Predictive Low-Glucose Suspend Reduces Hypoglycemia in Adults, Adolescents, and Children With Type 1 Diabetes in an At-Home Randomized Crossover Study: Results of the PROLOG Trial

Closed-Loop Insulin Delivery Systems: Past, Present, and Future Directions

Closed-loop (artificial pancreas) systems for automated insulin delivery have been likened to the holy grail of diabetes management. The first iterations of glucose-responsive insulin delivery were pioneered in the 1960s and 1970s, with the development of systems that used venous glucose measurements to dictate intravenous infusions of insulin and dextrose in order to maintain normoglycemia. Only recently have these bulky, bedside technologies progressed to miniaturized, wearable devices. These modern closed-loop systems use interstitial glucose sensing, subcutaneous insulin pumps, and increasingly sophisticated algorithms. As the number of commercially available hybrid closed-loop systems has grown, so too has the evidence supporting their efficacy. Future challenges in closed-loop technology include the development of fully closed-loop systems that do not require user input for meal announcements or carbohydrate counting. Another evolving avenue in research is the addition of glucagon to mitigate the risk of hypoglycemia and allow more aggressive insulin dosing.

7. Diabetes Technology: Standards of Medical Care in Diabetes-2022

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc22-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc22-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Hybrid Close-Loop Systems Versus Predictive Low-Glucose Suspend and Sensor-Augmented Pump Therapy in Patients With Type 1 Diabetes: A Single-Center Cohort Study

INTRODUCTION

Predictive low-glucose suspend (PLGS) and hybrid closed-loop (HCL) systems may improve glucose control and quality of life in type 1 diabetic individuals. This is a cross-sectional, single-center study to compare the effect on metabolic control and glucose variability of PLGS and HCL systems as compared to standard sensor-augmented pump (SAP) therapy.

METHODS

We retrospectively analyzed 136 adults (men/women 69/67, mean age 47.3 ± 13.9 years) with T1D on insulin pump therapy, divided accordingly to type of insulin pump system (group 1: SAP, 24 subjects; group 2: PLGS, 49 subjects; group 3: HCL, 63 subjects). The groups were matched for age, gender, years of disease, years of CSII use, and CGM wear time.

RESULTS

The analysis of CGM metrics, in the three groups, showed a statistically significant different percentage of time within the target range, defined as 70-180 mg/dl, with a higher percentage in group 3 and significantly less time spent in the hypoglycemic range in groups 2 and 3. The three groups were statistically different also for the glucose management indicator and coefficient of variation percentage, which were progressively lower moving from group 1 to group 3. In the HCL group, 52.4% of subjects reached a percentage of time passed in the euglycemic range above 70%, as compared to 32.7% in those with PLGS and 20.2% in those with SAP. A positive correlation between the higher percentage of TIR and the use of auto-mode was evident in the HCL group. Finally, the three groups did not show any statistical differences regarding the quality-of-life questionnaire, but there was a significant negative correlation between CV and perceived CSII-use convenience (r = -0.207, p = 0.043).

CONCLUSION

HCL systems were more effective in improving glucose control and in reducing the risk of hypoglycemia in patients with type 1 diabetes, thereby mitigating risk for acute and chronic complications and positively affecting diabetes technologies' acceptance.

Review of Automated Insulin Delivery Systems for Type 1 Diabetes and Associated Time in Range Outcomes

Automated insulin delivery (AID) systems play an important role in the management of type 1 diabetes mellitus (T1DM). These systems include three components: a continuous glucose monitor (CGM), an insulin pump and an algorithm that adjusts the pump based on the CGM sensor glucose readings. They are not fully automated and still require the user to administer bolus insulin doses for food. Some AID systems have automatic correction boluses, while others only have automatic basal or background insulin adjustments. As CGM has become more accurate and the technology has evolved, AID systems have demonstrated improved glycaemic outcomes. The clinical evaluation of AID systems in randomized controlled trials and real-world studies have shown their utility in helping glycaemic management. In this review, we compare AID systems that are commercially available in the US and summarize the literature, with a special focus on time in range in T1DM. The review also discusses new AID systems on the horizon and explores considerations for personalized care.

The automated pancreas: A review of technologies and clinical practice

Insulin pumps and glucose sensors are effective in improving diabetes therapy and reducing acute complications. The combination of both devices using an algorithm-driven interoperable controller makes automated insulin delivery (AID) systems possible. Many AID systems have been tested in clinical trials and have proven safety and effectiveness. However, currently, none of these systems are available for routine use in children younger than 6 years in Europe. For continued use, both users and prescribers must have sound knowledge of the features of the individual AID systems. Presently, all systems require various user interactions (e.g. meal announcements) because fully automated systems are not yet developed. Open-source systems are non-regulated variants to circumvent existing regulatory conditions. There are risks here for both users and prescribers. To evaluate AID therapy, the metric data of the glucose sensors, 'time in target range' and 'glucose management index', are novel recognized and suitable parameters allowing a consultation based on real glucose and insulin pump download data from the daily life of people with diabetes. Read out via cloud-based software or automatic download of such individual treatment data provides the ideal technical basis for shared decision-making through telemedicine, which must be further evaluated for general use.

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.

15. Management of Diabetes in Pregnancy: Standards of Medical Care in Diabetes-2022

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc22-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc22-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Hybrid Closed-Loop Systems for the Treatment of Type 1 Diabetes: A Collaborative, Expert Group Position Statement for Clinical Use in Central and Eastern Europe

