The role of real-time continuous glucose monitoring in diabetes management and how it should link to integrated personalized diabetes management.

Diabetes is a complex metabolic condition that demands tailored, individualized approaches for effective management. Real-time continuous glucose monitoring (rtCGM) systems have improved in terms of design, usability and accuracy over the years and play a pivotal role in the delivery of integrated personalized diabetes management (iPDM). iPDM is a comprehensive multidisciplinary approach that combines individualized care strategies utilizing technologies and interventions and encourages the active involvement of the person with diabetes in the care provided. The use of stand-alone rtCGM and its integration with other diabetes technologies, such as hybrid automated insulin delivery, have enabled improved glycaemic and quality of life outcomes for people with diabetes. As the uptake of rtCGM and associated technologies is increasing and becoming the standard of care for people with diabetes, it is important that efforts are focused on associated goals such as reducing health inequalities in terms of access, aligning structured education with rtCGM usage, choosing the right technology based on needs and preferences, and minimizing burden while aiming for optimal glucose outcomes. Utilizing rtCGM in other settings than outpatients and in diabetes cohorts beyond type 1 and type 2 diabetes needs further exploration. This review aims to provide an overview of the role of rtCGM and how best to link rtCGM to iPDM, highlighting its role in enhancing personalized treatment strategies.

Real-time continuous glucose monitoring immediately after severe hypoglycaemia requiring emergency medical services: A randomised controlled trial.

AIMS: Severe hypoglycaemia requiring emergency medical services remains prevalent despite advances in all aspects of diabetes self-management. Real-time continuous glucose monitoring (RTCGM) technologies can reduce the risk of severe hypoglycaemia for adults with type 1 diabetes, but the impact of these devices has not been assessed in the acute phase after an episode of severe hypoglycaemia. METHODS: We recruited and randomised 35 adults with type 1 diabetes in the acute period after an episode of severe hypoglycaemia requiring emergency medical services and randomised participants to RTCGM with alerts and alarms, or usual care with self-monitored blood glucose for 12 weeks with intermittent blinded CGM. The primary outcome was the difference between groups in percentage time spent in hypoglycaemia (≤3.0 mmol/L, 55 mg/dL). RESULTS: Thirty participants completed the study (median (IQR) age, duration of diabetes, and BMI was 43 (36-56) years, 26 (19-37) years, and 24.9 (21.9-29.0) kg/m2 , respectively). Sufficient CGM data was available for 15 participants in RT-CGM group and 8 in SMBG group for the primary outcome analysis. The RTCGM group had a significantly larger reduction in exposure to glucose below 3.0 mmol/L (RTCGM -0.16 [-1.23 to 0.01] vs. SMBG 1.58 [0.41 to 3.48], p = 0.03) and episodes of nocturnal hypoglycaemia (RT-CGM -0.03 [-0.15 to 0.02] vs. SMBG 0.05 [-0.03 to 0.40], p = 0.02). Episodes of severe hypoglycaemia were significantly lower in the RTCGM group (RTCGM 0.0 vs. SMBG 4.0, p 0.04). CONCLUSIONS: RTCGM implemented acutely after an episode of severe hypoglycaemia is feasible and clinically effective with important implications for hypoglycaemia management pathways and self-monitoring cost effectiveness.

Adaptive bolus calculators for people with type 1 diabetes: A systematic review.

AIM: To conduct a systematic review of studies assessing adaptive insulin bolus calculators for people with type 1 diabetes (T1D). METHODS: Electronic databases (Medline, Embase and Web of Science) were systematically searched from date of inception to 13 October 2022 for single-arm or randomized controlled studies assessing adaptive bolus calculators only, in children or adults with T1D on multiple daily injections or insulin pumps with glycaemic outcomes reported. The Clinicaltrials.gov registry was searched for recently completed studies evaluating decision support in T1D. The quality of extracted studies was assessed using the Standard Quality Assessment criteria and the Cochrane Risk of Bias assessment tool. RESULTS: Six studies were identified. Extracted data were synthesized in a descriptive review because of heterogeneity. All the studies were small feasibility studies or were not suitably powered, and all were deemed to be at a high risk of performance and detection bias because they were unblinded. Overall, these studies did not show a significant glycaemic improvement. Two studies showed a reduction in postprandial time below range or an incremental change in blood glucose concentration; however, these were in controlled environments over a short duration. CONCLUSIONS: There are limited clinical trials evaluating adaptive bolus calculators. Although results from small trials or in-silico data are promising, further studies are required to support personalized and adaptive management of T1D.

