Closed-loop Insulin Delivery System Research Articles

Closed-Loop Insulin Pump Therapy in Young Type 1 Diabetic Children

Source: Ware J, Allen JM, Boughton CK, et al. Randomized trial of closed-loop control in very young children with type 1 diabetes. N Engl J Med. 2022;386:209-219; doi: 10.1056/NEJMoa2111673Investigators from multiple institutions conducted a crossover randomized trial to compare the effectiveness and safety of a closed-loop system (ie, artificial pancreas) to sensor-augmented insulin pump therapy in young children with type 1 diabetes. Participants were children 1-7 years old with a diagnosis of type 1 diabetes for at least 6 months, recruited from diabetes centers in 4 European countries. The closed-loop system consisted of a continuous glucose sensor and insulin pump connected to a smartphone app. The app included an algorithm that automatically adjusted insulin delivery based on real-time sensor glucose readings. With the sensor-augmented pump, insulin therapy was stopped if an algorithm predicted that sensor glucose levels were likely to drop below a specified threshold. Study children were randomized to receive 16 weeks of treatment with the closed loop system, followed by 16 weeks using the augmented-sensor pump, or vice versa. The primary outcome was percentage of time spent in the target glucose range (70 to 170 mg/dL) during each treatment period. Secondary outcomes included percentage of time spent in a hyperglycemic range (>180 mg/dL) and hypoglycemic range (<70 mg/dL) and mean glucose levels. These outcomes were based on readings from the glucose sensors. Glycated hemoglobin levels were measured at baseline and at the end of each treatment period. Differences in outcomes between the closed-loop and sensor-augmented pump treatment period were assessed with repeated-measures linear regression models. Data on adverse events also were collected.Data were analyzed on 74 children with a mean age of 5.6 ±1.6 years. The mean glycated hemoglobin level at baseline was 7.3 ±0.7%. The mean time spent in the target glucose range were 71.6 ±5.9% during the closed-loop period and 62.9 ±9.0% in the sensor-augmented pump period (adjusted difference, 8.7 percentage points; 95% CI, 7.4, 9.9; P <0.001). The median time spent at glucose level >180 mg/dL was significantly less during the closed-loop period than during the control period (22.9% vs 31.7%; adjusted difference, -8.5 percentage points; 95% CI, -.9.9, -7.1; P <0.001); mean sensor glucose levels (145 ±11.8 mg/dL and 158.1 ±18.5 mg/dL, respectively; P <0.001) and mean glycated hemoglobin levels (6.6 ±0.6% and 7.0 ±0.7%) were significantly lower during the closed-loop period than during treatment with the segmented-sensor pump. There was no difference between treatment periods for percentage of time with glucose <70 mg/dL (P = 0.74). One participant had a serious adverse event (severe hypoglycemia) during their closed-loop treatment period.The authors conclude that a closed-loop system improved glycemic control in young children with type 1 diabetes.Dr Fechner has disclosed no financial relationship relevant to this commentary. This commentary does not contain a discussion of an unapproved/investigative use of a commercial product/device.Type 1 diabetes is very difficult to manage in the young child. The therapeutic window for insulin is very narrow, with too much insulin resulting in hypoglycemia and too little insulin resulting in hyperglycemia. The last 5 years have seen tremendous advances in the technology available to children with type 1 diabetes. A 2019 study comparing the insulin pump to multiple daily injections showed that the insulin pump was superior to multiple daily injections. (See AAP Grand Rounds. 2019;42[1]:4.) Continuous glucose monitoring has decreased parental anxiety regarding risk of hypoglycemia and diabetic ketoacidosis;1 however, parents still are responsible for making insulin dose adjustments, and glycemic control overall is not improved.2 The results of a 2020 study of 6- to 13-year-olds with type 1 diabetes demonstrated that glucose levels were in the target range for a longer period of time in those randomized to a closed-loop system than those using a sensor-augmented pump. (See AAP Grand Rounds. 2020;44[6]:69.)3 The current researchers compared a hybrid closed-loop insulin delivery system to sensor-augmented pump therapy in younger children with type I diabetes.The strengths of the current study are the randomized, crossover design using the same algorithm with the closed-loop functionality either abled or disabled; each therapy was 16 weeks, which allowed a meaningful glycated hemoglobin to be obtained. There were no restrictions on previous episodes of hypoglycemia and diabetic ketoacidosis, and the study included a very young children with diabetes, who are more vulnerable to hypo- and hyperglycemia. Weaknesses of this study include underrepresentation of ethnic minorities and a patient population not representative of the typical type 1 diabetes population, as the average glycated hemoglobin level was 7.3% at baseline.4 Decreased patient compliance outside of the confines of a study may be reflected in poorer glucose control regardless of the insulin delivery system used.Hybrid closed-loop insulin therapy in young children with type 1 diabetes has the potential to significantly improve long-term outcomes through improved glycemic control.