In both pediatric and adult populations with type 1 diabetes (T1D), technologies such as continuous subcutaneous insulin infusion (CSII), continuous glucose monitoring (CGM), or sensor-augmented pumps (SAP) can consistently improve glycemic control [measured as glycated hemoglobin (HbA1c) and time in range (TIR)] while reducing the risk of hypoglycemia. Use of technologies can thereby improve quality of life and reduce the burden of diabetes management compared with self-injection of multiple daily insulin doses (MDI). Novel hybrid closed-loop (HCL) systems represent the latest treatment modality for T1D, combining modern glucose sensors and insulin pumps with a linked control algorithm to offer automated insulin delivery in response to blood glucose levels and trends. HCL systems have been associated with increased TIR, improved HbA1c, and fewer hypoglycemic events compared with CSII, SAP, and MDI, thereby potentially improving quality of life for people with diabetes (PwD) while reducing the costs of treating short- and long-term diabetes-related complications. However, many barriers to their use and regional inequalities remain in Central and Eastern Europe (CEE). Published data suggest that access to diabetes technologies is hindered by lack of funding, underdeveloped health technology assessment (HTA) bodies and guidelines, unfamiliarity with novel therapies, and inadequacies in healthcare system capacities. To optimize the use of diabetes technologies in CEE, an international meeting comprising experts in the field of diabetes was held to map the current regional access, to present the current national reimbursement guidelines, and to recommend solutions to overcome uptake barriers. Recommendations included regional and national development of HTA bodies, efficient allocation of resources, and structured education programs for healthcare professionals and PwD. The responsibility of the healthcare community to ensure that all individuals with T1D gain access to modern technologies in a timely and economically responsible manner, thereby improving health outcomes, was emphasized, particularly for interventions that are cost-effective.

Real-world outcomes of two different sensor-augmented insulin pumps with predictive low glucose suspend function in type 1 diabetes patients

AIM

To analyse the real-life outcomes of two sensor-augmented pumps (SAP) with predictive low glucose suspend (PLGS) function, Medtronic Minimed 640G™ with SmartGuard (MM640G) and Tandem T Slim X2™ with Basal-IQ™ (TTSX2), in Type 1 Diabetes Mellitus (T1DM) patients.

METHODS

Observational cross-sectional study using data obtained from computerized clinical records. All T1DM patients on TTSX2 therapy were compared (1:1) with MM640G treated patients selected through stratified sampling. Primary efficacy outcome was to describe time in rage (TIR, 70-180 mg/dL, 3.9-10 mmol/L) interstitial glucose differences according to a non-inferiority hypothesis with TTSX2 compared to MM640G.

RESULTS

Forty-four patients were analyzed (female 66%). Mean age was 38.9 yrs. (range 23-59 yrs.) and mean diabetes duration was 23.4 ± 9.2 yrs. Patients treated with TTSX2 showed a numerically slightly lower, but non-statistically significantly different, TIR from the MM640G pump group (64.9 ± 16.4% vs. 72.4 ± 17.0%, P = 0.108). Similarly, we did no find differences in HbA1c between T1D patients treated with TTSX2 and MM640G (6.8 ± 1.0% vs. 7.0 ± 0.9%, 51 ± 11 mmol/mol vs. 53 ± 10 mmol/mol, P = 0.312). Moreover, rest of evaluated glycemic outcomes were similar between both treatment groups.

CONCLUSIONS

Patients using two different SAP with PLGS automatic function showed similar glycaemic control in a real-world scenario. NCT04741685.

Predictive Low Glucose Suspend Algorithm in Real Life: A Five-Year Follow-Up Retrospective Analysis

AIM

Sensor-augmented pumps with predictive low glucose suspend function (PLGS-SAP) help patients avoid hypoglycemia and improve quality of life: in this retrospective study, we investigated long-term effects of PLGS-SAP on metabolic outcomes, acute and chronic diabetic complications, in particular cardiovascular events.

MATERIALS AND METHODS

One hundred thirty-nine adults with type 1 diabetes (T1D) treated for more than 10 years with continuous subcutaneous insulin infusion (CSII) were followed for 5 years; 71 (Group 1) started to use PLGS-SAP, and 68 (Group 2) maintained on their non-PLGM insulin pump. Glucose control measures (hemoglobin A1c [HbA1c], acute diabetic complications), clinical outcomes (body mass index [BMI], arterial hypertension, dyslipidemia), chronic diabetes-related complications, and device utilization (continuous glucose monitoring utilization, use of temporary basal rates or special boluses, carbohydrate counting usage) were assessed.

RESULTS

The reduction of HbA1c was significant in Group 1 (from 7.5% ± 1.1% to 7.0% ± 1.0%, P = .02), while in Group 2 it did not reach statistical significance (from 7.5% ± 1.1% to 7.4% ± 0.9%, P = .853). BMI increased significantly in Group 2 (from 25.3 ± 2.8 to 25.7 ± 3.4, P < .001), but not in Group 1 (from 25.2 ± 3.5 to 25.2 ± 2.8, P = .887). There were no statistically significant differences in occurrence of acute diabetes complications, other clinical outcomes, prevalence of diabetes-related complications, or device utilization between the groups.

CONCLUSIONS

In our five-year follow-up experience with T1D CSII users, PLGS-SAP has resulted efficient in improving metabolic control and maintaining the body weight.

The Cost-Effectiveness of an Advanced Hybrid Closed-Loop System in People with Type 1 Diabetes: a Health Economic Analysis in Sweden

INTRODUCTION

Swedish National Diabetes Registry data show a correlation of improved glycemic control in people with type 1 diabetes (T1D) with increased use of diabetes technologies over the past 25 years. However, novel technologies are often associated with a high initial outlay. The aim of the present study was to evaluate the long-term cost-effectiveness of the advanced hybrid closed-loop (AHCL) MiniMed 780G system versus intermittently scanned continuous glucose monitoring (isCGM) plus self-injection of multiple daily insulin (MDI) or continuous subcutaneous insulin infusion (CSII) in people with T1D in Sweden.

METHODS

Outcomes were projected over patients' lifetimes using the IQVIA CORE Diabetes Model (v9.0). Clinical data, including changes in glycated hemoglobin (HbA1c) and hypoglycemia rates, were sourced from observational studies and a randomized crossover trial. Modeled patients were assumed to receive the treatments for their lifetimes, with HbA1c kept constant following the application of treatment effects. Costs were accounted from a societal perspective and expressed in Swedish krona (SEK). Utilities and days off work estimates were taken from published sources.

RESULTS

The MiniMed 780G system was associated with an improvement in life expectancy of 0.16 years and an improvement in quality-adjusted life expectancy of 1.95 quality-adjusted life years (QALYs) versus isCGM plus MDI or CSII. These clinical benefits were due to a reduced incidence and a delayed time to onset of diabetes-related complications. Combined costs were estimated to be SEK 727,408 (EUR 72,741) higher with MiniMed 780G, with treatment costs partially offset by direct cost savings from the avoidance of diabetes-related complications and indirect cost savings from the avoidance of lost workplace productivity. The MiniMed 780G system was associated with an incremental cost-effectiveness ratio of SEK 373,700 per QALY gained.