The impact of socio-economic deprivation on access to diabetes technology in adults with type 1 diabetes.

BACKGROUND: With advances in technology, there is an emerging concern that inequalities exist in provision and diabetes outcomes in areas of greater deprivation. We assess the relationship between socio-economic status and deprivation with access to diabetes technology and their outcomes in adults with type 1 diabetes. METHODS: Retrospective, observational analysis of adults attending a tertiary centre, comprising three urban hospitals in the UK. Socio-economic deprivation was assessed by the English Indices of Deprivation 2019. Data analysis was performed using one-way ANOVAs and chi-squared tests. RESULTS: In total, 1631 adults aged 44 ± 15 years and 758 (47%) women were included, with 391 (24%) using continuous subcutaneous insulin infusion, 312 (19%) using real-time continuous glucose monitoring and 558 (34%) using intermittently scanned continuous glucose monitoring. The highest use of diabetes technology was in the least deprived quintile compared to the most deprived quintile (67% vs. 45%, respectively; p < 0.001). HbA1c outcomes were available in 400 participants; no association with deprivation was observed (p = 0.872). Participation in structured education was almost twice as high from the most deprived to the least deprived groups (23% vs. 43%; p < 0.001). Adults with white or mixed ethnicity were more likely to use technology compared to black ethnicity (60% vs. 40%; p < 0.001). CONCLUSIONS: Adults living in the most deprived quintile had less technology use. Irrespective of socio-economic status or ethnicity, glycaemia was positively affected in all groups. It is imperative that health disparities are further addressed.

Cross-sectional analysis of emergency hypoglycaemia and outcome predictors among people with diabetes in an urban population.

AIMS/HYPOTHESIS: Out-of-hospital hypoglycaemia is a common complication for individuals with diabetes mellitus and represents a significant burden to emergency medical services (EMS). We aim to identify the factors associated with receiving parenteral treatment and hospital conveyance. METHODS: We retrospectively analysed a 6-month data set of all London EMS hypoglycaemia. Individuals with a known diabetes diagnosis were included in our analysis and stratified as either having type 1 diabetes or type 2 diabetes. RESULTS: A total of 2862 incidents occurred within the area served by London Ambulance Service between January and June 2018. Fifty percent of incidents required parenteral treatment (intravenous glucose or intramuscular glucagon) and were conveyed to hospital. A higher arrival of blood glucose, intact consciousness and receiving oral glucose treatment were all negative predictors for requiring parenteral therapy. Forty-three percent of incidents were labelled as 'hypoglycaemia' by the EMS call handler, and greater odds of hospitalisation were observed among incidents that received parenteral treatment (OR 2.52 [95% CI 1.46, 4.33] p < 0.01) and individuals with type 2 diabetes (OR 2.67 [95% CI 1.52, 4.71] p < 0.01). Repeated callouts from 2% (n = 50) of individuals accounted for 10% (286) of all incidents attended, and 56.4% of individuals attended by EMS on more than one occasion had type 1 diabetes. CONCLUSIONS/INTERPRETATION: Severe hypoglycaemia requiring emergency service attendance remains common, as does the requirement for parenteral therapy and conveyance to hospital. Early intervention, education and improved accessibility to risk prevention strategies may reduce the necessity for emergency parenteral treatment and hospitalisation, especially among individuals suffering from recurrent hypoglycaemia and high-risk individuals with type 2 diabetes.

Optimizing type 1 diabetes after multiple daily injections and capillary blood monitoring: Pump or sensor first? A meta-analysis using pooled differences in outcome measures.