Recent advances in glucose-responsive insulin delivery systems: novel hydrogels and future applications.

Over the past several decades, there have been major advancements in the field of glucose sensing and insulin delivery for the treatment of type I diabetes mellitus. The introduction of closed-loop insulin delivery systems that deliver insulin in response to specific levels of glucose in the blood has shifted significantly the research in this field. These systems consist of encapsulated glucose-sensitive components such as glucose oxidase or phenylboronic acid in hydrogels, microgels or nanoparticles. Since our previous evaluation of these systems in a contribution in 2004, new systems have been developed. Important improvements in key issues, such as consistent insulin delivery over an extended period of time have been addressed. In this contribution, we discuss recent advancements over the last 5 years and present persisting issues in these technologies that must be overcome in order for these systems to be applicable in patients.

Cambridge hybrid closed-loop algorithm in children and adolescents with type 1 diabetes: a multicentre 6-month randomised controlled trial

Closed-loop insulin delivery systems have the potential to address suboptimal glucose control in children and adolescents with type 1 diabetes. We compared safety and efficacy of the Cambridge hybrid closed-loop algorithm with usual care over 6 months in this population. In a multicentre, multinational, parallel randomised controlled trial, participants aged 6-18 years using insulin pump therapy were recruited at seven UK and five US paediatric diabetes centres. Key inclusion criteria were diagnosis of type 1 diabetes for at least 12 months, insulin pump therapy for at least 3 months, and screening HbA1c levels between 53 and 86 mmol/mol (7·0-10·0%). Using block randomisation and central randomisation software, we randomly assigned participants to either closed-loop insulin delivery (closed-loop group) or to usual care with insulin pump therapy (control group) for 6 months. Randomisation was stratified at each centre by local baseline HbA1c. The Cambridge closed-loop algorithm running on a smartphone was used with either (1) a modified Medtronic 640G pump, Medtronic Guardian 3 sensor, and Medtronic prototype phone enclosure (FlorenceM configuration), or (2) a Sooil Dana RS pump and Dexcom G6 sensor (CamAPS FX configuration). The primary endpoint was change in HbA1c at 6 months combining data from both configurations. The primary analysis was done in all randomised patients (intention to treat). Trial registration ClinicalTrials.gov, NCT02925299. Of 147 people initially screened, 133 participants (mean age 13·0 years [SD 2·8]; 57% female, 43% male) were randomly assigned to either the closed-loop group (n=65) or the control group (n=68). Mean baseline HbA1c was 8·2% (SD 0·7) in the closed-loop group and 8·3% (0·7) in the control group. At 6 months, HbA1c was lower in the closed-loop group than in the control group (between-group difference -3·5 mmol/mol (95% CI -6·5 to -0·5 [-0·32 percentage points, -0·59 to -0·04]; p=0·023). Closed-loop usage was low with FlorenceM due to failing phone enclosures (median 40% [IQR 26-53]), but consistently high with CamAPS FX (93% [88-96]), impacting efficacy. A total of 155 adverse events occurred after randomisation (67 in the closed-loop group, 88 in the control group), including seven severe hypoglycaemia events (four in the closed-loop group, three in the control group), two diabetic ketoacidosis events (both in the closed-loop group), and two non-treatment-related serious adverse events. There were 23 reportable hyperglycaemia events (11 in the closed-loop group, 12 in the control group), which did not meet criteria for diabetic ketoacidosis. The Cambridge hybrid closed-loop algorithm had an acceptable safety profile, and improved glycaemic control in children and adolescents with type 1 diabetes. To ensure optimal efficacy of the closed-loop system, usage needs to be consistently high, as demonstrated with CamAPS FX. National Institute of Diabetes and Digestive and Kidney Diseases.

