of the COVID-19 pandemic, a central pillar of building more attention to sedentary behavior and lifestyle factor management in patients with T1DM if unprecedented waves of the global pandemic hit and beyond.
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Introduction: During the COVID-19 pandemic lockdown, glycemic control in children with Type 1 diabetes under our care improved. One of the reasons could be due to inconsistency in the management of diabetes at school. In order to address this possibility, our unit commenced a Quality Improvement Project to improve glycemic control of children with diabetes at school. Objective(s): Our Quality Improvement Project involved developing an educational tool called AIM - Activity, Insulin, Meals, an aid to improve glycemic control at school based on insulin regime and type of glucose monitoring. AIM tool provides advice on insulin management at meal times and PE [Physical Exercise] along with a PE guide with specific advice on glycemic control around exercise. Method(s): We produced separate AIM guidance for the following groups: 1. Multiple Dose Insulin regime [MDI]using self-monitoring of blood glucose [SMBG] 2. MDI regime using CGM [Dexcom G6] 3. MDI regime using Flash Glucose scanning [Libre] 4. Continuous Subcutaneous Insulin Infusion [CSII] using SMBG 5. CSII using CGM 6. CSII using Libre A sample AIM tool [1st & last pages] for the group using MDI with CGM is inserted below. We introduced the AIM guidance to patients, parents, and school staff from mid October 2021 to end of February 2022. We are allowing 3 months to embed the guidance in the day to day management of children and young people with diabetes at school. We will then analyze the impact of the AIM educational tool on glycemic control by comparing HbA1c, time in range, average glucose, and coefficient of variability during a 6 weeks period. Post introduction data collection with start in June. Result(s): Awaiting post introduction data collection for analysis. We are likely to have 25 patients with complete pre and post introductory data. Conclusion(s): Based on the results. However we hope clear instructions using our educational tool 'AIM' will improve glycemic control in school going children and young people.
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Objectives To evaluate whether intermittently scanned continuous glucose monitoring (isCGM) with optional alarms (FreeStyle Libre 2) improves glycaemia as measured by HbA1c and sensor-based gluco-metrics, patient reported outcome measures (PROMS) and cost-effectiveness compared with selfmonitoring of blood glucose (SMBG). Design Flash UK is a multicenter, open-label, two arm, parallel, randomised controlled trial delivered in 7 specialist hospital diabetes clinics and 1 primary care centre. Participants 156 people with Type 1 diabetes, age 16 years and over treated with either multiple daily insulin injections or insulin pump therapy with HbA1c 7.5%-11% were randomised. Interventions Participants were randomised (1:1) to the FreeStyle Libre 2 (n = 72) or standard care with SMBG (n = 69). Participants were reviewed at 4, 12 and 24 weeks post-randomisation. Education and treatment optimisation was provided to both groups at randomisation, 4 and 12 weeks. Participants in the SMBG arm wore blinded glucose sensor (Freestyle Libre Pro) during the last 2 weeks of the study;all participants wore a 2-week blinded sensor prior to randomisation. All study visits were conducted either inperson or virtually owing to the COVID-19 pandemic. Main outcome measures The primary outcome was HbA1c at 24 weeks, analysed by intention to treat. Secondary outcomes included glucose time in range (3.9 to 10mmol/l), time below and above range and glucose variability. PROMS included EQ-5DL-5L, Type 1 Diabetes Distress Scale, Diabetes fear of injecting and self-testing, Diabetes Eating Problem Survey, Diabetes Treatment Satisfaction, Patient Health Questionnaire and The Glucose Monitoring Satisfaction Survey. Economic evaluation included healthcare resource use, insulin usage and Freestyle Libre 2 utilisation. Results & Conclusion Results and conclusions will be presented during the 15th International Conference on Advanced Technologies & Treatments for Diabetes, April 27 to 30th Barcelona, Spain and Online.