AIMS: Most people living with type 1 diabetes self-manage using multiple daily injection (MDI) insulin regimens and self-monitoring of blood glucose (SMBG). Continuous subcutaneous insulin infusion (CSII) and continuous glucose monitoring (CGM) are adjuncts to education and support self-management optimization. The aim of this systematic review and meta-analysis was to assess which first-line technology is most effective. METHODS: Electronic databases (MEDLINE, EMBASE and WEB OF SCIENCE) were systematically searched from 1999 to September 2020. Randomized controlled trials comparing either CSII with MDI or CGM with SMBG in adults with type 1 diabetes were included. Data were extracted in duplicate by two reviewers, and were analysed to assess individual and overall treatment effect measures (PROSPERO registration: CRD42020149915). RESULTS: Glycated haemoglobin was significantly reduced for CGM when compared with SMBG [Cohen's d - 0.62 (95% CI -0.79 to -0.45)] and for CSII when compared with MDI [Cohen's d - 0.44 (95% CI -0.67 to -0.22)]. Rates of severe hypoglycaemia were significantly reduced with CGM compared with SMBG, but did not change for CSII when compared with MDI. Episodes of diabetic ketoacidosis were more likely to occur with CSII than MDI. Both CSII and CGM reduced glucose standard deviation, compared with MDI and SMBG respectively. CONCLUSIONS: Both CGM and CSII remain impactful interventions compared with SMBG and MDI but in adults with type 1 diabetes and in the contexts in which they have been studied, CGM might have a greater positive impact on glycaemic variability and severe hypoglycaemia than CSII, when added to MDI and SMBG. A head-to-head study, including patient reported outcomes, is required to explore these findings further.

Rationale and protocol for the Assessment of Impact of Real-time Continuous Glucose Monitoring on people presenting with severe Hypoglycaemia (AIR-CGM) study.

BACKGROUND: Severe hypoglycaemia carries a significant risk of morbidity and mortality for people with type 1 diabetes. Economic costs are also high, estimated at approximately £13 million annually in England, UK. Continuous glucose monitoring (CGM) has been shown to reduce hypoglycaemia and associated fear, improve overall glycaemia and quality of life, and is cost-effective. Despite effective pathways in place with high levels of resource utilization, it has been reported there are low levels of follow-up, therapy change and specialist intervention after severe hypoglycaemia. This study is designed to assess the impact of providing real-time CGM to people with type 1 diabetes, who have had a recent episode of severe hypoglycaemia (within 72 h), compared to standard care. METHODS/DESIGN: Fifty-five participants with type 1 diabetes and a recent episode of severe hypoglycaemia, who are CGM naïve, will be recruited to the study. Participants will be randomised to CGM or standard care. The primary outcome is percentage time spent in hypoglycaemia (< 3.0 mmol/L, 55 mg/dL). Secondary outcomes include other measures of hypoglycaemia, time in euglycaemia, overall glucose status and patient reported qualitative measures. DISCUSSION: This study assesses the impact of providing continuous glucose monitoring at the outset in individuals at highest risk of hypoglycaemia. Changing demand means that novel approaches need to be taken to healthcare provision. This study has the potential to shape future national standards. TRIAL REGISTRATION: NCT03748433 , November 2018 (UK).

Long-Term Glucose Forecasting Using a Physiological Model and Deconvolution of the Continuous Glucose Monitoring Signal.

(1) Objective: Blood glucose forecasting in type 1 diabetes (T1D) management is a maturing field with numerous algorithms being published and a few of them having reached the commercialisation stage. However, accurate long-term glucose predictions (e.g., >60 min), which are usually needed in applications such as precision insulin dosing (e.g., an artificial pancreas), still remain a challenge. In this paper, we present a novel glucose forecasting algorithm that is well-suited for long-term prediction horizons. The proposed algorithm is currently being used as the core component of a modular safety system for an insulin dose recommender developed within the EU-funded PEPPER (Patient Empowerment through Predictive PERsonalised decision support) project. (2) Methods: The proposed blood glucose forecasting algorithm is based on a compartmental composite model of glucose-insulin dynamics, which uses a deconvolution technique applied to the continuous glucose monitoring (CGM) signal for state estimation. In addition to commonly employed inputs by glucose forecasting methods (i.e., CGM data, insulin, carbohydrates), the proposed algorithm allows the optional input of meal absorption information to enhance prediction accuracy. Clinical data corresponding to 10 adult subjects with T1D were used for evaluation purposes. In addition, in silico data obtained with a modified version of the UVa-Padova simulator was used to further evaluate the impact of accounting for meal absorption information on prediction accuracy. Finally, a comparison with two well-established glucose forecasting algorithms, the autoregressive exogenous (ARX) model and the latent variable-based statistical (LVX) model, was carried out. (3) Results: For prediction horizons beyond 60 min, the performance of the proposed physiological model-based (PM) algorithm is superior to that of the LVX and ARX algorithms. When comparing the performance of PM against the secondly ranked method (ARX) on a 120 min prediction horizon, the percentage improvement on prediction accuracy measured with the root mean square error, A-region of error grid analysis (EGA), and hypoglycaemia prediction calculated by the Matthews correlation coefficient, was 18.8 % , 17.9 % , and 80.9 % , respectively. Although showing a trend towards improvement, the addition of meal absorption information did not provide clinically significant improvements. (4) Conclusion: The proposed glucose forecasting algorithm is potentially well-suited for T1D management applications which require long-term glucose predictions.

