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

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."

Closed-loop insulin delivery systems in children and adolescents with type 1 diabetes

Introduction: Optimal glycemic control remains challenging in children and adolescents with type 1 diabetes due to highly variable day-to-day and night-to-night insulin requirements. This hurdle could be addressed by glucose-responsive insulin delivery based on real-time continuous glucose measurements.Areas covered: This review summaries recent advances of closed-loop systems in children and adolescents with type 1 diabetes, using both single- and dual-hormone closed-loop systems. The main outcomes, proportions of time spent in target range 70-180 mg/dl, and time spent in hypoglycemia below 70 mg/dl, are assessed particularly during unsupervised free-living randomized controlled trials.Expert opinion: Noteworthy and clinically meaningful translation of experimental investigations from controlled in-hospital settings to unrestricted home studies have been achieved over the past years, resulting in the regulatory approval of the first hybrid closed-loop system also in the pediatric population and with several other advanced devices in the pipeline. Large multinational and pivotal clinical trials including broad age populations are underway to facilitate the use of closed-loop systems in routine clinical practice.

Glycemic Status Assessment by the Latest Glucose Monitoring Technologies

The advanced and performing technologies of glucose monitoring systems provide a large amount of glucose data that needs to be properly read and interpreted by the diabetology team in order to make therapeutic decisions as close as possible to the patient's metabolic needs. For this purpose, new parameters have been developed, to allow a more integrated reading and interpretation of data by clinical professionals. The new challenge for the diabetes community consists of promoting an integrated and homogeneous reading, as well as interpretation of glucose monitoring data also by the patient himself. The purpose of this review is to offer an overview of the glycemic status assessment, opened by the current data management provided by latest glucose monitoring technologies. Furthermore, the applicability and personalization of the different glycemic monitoring devices used in specific insulin-treated diabetes mellitus patient populations will be evaluated.

The efficacy and safety of insulin pump therapy with predictive low glucose suspend feature in decreasing hypoglycemia in children with type 1 diabetes mellitus: A systematic review and meta-analysis

BACKGROUND

Automated insulin delivery with predictive low glucose suspend (PLGS) feature has the potential to reduce risk of hypoglycemia in patients with type 1 diabetes mellitus (T1DM). We aim to systematically synthesize the evidence on the efficacy and safety of PLGS in children and adolescents with T1DM.

METHODS

We performed a systematic search through Ovid/MEDLINE, Ovid/Embase, and other search engines. We included randomized controlled trials (RCTs) evaluating the effect of sensor augmented pump (SAP) with PLGS feature compared to SAP or insulin pump therapy without SAP in decreasing hypoglycemia in children and adolescents aged 2 to 18 years with T1DM, with at least 2 weeks of follow-up. Two reviewers independently selected studies, extracted data, and evaluated the risk of bias (ROB).

RESULTS

Five RCTs with total sample size of 493 children aged 6 to 18 years met the inclusion criteria. The overall ROB of included studies was low. There is high quality evidence that PLGS is superior to SAP in decreasing time spent in hypoglycemia (sensor glucose [SG] <3.9 mmol/L [<70 mg/dL]/24 h) and nocturnal hypoglycemia (SG <3.9 mmol [<70 mg/dL]/L/night) with an absolute mean difference of 17.4 min/d (95% CI: -19.2, -15.5) and 26.3 min/night (95% CI: -35.5, -16.7), respectively, without increasing percentage of time spent in hyperglycemia or episodes of diabetic ketoacidosis (DKA). There was insufficient evidence for the impact of PLGS on health related quality of life (HRQL).

CONCLUSIONS

PLGS is superior to SAP in decreasing daytime and nocturnal hypoglycemia without increasing the risk of DKA or hyperglycemia. Future studies should address the impact of PLGS on children younger than 6-years-old and HRQL.

Insulinpumpe, kontinuierliche und kapilläre Glukosemessung bei Kindern, Jugendlichen und Erwachsenen mit Diabetes mellitus: Daten des DPV-Registers zwischen 1995 und 2019

Ziel dieser Beobachtungsstudie ist die Beschreibung der aktuellen Nutzung von Diabetestechnologien bei Patienten/-innen mit Diabetes mellitus.

Methode Auswertung von Daten des DPV-Registers zur Nutzung der Insulinpumpentherapie (CSII), kontinuierlicher Glukosemessung (CGM) und der Selbstmessung der Blutglukose (SMBG) aus 497 teilnehmenden Zentren in Deutschland, Österreich, Luxemburg und der Schweiz zwischen 1995 und 2019. Die Daten wurden bei Patienten/-innen mit Diabetes Typ 1 (Alter ≥ 0,5 Jahre) für 5 Altersgruppen ausgewertet. Zusätzlich wurden aktuelle (zwischen 2017 und 2019) Geschlechtsunterschiede in der Verwendung von Diabetestechnologie bei Typ-1-Patienten/-innen untersucht, ebenso wie die Nutzung von Insulinpumpen und CGM für Patienten/-innen mit Insulintherapie bei Typ-2-DM, bei zystischer Fibrose (CFRD), bei anderen Pankreaserkrankungen, neonatalem Diabetes und Maturity Onset Diabetes of the Young (MODY).

Ergebnisse Es zeigte sich bei Patienten/-innen mit Diabetes Typ 1 ein Anstieg der CSII-Nutzung von 1995 bis 2019 von 1 % auf 55 % (2019: < 6 Jahre: 89 %; 6–< 12 Jahre: 67 %; 12–< 18 Jahre: 52 %; 18–< 25 Jahre: 48 %; ≥ 25 Jahre: 34 %). Die CGM-Nutzung erhöhte sich ab 2016 bis 2019 von 9 % auf 56 % (2019: 67 %; 68 %; 61 %; 47 %; 19 % der jeweiligen Altersgruppe). Die SMBG nahmen von 1995 bis 2015 insbesondere in den jüngeren Altersgruppen zu, gefolgt von einem Rückgang seit dem Jahr 2016 (Alle Patienten: 1995: 3,3/Tag; 2016: 5,4/Tag; 2019: 3,8/Tag). Weibliche Patienten mit Typ-1-Diabetes führten häufiger eine CSII und mehr SMBG als männliche Patienten durch (56 %/48 %, jeweils p-Wert: < 0,0001), während sich bei der CGM-Nutzung keine signifikanten Unterschiede zeigten.