CONCLUSIONS

Based on a willingness-to-pay threshold of SEK 500,000 per QALY gained, the MiniMed 780G system was projected to be cost-effective versus isCGM plus MDI or CSII for the treatment of T1D in Sweden.

Current Advances of Artificial Pancreas Systems: A Comprehensive Review of the Clinical Evidence

Since Banting and Best isolated insulin in the 1920s, dramatic progress has been made in the treatment of type 1 diabetes mellitus (T1DM). However, dose titration and timely injection to maintain optimal glycemic control are often challenging for T1DM patients and their families because they require frequent blood glucose checks. In recent years, technological advances in insulin pumps and continuous glucose monitoring systems have created paradigm shifts in T1DM care that are being extended to develop artificial pancreas systems (APSs). Numerous studies that demonstrate the superiority of glycemic control offered by APSs over those offered by conventional treatment are still being published, and rapid commercialization and use in actual practice have already begun. Given this rapid development, keeping up with the latest knowledge in an organized way is confusing for both patients and medical staff. Herein, we explore the history, clinical evidence, and current state of APSs, focusing on various development groups and the commercialization status. We also discuss APS development in groups outside the usual T1DM patients and the administration of adjunct agents, such as amylin analogues, in APSs.

Automated Adaptation of Insulin Treatment in Type 1 Diabetes

Individuals with type 1 diabetes (T1D) need life-long insulin therapy to compensate for the lack of endogenous insulin due to the autoimmune damage to pancreatic beta-cells. Treatment is based on basal and bolus insulin, to cover fasting and postprandial periods, respectively, according to three insulin dosing parameters: basal rate (BR), carbohydrate-to-insulin ratio (CR), and correction factor (CF). Suboptimal BR, CR, and CF profiles leading to incorrect insulin dosing may be the cause of undesired glycemic events, which carry dangerous short-term and long-term effects. Therefore, correct tuning of these parameters is of the utmost importance. In this work, we propose a new algorithm to optimize insulin dosing parameters in individuals with T1D who use a continuous glucose monitor and an insulin pump. The algorithm was tested using the University of Virginia/Padova T1D Simulator and led to an improvement in the quality of glycemic control. Future efforts will be devoted to test the algorithm in human clinical trials.

Clinically Serious Hypoglycemia Is Rare and Not Associated With Time-in-range in Youth With New-onset Type 1 Diabetes

CONTEXT

Early initiation of continuous glucose monitoring (CGM) is advocated for youth with type 1 diabetes (T1D). Data to guide CGM use on time-in-range (TIR), hypoglycemia, and the role of partial clinical remission (PCR) are limited.

OBJECTIVE

Our aims were to assess whether 1) an association between increased TIR and hypoglycemia exists, and 2) how time in hypoglycemia varies by PCR status.

METHODS

We analyzed 80 youth who were started on CGM shortly after T1D diagnosis and were followed for up to 1-year post diagnosis. TIR and hypoglycemia rates were determined by CGM data and retrospectively analyzed. PCR was defined as (visit glycated hemoglobin A1c) + (4*units/kg/day) less than 9.

RESULTS

Youth were started on CGM 8.0 (interquartile range, 6.0-13.0) days post diagnosis. Time spent at less than 70 mg/dL remained low despite changes in TIR (highest TIR 74.6 ± 16.7%, 2.4 ± 2.4% hypoglycemia at 1 month post diagnosis; lowest TIR 61.3 ± 20.3%, 2.1 ± 2.7% hypoglycemia at 12 months post diagnosis). No events of severe hypoglycemia occurred. Hypoglycemia was rare and there was minimal difference for PCR vs non-PCR youth (54-70 mg/dL: 1.8% vs 1.2%, P = .04; < 54mg/dL: 0.3% vs 0.3%, P = .55). Approximately 50% of the time spent in hypoglycemia was in the 65 to 70 mg/dL range.

CONCLUSION

As TIR gradually decreased over 12 months post diagnosis, hypoglycemia was limited with no episodes of severe hypoglycemia. Hypoglycemia rates did not vary in a clinically meaningful manner by PCR status. With CGM being started earlier, consideration needs to be given to modifying CGM hypoglycemia education, including alarm settings. These data support a trial in the year post diagnosis to determine alarm thresholds for youth who wear CGM.

Clinical Considerations for Insulin Therapy in Older Adults with Type 1 Diabetes

Type 1 diabetes represents an autoimmune condition with a strong inherited background, and its incidence is increasing worldwide. About 25% of such cases are diagnosed in adulthood, some even as late as the ninth decade of life. The number of older adults with type 1 diabetes is increasing due to improvements in care and decreased mortality rate. However, there is a lack of clinical trials in people older than 70 years of age with type 1 diabetes complicated with comorbidities, frailty, and dependency. The management of type 1 diabetes and the goals of therapy should be individualized based on the patient's health status and life expectancy. In healthier older adults, insulin treatment regimens (multiple daily insulin injections or insulin pump therapy) that approximate the normal physiology of insulin secretion should be used to achieve lower glycemic goals, while reducing the risk of hypoglycemia with frequent glucose monitoring (preferably using continuous glucose monitoring systems). For frail individuals with poor health, simpler insulin regimens and less stringent glycemic targets would be more appropriate. Poor cognition, vision and hearing, impaired mobility, depression, and chronic pain can interfere with complex insulin regimens. In these individuals, the principal goals of therapy are to reduce the acute effects of hyperglycemia, minimize hypoglycemia risk, and optimize quality of life. The newer insulin preparations and technological advances in insulin delivery and blood glucose monitoring have enhanced the management of type 1 diabetes in all age groups.

Lost in Translation: A Disconnect Between the Science and Medicare Coverage Criteria for Continuous Subcutaneous Insulin Infusion

Numerous studies have demonstrated the clinical value and safety of insulin pump therapy in type 1 diabetes and type 2 diabetes populations. However, the eligibility criteria for insulin pump coverage required by the Centers for Medicare & Medicaid Services (CMS) discount conclusive evidence that supports insulin pump use in diabetes populations that are currently deemed ineligible. This article discusses the limitations and inconsistencies of the insulin pump eligibility criteria relative to current scientific evidence and proposes workable solutions to address this issue and improve the safety and care of all individuals with diabetes.

