One Year Real-World Use of the Control-IQ Advanced Hybrid Closed-Loop Technology

The Evolution of Insulin Administration in Type 1 Diabetes

Insulin has been utilized in the treatment of type 1 diabetes (T1D) for 100 years. While there is still no cure for T1D, insulin administration has undergone a remarkable evolution which has contributed to improvements in quality of life and life expectancy in individuals with T1D. The advent of faster-acting and longer-acting insulins allowed for the implementation of insulin regimens more closely resembling normal insulin physiology. These improvements afforded better glycemic control, which is crucial for limiting microvascular complications and improving T1D outcomes. Suspension of insulin delivery in response to actual and forecasted hypoglycemia has improved quality of life and mitigated hypoglycemia without compromising glycemic control. Advances in continuous glucose monitoring (CGM) and insulin pumps, efforts to model glucose and insulin kinetics, and the application of control theory to T1D have made the automation of insulin delivery a reality. This review will summarize the past, present, and future of insulin administration in T1D.

Effectiveness of a closed-loop control system and a virtual educational camp for children and adolescents with type 1 diabetes: A prospective, multicentre, real-life study

AIM

To evaluate the impact of a virtual educational camp (vEC) on glucose control in children and adolescents with type 1 diabetes using a closed-loop control (CLC) system.

MATERIALS AND METHODS

This was a prospective multicentre study of children and adolescents with type 1 diabetes using the Tandem Basal-IQ system. Insulin pumps were upgraded to Control-IQ, and children and their parents participated in a 3-day multidisciplinary vEC. Clinical data, glucose metrics and HbA1c were evaluated over the 12 weeks prior to the Control-IQ update and over the 12 weeks after the vEC.

RESULTS

Forty-three children and adolescents (aged 7-16 years) with type 1 diabetes and their families participated in the vEC. The median percentage of time in target range (70-180 mg/dL; TIR) increased from 64% (interquartile range [IQR] 56%-73%) with Basal-IQ to 76% (IQR 71%-81%) with Control-IQ (P < .001). After the vEC, more than 75% of participants achieved a TIR of more than 70%. The percentage of time between 180 and 250 mg/dL and above 250 mg/dL decreased by 5% (P < .01) and 6% (P < .01), respectively, while the time between 70 and 54 mg/dL and below 54 mg/dL remained low and unaltered. HbA1c decreased by 0.5% (P < .01). There were no episodes of diabetic ketoacidosis or severe hypoglycaemia.

CONCLUSIONS

In this study of children managing their diabetes in a real-world setting, more than 75% of children who participated in a vEC after starting a CLC system could obtain and maintain a TIR of more than 70%. The vEC was feasible and resulted in a significant and persistent improvement in TIR in children and adolescents with type 1 diabetes.

Artificial intelligence and diabetes technology: A review

Artificial intelligence (AI) is widely discussed in the popular literature and is portrayed as impacting many aspects of human life, both in and out of the workplace. The potential for revolutionizing healthcare is significant because of the availability of increasingly powerful computational platforms and methods, along with increasingly informative sources of patient data, both in and out of clinical settings. This review aims to provide a realistic assessment of the potential for AI in understanding and managing diabetes, accounting for the state of the art in the methodology and medical devices that collect data, process data, and act accordingly. Acknowledging that many conflicting definitions of AI have been put forth, this article attempts to characterize the main elements of the field as they relate to diabetes, identifying the main perspectives and methods that can (i) affect basic understanding of the disease, (ii) affect understanding of risk factors (genetic, clinical, and behavioral) of diabetes development, (iii) improve diagnosis, (iv) improve understanding of the arc of disease (progression and personal/societal impact), and finally (v) improve treatment.

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.

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.