Zwischen 2017 und 2019 erfolgte eine Nutzung von Insulinpumpen und CGM bei neonatalem Diabetes (CSII 87 %; CGM 38 %), bei MODY (CSII 14 %; CGM 28 %) und bei CFRD (CSII 18 %; CGM 22 %). CGM und CSII wurden dagegen nur selten von Menschen mit Insulintherapie und Diabetes Typ 2 (CSII < 1 %; CGM 1 %) und bei anderen Pankreaserkrankungen (CSII 3 %; CGM 4 %) genutzt.

Schlussfolgerung Moderne Diabetestechnologien werden derzeit insbesondere von pädiatrischen Patienten/-innen mit Diabetes Typ 1, aber auch von Menschen mit neonatalem Diabetes breit genutzt, von Patienten/-innen mit MODY und CFRD sowie Erwachsenen mit Diabetes Typ 1 in etwas geringerem Maße mit ansteigendem Trend. Dagegen sind diese Technologien in der Therapie des Typ-2-Diabetes und bei anderen Pankreaserkrankungen zurzeit nur wenig verbreitet.

A clinical review of the t:slim X2 insulin pump

Insulin pumps are commonly used for intensive insulin therapy to treat type 1 diabetes in adults and youth. Insulin pump technologies have advanced dramatically in the last several years to integrate with continuous glucose monitors (CGM) and incorporate control algorithms. These control algorithms automate some insulin delivery in response to the glucose information received from the CGM to reduce the occurrence of hypoglycemia and hyperglycemia and improve overall glycemic control. The t:slim X2 insulin pump system became commercially available in 2016. It is an innovative insulin pump technology that can be updated remotely by the user to install new software onto the pump device as new technologies become available. Currently, the t:slim X2 pairs with the Dexcom G6 CGM and there are two advanced software options available: Basal-IQ, which is a predictive low glucose suspend (PLGS) technology, and Control-IQ, which is a Hybrid Closed Loop (HCL) technology. This paper will describe the different types of advanced insulin pump technologies, review how the t:slim X2 insulin pump works, and summarize the clinical studies leading to FDA approval and commercialization of the Basal-IQ and Control-IQ technologies.

A Randomized Trial of Closed-Loop Control in Children with Type 1 Diabetes

BACKGROUND

A closed-loop system of insulin delivery (also called an artificial pancreas) may improve glycemic outcomes in children with type 1 diabetes.

METHODS

In a 16-week, multicenter, randomized, open-label, parallel-group trial, we assigned, in a 3:1 ratio, children 6 to 13 years of age who had type 1 diabetes to receive treatment with the use of either a closed-loop system of insulin delivery (closed-loop group) or a sensor-augmented insulin pump (control group). The primary outcome was the percentage of time that the glucose level was in the target range of 70 to 180 mg per deciliter, as measured by continuous glucose monitoring.

RESULTS

A total of 101 children underwent randomization (78 to the closed-loop group and 23 to the control group); the glycated hemoglobin levels at baseline ranged from 5.7 to 10.1%. The mean (±SD) percentage of time that the glucose level was in the target range of 70 to 180 mg per deciliter increased from 53±17% at baseline to 67±10% (the mean over 16 weeks of treatment) in the closed-loop group and from 51±16% to 55±13% in the control group (mean adjusted difference, 11 percentage points [equivalent to 2.6 hours per day]; 95% confidence interval, 7 to 14; P<0.001). In both groups, the median percentage of time that the glucose level was below 70 mg per deciliter was low (1.6% in the closed-loop group and 1.8% in the control group). In the closed-loop group, the median percentage of time that the system was in the closed-loop mode was 93% (interquartile range, 91 to 95). No episodes of diabetic ketoacidosis or severe hypoglycemia occurred in either group.

CONCLUSIONS

In this 16-week trial involving children with type 1 diabetes, the glucose level was in the target range for a greater percentage of time with the use of a closed-loop system than with the use of a sensor-augmented insulin pump. (Funded by Tandem Diabetes Care and the National Institute of Diabetes and Digestive and Kidney Diseases; ClinicalTrials.gov number, NCT03844789.).

Glycemic Outcomes of Use of CLC Versus PLGS in Type 1 Diabetes: A Randomized Controlled Trial

OBJECTIVE

Limited information is available about glycemic outcomes with a closed-loop control (CLC) system compared with a predictive low-glucose suspend (PLGS) system.

RESEARCH DESIGN AND METHODS

After 6 months of use of a CLC system in a randomized trial, 109 participants with type 1 diabetes (age range, 14-72 years; mean HbA_1c, 7.1% [54 mmol/mol]) were randomly assigned to CLC (N = 54, Control-IQ) or PLGS (N = 55, Basal-IQ) groups for 3 months. The primary outcome was continuous glucose monitor (CGM)-measured time in range (TIR) for 70-180 mg/dL. Baseline CGM metrics were computed from the last 3 months of the preceding study.

RESULTS

All 109 participants completed the study. Mean ± SD TIR was 71.1 ± 11.2% at baseline and 67.6 ± 12.6% using intention-to-treat analysis (69.1 ± 12.2% using per-protocol analysis excluding periods of study-wide suspension of device use) over 13 weeks on CLC vs. 70.0 ± 13.6% and 60.4 ± 17.1% on PLGS (difference = 5.9%; 95% CI 3.6%, 8.3%; P < 0.001). Time >180 mg/dL was lower in the CLC group than PLGS group (difference = -6.0%; 95% CI -8.4%, -3.7%; P < 0.001) while time <54 mg/dL was similar (0.04%; 95% CI -0.05%, 0.13%; P = 0.41). HbA_1c after 13 weeks was lower on CLC than PLGS (7.2% [55 mmol/mol] vs. 7.5% [56 mmol/mol], difference -0.34% [-3.7 mmol/mol]; 95% CI -0.57% [-6.2 mmol/mol], -0.11% [1.2 mmol/mol]; P = 0.0035).