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“Newer Continuous Glucose Monitoring Systems” Satish K. Garg, MD Professor of Medicine and Pediatrics, Director of adult Diabetes program, University of Colorado Denver and Barbara Davis Center for Diabetes, Aurora, Colorado. Over the past decade there have been many advances in diabetes technologies, such as Continuous Glucose Monitoring devices/systems (CGMs), insulin-delivery devices, and hybrid closed-loop systems. There have been significant advances in CGMs in the past decade. In fact, ten years ago very few people use to believe in the use of CGMs, even though they had been available for the past two decades. Many providers used to question who, why, and when will patients ever use CGMs similar to the questions asked about Self-Monitoring of Blood Glucose (SMBG) about four decades ago. At the time of this writing, more than five million people world-wide are using a CGM for their diabetes management, especially those who require insulin (all patients with Type 1 diabetes (T1D) and about 20% of patients with Type 2 diabetes (T2D)). Total sales of all CGMs now exceeds more than $7 billion and the use of SMBG is going down every day. Most of the CGMs have improved their accuracy significantly in the past two decades. I still remember doing studies on the GlucoWatch and earlier versions of Dexcom STS where mean absolute relative difference (MARD) used to be in the range of 15-26%. Now most of the CGMs (Guardian by Medtronic, G6 by Dexcom, and Libre 2 by Abbott) have single-digit MARD. In addition, the majority of the new CGMs do not require calibrations and the newer CGMs last for 10-14 days. An implantable CGM by Senseonics (Eversense®) is approved in the USA for 3 months and a different version is approved in Europe for 6 months. FDA has still not approved the 6-month version of Eversense® implantable sensor in the USA, which also has single-digit accuracy. The newer CGMs that are likely to be launched in the next 3-6 months;hopefully around the ATTD Conference, include 10.5-day Dexcom G7 (60% smaller than the existing G6), 7-day Medtronic Guardian 4, 14-day Libre 3, and 6-month Eversense®. Most of the newer CGM data can be viewed on Android or iOS/iPhone smart devices, and in many instances they have several features like predictive alarms and alerts, easy insertion, automatic initialization (in some instances down to 27 mins, Dexcom G7) with single-digit MARDs. It has also been noticed that arm insertion site might have better accuracy than abdomen or other sites like the buttock for kids. Lag time between YSI and different sensors have been reported differently, sometimes it's down to 2-3 mins;however, in many instances, it's still 15-20 mins. Diabetes effects communities of color disproportionately higher. For example, the highest prevalence of diabetes in the USA is amongst Native Americans (14.7%), which is nearly two times higher than Caucasians. African Americans and Hispanics also have higher prevalence of diabetes in the USA. It's also known that LatinX, African Americans, and Native Americans are much less likely to be offered new technologies like continuous subcutaneous insulin infusion (CSII/insulin pumps) and CGMs. Use of technology, especially CGMs, is expected to remove many of the social barriers and disparities in care for people with diabetes. A large database during the COVID-19 pandemic recently reported better Time-in-Range (TIR) in patients with diabetes irrespective of their ethnic background. However, the baseline TIR was significantly lower for minorities as compared to Caucasians. I believe the future will bring a larger increase in the use of CGMs for people with insulin-requiring diabetes (estimated at more than 100 million people globally) and those with T2D on non-insulin therapies (estimated at more than 400 million people globally). I also envision an increase in the number of pre-diabetes patients (estimated at more than 200 million people globally) using CGMs so that early medical intervention for diabetes management can be entertained. The intermittent or continuou use of CGM would depend upon the clinical needs. Needless to say, healthy individuals without diabetes (who can afford CGMs) might even use these technologies for self-evaluation of their glucose profiles after meals.
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Background and Aims: After the artificial pancreas (AP) trials performed in 2016-7 with DiAs system, during the COVID-19 pandemic the first outpatient clinical trial was carried out in Argentina. The main objective was to evaluate the feasibility of running full closed-loop (FCL) algorithms in an own and free platform developed from open-source resources. Methods: The ARG project (Automatic Regulation of Glucose) aims at developing a robust AP algorithm prioritizing patient autonomy. The evolution of the project phases is summarized in the figure. The last step towards this objective was the implementation of a FCL algorithm in our InsuMate platform and its evaluation in an outpatient setting. Five adults with DMT1 completed one week of study, consisting in 3 days of open-loop (OL) followed by 3 days of FCL (i.e., without CHO counting and without delivering meal priming insulin boluses). Accu-Chek pumps and Dexcom G6 CGMs were used. Results: When analyzing the full duration of the trial, the time in range increased in FCL control vs. OL, while the time above range decreased, as did the mean BG. On the other hand, the time below range and the time in severe hypoglycemia remain similar across methods, both achieving the ADA recommended values. The FCL showed greater improvement by the end of the trial, particularly for daytime metrics. InsuMate properly operated in FCL for an average of 95.4% of time. Conclusions: It can be concluded from this experience that the outpatient automatic regulation of glucose levels using the ARG algorithm and Insumate platform is feasible, safe, and effective. (Figure Presented).