Switching from Intermittently Scanned Continuous Glucose Monitoring to Real-Time Continuous Glucose Monitoring with a Predictive Urgent Low Soon Alert Reduces Exposure to Hypoglycemia.

Differences in the effectiveness of real-time continuous glucose monitoring (rtCGM) and intermittently scanned continuous glucose monitoring (isCGM) in type 1 diabetes (T1D) are reported. The impact on percent time in range of switching from an isCGM with glucose threshold-based optional alerts only (FreeStyle Libre 2 [FSL2]) to an rtCGM (Dexcom G7) with an urgent low soon predictive alert was assessed, alongside other secondary outcomes including hemoglobin A1c (HbA1c) and other continuous glucose monitoring metrics. Adults with T1D using FSL2 were switched to Dexcom G7 for 12 weeks. HbA1c and continuous glucose data during FSL2 and Dexcom G7 use were compared. Data from 29 participants (aged 44.8 ± 16.5 years, 12 male and 17 female) were analyzed. After switching to rtCGM, participants spent less time in hypoglycemia below 3.9 mmol/L (70 mg/dL) (3.0% [1.0%, 5.0%] vs. 2.0% [1.0%, 3.0%], P = 0.006) and had higher percentage achievement of time below 3.9 mmol/L (70 mg/dL) of <4% (55.2% vs. 82.8%, P = 0.005). Coefficient of variation was lower (39.3 ± 6.6% vs. 37.2 ± 5.6%, P = 0.008). In conclusion, adults with T1D who switched from isCGM to rtCGM may benefit from reduced exposure to hypoglycemia and glycemic variability.

Development and Validation of Binary Classifiers to Predict Nocturnal Hypoglycemia in Adults With Type 1 Diabetes.

BACKGROUND: One of the biggest challenges for people with type 1 diabetes (T1D) using multiple daily injections (MDIs) is nocturnal hypoglycemia (NH). Recurrent NH can lead to serious complications; hence, prevention is of high importance. In this work, we develop and externally validate, device-agnostic Machine Learning (ML) models to provide bedtime decision support to people with T1D and minimize the risk of NH. METHODS: We present the design and development of binary classifiers to predict NH (blood glucose levels occurring below 70 mg/dL). Using data collected from a 6-month study of 37 adult participants with T1D under free-living conditions, we extract daytime features from continuous glucose monitor (CGM) sensors, administered insulin, meal, and physical activity information. We use these features to train and test the performance of two ML algorithms: Random Forests (RF) and Support Vector Machines (SVMs). We further evaluate our model in an external population of 20 adults with T1D using MDI insulin therapy and wearing CGM and flash glucose monitoring sensors for two periods of eight weeks each. RESULTS: At population-level, SVM outperforms RF algorithm with a receiver operating characteristic-area under curve (ROC-AUC) of 79.36% (95% CI: 76.86%, 81.86%). The proposed SVM model generalizes well in an unseen population (ROC-AUC = 77.06%), as well as between the two different glucose sensors (ROC-AUC = 77.74%). CONCLUSIONS: Our model shows state-of-the-art performance, generalizability, and robustness in sensor devices from different manufacturers. We believe it is a potential viable approach to inform people with T1D about their risk of NH before it occurs.

Safety and Efficacy of an Adaptive Bolus Calculator for Type 1 Diabetes: A Randomized Controlled Crossover Study.