CONCLUSIONS

Following 6 months of CLC, switching to PLGS reduced TIR and increased HbA_1c toward their pre-CLC values, while hypoglycemia remained similarly reduced with both CLC and PLGS.

Modern diabetes devices in the school setting: Perspectives from school nurses

OBJECTIVE

To explore the experiences, practices, and attitudes of school nurses related to modern diabetes devices (insulin pumps, continuous glucose monitors, and hybrid-closed loop systems).

RESEARCH DESIGN AND METHODS

Semistructured interviews were conducted with 40 public school nurses caring for children in elementary and middle schools. Developed with stakeholder input, the interview questions explored experiences working with devices and communicating with the health care system. Deidentified transcripts were analyzed through an iterative process of coding to identify major themes.

RESULTS

School nurses reported a range of educational backgrounds (58% undergraduate, 42% graduate), geographic settings (20% urban, 55% suburban, 25% rural), and years of experience (20% <5 years, 38%, 5-15 years, 42% >15 years). Four major themes emerged: (a) As devices become more common, school nurses must quickly develop new knowledge and skills yet have inconsistent training opportunities; (b) Enthusiasm for devices is tempered by concerns about implementation due to poor planning prior to the school year and potential disruptions by remote monitors; (c) Barriers exist to integrating devices into schools, including school/classroom policies, liability/privacy concerns, and variable staff engagement; and (d) Collaboration between school nurses and providers is limited; better communication may benefit children with diabetes.

CONCLUSIONS

Devices are increasingly used by school-aged children. School nurses appreciate device potential but share structural and individual-level challenges. Guiding policy is needed as the technology progressively becomes standard of care. Enhanced training and collaboration with diabetes providers may help to optimize school-based management for children in the modern era.

Review of the Omnipod® 5 Automated Glucose Control System Powered by Horizon™ for the treatment of Type 1 diabetes

Type 1 diabetes (T1D) is a medical condition that requires constant management, including monitoring of blood glucose levels and administration of insulin. Advancements in diabetes technology have offered methods to reduce the burden on people with T1D. Several hybrid closed-loop systems are commercially available or in clinical trials, each with unique features to improve care for patients with T1D. This article reviews the Omnipod® 5 Automated Glucose Control System Powered by Horizon™ and the safety and efficacy data to support its use in the management of T1D.

Real-World Improvements in Hypoglycemia in an Insulin-Dependent Cohort With Diabetes Mellitus Pre/Post Tandem Basal-Iq Technology Remote Software Update

OBJECTIVE

Software updatable insulin pumps, such as the t:slim X2 pump from Tandem Diabetes Care, enable access to new technology as soon as it is commercialized. The remote software update process allows for minimal interruption in therapy compared to purchasing a new pump; however, little quantitative data exist on the software update process or on pre/post therapeutic outcomes. We examined real-world usage and impact of a remote software updatable predictive low-glucose suspend (PLGS) technology designed to reduce hypoglycemic events in people with insulin-dependent diabetes.

METHODS

Approximately 15,000 U.S. Tandem pump users remotely updated their t:slim X2 software to Basal-IQ PLGS technology since its commercial release. We performed a retrospective analysis of users who uploaded at least 21 days of pre/post PLGS update usage data to the Tandem t:connect web application between August 28, 2018, and October 21, 2019 (N = 6,170). Insulin delivery and sensor-glucose values were analyzed per recent international consensus and American Diabetes Association guidelines. Software update performance was also assessed.

RESULTS

Median software update time was 5.36 minutes. Overall glycemic outcomes for pre and post software update showed a decrease in sensor time <70 mg/dL from 2.14 to 1.18% (-1.01; 95% confidence interval [CI], -0.97, -1.05; P<.001), with overall sensor time 70 to 180 mg/dL increasing from 57.8 to 58.5% (0.64; 95% CI, 0.04, 1.24; P<.001). These improvements were sustained at 3, 6, and 9 months after the update.

CONCLUSION

Introduction of a software updatable PLGS algorithm for the Tandem t:slim X2 insulin pump resulted in sustained reductions of hypoglycemia.

ABBREVIATIONS

ADA = American Diabetes Association; CGM = continuous glucose monitoring; CI = confidence interval; PLGS = predictive low-glucose suspend; SG = sensor glucose; T1D = type 1 diabetes; T2D = type 2 diabetes; TIR = time-in-range.

Closed-loop control in insulin pumps for type-1 diabetes mellitus: safety and efficacy

INTRODUCTION

Type 1 diabetes is a lifelong disease with high management burden. The majority of people with type 1 diabetes fail to achieve glycemic targets. Algorithm-driven automated insulin delivery (closed-loop) systems aim to address these challenges. This review provides an overview of commercial and emerging closed-loop systems.

AREAS COVERED

We review safety and efficacy of commercial and emerging hybrid closed-loop systems. A literature search was conducted and clinical trials using day-and-night closed-loop systems during free-living conditions were used to report on safety data. We comment on efficacy where robust randomized controlled trial data for a particular system are available. We highlight similarities and differences between commercial systems.

EXPERT OPINION

Study data shows that hybrid closed-loop systems are safe and effective, consistently improving glycemic control when compared to standard therapy. While a fully closed-loop system with minimal burden remains the end-goal, these hybrid closed-loop systems have transformative potential in diabetes care.

Where Do We Stand with Closed-Loop Systems and Their Challenges?

Treatments for type 1 diabetes have advanced significantly over recent years. There are now multiple hybrid closed-loop systems commercially available and additional systems are in development. Challenges remain, however. This review outlines the recent advances in closed-loop systems and outlines the remaining challenges, including post-prandial hyperglycemia and exercise-related dysglycemia.