A Multitask Learning Approach to Personalized 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 network 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 ±4.6mg/dL at 30, 45, 60, 90, and 120min 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 show 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.

Cost-Effectiveness Analysis of an Advanced Hybrid Closed-Loop Insulin Delivery System in People with Type 1 Diabetes in Greece.

AbstractIntroduction: Usage of automated insulin delivery systems is increasing for the treatment of people with type 1 diabetes (T1D). This study compared long-term cost-effectiveness of the Advanced Hybrid Closed Loop MiniMed 780G (AHCL) system versus sensor augmented pump (SAP) system with predictive low glucose management (PLGM) or multiple daily injections (MDI) plus intermittently scanned continuous glucose monitoring (isCGM) in people with T1D in Greece. Methods: Analyses were performed using the IQVIA CORE Diabetes Model, with clinical input data sourced from various studies. In the AHCL versus SAP plus PLGM analysis, patients were assumed to have 7.5% baseline glycated hemoglobin (HbA1c), when comparing AHCL with MDI plus isCGM baseline HbA1c was assumed to be 7.8%. HbA1c was reduced to 7.0% following AHCL treatment initiation but remained at baseline levels in the comparator arms. Analyses were performed from a societal perspective over a lifetime time horizon. Future costs and clinical outcomes were discounted at 1.5% per annum. Results: AHCL was associated with increased quality-adjusted life expectancy of 0.284 quality-adjusted life years (QALYs) and EUR 10,173 lower mean total lifetime costs with SAP plus PLGM. Compared with MDI plus isCGM, AHCL was associated with increased quality-adjusted life expectancy of 2.708 QALYs, EUR 76,396 higher mean total lifetime costs, and an incremental cost-effectiveness ratio of EUR 29,869 per QALY. Extensive sensitivity analysis confirmed the robustness of results. Conclusions: Over patient lifetime, the MiniMed 780G system is likely to be cost saving compared with the SAP plus PLGM system and cost-effective compared with MDI plus isCGM in people with T1D in Greece.

Opportunities and challenges in closed-loop systems in type 1 diabetes

Commercial automated insulin delivery therapy can improve glucose outcomes by increasing glucose time-in-range (3·9–10·0 mmol/L; 70–180 mg/dL) and reducing hypoglycaemia (<3·9 mmol/L; <70 mg/dL) in people with type 1 diabetes. Commercial closed-loop systems have shown modest improvement in percentage time-in-range, with an increase of 5 percentage points for MiniMed 670G 1 Bergenstal RM Garg S Weinzimer SA et al. Safety of a hybrid closed-loop insulin delivery system in patients with type 1 diabetes. JAMA. 2016; 316: 1407-1408 Crossref PubMed Scopus (377) Google Scholar and of 10 percentage points for Control-IQ 2 Brown SA Kovatchev BP Raghinaru D et al. Six-month randomized, multicenter trial of closed-loop control in type 1 diabetes. N Engl J Med. 2019; 381: 1707-1717 Crossref PubMed Scopus (266) Google Scholar compared with open-loop therapy. New developments with closed-loop systems are likely to enable further improvements in glucose outcomes while reducing user burden for individuals able to access closed-loop systems. These developments are focused on addressing deficiencies with current closed-loop systems by, for example, reducing or eliminating the requirement of prandial insulin dosing, overcoming the slow kinetics of subcutaneous insulin compared with endogenous insulin production, and improving the handling of insulin before, during, and after exercise. These advances will ideally lead to improved performance and reduced patient burden, and enable more widespread usage.

Real-World, Patient-Reported and Clinic Data from Individuals with Type 1 Diabetes Using the MiniMed 670G Hybrid Closed-Loop System.