Background: The Advanced Bolus Calculator for Type 1 Diabetes (ABC4D) is a decision support system using the artificial intelligence technique of case-based reasoning to adapt and personalize insulin bolus doses. The integrated system comprises a smartphone application and clinical web portal. We aimed to assess the safety and efficacy of the ABC4D (intervention) compared with a nonadaptive bolus calculator (control). Methods: This was a prospective randomized controlled crossover study. Following a 2-week run-in period, participants were randomized to ABC4D or control for 12 weeks. After a 6-week washout period, participants crossed over for 12 weeks. The primary outcome was difference in % time in range (%TIR) (3.9-10.0 mmol/L [70-180 mg/dL]) change during the daytime (07:00-22:00) between groups. Results: Thirty-seven adults with type 1 diabetes on multiple daily injections of insulin were randomized, median (interquartile range [IQR]) age 44.7 (28.2-55.2) years, diabetes duration 15.0 (9.5-29.0) years, and glycated hemoglobin 61.0 (58.0-67.0) mmol/mol (7.7 [7.5-8.3]%). Data from 33 participants were analyzed. There was no significant difference in daytime %TIR change with ABC4D compared with control (median [IQR] +0.1 [-2.6 to +4.0]% vs. +1.9 [-3.8 to +10.1]%; P = 0.53). Participants accepted fewer meal dose recommendations in the intervention compared with control (78.7 [55.8-97.6]% vs. 93.5 [73.8-100]%; P = 0.009), with a greater reduction in insulin dosage from that recommended. Conclusion: The ABC4D is safe for adapting insulin bolus doses and provided the same level of glycemic control as the nonadaptive bolus calculator. Results suggest that participants did not follow the ABC4D recommendations as frequently as control, impacting its effectiveness. Clinical Trials Registration: clinicaltrials.gov NCT03963219 (Phase 5).

The Accuracy of Continuous Glucose Sensors in People with Diabetes Undergoing Hemodialysis (ALPHA Study).

Objectives: Real-time and intermittently scanned continuous glucose monitoring are increasingly used for glucose monitoring in people with diabetes requiring renal replacement therapy, with limited data reporting their accuracy in this cohort. We evaluated the accuracy of Dexcom G6 and Abbott Freestyle Libre 1 glucose monitoring systems in people with diabetes undergoing hemodialysis. Methods: Participants on hemodialysis with diabetes (on insulin or sulfonylureas) were recruited. Paired sensor glucose from Dexcom G6 and Freestyle Libre 1 were recorded with plasma glucose analyzed using the Yellow Springs Instrument (YSI) method at frequent intervals during hemodialysis. Analysis of accuracy metrics included mean absolute relative difference (MARD), Clarke error grid (CEG) analysis and proportion of CGM values within 15% and 20% or 15 and 20 mg/dL of YSI reference values for blood glucose >100 or ≤100 mg/dL, respectively (% 15/15, % 20/20). Results: Forty adults (median age 64.7 [60.2-74.4] years) were recruited. Overall MARD for Dexcom G6 was 22.7% (2656 matched glucose pairs), and 11.3% for Libre 1 (n = 2785). The proportions of readings meeting %15/15 and %20/20 were 29.1% and 45.4% for Dexcom G6, respectively, and 73.5% and 85.6% for Libre 1. CEG analysis showed 98.9% of all values in zones A and B for Dexcom G6 and 99.8% for Libre 1. Conclusions: Our results indicate Freestyle Libre 1 is a reliable tool for glucose monitoring in adults on hemodialysis. Further studies are required to evaluate Dexcom G6 accuracy in people on hemodialysis.

Identifying Continuous Glucose Monitoring Data Using Machine Learning.