Recreational diving in persons with type 1 and type 2 diabetes: Advancing capabilities and recommendations

Diving by persons with diabetes has long been conducted, with formal guidelines published in the early 1990s. Subsequent consensus guidelines produced following a 2005 workshop helped to advance the recognition of relevant issues and promote discussion. The guidelines were intended as an interim step in guidance, with the expectation that revisions should follow the gathering of additional data and experience. Recent and ongoing developments in pharmacology and technology can further aid in reducing the risk of hypoglycemia, a critical acute concern of diving with diabetes. Careful and periodic evaluation remains crucial to ensure that participation in diving activity is appropriate. Close self-monitoring, thoughtful adjustments of medications and meals, and careful review of the individual response to diving can assist in optimising control and ensuring safety. Open communication with diving partners, support personnel, and medical monitors is important to ensure that all are prepared to effectively assist in case of need. Ongoing vigilance, best practice, including graduated clearance for diving exposures and adverse event reporting, are all required to ensure the safety of diving with diabetes and to promote community understanding and acceptance.

Artificial Intelligence and Machine Learning Applied at the Point of Care

Introduction

The increasing availability of healthcare data and rapid development of big data analytic methods has opened new avenues for use of Artificial Intelligence (AI)- and Machine Learning (ML)-based technology in medical practice. However, applications at the point of care are still scarce.

Objective

Review and discuss case studies to understand current capabilities for applying AI/ML in the healthcare setting, and regulatory requirements in the US, Europe and China.

Methods

A targeted narrative literature review of AI/ML based digital tools was performed. Scientific publications (identified in PubMed) and grey literature (identified on the websites of regulatory agencies) were reviewed and analyzed.

Results

From the regulatory perspective, AI/ML-based solutions can be considered medical devices (i.e., Software as Medical Device, SaMD). A case series of SaMD is presented. First, tools for monitoring and remote management of chronic diseases are presented. Second, imaging applications for diagnostic support are discussed. Finally, clinical decision support tools to facilitate the choice of treatment and precision dosing are reviewed. While tested and validated algorithms for precision dosing exist, their implementation at the point of care is limited, and their regulatory and commercialization pathway is not clear. Regulatory requirements depend on the level of risk associated with the use of the device in medical practice, and can be classified into administrative (manufacturing and quality control), software-related (design, specification, hazard analysis, architecture, traceability, software risk analysis, cybersecurity, etc.), clinical evidence (including patient perspectives in some cases), non-clinical evidence (dosing validation and biocompatibility/toxicology) and other, such as e.g. benefit-to-risk determination, risk assessment and mitigation. There generally is an alignment between the US and Europe. China additionally requires that the clinical evidence is applicable to the Chinese population and recommends that a third-party central laboratory evaluates the clinical trial results.

Conclusions

The number of promising AI/ML-based technologies is increasing, but few have been implemented widely at the point of care. The need for external validation, implementation logistics, and data exchange and privacy remain the main obstacles.

Effect of Continuous Glucose Monitoring on Hypoglycemia in Older Adults With Type 1 Diabetes: A Randomized Clinical Trial

IMPORTANCE

Continuous glucose monitoring (CGM) provides real-time assessment of glucose levels and may be beneficial in reducing hypoglycemia in older adults with type 1 diabetes.

OBJECTIVE

To determine whether CGM is effective in reducing hypoglycemia compared with standard blood glucose monitoring (BGM) in older adults with type 1 diabetes.

DESIGN, SETTING, AND PARTICIPANTS

Randomized clinical trial conducted at 22 endocrinology practices in the United States among 203 adults at least 60 years of age with type 1 diabetes.

INTERVENTIONS

Participants were randomly assigned in a 1:1 ratio to use CGM (n = 103) or standard BGM (n = 100).

MAIN OUTCOMES AND MEASURES

The primary outcome was CGM-measured percentage of time that sensor glucose values were less than 70 mg/dL during 6 months of follow-up. There were 31 prespecified secondary outcomes, including additional CGM metrics for hypoglycemia, hyperglycemia, and glucose control; hemoglobin A1c (HbA1c); and cognition and patient-reported outcomes, with adjustment for multiple comparisons to control for false-discovery rate.

RESULTS

Of the 203 participants (median age, 68 [interquartile range {IQR}, 65-71] years; median type 1 diabetes duration, 36 [IQR, 25-48] years; 52% female; 53% insulin pump use; mean HbA1c, 7.5% [SD, 0.9%]), 83% used CGM at least 6 days per week during month 6. Median time with glucose levels less than 70 mg/dL was 5.1% (73 minutes per day) at baseline and 2.7% (39 minutes per day) during follow-up in the CGM group vs 4.7% (68 minutes per day) and 4.9% (70 minutes per day), respectively, in the standard BGM group (adjusted treatment difference, -1.9% (-27 minutes per day); 95% CI, -2.8% to -1.1% [-40 to -16 minutes per day]; P <.001). Of the 31 prespecified secondary end points, there were statistically significant differences for all 9 CGM metrics, 6 of 7 HbA1c outcomes, and none of the 15 cognitive and patient-reported outcomes. Mean HbA1c decreased in the CGM group compared with the standard BGM group (adjusted group difference, -0.3%; 95% CI, -0.4% to -0.1%; P <.001). The most commonly reported adverse events using CGM and standard BGM, respectively, were severe hypoglycemia (1 and 10), fractures (5 and 1), falls (4 and 3), and emergency department visits (6 and 8).

CONCLUSIONS AND RELEVANCE

Among adults aged 60 years or older with type 1 diabetes, continuous glucose monitoring compared with standard blood glucose monitoring resulted in a small but statistically significant improvement in hypoglycemia over 6 months. Further research is needed to understand the long-term clinical benefit.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT03240432.

Pumps that predict and manage low blood glucose are superior to pumps with stand-alone CGM for reducing hypoglycaemia in type 1 diabetes patients in a real-world setting

BACKGROUND

This study aimed to assess the efficacy of insulin pumps with automated predictive low-glucose insulin suspension in a real-world setting compared with stand-alone flash glucose monitoring (FGM).