Felix dies natalis, insulin… ceterum autem censeo "beta is better"

One hundred years after its discovery, insulin remains the life-saving therapy for many patients with diabetes. It has been a 100-years-old success story thanks to the fact that insulin therapy has continuously integrated the knowledge developed over a century. In 1982, insulin becomes the first therapeutic protein to be produced using recombinant DNA technology. The first "mini" insulin pump and the first insulin pen become available in 1983 and 1985, respectively. In 1996, the first generation of insulin analogues were produced. In 1999, the first continuous glucose-monitoring device for reading interstitial glucose was approved by the FDA. In 2010s, the ultra-long action insulins were introduced. An equally exciting story developed in parallel. In 1966. Kelly et al. performed the first clinical pancreas transplant at the University of Minnesota, and now it is a well-established clinical option. First successful islet transplantations in humans were obtained in the late 1980s and 1990s. Their ability to consistently re-establish the endogenous insulin secretion was obtained in 2000s. More recently, the possibility to generate large numbers of functional human β cells from pluripotent stem cells was demonstrated, and the first clinical trial using stem cell-derived insulin producing cell was started in 2014. This year, the discovery of this life-saving hormone turns 100 years. This provides a unique opportunity not only to celebrate this extraordinary success story, but also to reflect on the limits of insulin therapy and renew the commitment of the scientific community to an insulin free world for our patients.

Landscape of Continuous Glucose Monitoring (CGM) and Integrated CGM: Accuracy Considerations

Continuous glucose monitoring devices have significantly improved in many respects compared with earlier versions. As sensor accuracy improved, U.S. Food and Drug Administration approved a nonadjunctive indication for use that no longer required confirmatory blood glucose monitoring. This article discusses the performance characteristics and regulatory classifications for the current systems that are relevant to informed clinical decision-making.

Advances in Pharmacotherapeutics, Metabolic Surgery, and Technology for Diabetes

Remarkable advances in diabetes management have occurred since the discovery of insulin 100 years ago. Advances across a therapeutic spectrum, including pharmacotherapy, metabolic surgery, and diabetes technology, offer superior treatment options for diabetes management. New medication classes (glucagon-like peptide-1 receptor analogs and SGLT-2 inhibitors) have demonstrated cardiorenal benefits beyond glycemic control in type 2 diabetes mellitus, while evolving metabolic surgical interventions also help patients achieve diabetes remission. The use of artificial pancreas systems has shown consistent improvement in glycemic control in type 1 diabetes mellitus. It is time for policy changes to expand access to such advantageous therapies.

Type 1 diabetes glycemic management: Insulin therapy, glucose monitoring, and automation

Despite innovations in insulin therapy since its discovery, most patients living with type 1 diabetes do not achieve sufficient glycemic control to prevent complications, and they experience hypoglycemia, weight gain, and major self-care burden. Promising pharmacological advances in insulin therapy include the refinement of extremely rapid insulin analogs, alternate insulin-delivery routes, liver-selective insulins, add-on drugs that enhance insulin effect, and glucose-responsive insulin molecules. The greatest future impact will come from combining these pharmacological solutions with existing automated insulin delivery methods that integrate insulin pumps and glucose sensors. These systems will use algorithms enhanced by machine learning, supplemented by technologies that include activity monitors and sensors for other key metabolites such as ketones. The future challenges facing clinicians and researchers will be those of access and broad clinical implementation.

A Multitask Learning Approach to Personalised Blood Glucose Prediction

Blood glucose prediction algorithms are key tools in the development of decision support systems and closed-loop insulin delivery systems for blood glucose control in diabetes. Deep learning models have provided leading results among machine learning algorithms to date in glucose prediction. However these models typically require large amounts of data to obtain best personalised glucose prediction results. Multitask learning facilitates an approach for leveraging data from multiple subjects while still learning accurate personalised models. In this work we present results comparing the effectiveness of multitask learning over sequential transfer learning, and learning only on subject-specific data with neural networks and support vector regression. The multitask learning approach shows consistent leading performance in predictive metrics at both short-term and long-term prediction horizons. We obtain a predictive accuracy (RMSE) of 18.8 2.3, 25.3 2.9, 31.8 3.9, 41.2 4.5, 47.2 4.6 mg/dL at 30, 45, 60, 90, and 120 min prediction horizons respectively, with at least 93\% clinically acceptable predictions using the Clarke Error Grid (EGA) at each prediction horizon. We also identify relevant prior information such as glycaemic variability that can be incorporated to improve predictive performance at long-term prediction horizons. Furthermore, we demonstrate consistent performance - 5% change in both RMSE and EGA (Zone A) - in rare cases of adverse glycaemic events with 1-6 weeks of training data. In conclusion, a multitask approach can allow for deploying personalised models even with significantly less subject-specific data without compromising performance.

Predictive Low-Glucose Suspend Necessitates Less Carbohydrate Supplementation to Rescue Hypoglycemia: Need to Revisit Current Hypoglycemia Treatment Guidelines

Current guidelines recommend 15-20 g of carbohydrate (CHO) for treatment of mild to moderate hypoglycemia. However, these guidelines do not account for reduced insulin during suspensions with predictive low-glucose suspend (PLGS). We assessed insulin suspensions, hypoglycemic events, and CHO treatment during a 20-h inpatient evaluation of an investigational system with a PLGS feature, including an overnight basal up-titration period to activate the PLGS. Among 10 adults with type 1 diabetes, there were 59 suspensions; 7 suspensions were associated with rescue CHO and 5 with hypoglycemia. Rescue treatment consisted of median 9 g CHO (range: 5-16 g), with no events requiring repeat CHO. No rescue CHO were given during or after insulin suspension for the overnight basal up-titration. To minimize rebound hyperglycemia and needless calorie intake from hypoglycemia overtreatment, updated guidance for PLGS systems should reflect possible need to reduce CHO amounts for hypoglycemia rescue associated with an insulin suspension. The clinical trial was registered with ClinicalTrials.gov (NCT03890003).