Background and Aims: The recent increase in wearable devices for diabetes care, and in particular the use of continuous glucose monitoring (CGM), generates large data sets and associated cybersecurity challenges. In this study, we demonstrate that it is possible to identify CGM data at an individual level by using standard machine learning techniques. Methods: The publicly available REPLACE-BG data set (NCT02258373) containing 226 adult participants with type 1 diabetes (T1D) wearing CGM over 6 months was used. A support vector machine (SVM) binary classifier aiming to determine if a CGM data stream belongs to an individual participant was trained and tested for each of the subjects in the data set. To generate the feature vector used for classification, 12 standard glycemic metrics were selected and evaluated at different time periods of the day (24 h, day, night, breakfast, lunch, and dinner). Different window lengths of CGM data (3, 7, 15, and 30 days) were chosen to evaluate their impact on the classification performance. A recursive feature selection method was employed to select the minimum subset of features that did not significantly degrade performance. Results: A total of 40 features were generated as a result of evaluating the glycemic metrics over the selected time periods (24 h, day, night, breakfast, lunch, and dinner). A window length of 15 days was found to perform the best in terms of accuracy (86.8% ± 12.8%) and F1 score (0.86 ± 0.16). The corresponding sensitivity and specificity were 85.7% ± 19.5% and 87.9% ± 17.5%, respectively. Through recursive feature selection, a subset of 9 features was shown to perform similarly to the 40 features. Conclusion: It is possible to determine with a relatively high accuracy if a CGM data stream belongs to an individual. The proposed approach can be used as a digital CGM "fingerprint" or for detecting glycemic changes within an individual, for example during intercurrent illness.

An In Silico Head-to-Head Comparison of the Do-It-Yourself Artificial Pancreas Loop and Bio-Inspired Artificial Pancreas Control Algorithms.

BACKGROUND: User-developed automated insulin delivery systems, also referred to as do-it-yourself artificial pancreas systems (DIY APS), are in use by people living with type 1 diabetes. In this work, we evaluate, in silico, the DIY APS Loop control algorithm and compare it head-to-head with the bio-inspired artificial pancreas (BiAP) controller for which clinical data are available. METHODS: The Python version of the Loop control algorithm called PyLoopKit was employed for evaluation purposes. A Python-MATLAB interface was created to integrate PyLoopKit with the UVa-Padova simulator. Two configurations of BiAP (non-adaptive and adaptive) were evaluated. In addition, the Tandem Basal-IQ predictive low-glucose suspend was used as a baseline algorithm. Two scenarios with different levels of variability were used to challenge the algorithms on the adult (n = 10) and adolescent (n = 10) virtual cohorts of the simulator. RESULTS: Both BiAP and Loop improve, or maintain, glycemic control when compared with Basal-IQ. Under the scenario with lower variability, BiAP and Loop perform relatively similarly. However, BiAP, and in particular its adaptive configuration, outperformed Loop in the scenario with higher variability by increasing the percentage time in glucose target range 70-180 mg/dL (BiAP-Adaptive vs Loop vs Basal-IQ) (adults: 89.9% ± 3.2%* vs 79.5% ± 5.3%* vs 67.9% ± 8.3%; adolescents: 74.6 ± 9.5%* vs 53.0% ± 7.7% vs 55.4% ± 12.0%, where * indicates the significance of P < .05 calculated in sequential order) while maintaining the percentage time below range (adults: 0.89% ± 0.37% vs 1.72% ± 1.26% vs 3.41 ± 1.92%; adolescents: 2.87% ± 2.77% vs 4.90% ± 1.92% vs 4.17% ± 2.74%). CONCLUSIONS: Both Loop and BiAP algorithms are safe and improve glycemic control when compared, in silico, with Basal-IQ. However, BiAP appears significantly more robust to real-world challenges by outperforming Loop and Basal-IQ in the more challenging scenario.

Looking beyond Pertussis in Prolonged Cough Illness of Young Children.

Pertussis, commonly known as whooping cough, is one of the most poorly controlled vaccine-preventable diseases in the world. South-East Asia is estimated to contribute the most to childhood disease burden while this remains largely unexplored in India. The clinical diagnosis of pertussis in young children is a challenge as the classical four-stage presentation with paroxysmal cough or whoop may be absent. It is also difficult to differentiate from other respiratory infections which can cause pertussis-like illness. Children below two years with prolonged cough illness attending an urban pediatric center in western India, were evaluated for pertussis and viral infections by molecular methods. Bordetella pertussis and B. holmesii were confirmed in three each of 45 suspected cases, and RSV-A and hMPV were the most common viruses that were detected. These organisms can mimic mild cases of pertussis and need to be considered in differential diagnosis of prolonged cough illness in young children. The accurate etiology of prolonged cough illness needs to be detected and documented to ensure appropriate management and accurate estimates of disease burden.