METHODS

The data analyzed were uploaded by patients with type 1 diabetes (n=195) treated with external insulin pumps [either a MiniMed 640G system (Medtronic) including SmartGuard technology that predicts and manages low glucose (n=61) or an Omnipod patch pump accompanied by a FreeStyle Libre sensor (Abbott) for FGM (n=134)].

RESULT

The median (25th-75th percentile) time spent with sensor glucose values≤3.9mmol/L was 0.9% (0.4-1.55) vs. 5.6% (3.05-9.55) in the predictive low-glucose suspend group vs. FGM users, respectively (P<0.0001), with similar results obtained for median time spent with sensor glucose values≤3mmol/L (P<0.0001). The group using sensor-integrated pumps had lower % coefficient of variation (CV) values and lower mean amplitude glycaemic excursions (P<0.0001). Mean glucose values as well as measured HbA_1c levels were also lower.

CONCLUSION

These real-world data show that predictive low-glucose insulin suspension is more effective than pumps with stand-alone FGM for reducing hypoglycaemic events, and could be of benefit to patients at risk of hypoglycaemia as well as those lacking in hypoglycaemic awareness.

Effect of real-life insulin pump with predictive low-glucose management use for 3 months: Analysis of the patients treated in a Japanese center

Aims/Introduction

In Japan, an insulin pump with predictive low‐glucose management (PLGM) was launched in 2018. It automatically suspends insulin delivery when the sensor detects or predicts low glucose values. The aim of this study was to analyze the safety and efficacy of PLGM in patients treated in a Japanese center.

Materials and Methods

We carried out a retrospective observational analysis of 16 patients with type 1 diabetes mellitus and one patient after pancreatectomy. They switched from the MiniMed 620G device to the 640G device with PLGM. The primary outcome was the change in the percentage of time in hypoglycemia. The secondary outcome was the change in HbA1c (%) over a period of 3 months. We also explored the presence of “post‐suspend hyperglycemia” with the 640G device.

Results

After changing to the 640G device, the percentage of time in hypoglycemia (glucose <50 mg/dL) significantly decreased from 0.39% (0–1.51%) to 0% (0–0.44%; P = 0.0407). The percentage of time in hyperglycemia (glucose >180 mg/dL) significantly increased from 25.53% (15.78–44.14%) to 32.9% (24.71–45.49%; P = 0.0373). HbA1c significantly increased from 7.6 ± 1.0% to 7.8 ± 1.1% (P = 0.0161). From 1.5 to 4.5 h after the resumption of insulin delivery, the percentage of time in hyperglycemia was 32.23% (24.2–53.75%), but it was significantly lower, 2.78% (0–21.6%), when patients manually restarted the pump within 30 min compared with automatic resumption 31.2% (20–61.66%; P = 0.0063).

Conclusions

Predictive low‐glucose management is an effective tool for reducing hypoglycemia, but possibly elicits “post‐suspend hyperglycemia.” This information is useful for achieving better blood glucose control in the patients treated with PLGM.

Effects of Continuous Glucose Monitoring on Metrics of Glycemic Control in Diabetes: A Systematic Review With Meta-analysis of Randomized Controlled Trials

BACKGROUND

Continuous glucose monitoring (CGM) provides important information to aid in achieving glycemic targets in people with diabetes.

PURPOSE

We performed a meta-analysis of randomized controlled trials (RCTs) comparing CGM with usual care for parameters of glycemic control in both type 1 and type 2 diabetes.

DATA SOURCES

Many electronic databases were searched for articles published from inception until 30 June 2019.

STUDY SELECTION

We selected RCTs that assessed both changes in HbA_1c and time in target range (TIR), together with time below range (TBR), time above range (TAR), and glucose variability expressed as coefficient of variation (CV).

DATA EXTRACTION

Data were extracted from each trial by two investigators.

DATA SYNTHESIS

All results were analyzed by a random effects model to calculate the weighted mean difference (WMD) with the 95% CI. We identified 15 RCTs, lasting 12-36 weeks and involving 2,461 patients. Compared with the usual care (overall data), CGM was associated with modest reduction in HbA_1c (WMD -0.17%, 95% CI -0.29 to -0.06, I ² = 96.2%), increase in TIR (WMD 70.74 min, 95% CI 46.73-94.76, I ² = 66.3%), and lower TAR, TBR, and CV, with heterogeneity between studies. The increase in TIR was significant and robust independently of diabetes type, method of insulin delivery, and reason for CGM use. In preplanned subgroup analyses, real-time CGM led to the higher improvement in mean HbA_1c (WMD -0.23%, 95% CI -0.36 to -0.10, P < 0.001), TIR (WMD 83.49 min, 95% CI 52.68-114.30, P < 0.001), and TAR, whereas both intermittently scanned CGM and sensor-augmented pump were associated with the greater decline in TBR.

LIMITATIONS

Heterogeneity was high for most of the study outcomes; all studies were sponsored by industry, had short duration, and used an open-label design.

CONCLUSIONS

CGM improves glycemic control by expanding TIR and decreasing TBR, TAR, and glucose variability in both type 1 and type 2 diabetes.

How does a predictive low glucose suspend (PLGS) system tackle pediatric lifespan challenges in diabetes treatment? Real world data analysis

OBJECTIVES

The aim of the study was to assess the benefits of a predictive low glucose suspend (PLGS) system in real-life in children and adolescents with type 1 diabetes of different age and age-related clinical challenges.

METHODS

Real life retrospective and descriptive analysis included 44 children (26 girls) with type 1 diabetes who were introduced to PLGS system. We divided them in three age groups: I (3-6 years old, n = 12), II (7-10 y/o, n = 16), III (11-19 y/o, n = 16). All children and their caregivers received unified training in self-management during PLGS therapy. Patients' data included: age, HbA1C levels, sex. While from the CGM metric, we obtained: time of sensor use (SENSuse), time in range (TiR): in, below and over target range and average blood glycemia (AVG), insulin suspension time (INSsusp).