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Background and Aims: During the recent COVID-19 pandemic, telemedicine has been used in type 1 diabetic patients to monitor and check metabolic balance, through specific platforms for downloading data. Aim of our study is to describe the experience of remote training, initiation and one-year follow-up of insulin pump therapy and continuous glycemic monitoring in four poorly controlled type 1 diabetic patients, presenting several hypoglycemic episodes. Methods: In April 2020 four patients were determined to be CSII therapy candidates, primarily to reduce hypoglycemic episodes. The remote training consisted of 3 or 4 sessions focused on self-management of advanced insulin therapy and technical aspects of pumps. They occurred in patients' homes using Skype™ for synchronous teleconferencing. After the training, two patients transitioned to the MiniMed 670G system, one to Omnipod and one to Accu-Chek Solo. Insulin pump informations and CGM data were remotely downloaded, and follow-up telemedicine visits were scheduled. Results: As early as two weeks after the insulin pump has been implanted, a hypoglycemic episode reset was recorded in all patients and the time in range (TIR) was greater than 90% in three of the four patients. During one-year remote follow-up, all patients maintained a satisfactory %TIR and glycemic variability, with a limited number of hypoglycemic events. One patient had COVID-19 disease and one became pregnant: these conditions were well managed by telemedicine service. Conclusions: These findings support the effectiveness of telemedicine for remote training, initiation, and follow-up of insulin pump therapy, ensuring a positive control of glycometabolic outcomes.
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Aims: Regional variations in adoption of real-time continuous glucose monitoring (RT-CGM) may be reflected in population-level metrics of glycaemic control. In this observational study, we characterised the impact of two different RT-CGM systems in three European countries. Methods: Anonymised data from users in Germany, Sweden, and the UK who transitioned from Dexcom's G5 to its G6 RT-CGM System in 2018 and uploaded data from both systems were analysed. The G6 (but not G5) feature set includes a predictive alert designed to mitigate hypoglycaemia. Endpoints were time in range (TIR, 3.9-10.0mmol/ L), retention rates, and intraday/interday device utilisation. Metrics were computed for three month intervals in the two year study window following G6 launches. Results: Utilisation among G5-to- G6 transitioners improved across all countries, and the user retention rate at the end of the study was 85.5%. Overall mean TIR increased from 60.1% (final three months of G5) to 62.8% (two years after switching to G6), and the proportion achieving >70% TIR increased from 28.3% to 37.9%.Regional TIR differences were observed in 2020 and may have been influenced by covid-19 lockdown approaches. Pandemic-related increases in TIR were evident in the UK and Germany, where stringent lockdown measures were introduced;TIR changes in Sweden, where lockdowns were less restrictive, were negligible. Conclusions: Population-level analysis of RT-CGM data can reveal nationwide trends and disparities in the adequacy of glycaemic control. These may be impacted by factors including features and performance attributes of the RT-CGM system itself, and by public health measures such as lockdowns.
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Aims: To evaluate the effectiveness of an interactive virtual carbohydrate counting course on glycaemic indicators in people with type 1 diabetes. Methods: An observational study of glycaemic management following a virtual carbohydrate counting course, comprising of a weekly two hour session over three weeks on Microsoft Teams. Key metrics monitored from flash glucose monitoring (FGM) at baseline, three and six months: time in range (TIR), time below range (TBR) estimated glycated haemoglobin (eHbA1c) and glucose variability (GV). A paired two sample for means T-test was used to determine statistical significance. Results: 26 participants completed the course (14 male, 12 female). Baseline and three month data was available for 17 participants (11 male, 6 female). Six month data was available for 14 participants, (9 male, 5 female). Significant improvements were observed at six months for GV (p = 0.05). No significant differences were observed at three months. Conclusions: One metric (GV) showed significant improvement at six months. The lack of significant improvements in other parameters and at three months could be related to many factors. There is a paucity of research on virtual carbohydrate counting courses for comparison purposes. The National Institute of Clinical Excellence (NICE) recommends offering structured education to all people with type 1 diabetes. The option of a virtual course has allowed for continuation of education during the covid-19 pandemic, which otherwise would have been absent. Further research is required to inform clinical practice and service development and provide further insight into lack of improvement in some parameters.
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Objective: Flash Continuous Glucose Monitoring (flash CGM) has been rapidly accepted in real life clinical setting. Methods: We conducted a cross sectional study across two centres, delivering the similar standard of care, over three years (n=362), in patients who utilised FreeStyle Libre Pro CGM to understand glycemic metrics and variability. The key glycemic metrics;TIR, Time Below Range (TBR), Time Above Range (TAR), estimated HbA1c, average glucose was analysed. Descriptive statistics, Pearson r and ANOVA were utilised for analysis. Results: Overall, in total 24.8% (90/362) were in TIR >70%, with 14.7% (18/122) patients in 2018, 17.6% (30/170) in 2019 and 60% (42/70) in 2020. In total 37% (134/362) were in TAR < 25%, 29.5% (36/122) in 2018, 28.2% (48/170) in 2019 and 71.4% (50/70) in 2020. In total 45.3% (164/362) were in TBR < 4%, 44.2% (54/122) in 2018, 46.4% (79/170) in 2019 and 44.2% (31/70) in 2020. Overall, 9.3% (34/362) achieved all three metrices (TIR >70%, TAR < 25%, TBR < 4%), with 4.9% (6/122) in 2018, 7.6% (13/170) in 2019, 24.2 (17/70) in 2020. There was a significant negative correlation between the eHbA1c and TIR (Pearson r – 0.74, 95% CI -0.79 to -0.69, p < 0.0001). There was significant improvement in TIR and TAR over three years. The eHbA1c (6.5%) and average glucose (139.7mg/dl) were lowest in the year 2020, which were comparable with values in previous years. Lesser hypoglycaemic events were noticed in CGM. (figure). [Formula presented] Discussion/Conclusion: There was a significant change in the glycemic metrics. We attribute the remarkable improvement, over three years, to the better awareness in the patients to manage diabetes, greater adoption of guideline directed, contemporary therapeutics including SGLT2 inhibitors, advanced insulins. This coincided with the COVID-19 induced fear of mortality and lockdown led better metabolic health, that resulted in better self-care of diabetes.