2021 Clinical Practice Guidelines for Diabetes Mellitus of the Korean Diabetes Association

The Committee of Clinical Practice Guidelines of the Korean Diabetes Association (KDA) updated the previous clinical practice guidelines for Korean adults with diabetes and prediabetes and published the seventh edition in May 2021. We performed a comprehensive systematic review of recent clinical trials and evidence that could be applicable in real-world practice and suitable for the Korean population. The guideline is provided for all healthcare providers including physicians, diabetes experts, and certified diabetes educators across the country who manage patients with diabetes or the individuals at the risk of developing diabetes mellitus. The recommendations for screening diabetes and glucose-lowering agents have been revised and updated. New sections for continuous glucose monitoring, insulin pump use, and non-alcoholic fatty liver disease in patients with diabetes mellitus have been added. The KDA recommends active vaccination for coronavirus disease 2019 in patients with diabetes during the pandemic. An abridgement that contains practical information for patient education and systematic management in the clinic was published separately.

Feature-Based Machine Learning Model for Real-Time Hypoglycemia Prediction

BACKGROUND

Hypoglycemia is a serious health concern in youth with type 1 diabetes (T1D). Real-time data from continuous glucose monitoring (CGM) can be used to predict hypoglycemic risk, allowing patients to take timely intervention measures.

METHODS

A machine learning model is developed for probabilistic prediction of hypoglycemia (<70 mg/dL) in 30- and 60-minute time horizons based on CGM datasets obtained from 112 patients over a range of 90 days consisting of over 1.6 million CGM values under normal living conditions. A comprehensive set of features relevant for hypoglycemia are developed and a parsimonious subset with most influence on predicting hypoglycemic risk is identified. Model performance is evaluated both with and without contextual information on insulin and carbohydrate intake.

RESULTS

The model predicted hypoglycemia with >91% sensitivity for 30- and 60-minute prediction horizons while maintaining specificity >90%. Inclusion of insulin and carbohydrate data yielded performance improvement for 60-minute but not for 30-minute predictions. Model performance was highest for nocturnal hypoglycemia (~95% sensitivity). Shortterm (less than one hour) and medium-term (one to four hours) features for good prediction performance are identified.

CONCLUSIONS

Innovative feature identification facilitated high performance for hypoglycemia risk prediction in pediatric youth with T1D. Timely alerts of impending hypoglycemia may enable proactive measures to avoid severe hypoglycemia and achieve optimal glycemic control. The model will be deployed on a patient-facing smartphone application in an upcoming pilot study.

The Feasibility and Applicability of Stem Cell Therapy for the Cure of Type 1 Diabetes

Stem cell therapy using islet-like insulin-producing cells derived from human pluripotent stem cells has the potential to allow patients with type 1 diabetes to withdraw from insulin therapy. However, several issues exist regarding the use of stem cell therapy to treat type 1 diabetes. In this review, we will focus on the following topics: (1) autoimmune responses during the autologous transplantation of stem cell-derived islet cells, (2) a comparison of stem cell therapy with insulin injection therapy, (3) the impact of the islet microenvironment on stem cell-derived islet cells, and (4) the cost-effectiveness of stem cell-derived islet cell transplantation. Based on these various viewpoints, we will discuss what is required to perform stem cell therapy for patients with type 1 diabetes.

Basal-IQ technology in the real world: satisfaction and reduction of diabetes burden in individuals with type 1 diabetes

AIM

To describe person-reported outcomes of the Basal-IQ predictive low-glucose-suspend system (Tandem Diabetes Care, San Diego, CA, USA) in real-world use.

METHODS

Adults with type 1 diabetes/caregivers of minors with type 1 diabetes completed the Diabetes Impact and Device Satisfaction questionnaire (11 items scored on 10-point Likert scales) prior to Basal-IQ system initiation, and at 2, 4 and 6 months post-initiation. Analysis was stratified by previous insulin treatment method. Beta mixed models were used to measure change in device satisfaction (e.g. trust, ease of use) and diabetes impact (e.g. hypoglycaemia fear, poor sleep) scores between time points, adjusting for baseline covariates.

RESULTS

A total of 967 adults and caregivers [54% women, mean (sd) age 36 (17) years, 57% Tandem pump users, 27% non-Tandem pump users, 17% multiple daily injection users] completed surveys. Device satisfaction significantly increased from baseline to 2 months in all groups (P<0.001 multiple daily injection and non-Tandem pump users; P=0.048 Tandem pump users), and was sustained from 2 to 6 months in all groups. Diabetes impact decreased significantly from baseline to 2 months in all groups (P<0.001 for all), was sustained from 2 to 6 months in multiple daily injection and Tandem pump users, and increased slightly at 4 months/decreased at 6 months in non-Tandem users.

CONCLUSION

The Basal-IQ system increased device satisfaction and reduced diabetes impact in all users in the first 2 months of use, and satisfaction was sustained over 6 months, with small fluctuations.

American Association of Clinical Endocrinology Clinical Practice Guideline: The Use of Advanced Technology in the Management of Persons With Diabetes Mellitus

OBJECTIVE

To provide evidence-based recommendations regarding the use of advanced technology in the management of persons with diabetes mellitus to clinicians, diabetes-care teams, health care professionals, and other stakeholders.

METHODS

The American Association of Clinical Endocrinology (AACE) conducted literature searches for relevant articles published from 2012 to 2021. A task force of medical experts developed evidence-based guideline recommendations based on a review of clinical evidence, expertise, and informal consensus, according to established AACE protocol for guideline development.

MAIN OUTCOME MEASURES

Primary outcomes of interest included hemoglobin A1C, rates and severity of hypoglycemia, time in range, time above range, and time below range.

RESULTS

This guideline includes 37 evidence-based clinical practice recommendations for advanced diabetes technology and contains 357 citations that inform the evidence base.

RECOMMENDATIONS

Evidence-based recommendations were developed regarding the efficacy and safety of devices for the management of persons with diabetes mellitus, metrics used to aide with the assessment of advanced diabetes technology, and standards for the implementation of this technology.

CONCLUSIONS

Advanced diabetes technology can assist persons with diabetes to safely and effectively achieve glycemic targets, improve quality of life, add greater convenience, potentially reduce burden of care, and offer a personalized approach to self-management. Furthermore, diabetes technology can improve the efficiency and effectiveness of clinical decision-making. Successful integration of these technologies into care requires knowledge about the functionality of devices in this rapidly changing field. This information will allow health care professionals to provide necessary education and training to persons accessing these treatments and have the required expertise to interpret data and make appropriate treatment adjustments.