RESULTS

SENSuse was 93% in total, with 92%, 94%, and 87% in age groups I, II, III, respectively. In total the reduction of mean HbA1C from 7.61% to 6.88% (P < .05), while for the I, II, and III it was 7.46% to 6.72%, 6.91% to 6.41%, and 8.46 to 7.44%, respectively (P < .05). Although we observed a significant reduction of HbA1C, the time below target range was minimal. Specific findings included: group I-longest INSsusp (17%), group II-lowest glycemic variability (CV) (36%), and group III-highest AVG (169 mg/dL). There was a reverse correlation between suspend before low and age (-0.32, P < .05). In group I CV reduced TiR in target range (TiRin) (-0.82, P < .05), in group II use of complex boluses increased TiRin (0.52, P < .05). In group III higher CV increased HbA1C (0.64, P < .05) while reducing TiRin (-0.72, P < .05).

CONCLUSIONS

PLGS is a suitable and safe therapeutic option for children with diabetes of all age and it is effective in addressing age-specific challenges. PLGS improves glycemic control in children of all age, positively affecting its different parameters.

Real world hybrid closed-loop discontinuation: Predictors and perceptions of youth discontinuing the 670G system in the first 6 months

OBJECTIVE

To describe predictors of hybrid closed loop (HCL) discontinuation and perceived barriers to use in youth with type 1 diabetes.

SUBJECTS

Youth with type 1 diabetes (eligible age 2-25 y; recruited age 8-25 y) who initiated the Minimed 670G HCL system were followed prospectively for 6 mo in an observational study.

RESEARCH DESIGN AND METHODS

Demographic, glycemic (time-in-range, HbA1c), and psychosocial variables [Hypoglycemia Fear Survey (HFS); Problem Areas in Diabetes (PAID)] were collected for all participants. Participants who discontinued HCL (<10% HCL use at clinical visit) completed a questionnaire on perceived barriers to HCL use.

RESULTS

Ninety-two youth (15.7 ± 3.6 y, HbA1c 8.8 ± 1.3%, 50% female) initiated HCL, and 28 (30%) discontinued HCL, with the majority (64%) discontinuing between 3 and 6 mo after HCL start. Baseline HbA1c predicted discontinuation (P = .026) with the odds of discontinuing 2.7 times higher (95% CI: 1.123, 6.283) for each 1% increase in baseline HbA1c. Youth who discontinued HCL rated difficulty with calibrations, number of alarms, and too much time needed to make the system work as the most problematic aspects of HCL. Qualitatively derived themes included technological difficulties (error alerts, not working correctly), too much work (calibrations, fingersticks), alarms, disappointment in glycemic control, and expense (cited by parents).

CONCLUSIONS

Youth with higher HbA1c are at greater risk for discontinuing HCL than youth with lower HbA1c, and should be the target of new interventions to support device use. The primary reasons for discontinuing HCL relate to the workload required to use HCL.

Evolution of Diabetes Technology

Technological innovations have fundamentally changed diabetes care. Insulin pump use and continuous glucose monitoring are associated with improved glycemic control along with a better quality of life; automated insulin-dosing advisors facilitate and improve decision making. Glucose-responsive automated insulin delivery enables the highest targets for time in range, lowest rate and duration of hypoglycemia, and favorable quality of life. Clear targets for time in ranges and a standard visualization of the data will help the diabetes technology to be used more efficiently. Decision support systems within and integrated cloud environment will further simplify, unify, and improve modern routine diabetes care.

Benefits and Challenges of Diabetes Technology Use in Older Adults

With successful aging of adults with type 1 diabetes, there is an increased opportunity to use technology for diabetes management. Technology can ease the burden of self-care and provide a sense of security. However, age-related cognitive and physical decline can make technology use difficult. Guidelines using technology in the aging population are urgently needed, along with educational material for the clinicians and caregivers. In this article, we review the evidence supporting the use of diabetes-related technologies in the older population and discuss recommendations based on current data and the authors' clinical knowledge and experience.

Diabetes Technology Use in Adults with Type 1 and Type 2 Diabetes

In the last 2 decades, diabetes technology has emerged as a branch of diabetes management thanks to the advent of continuous glucose monitoring (CGM) and increased availability of continuous subcutaneous insulin infusion systems, or insulin pumps. These tools have progressed from rudimentary instruments to sophisticated therapeutic options for advanced diabetes management. This article discusses the available CGM and insulin pump systems and the clinical benefits of their use in adults with type 1 diabetes, intensively insulin-treated type 2 diabetes, and pregnant patients with preexisting diabetes.

Automated Insulin Delivery in Children with Type 1 Diabetes

The advent of insulin pump therapy marked an important milestone in diabetes treatment in the past few decades and has become the tipping point for the development of automated insulin delivery systems (AID). Standalone insulin pump systems have evolved over the course of years and have been replaced by modern high-technology insulin pumps with continuous glucose monitor interface allowing real-time insulin dose adjustment to optimize treatment. This review summarizes evidence from AID studies conducted in children with type 1 diabetes and discusses the outlook for future generation AID systems from a pediatric treatment perspective.

Therapeutic Inertia in Pediatric Diabetes: Challenges to and Strategies for Overcoming Acceptance of the Status Quo

Despite significant advances in therapies for pediatric type 1 diabetes, achievement of glycemic targets remains elusive, and management remains burdensome for patients and their families. This article identifies common challenges in diabetes management at the patient-provider and health care system levels and proposes practical approaches to overcoming therapeutic inertia to enhance health outcomes for youth with type 1 diabetes.

A Modular Safety System for an Insulin Dose Recommender: A Feasibility Study

BACKGROUND

Delivering insulin in type 1 diabetes is a challenging, and potentially risky, activity; hence the importance of including safety measures as part of any insulin dosing or recommender system. This work presents and clinically evaluates a modular safety system that is part of an intelligent insulin dose recommender platform developed within the EU-funded PEPPER project.