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Objective: Continuous glucose monitoring (CGM) has demonstrated benefits in managing inpatient diabetes. We initiated this prospective pilot study to determine the feasibility and accuracy of CGM in high-risk cardiac surgery patients with diabetes after their transition of care from the intensive care unit(ICU). Methods: Clarke Error Grid(CEG) analysis was used to compare CGM and point-of-care(POC) measurements. Mean absolute relative difference(MARD) of the paired measurements was calculated to assess the accuracy of the CGM for glucose measurements during the first 24 hours on CGM, the remainder of time on the CGM as well as for different chronic kidney disease(CKD) strata. Results: Overall MARD between POC and CGM measurements was 14.80%. MARD for patients without CKD IV and V with eGFR < 20 ml/min/1.73m2 was 12.13%. Overall, 97% of the CGM values were within the no-risk zone of the CEG analysis. For the first 24 hours, a sensitivity analysis of the overall MARD for all subjects and for those with eGFR > 20 ml/min/1.73m2 was 15.42% (+/- 14.44) and 12.80% (+/- 7.85) respectively. Beyond the first 24 hours, overall MARD for all subjects and for those with eGFR > 20 ml/min/1.73m2 was 14.54% (+/- 13.21) and 11.86% (+/- 7.64) respectively. Discussion/Conclusion: CGM has great promise to optimize inpatient diabetes management in the noncritical care setting and after the transition of care from the ICU with high clinical reliability, accuracy, and superior detection of hypoglycemia. More studies are needed to further assess CGM in patients with advanced CKD.
ABSTRACT
Objective: The MiniMed 670G hybrid closed-loop (HCL) system was the first FDA-approved automated insulin delivery system for patients with type 1 diabetes (T1D). We aimed in this study to evaluate a 10-day initiation protocol in adults with T1D from multiple daily injections (MDI) to MiniMed 670G HCL system in achieving glycemic control. Methods: We recruited individuals with T1D on MDI, aged 18-65 years with HbA1C less than 12.5% (113 mmol/mol) in an open-label, single-arm study for 3 months. The primary outcome was achievable Time in Range (TIR), 3.9-10 mmol/L (70-180 mg/dL), over the first 84 days after initiation of the Auto Mode of the HCL system. The participants went through a planned 10-day protocol of 2 days to assess their readiness for the HCL system, followed by 5 days of system training in groups of 3-5 individuals, then 3 days of Manual Mode use before starting the Auto Mode. We collected the real-time continuous glucose monitoring (CGM) data at baseline and the CGM, pump settings, and system usage data over the first 84 days of Auto Mode use. Statistical analysis was performed using STATISTICA 12 (StatSoft, Tulsa, USA). Results: We enrolled 24 individuals (13 females), aged 28.8±9 years with T1D for 12.1±7.4 years, mean HbA1C of 8.9±1.4% (74±15.3 mmol/mol), TIR of 48.96±17.9%, time below range (TBR) of 5.96±7.6%, and time above range (TAR) of 43.42±16.8%. One female did not complete the study as she became pregnant. During the first 84 days on the Auto mode of the HCL system, the participants had a median sensor usage of 86% of the time and spent a median time of 83% in Auto Mode. TIR increased to 67.22±13.2% (P = 0.0003), TBR decreased to 3.57±2.9% (P = 0.16), and TAR decreased to 29.22±13.2% (P = 0.0024). The mean HbA1C improved to 7.5±0.8% (59±9.3 mmol/mol) by the end of the study (P = 0.0001). No diabetic ketoacidosis or severe hypoglycemia episodes were recorded during the study. Discussion/Conclusion: Previous studies on MiniMed 670G recruited participants with previous insulin pump experience, while our patients were insulin pump naïve. We used the same 10-day onboarding protocol that was tested before to initiate the 670G HCL system in a study of the pediatric age group reaching a TIR of 75.6±6.9% three months after initiation. The adult participants in our study had significant improvement in HbA1C and TIR, although it was less than what was reported in the pediatric population and this could be related to the conduction of the study during the COVID-19 pandemic. The 670G HCL system improved glycemic control without worsening TBR.