Background: The purpose of this study was to collect 1 year of real-world data from individuals with type 1 diabetes (T1D) initiating the Medtronic 670G hybrid closed-loop insulin delivery system as part of usual care. We sought to expand current knowledge to understand how use of the system impacts patient-reported outcomes, in addition to clinical outcomes, for children and adults with T1D. Methods: Questionnaires were completed by the participant (and/or parent) before initiation of the 670G system (baseline) and at 6 weeks, 6 months, and 12 months from enrollment. Clinical data were obtained at routine clinical visits. Results: Of 132 participants who initiated Auto Mode, 80 completed the 12-month questionnaires while persisting with the system. Nearly all reported receiving adequate training on the 670G. Participant and parent-reported fear of hypoglycemia decreased by 6 and 11 points, respectively, from baseline to 12 months. More than half reported issues with sleep interruption at night due to alarms and 40% did not like frequent exits from Auto Mode. For the subset who had complete continuous glucose monitor data (n = 27), mean percent time in target range (70-180 mg/dL) was 66% at baseline, and 74% and 68% at 6 and 12 months, respectively. Conclusions: With this study, we have captured real-time feedback from patients with T1D who initiated the 670G system and continued to use it over 12 months regarding their experience with the system. This has helped to illuminate both benefits and burdens associated with the first commercially available hybrid closed-loop system.

Short course corticosteroid treatment and closed-loop insulin delivery system: The experience of the DBLG1 pre-launch

No abstract (brief report).

Fully closed-loop insulin delivery—are we nearly there yet?

Fully closed-loop insulin delivery—are we nearly there yet?

Adjunctive Therapies to Optimize Closed-loop Glucose Control.

Closed-loop insulin delivery systems are fast becoming the standard of care in the management of type 1 diabetes and have led to significant improvements in diabetes management. Nevertheless, there is still room for improvement for the closed-loop systems to optimize treatment and meet target glycemic control. Adjunct treatments have been introduced as an alternative method to insulin-only treatment methods to overcome diabetes treatment challenges and improve clinical and patient reported outcomes during closed-loop treatment. The adjunct treatment agents mostly consist of medications that are already approved for type 2 diabetes treatment and aim to complete the missing physiologic factors, such as the entero-endocrine system, that regulate glycemia in addition to insulin. This paper will review many of these adjunct therapies, including the basic mechanisms of action, potential benefits, side effects, and the evidence supporting their use during closed-loop treatment.

Artificial intelligence in diabetology

This review presents the applications of artificial intelligence for the study of the mechanisms of diabetes development and generation of new technologies of its prevention, monitoring and treatment. In recent years, a huge amount of molecular data has been accumulated, revealing the pathogenic mechanisms of diabetes and its complications. Data mining and text mining open up new possibilities for processing this information. Analysis of gene networks makes it possible to identify molecular interactions that are important for the development of diabetes and its complications, as well as to identify new targeted molecules. Based on the big data analysis and machine learning, new platforms have been created for prediction and screening of diabetes, diabetic retinopathy, chronic kidney disease, and cardiovascular disease. Machine learning algorithms are applied for personalized prediction of glucose trends, in the closed-loop insulin delivery systems and decision support systems for lifestyle modification and diabetes treatment. The use of artificial intelligence for the analysis of large databases, registers, and real-world evidence studies seems to be promising. The introduction of artificial intelligence systems is in line with global trends in modern medicine, including the transition to digital and distant technologies, personification of treatment, high-precision forecasting and patient-centered care. There is an urgent need for further research in this field, with an assessment of the clinical effectiveness and economic feasibility.

Искусственный интеллект в диабетологии

This review presents the applications of artificial intelligence for the study of the mechanisms of diabetes development and generation of new technologies of its prevention, monitoring and treatment. In recent years, a huge amount of molecular data has been accumulated, revealing the pathogenic mechanisms of diabetes and its complications. Data mining and text mining open up new possibilities for processing this information. Analysis of gene networks makes it possible to identify molecular interactions that are important for the development of diabetes and its complications, as well as to identify new targeted molecules. Based on the big data analysis and machine learning, new platforms have been created for prediction and screening of diabetes, diabetic retinopathy, chronic kidney disease, and cardiovascular disease. Machine learning algorithms are applied for personalized prediction of glucose trends, in the closed-loop insulin delivery systems and decision support systems for lifestyle modification and diabetes treatment. The use of artificial intelligence for the analysis of large databases, registers, and real-world evidence studies seems to be promising. The introduction of artificial intelligence systems is in line with global trends in modern medicine, including the transition to digital and distant technologies, personification of treatment, high-precision forecasting and patient-centered care. There is an urgent need for further research in this field, with an assessment of the clinical effectiveness and economic feasibility.