METHODS

The proposed safety system is composed of four modules which use a novel glucose forecasting algorithm. These modules are predictive glucose alerts and alarms; a predictive low-glucose basal insulin suspension module; an advanced rescue carbohydrate recommender for resolving hypoglycemia; and a personalized safety constraint applied to insulin recommendations. The technical feasibility of the proposed safety system was evaluated in a pilot study including eight adult subjects with type 1 diabetes on multiple daily injections over a duration of six weeks. Glycemic control and safety system functioning were compared between the two-weeks run-in period and the end point at eight weeks. A standard insulin bolus calculator was employed to recommend insulin doses.

RESULTS

Overall, glycemic control improved over the evaluated period. In particular, percentage time in the hypoglycemia range (<3.0 mmol/l) significantly decreased from 0.82% (0.05-4.79) at run-in to 0.33% (0.00-0.93) at endpoint (P = .02). This was associated with a significant increase in percentage time in target range (3.9-10.0 mmol/l) from 52.8% (38.3-61.5) to 61.3% (47.5-71.7) (P = .03). There was also a reduction in number of carbohydrate recommendations.

CONCLUSION

A safety system for an insulin dose recommender has been proven to be a viable solution to reduce the number of adverse events associated to glucose control in type 1 diabetes.

Real-World Hypoglycemia Avoidance With a Predictive Low Glucose Alert Does Not Depend on Frequent Screen Views

BACKGROUND

Frequent real-time continuous glucose monitoring (rtCGM) data viewing has been associated with reduced mean glucose and frequent scanning of an intermittently scanned continuous glucose monitoring (isCGM) system has been associated with reduced hypoglycemia for patients with diabetes. However, requiring patients to frequently interact with their glucose monitoring devices to detect actual or impending hypoglycemia is burdensome. We hypothesized that a predictive low glucose alert, which forecasts glucose ≤55 mg/dL within 20 minutes and is included in a new rtCGM system, could mitigate hypoglycemia without requiring frequent device interaction.

METHODS

We analyzed estimated glucose values (EGVs) from an anonymized convenience sample of 15,000 patients who used Dexcom G6 (Dexcom, Inc, San Diego, CA, USA) and its mobile app for at least 30 days with or without the "Urgent Low Soon" alert (ULS) enabled. Screen view frequency was determined as the frequency with which the trend screen was accessed on the app. Multiple screen views within any 5-minute interval were counted as one. Hypoglycemia exposure for patients in the top and bottom quartiles of screen view frequency (>8.25 and <3.30 per day, respectively) was calculated as the percentage of EGVs below various thresholds.

RESULTS

Over 93% of users enabled the ULS alert; its use was associated with significantly reduced hypoglycemia <55 and <70 mg/dL, independent of screen view frequency.

CONCLUSION

Use of the G6 ULS alert may disencumber rtCGM users by promoting significant reductions in hypoglycemia without requiring frequent device interactions.

Insulin-delivery methods for children and adolescents with type 1 diabetes

Efforts directed toward restoring normal metabolic levels by mimicking the physiological insulin secretion, thereby ensuring safety, efficacy, minimal invasiveness and conveniences, are of great significance in the management of type 1 diabetes among children and adolescents. Regardless of the various technologies being discovered in addressing invasiveness and enhancing medication adherence in the management of type 1 diabetes, yet limited success had been observed among children and adolescents. The multiple daily subcutaneous insulin injections route using vial and syringe, and occasionally insulin pens, remain the most predictable route for insulin administration among children and adolescents. However, this route has been associated with compromised patient compliance, fear of injections and unacceptability, resulting in poor glycemic control, which promote the demand for alternative routes of insulin administration. Alternative routes for delivering insulin are being investigated in children and adolescents with type 1 diabetes; these include the hybrid closed-loop 'artificial pancreas' system, oral, inhalation, intranasal routes, and others. This review article explores the current advances in insulin-delivery methods that address the needs of children and adolescents in the treatment of type 1 diabetes.

7. Diabetes Technology: Standards of Medical Care in Diabetes-2020

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/dc20-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/dc20-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Cost-Effectiveness of Sensor-Augmented Insulin Pump Therapy Versus Continuous Insulin Infusion in Patients with Type 1 Diabetes in Turkey

Background and Aims: Sensor-augmented pump therapy (SAP) combines continuous glucose monitoring with continuous subcutaneous insulin infusion (CSII). SAP is costlier than CSII but provides additional clinical benefits relative to CSII alone. A long-term cost-effectiveness analysis was performed to determine whether SAP is cost-effective relative to CSII in patients with type 1 diabetes (T1D) in Turkey. Methods: Analyses were performed in two different patient cohorts, one with poor glycemic control at baseline (mean glycated hemoglobin 9.0% [75 mmol/mol]) and a second cohort considered to be at increased risk of hypoglycemic events. Clinical input data and direct medical costs were sourced from published literature. The analysis was performed from a third-party payer perspective over patient lifetimes and future costs and clinical outcomes were discounted at 3.5% per annum. Results: In both patient cohorts, SAP was associated with a gain in quality-adjusted life expectancy but higher costs relative to CSII (incremental gain of 1.40 quality-adjusted life years [QALYs] in patients with poor baseline glycemic control and 1.73 QALYs in patients at increased risk of hypoglycemic events). Incremental cost-effectiveness ratios for SAP versus CSII were TRY 76,971 (EUR 11,612) per QALY gained for patients with poor baseline glycemic control and TRY 69,534 (EUR 10,490) per QALY gained for patients at increased risk for hypoglycemia. Conclusions: SAP is associated with improved long-term clinical outcomes versus CSII, and in Turkey, SAP is likely to represent good value for money compared with CSII in T1D patients with poor glycemic control and/or with frequent severe hypoglycemic events.