Self-Monitoring of Blood Glucose Requirements with the Use of Intermittently Scanned Continuous Glucose Monitoring: A Follow-Up Analysis Using Real-Life Data.

Hypoglycemia-prone individuals with type 1 diabetes (T1D) who use intermittently scanned continuous glucose monitoring (isCGM) systems spend about 5 h/day in states where self-monitoring of blood glucose (SMBG) is indicated. Here we present estimates of the need for SMBG testing by retrospectively analyzing isCGM data from a cohort of real-world isCGM users. Data from 67 individuals were included in the analysis. Mean (SD) 3.18 (1.63) h/day was spent in an SMBG-indicated state and the number of transitions to an SMBG-indicated state was 3.86 (1.46)/day. Frequency of clinically important hypoglycemia [<3.0 mmol/L (<54 mmol/dL)] was median (IQR) 1.5 (0.6-3.4) episodes/week, of which only 50% were associated with a scan during the episode, and the average duration was 75.2 (63.9-91.8) min/episode. The need for continued SMBG testing remains important for all isCGM users and may affect the overall cost-effectiveness of isCGM. Impaired awareness of hypoglycemia and incidence of asymptomatic hypoglycemia may be underreported among real-life isCGM users in clinical practice.

A Pilot Study of Flat and Circadian Insulin Infusion Rates in Continuous Subcutaneous Insulin Infusion (CSII) in Adults with Type 1 Diabetes (FIRST1D).

BACKGROUND: Initiation of continuous subcutaneous insulin therapy (CSII) in type 1 diabetes requires conversion of a basal insulin dose into a continuous infusion regimen. There are limited data to guide the optimal insulin profile to rapidly achieve target glucose and minimize healthcare professional input. The aim of this pilot study was to compare circadian and flat insulin infusion rates in CSII naïve adults with type 1 diabetes. METHODS: Adults with type 1 diabetes commencing CSII were recruited. Participants were randomized to circadian or flat basal profile calculated from the total daily dose. Basal rate testing was undertaken on days 7, 14 and 28 and basal rates were adjusted. The primary outcome was the between-group difference in absolute change in insulin basal rate over 24 hours following three rounds of basal testing. Secondary outcomes included the number of basal rate changes and the time blocks. RESULTS: Seventeen participants (mean age 33.3 (SD 8.6) years) were recruited. There was no significant difference in absolute change in insulin basal rates between groups (P = .85). The circadian group experienced significant variation in the number of changes made with the most changes in the morning and evening (P = .005). The circadian group received a greater reduction in total insulin (-14.1 (interquartile range (IQR) -22.5-12.95) units) than the flat group (-7.48 (IQR -11.90-1.23) units) (P = .021). CONCLUSION: The initial insulin profile does not impact on the magnitude of basal rate changes during optimization. The circadian profile requires changes at specific time points. Further development of the circadian profile may be the optimal strategy.

Robust Determination of the Optimal Continuous Glucose Monitoring Length of Intervention to Evaluate Long-Term Glycemic Control.

Objective: Consensus continuous glucose monitoring (CGM) guidance includes a recommendation that a minimum of 14 days of CGM data are used to report times in ranges. The previously employed approaches to determine the optimal duration for CGM data have limitations. In this study, we present a robust approach to define the minimum duration of CGM data to report times in ranges, as well as other glycemic metrics. Methods: The approach is based on the median absolute percentage error and employs a sliding time window to reduce the impact of inter-time interval variability, hence allowing smaller data sets to be used. A 10% and 5% threshold were employed to assess the optimal duration of CGM data for a set of commonly employed metrics to assess quality of glycemic control and glycemic variability. To evaluate the impact of the data set size and type of intervention, data from two randomized controlled trials involving participants with type 1 diabetes were used (n = 236 and n = 25). Results: Results suggest that mean glucose reaches the 5% threshold for mean absolute percentage error within 2 weeks, whereas percentage time in target 70-180 mg/dL, mean absolute glucose, standard deviation, and coefficient of variation reach the same threshold within 4 weeks in both data sets, suggesting that these metrics can be robustly assessed from CGM data for a 4-week period, whereas some other metrics require much longer window lengths, especially those evaluating hypoglycemia. Conclusions: Our data suggest that there is no optimal duration for CGM data to robustly assess all outcomes and that the duration required for a robust outcome depends on the population being studied, the sampling frequency, and the primary outcomes selected.