Deep learning and regression approaches to forecasting blood glucose levels for type 1 diabetes

Objective: Controlling blood glucose in the euglycemic range is the main goal of developing the closed-loop insulin delivery system for type 1 diabetes patients. The closed-loop system delivers the amount of insulin dose determined by glucose predictions through the use of computational algorithms. A computationally efficient and accurate model that can capture the physiological nonlinear dynamics is critical for developing an efficient closed-loop system. Methods: Four data-driven models are compared, including different neural network architectures, a reservoir computing model, and a novel linear regression approach. Model predictions are evaluated over continuous 30 and 60 min time horizons using real-world data from wearable sensor measurements, a continuous glucose monitor, and self-reported events through mobile applications. The four data-driven models are trained on 12 data contributors for around 32 days, 8 days of data are used for validation, with an additional 10 days of data for out-of-sample testing. Model performance was evaluated by the root mean squared error and the mean absolute error. Results: A neural network model using an encoder-decoder architecture has the most stable performance and is able to recover missing dynamics in short time intervals. Regression models performed better at long-time prediction horizons (i.e., 60 min) and with lower computational costs. Significance: The performance of several distinct models was tested for individual-level data from a type 1 diabetes data set. These results may enable a feasible solution with low computational cost for the time-dependent adjustment of artificial pancreas for diabetes patients.

Machine learning-based glucose prediction with use of continuous glucose and physical activity monitoring data: The Maastricht Study.

Closed-loop insulin delivery systems, which integrate continuous glucose monitoring (CGM) and algorithms that continuously guide insulin dosing, have been shown to improve glycaemic control. The ability to predict future glucose values can further optimize such devices. In this study, we used machine learning to train models in predicting future glucose levels based on prior CGM and accelerometry data. We used data from The Maastricht Study, an observational population-based cohort that comprises individuals with normal glucose metabolism, prediabetes, or type 2 diabetes. We included individuals who underwent >48h of CGM (n = 851), most of whom (n = 540) simultaneously wore an accelerometer to assess physical activity. A random subset of individuals was used to train models in predicting glucose levels at 15- and 60-minute intervals based on either CGM data or both CGM and accelerometer data. In the remaining individuals, model performance was evaluated with root-mean-square error (RMSE), Spearman's correlation coefficient (rho) and surveillance error grid. For a proof-of-concept translation, CGM-based prediction models were optimized and validated with the use of data from individuals with type 1 diabetes (OhioT1DM Dataset, n = 6). Models trained with CGM data were able to accurately predict glucose values at 15 (RMSE: 0.19mmol/L; rho: 0.96) and 60 minutes (RMSE: 0.59mmol/L, rho: 0.72). Model performance was comparable in individuals with type 2 diabetes. Incorporation of accelerometer data only slightly improved prediction. The error grid results indicated that model predictions were clinically safe (15 min: >99%, 60 min >98%). Our prediction models translated well to individuals with type 1 diabetes, which is reflected by high accuracy (RMSEs for 15 and 60 minutes of 0.43 and 1.73 mmol/L, respectively) and clinical safety (15 min: >99%, 60 min: >91%). Machine learning-based models are able to accurately and safely predict glucose values at 15- and 60-minute intervals based on CGM data only. Future research should further optimize the models for implementation in closed-loop insulin delivery systems.