Leveraging Artificial Intelligence to Improve Chronic Disease Care: Methods and Application to Pharmacotherapy Decision Support for Type-2 Diabetes Mellitus

OBJECTIVES

Artificial intelligence (AI), including predictive analytics, has great potential to improve the care of common chronic conditions with high morbidity and mortality. However, there are still many challenges to achieving this vision. The goal of this project was to develop and apply methods for enhancing chronic disease care using AI.

METHODS

Using a dataset of 27,904 patients with diabetes, an analytical method was developed and validated for generating a treatment pathway graph which consists of models that predict the likelihood of alternate treatment strategies achieving care goals. An AI-driven clinical decision support system (CDSS) integrated with the electronic health record (EHR) was developed by encapsulating the prediction models in an OpenCDS Web service module and delivering the model outputs through a SMART on FHIR (Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources) web-based dashboard. This CDSS enables clinicians and patients to review relevant patient parameters, select treatment goals, and review alternate treatment strategies based on prediction results.

RESULTS

The proposed analytical method outperformed previous machine-learning algorithms on prediction accuracy. The CDSS was successfully integrated with the Epic EHR at the University of Utah.

CONCLUSION

A predictive analytics-based CDSS was developed and successfully integrated with the EHR through standards-based interoperability frameworks. The approach used could potentially be applied to many other chronic conditions to bring AI-driven CDSS to the point of care.

A Virtual Training Program for the Tandem t:slim X2 Insulin Pump: Implementation and Outcomes

Insulin pump training has traditionally been performed in-person. The coronavirus disease 2019 (COVID-19) pandemic necessitated vast increases in the number of virtual pump trainings for Tandem t:slim X2 insulin pump starts. A customized structured pump training curriculum specifically tailored to virtual learning was deployed in early 2020, and included (1) preparation for training with use of the t:simulator app, (2) use of the teach-back method during video training, and (3) automating data uploads for follow-up. Retrospective analysis from >23,000 pump training sessions performed from January 1, 2020 to July 28, 2020 showed sensor time-in-range for up to 6 months after training was 72% (60%-81%) for virtual training versus 67% (54%-78%) for in-person training. Higher user satisfaction (4.78 ± 0.52 vs. 4.64 ± 0.68; P < 0.01) and higher user confidence (4.61 ± 0.75 vs. 4.47 ± 0.0.85; P < 0.01) were reported after the virtual sessions. Virtual pump training was well received and proved safe and effective with the new virtual training curriculum.

Glucose Prediction under Variable-Length Time-Stamped Daily Events: A Seasonal Stochastic Local Modeling Framework

Accurate glucose prediction along a long-enough time horizon is a key component for technology to improve type 1 diabetes treatment. Subjects with diabetes might benefit from supervision and control systems that accurately predict risks and trigger corrective actions early enough with improved mitigation. However, large intra-patient variability poses big challenges to glucose prediction. In previous works by the authors, clustering and local modeling techniques with seasonal stochastic models proved to be efficient, allowing for good glucose prediction accuracy for long prediction horizons. Continuous glucose monitoring (CGM) data were partitioned into fixed-length postprandial time subseries and clustered with Fuzzy C-Means to collect similar behaviors, enforcing seasonality at each cluster after subseries concatenation. Then, seasonal stochastic models were identified for each cluster and local predictions were integrated into a global prediction. However, free-living conditions do not support the fixed-length partition of CGM data since daily events duration is variable. In this work, a new algorithm is provided to overcome this constraint, allowing better coping with patient's variability under variable-length time-stamped daily events in supervision and control applications. Besides predicted glucose, two real-time indices are additionally provided-a crispness index, indicating good representation of current glucose behavior by a single model, and a normality index, allowing for the detection of an abnormal glucose behavior (unusual according to registered historical data). The framework is tested in a proof-of-concept in silico study with ten patients over four month training data and two independent two month validation datasets, with and without abnormal behaviors, from the distribution version of the UVA/Padova simulator extended with diverse sources of intra-patient variability.

New closed-loop insulin systems

Advances in diabetes technologies have enabled the development of automated closed-loop insulin delivery systems. Several hybrid closed-loop systems have been commercialised, reflecting rapid transition of this evolving technology from research into clinical practice, where it is gradually transforming the management of type 1 diabetes in children and adults. In this review we consider the supporting evidence in terms of glucose control and quality of life for presently available closed-loop systems and those in development, including dual-hormone closed-loop systems. We also comment on alternative 'do-it-yourself' closed-loop systems. We remark on issues associated with clinical adoption of these approaches, including training provision, and consider limitations of presently available closed-loop systems and areas for future enhancements to further improve outcomes and reduce the burden of diabetes management.

New closed-loop insulin systems

Advances in diabetes technologies have enabled the development of automated closed-loop insulin delivery systems. Several hybrid closed-loop systems have been commercialised, reflecting rapid transition of this evolving technology from research into clinical practice, where it is gradually transforming the management of type 1 diabetes in children and adults. In this review we consider the supporting evidence in terms of glucose control and quality of life for presently available closed-loop systems and those in development, including dual-hormone closed-loop systems. We also comment on alternative 'do-it-yourself' closed-loop systems. We remark on issues associated with clinical adoption of these approaches, including training provision, and consider limitations of presently available closed-loop systems and areas for future enhancements to further improve outcomes and reduce the burden of diabetes management.

Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study

BACKGROUND

Predictive alerts for impending hypoglycemic events enable persons with type 1 diabetes to take preventive actions and avoid serious consequences.

OBJECTIVE

This study aimed to develop a prediction model for hypoglycemic events with a low false alert rate, high sensitivity and specificity, and good generalizability to new patients and time periods.

METHODS

Performance improvement by focusing on sustained hypoglycemic events, defined as glucose values less than 70 mg/dL for at least 15 minutes, was explored. Two different modeling approaches were considered: (1) a classification-based method to directly predict sustained hypoglycemic events, and (2) a regression-based prediction of glucose at multiple time points in the prediction horizon and subsequent inference of sustained hypoglycemia. To address the generalizability and robustness of the model, two different validation mechanisms were considered: (1) patient-based validation (model performance was evaluated on new patients), and (2) time-based validation (model performance was evaluated on new time periods).