Improved glycaemia during the Covid-19 pandemic lockdown is sustained post-lockdown and during the "Eat Out to Help Out" Government Scheme, in adults with Type 1 diabetes in the United Kingdom.

AIMS: The majority of studies report that the Covid-19 pandemic lockdown did not have a detrimental effect on glycaemia. We sought to explore the impact of lockdown on glycaemia and whether this is sustained following easing of restrictions. METHODS: Retrospective, observational analysis in adults and children with type 1 diabetes attending a UK specialist centre, using real-time or intermittently scanned continuous glucose monitoring. Data from the following 28-day time periods were collected: (i) pre-lockdown; (ii) during lockdown; (iii) immediately after lockdown; and (iv) a month following relaxation of restrictions (coinciding with Government-subsidised restaurant food). Data were analysed for times in glycaemic ranges and are expressed as median (IQR). RESULTS: 145 adults aged 35.5 (25.8-51.3) years with diabetes duration of 19.0 (7.0-29.0) years on multiple daily injections of insulin (60%) and continuous insulin infusion (40%) were included. In adults, % time in range (70-180mg/dL) increased during lockdown (60.2 (45.2-69.3)%) compared to pre-lockdown (56.7 (43.5-65.3)%; p<0.001). This was maintained in the post-lockdown time periods. Similarly, % time above range (>180mg/dL) reduced in lockdown compared to pre-lockdown (p = 0.01), which was sustained thereafter. In children, no significant changes to glycaemia were observed during lockdown. In multivariable analysis, a greater increase in %TIR 3.9-10mmol/L (70-180mg/dL) during lockdown was associated with higher levels of deprivation (coefficient: 4.208, 95% CI 0.588 to 7.828; p = 0.02). CONCLUSIONS: Glycaemia in adults improved during lockdown, with people from more deprived areas most likely to benefit. This effect was sustained after easing of restrictions, with government-subsidised restaurant eating having no adverse impact on glycaemia.

Safety and Feasibility of the PEPPER Adaptive Bolus Advisor and Safety System: A Randomized Control Study.

Background: The Patient Empowerment through Predictive Personalized Decision Support (PEPPER) system provides personalized bolus advice for people with type 1 diabetes. The system incorporates an adaptive insulin recommender system (based on case-based reasoning, an artificial intelligence methodology), coupled with a safety system, which includes predictive glucose alerts and alarms, predictive low-glucose suspend, personalized carbohydrate recommendations, and dynamic bolus insulin constraint. We evaluated the safety and efficacy of the PEPPER system compared to a standard bolus calculator. Methods: This was an open-labeled multicenter randomized controlled crossover study. Following 4-week run-in, participants were randomized to PEPPER/Control or Control/PEPPER in a 1:1 ratio for 12 weeks. Participants then crossed over after a washout period. The primary end-point was percentage time in range (TIR, 3.9-10.0 mmol/L [70-180 mg/dL]). Secondary outcomes included glycemic variability, quality of life, and outcomes on the safety system and insulin recommender. Results: Fifty-four participants on multiple daily injections (MDI) or insulin pump completed the run-in period, making up the intention-to-treat analysis. Median (interquartile range) age was 41.5 (32.3-49.8) years, diabetes duration 21.0 (11.5-26.0) years, and HbA1c 61.0 (58.0-66.1) mmol/mol. No significant difference was observed for percentage TIR between the PEPPER and Control groups (62.5 [52.1-67.8] % vs. 58.4 [49.6-64.3] %, respectively, P = 0.27). For quality of life, participants reported higher perceived hypoglycemia with the PEPPER system despite no objective difference in time spent in hypoglycemia. Conclusions: The PEPPER system was safe, but did not change glycemic outcomes, compared to control. There is wide scope for integrating PEPPER into routine diabetes management for pump and MDI users. Further studies are required to confirm overall effectiveness. Clinical trial registration: ClinicalTrials.gov NCT03849755.