Hybrid Closed-Loop Insulin Delivery Systems for People With Type 1 Diabetes

&#x0D; The CADTH Health Technology Expert Review Panel (HTERP) suggests that hybrid closed-loop insulin delivery (HCL) systems hold promise for the care of people with type 1 diabetes. HTERP considers that, at present, there are insufficient long-term data on clinically relevant and patient-important outcomes to recommend how extensive the role of HCL systems should be in care.&#x0D; HTERP recommends the collection of robust and comparative data for consideration of future reassessments that compare HCL systems to existing insulin delivery and glucose monitoring methods in terms of glycated hemoglobin (hemoglobin A1C); time-in-range; time above and below range; glycemic variability; quality of life; patient, parent or caregiver, and health care provider satisfaction; diabetes-related complications; discontinuation rates; and health system impact. Robust data are collected in well-designed comparative studies that are, among other considerations, of sufficient duration to ensure a clinically meaningful outcome assessment.&#x0D;

147-OR: Use of Closed-Loop Insulin Therapy in Older Adults with Type 1 Diabetes: Real-World Experience

Background: Closed-loop control insulin delivery systems (CLC) have been available to adults with type 1 diabetes (T1D) for many years. However, for older adults with T1D, Tandem Control IQ is the first CLC available through Medicare. We evaluated the use of CLC in older adults with T1D in a real-world scenario. Methods: We retrospectively analyzed electronic health record (EHR) data for older adults (&amp;gt;65) with T1D started on CLC in a single tertiary center between January and December 2020. Continuous glucose monitoring (CGM) data from the 2 weeks prior (baseline) and 2 weeks post CLC system (post-CLC) initiation were analyzed. We evaluated glycemic measures, including coefficient of variation (CV) for glucose variability and glucose management indicator (GMI) for overall glycemic control. Demographic, clinical and diabetes technology data were gathered from EHR. Results: We present interim data from 25 older adults (Mean age 69±4 yrs, duration of diabetes 43±13 yrs) who started using CLC over the 12 month period. Overall, 23/25 were prior pump users, and 23/25 were prior CGM users. Only 2/25 discontinued CLC use. Comparison of CGM metrics measured at baseline vs. post-CLC showed an increase in Time in Range (TIR) from 63±13% to 77±9% (p&amp;lt;0.001) and a reduction in time &amp;gt;180 mg/dL from 30±12% to 18±9% (p=0.004). While there was a trend in decrease of time &amp;lt;70 mg/dL from baseline to post-CLC, this change was not statistically significant (2.8±2.4% vs. 2.2±2.5% (p=0.5)). CV improved from 0.33±0.04% to 0.30±0.04% (p=0.04) and GMI decreased from 7.0±0.5% to 6.7±0.4% (p=0.06). CLC use averaged 92% of the time over 2 weeks. Conclusion: In a small cohort of older adults with T1D using CLC in a real-world scenario, the majority of the cohort was able to use the system successfully, resulting in a higher TIR and improved glycemic variability, as seen by a decrease in CV over a 3 month period. Thus, CLC has the potential to improve glycemic outcomes in this elderly population. Disclosure E. Toschi: Consultant; Self; Medtronic. C. Slyne: None. A. Atakov-castillo: None. M. Munshi: Consultant; Self; Sanofi. Funding National Institutes of Health (1DP3DK112214-01)

695-P: Comparison of Inpatient Glycemic Control in Hybrid Closed-Loop vs. Sensor-Augmented Insulin Delivery Systems

The hybrid closed-loop (HCL) insulin delivery system is at the forefront of continuous subcutaneous insulin infusion technology. However, to our knowledge, no studies to date have compared inpatient glycemic control between the HCL and non-HCL delivery systems of the HCL insulin pumps. We aim to compare the efficacy and safety of glycemic control between these two insulin delivery systems in the hospital setting. We are performing a prospective study on patients using a HCL insulin pump who are admitted to a non-critical care floor. We included patients who were ≥18 years of age, diagnosed with type 1 or type 2 diabetes, on a HCL insulin pump for more than 4 weeks, and admitted for ≥2 days. The primary outcome is the comparison of percentage of point of care blood glucose readings between 70 and 180 mg/dL in the HCL system versus the non-HCL system. Secondary outcomes include percentage of glucose readings >180 mg/dL and Here, we report preliminary findings of 9 patients using the non-HCL system and 4 patients using the HCL system (Table 1). In the HCL group, >70% of the time was spent in the target blood glucose range of 70-180 mg/dL, 180 mg/dL, and Disclosure Y. Tsushima: None. A. Iqbal: None. J. F. Bena: None. M. Lansang: Consultant; Self; Sanofi. Research Support; Self; Dexcom, Inc.