Benefits of a Switch from Intermittently Scanned Continuous Glucose Monitoring (isCGM) to Real-Time (rt) CGM in Diabetes Type 1 Suboptimal Controlled Patients in Real-Life: A One-Year Prospective Study §

The switch from intermittently scanned continuous glucose monitoring (isCGM) to real-time (rt) CGM could improve glycemic management in suboptimal controlled type 1 diabetes patients, but long-term study is lacking. We evaluated retrospectively the ambulatory glucose profile (AGP) in such patients after switching from Free Style Libre 1 (FSL1) to Dexcom G4 (DG4) biosensors over 1 year. Patients (n = 21, 43 ± 15 years, BMI 25 ± 5, HbA1c 8.1 ± 1.0%) had severe hypoglycemia and/or HbA1c ≥ 8%. AGP metrics (time-in-range (TIR) 70–180 mg/dL, time-below-range (TBR) <70 mg/dL or <54 mg/dL, glucose coefficient of variation (%CV), time-above-range (TAR) >180 mg/dL or >250 mg/dL, glucose management indicator (GMI), average glucose) were collected the last 3 months of FSL1 use (M0) and of DG4 for 3, 6 (M6) and 12 (M12) months of use. Values were means ± standard deviation or medians [Q1;Q3]. At M12 versus M0, the higher TIR (50 ± 17 vs. 45 ± 16, p = 0.036), and lower TBR < 70 mg/dL (2.5 [1.6;5.5] vs. 7.0 [4.5;12.5], p = 0.0007), TBR < 54 mg/dL (0.7 [0.4;0.8] vs. 2.3 [0.8;7.0], p = 0.007) and %CV (39 ± 5 vs. 45 ± 8, p = 0.0009), evidenced a long-term effectiveness of the switch. Compared to M6, TBR < 70 mg/dL decreased, %CV remained stable, while the improvement on hyperglycemia exposure decreased (higher GMI, TAR and average glucose). This switch was a relevant therapeutic option, though a loss of benefit on hyperglycemia stressed the need for optimized management of threshold alarms. Nevertheless, few patients attained the recommended values for AGP metrics, and the reasons why some patients are “responders” vs. “non-responders” warrant to be investigated.

A data-driven approach to classifying daily continuous glucose monitoring (CGM) time series

According to the World Health Organization, about 422 million people worldwide have type 1 or type 2 diabetes (T1D, T2D), with the latter accounting for 90-95% of cases. Safe and effective treatment of patients with diabetes requires accurate and frequent monitoring of their blood sugar levels. Continuous glucose monitoring (CGM) is a monitoring technology developed to address this need, and its use among U.S. T1D patients has increased from 6% in 2011 to 38% in 2018 and continues to increase worldwide in both T1D and T2D. This paper presents a data-driven approach to determine Ω, a finite set of representative daily profiles (motifs) such that almost any daily CGM profile generated by a patient can be matched to one of the motifs in Ω. The training data set (9,741 profiles) was used to identify 8 candidate sets of motifs, while the validation data set (14,175 profiles) was used to select the final set Ω. The robustness of Ω was established by using it to successfully classify (match against a representative daily profile in Ω) 99.0% of 42,595 daily CGM profiles in the testing data set. All data sets contained daily CGM profiles from six studies involving T1D and T2D patients using a variety of treatment modes, including daily insulin injections, insulin pumps, or artificial pancreas (AP). The classified profiles can be used in predictive modeling, decision support, and automated control systems (e.g., AP).

A Comparison of Two Hybrid Closed-Loop Systems in Italian Children and Adults With Type 1 Diabetes

Tandem Control-IQ and Minimed 780G represent the most Advanced Hybrid Closed Loop (AHCL) systems currently available in pediatric and adult subjects with Type 1 Diabetes (T1D). We retrospectively compared clinical and continuous glucose monitoring data from 51 patients who upgraded to Minimed 780G system and have completed 1-month observation period with data from 39 patients who upgraded to Tandem Control-IQ. Inverse probability weighting was used to minimize the basal characteristics imbalances. Both AHCL systems showed a significant improvement in glycemic parameters. Minimed 780G group achieved higher TIR increase (p= 0.004) and greater reduction of blood glucose average (p= 0.001). Tandem Control-IQ system significantly reduced the occurrence of TBR (p= 0.010) and the Coefficient of Variation of glucose levels (p= 0.005). The use of ACHL systems led to a significant improvement of glycemic control substantially reaching the International recommended glycemic targets. Minimed 780G appears to be more effective in managing hyperglycemia, while Tandem Control-IQ seems to be more effective in reducing time in hypoglycemia.