RESULTS

This study utilized data from 110 patients over 30-90 days comprising 1.6 million continuous glucose monitoring values under normal living conditions. The model accurately predicted sustained events with >97% sensitivity and specificity for both 30- and 60-minute prediction horizons. The false alert rate was kept to <25%. The results were consistent across patient- and time-based validation strategies.

CONCLUSIONS

Providing alerts focused on sustained events instead of all hypoglycemic events reduces the false alert rate and improves sensitivity and specificity. It also results in models that have better generalizability to new patients and time periods.

Basal Glucose Control in Type 1 Diabetes Using Deep Reinforcement Learning: An In Silico Validation

People with Type 1 diabetes (T1D) require regular exogenous infusion of insulin to maintain their blood glucose concentration in a therapeutically adequate target range. Although the artificial pancreas and continuous glucose monitoring have been proven to be effective in achieving closed-loop control, significant challenges still remain due to the high complexity of glucose dynamics and limitations in the technology. In this work, we propose a novel deep reinforcement learning model for single-hormone (insulin) and dual-hormone (insulin and glucagon) delivery. In particular, the delivery strategies are developed by double Q-learning with dilated recurrent neural networks. For designing and testing purposes, the FDA-accepted UVA/Padova Type 1 simulator was employed. First, we performed long-term generalized training to obtain a population model. Then, this model was personalized with a small data-set of subject-specific data. In silico results show that the single and dual-hormone delivery strategies achieve good glucose control when compared to a standard basal-bolus therapy with low-glucose insulin suspension. Specifically, in the adult cohort (n = 10), percentage time in target range 70, 180 mg/dL improved from 77.6% to 80.9% with single-hormone control, and to 85.6% with dual-hormone control. In the adolescent cohort (n = 10), percentage time in target range improved from 55.5% to [Formula: see text] with single-hormone control, and to 78.8% with dual-hormone control. In all scenarios, a significant decrease in hypoglycemia was observed. These results show that the use of deep reinforcement learning is a viable approach for closed-loop glucose control in T1D.

Type 1 diabetes woman with repeated miscarriages successfully gave birth after introducing an insulin pump with a predictive low glucose suspend feature

Sensor-augmented insulin pump therapy with a predictive low glucose suspend (SAP-PLGS) feature is a remarkably progressed modality for the glycemic management of patients with type 1 diabetes. This technology avoids nocturnal hypoglycemia and severe hypoglycemia. A Brazilian woman developed type 1 diabetes at age 11 and was treated with multiple daily insulin injections. At age 20, she was admitted to our internal medicine department for her first pregnancy. Her HbA1c was 7.9% in the 6 weeks of gestation. Although the combination of continuous subcutaneous insulin infusion and a sensor-augmented pump was introduced, she had a miscarriage in the next week. After 6 months, she became pregnant again. Despite an HbA1c of 7.2%, she had another miscarriage. Thereafter, she returned to multiple daily insulin injections and began using intermittently scanned continuous glycemic monitoring. At age 22, she had her third pregnancy. Her HbA1c was 7.3%. SAP-PLGS was then introduced, which reduced her frequent hypoglycemic events and blood glucose fluctuations. She gave birth to a 4137 g boy at 39 weeks without significant complications. Successful delivery can be obtained in women with type 1 diabetes following repeated miscarriages after introducing SAP-PLGS. We hypothesize that the modality might contributed to our patient's miscarriage avoidance by reducing her glycemic fluctuations.

Pancreas transplant versus islet transplant versus insulin pump therapy: in which patients and when?

PURPOSE OF REVIEW

The aim of the present review is to gather recent reports on the use of pancreas and islet transplantation and conventional insulin therapy for treating patients experiencing diabetes and its related complications. The present review directs attention to the current status, challenges and perspectives of these therapies and sheds light on potential future cellular therapies.

RECENT FINDINGS

The risks and benefits of diabetes treatment modalities continue to evolve, altering the risk versus benefit calculation for patients. As continuous subcutaneous insulin infusion and monitoring technologies demonstrate increasing effectiveness in achieving better diabetes control and reducing hypoglycemia frequency, so are pancreas and islet transplantation improving and becoming more effective and safer. Both beta-cell replacement therapies, however, are limited by a dependence on immunosuppression and a shortage of cadaver donors, restricting more widespread and safer deployment. Based on the effectiveness of clinical beta-cell replacement for lengthening lifespan and improving quality of life, scientists are aggressively investigating alternative cell sources, transplant platforms, and means of preventing immunological damage of transplanted cells to overcome these principle limitations.

SUMMARY

Essential goals of diabetes therapy are euglycemia, avoidance of hypoglycemia, and prevention or stabilization of end-organ damage. With these goals in mind, all therapeutic options should be considered.

Real-World Patient-Reported Outcomes and Glycemic Results with Initiation of Control-IQ Technology

Background: The t:slim X2™ insulin pump with Control-IQ™ technology, an advanced hybrid closed-loop system, became available in the United States in early 2020. Real-world outcomes with use of this system have not yet been comprehensively reported. Methods: Individuals with type 1 diabetes (T1D) (≥14 years of age) who had ≥21 days of pump usage data were invited via email to participate. Participants completed psychosocial questionnaires (Technology Acceptance Scale [TAS], well-being index [WHO-5], and Diabetes Impact and Devices Satisfaction [DIDS] scale) at timepoint 1 (T1) (at least 3 weeks after starting Control-IQ technology) and the DIDS and WHO-5 at timepoint 2 (T2) (4 weeks from T1). Patient-reported outcomes (PROs) and glycemic outcomes were reviewed at each timepoint. Results: Overall, 9,085 potentially eligible individuals received the study invite. Of these, 3,116 consented and subsequently 1,435 participants completed questionnaires at both T1 and T2 and had corresponding glycemic data available on the t:connect® web application. Time in range was 78.2% (70.2%-85.1%) at T1 and 79.2% (70.3%-86.2%) at T2. PROs reflected high device-related satisfaction and reduced diabetes impact at T2. Factors contributing to high trust in the system included sensor accuracy, improved diabetes control, reduction in extreme blood glucose levels, and improved sleep quality. In addition, participants reported improved quality of life, ease of use, and efficient connectivity to the continuous glucose monitoring system as being valuable features of the system. Conclusions: Continued real-world use of the t:slim X2 pump with Control-IQ technology showed improvements in psychosocial outcomes and persistent achievement of recommended TIR glycemic outcomes in people with T1D.