524-P: Diabetes Device Use Linked with Lower Family Conflict in Teens with Type 1 Diabetes

Introduction: Use of hybrid closed-loop insulin delivery systems have been associated with better glycemic control and lower hemoglobin A1c (HbA1c) values, and better glycemic control is linked with less diabetes-specific family conflict among adolescents with type 1 diabetes (T1D). Little is known, however, about how the use of closed-loop systems relates to diabetes family conflict. Methods: Participants included 60 teens ages 13-17 who had been diagnosed with T1D for ≥1 year and who reported at least moderate diabetes distress (M age= 16.15±1.4, 68% white, non-Hispanic 68% female), and their parents (78% white, non-Hispanic, 78% Married, 83% female). As part of baseline data collection of an ongoing two-site trial of a behavioral intervention, parents and teens each reported on diabetes device use and diabetes-related family conflict. HbA1c values were obtained from teens’ medical records or mail-in kits. Nonparametric (Mann-Whitney) tests were conducted to examine differences between parent and teen-reported diabetes family conflict and HbA1c in relation to use of closed-loops systems. Results: Of the participants, 12 teens (20%) were using a closed-loop system. The use of closed-loop systems was associated with lower levels of parent-reported diabetes-related family conflict (z= -2.372, p=0.018). Use of closed-loop systems was also associated with lower HbA1c values (z= -2.865, p=.004). However, teens’ own report of family conflict was not significantly related to use of the closed-loop systems (z= -1.331, .183). Conclusion: Results suggest that the use of closed-loop systems is associated with both lower HbA1c and lower parent perceptions of diabetes-related family conflict. While future studies are needed to replicate these findings in larger samples and over time, our findings further support that closed-loop systems may reduce the burden of diabetes - particularly for parents - and improve both psychosocial and glycemic outcomes in families of youth with T1D. Disclosure H. R. Moore: None. S. S. Jaser: None. R. Streisand: None. H. Inverso: None. T. Morrow: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (R01DK121316)

Time in Range in Pregnancy: Is There a Role?

Continuous glucose monitoring (CGM) is being used with increasing frequency as an adjunct to self-monitoring of blood glucose in pregnancy, and novel targets based on CGM data are becoming standardized. This adoption of CGM is the result of its improving accuracy, patient preference, and evolving data demonstrating associations of novel targets such as time in range (TIR) with pregnancy and neonatal outcomes. A greater understanding of the relationship of various CGM metrics to outcomes in pregnancy complicated by diabetes is needed. It is clear that TIR parameters need to be uniquely lower for pregnant women than for nonpregnant individuals. CGM technology is also an integral part of hybrid closed-loop insulin delivery systems. These insulin delivery systems will be a significant advance in the management of diabetes during pregnancy if they can achieve the pre- and postprandial targets required for pregnancy and optimize TIR.

Benefits and Challenges of Current Closed-Loop Technologies in Children and Young People With Type 1 Diabetes.

Recent advances in diabetes technology have led to the development of closed-loop insulin delivery systems for the management of type 1 diabetes. Several such systems are now commercially available for children and young people. While all available systems have been shown to improve glycaemic control and quality of life in this population, qualitative data also highlights the challenges in using closed-loop systems, which vary among different pediatric age-groups. Very young children require systems that are able to cope with low insulin doses and significant glycaemic variability due to their high insulin sensitivity and unpredictable eating and exercise patterns. Adolescents' compliance is often related to size and number of devices, usability of the systems, need for calibrations, and their ability to interact with the system. Given the speed of innovations, understanding the capabilities and key similarities and differences of current systems can be challenging for healthcare professionals, caregivers and young people with type 1 diabetes alike. The aim of this review is to summarize the key evidence on currently available closed-loop systems for children and young people with type 1 diabetes, as well as commenting on user experience, where real-world data are available. We present findings on a system-basis, as well as identifying specific challenges in different pediatric age-groups and commenting on how current systems might address these. Finally, we identify areas for future research with regards to closed-loop technology tailored for pediatric use and how these might inform reimbursement and alleviate disease burden.