Time in range centered diabetes care

Optimal glycemic control remains challenging and elusive for many people with diabetes. With the comprehensive clinical evidence on safety and efficiency in large populations, and with broader reimbursement, the adoption of continuous glucose monitoring (CGM) is rapidly increasing. Standardized visual reporting and interpretation of CGM data and clear and understandable clinical targets will help professionals and individuals with diabetes use diabetes technology more efficiently, and finally improve long-term outcomes with less everyday disease burden. For the majority of people with type 1 or type 2 diabetes, time in range (between 70 and 180 mg/dL, or 3.9 and 10 mmol/L) target of more than 70% is recommended, with each incremental increase of 5% towards this target being clinically meaningful. At the same time, the goal is to minimize glycemic excursions: a recommended target for a time below range (< 70 mg/dL or < 3.9 mmol/L) is less than 4%, and time above range (> 180 mg/dL or 10 mmol/L) less than 25%, with less stringent goals for older individuals or those at increased risk. These targets should be individualized: the personal use of CGM with the standardized data presentation provides all necessary means to accurately tailor diabetes management to the needs of each individual with diabetes.

Using trend arrows in continuous glucose monitoring systems for insulin adjustment in clinical practice: Brazilian Diabetes Society Position Statement

This manuscript reports the Brazilian Diabetes Society Position Statement for insulin adjustments based on trend arrows observed in continuous glucose monitoring systems. The Brazilian Diabetes Society supports the utilization of trend arrows for insulin dose adjustments in patients with diabetes on basal-bolus insulin therapy, both with multiple daily insulin doses or insulin pumps without closed-loop features. For those on insulin pumps with predictive low-glucose suspend feature, we suggest that only upward trend arrows should be used for adjustments. In this paper, tables for insulin adjustment based on sensitivity factors are provided and strategies to optimize the use of trend arrows in clinical practice are discussed.

Developing Insulin Delivery Devices with Glucose Responsiveness

Individuals with type 1 and advanced type 2 diabetes require daily insulin therapy to maintain blood glucose levels in normoglycemic ranges to prevent associated morbidity and mortality. Optimal insulin delivery should offer both precise dosing in response to real-time blood glucose levels as well as a feasible and low-burden administration route to promote long-term adherence. A series of glucose-responsive insulin delivery mechanisms and devices have been reported to increase patient compliance while mitigating the risk of hypoglycemia. This review discusses currently available insulin delivery devices, overviews recent developments towards the generation of glucose-responsive delivery systems, and provides commentary on the opportunities and barriers ahead regarding the integration and translation of current glucose-responsive insulin delivery designs.

Improved Real-World Glycemic Control With Continuous Glucose Monitoring System Predictive Alerts

BACKGROUND

Most standalone real-time continuous glucose monitoring (RT-CGM) systems provide predictive low and high sensor glucose (SG) threshold alerts. The durations and risk of low and high SG excursions following Guardian™ Connect CGM system predictive threshold alerts were evaluated.

METHODS

Continuous glucose monitoring system data uploaded between January 2, 2017 and May 22, 2018 by 3133 individuals using multiple daily injections (MDIs) or continuous subcutaneous insulin infusion (CSII) therapy were deidentified and retrospectively analyzed. Glucose excursions were defined as SG values that went beyond a preset low or high SG threshold for ≥15 minutes. For a control group, thresholds were based on the median of the low SG threshold limit (70 mg/dL) and the high SG threshold limit (210 mg/dL) preset by all system users. During periods when alerts were not enabled, timestamps were identified when a predictive alert would have been triggered. The time before low horizon was 17.5 minutes and the time before high horizon was 15 minutes, of all users who enabled alerts. Excursions occurring after a low SG or high SG predictive alert were segmented into prevented, ≤20, 20-60, and >60 minutes.

RESULTS

Excursions were prevented after 59% and 39% of low and high SG predictive alerts, respectively. The risk of a low or high excursion occurring was 1.9 (P < 0.001, 95% CI, 1.88-1.93) and 3.3 (P < 0.001, 95% CI, 3.20-3.30) times greater, respectively, when alerts were not enabled.

CONCLUSIONS

The predictive alerts of the RT-CGM system under study can help individuals living with diabetes prevent some real-world low and high SG excursions. This can be especially important for those unable to reach or maintain glycemic control with basic RT-CGM or CSII therapy.

7. Diabetes Technology: Standards of Medical Care in Diabetes-2021

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc21-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc21-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Continuous glucose monitoring: The achievement of 100 years of innovation in diabetes technology

Monitoring of glucose levels is essential to effective diabetes management. Over the past 100 years, there have been numerous innovations in glucose monitoring methods. The most recent advances have centered on continuous glucose monitoring (CGM) technologies. Numerous studies have demonstrated that use of continuous glucose monitoring confers significant glycemic benefits on individuals with type 1 diabetes (T1DM) and type 2 diabetes (T2DM). Ongoing improvements in accuracy and convenience of CGM devices have prompted increasing adoption of this technology. The development of standardized metrics for assessing CGM data has greatly improved and streamlined analysis and interpretation, enabling clinicians and patients to make more informed therapy modifications. However, many clinicians many be unfamiliar with current CGM and how use of these devices may help individuals with T1DM and T2DM achieve their glycemic targets. The purpose of this review is to present an overview of current CGM systems and provide guidance to clinicians for initiating and utilizing CGM in their practice settings.

New and Emerging Technologies in Type 1 Diabetes

There has been a rapid advancement in the pace of development of new diabetes technologies and therapies for the management of type 1 diabetes over the past decade. The Diabetes Control and Complications Trial conclusively established that tight glycemic control with intensive insulin therapy decreases the rates of diabetes complications in proportion to glycemic control, and diabetes technologies have accordingly been developed to help patients reach these goals. In this review, the authors discuss new diabetes therapeutics and technologies, including new insulin analogues, insulin pumps, continuous glucose monitoring systems, and automated insulin delivery systems."