Realizing a Closed-Loop (Artificial Pancreas) System for the Treatment of Type 1 Diabetes

Recent, rapid changes in the treatment of type 1 diabetes have allowed for commercialization of an "artificial pancreas" that is better described as a closed-loop controller of insulin delivery. This review presents the current state of closed-loop control systems and expected future developments with a discussion of the human factor issues in allowing automation of glucose control. The goal of these systems is to minimize or prevent both short-term and long-term complications from diabetes and to decrease the daily burden of managing diabetes. The closed-loop systems are generally very effective and safe at night, have allowed for improved sleep, and have decreased the burden of diabetes management overnight. However, there are still significant barriers to achieving excellent daytime glucose control while simultaneously decreasing the burden of daytime diabetes management. These systems use a subcutaneous continuous glucose sensor, an algorithm that accounts for the current glucose and rate of change of the glucose, and the amount of insulin that has already been delivered to safely deliver insulin to control hyperglycemia, while minimizing the risk of hypoglycemia. The future challenge will be to allow for full closed-loop control with minimal burden on the patient during the day, alleviating meal announcements, carbohydrate counting, alerts, and maintenance. The human factors involved with interfacing with a closed-loop system and allowing the system to take control of diabetes management are significant. It is important to find a balance between enthusiasm and realistic expectations and experiences with the closed-loop system.

Glycaemic variability: The under-recognized therapeutic target in type 1 diabetes care

Type 1 diabetes mellitus (T1DM) remains one of the most challenging long-term conditions to manage. Despite robust evidence to demonstrate that near normoglycaemia minimizes, but does not completely eliminate, the risk of complications, its achievement has proved almost impossible in a real-world setting. HbA1c to date has been used as the gold standard marker of glucose control and has been shown to reflect directly the risk of diabetes complications. However, it has been recognized that HbA1c is a crude marker of glucose control. Continuous glucose monitoring (CGM) provides the ability to measure and observe inter- and intraday glycaemic variability (GV), a more meaningful measure of glycaemic control, more relevant to daily living for those with T1DM. This paper reviews the relationship between GV and hypoglycaemia, and micro- and macrovascular complications. It also explores the impact on GV of CGM, insulin pumps, closed-loop technologies, and newer insulins and adjunctive therapies. Looking to the future, there is an argument that GV should become a key determinant of therapeutic success. Further studies are required to investigate the pathological and psychological benefits of reducing GV.

Six-Month Randomized, Multicenter Trial of Closed-Loop Control in Type 1 Diabetes

BACKGROUND

Closed-loop systems that automate insulin delivery may improve glycemic outcomes in patients with type 1 diabetes.

METHODS

In this 6-month randomized, multicenter trial, patients with type 1 diabetes were assigned in a 2:1 ratio to receive treatment with a closed-loop system (closed-loop group) or a sensor-augmented pump (control group). The primary outcome was the percentage of time that the blood glucose level was within the target range of 70 to 180 mg per deciliter (3.9 to 10.0 mmol per liter), as measured by continuous glucose monitoring.

RESULTS

A total of 168 patients underwent randomization; 112 were assigned to the closed-loop group, and 56 were assigned to the control group. The age range of the patients was 14 to 71 years, and the glycated hemoglobin level ranged from 5.4 to 10.6%. All 168 patients completed the trial. The mean (±SD) percentage of time that the glucose level was within the target range increased in the closed-loop group from 61±17% at baseline to 71±12% during the 6 months and remained unchanged at 59±14% in the control group (mean adjusted difference, 11 percentage points; 95% confidence interval [CI], 9 to 14; P<0.001). The results with regard to the main secondary outcomes (percentage of time that the glucose level was >180 mg per deciliter, mean glucose level, glycated hemoglobin level, and percentage of time that the glucose level was <70 mg per deciliter or <54 mg per deciliter [3.0 mmol per liter]) all met the prespecified hierarchical criterion for significance, favoring the closed-loop system. The mean difference (closed loop minus control) in the percentage of time that the blood glucose level was lower than 70 mg per deciliter was -0.88 percentage points (95% CI, -1.19 to -0.57; P<0.001). The mean adjusted difference in glycated hemoglobin level after 6 months was -0.33 percentage points (95% CI, -0.53 to -0.13; P = 0.001). In the closed-loop group, the median percentage of time that the system was in closed-loop mode was 90% over 6 months. No serious hypoglycemic events occurred in either group; one episode of diabetic ketoacidosis occurred in the closed-loop group.

CONCLUSIONS

In this 6-month trial involving patients with type 1 diabetes, the use of a closed-loop system was associated with a greater percentage of time spent in a target glycemic range than the use of a sensor-augmented insulin pump. (Funded by the National Institute of Diabetes and Digestive and Kidney Diseases; iDCL ClinicalTrials.gov number, NCT03563313.).

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

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

Advances in technology for management of type 1 diabetes

Technological advances have had a major effect on the management of type 1 diabetes. In addition to blood glucose meters, devices used by people with type 1 diabetes include insulin pumps, continuous glucose monitors, and, most recently, systems that combine both a pump and a monitor for algorithm-driven automation of insulin delivery. In the next 5 years, as many advances are expected in technology for the management of diabetes as there have been in the past 5 years, with improvements in continuous glucose monitoring and more available choices of systems that automate insulin delivery. Expansion of the use of technology will be needed beyond endocrinology practices to primary-care settings and broader populations of patients. Tools to support decision making will also need to be developed to help patients and health-care providers to use the output of these devices to optimise diabetes management.

A Review of Predictive Low Glucose Suspend and Its Effectiveness in Preventing Nocturnal Hypoglycemia

To evaluate the effectiveness of predictive low glucose suspend (PLGS) systems within sensor-augmented insulin infusion pumps at preventing nocturnal hypoglycemia in patients with type 1 diabetes (DM1), we performed a systematic review and meta-analysis of randomized crossover trials. Pubmed and Google Scholar were searched for randomized crossover trials, published between January 2013 and July 2018, in nonpregnant outpatients with DM1, which compared event rates during PLGS overnight periods and non-PLGS overnight periods. The primary outcome was the proportion of overnight periods with one or more hypoglycemic measurement. When available, individual patient data were used to assess the effect of clustering measurements within patients. Four studies (272 patients, 10,735 patient-nights: 5422 PLGS and 5313 non-PLGS) were included in the meta-analysis. Two studies reported patient-level data that permitted assessment of the effect of clustering measurements within patients. The effect on the risk difference was minimal. The proportion of overnight periods with one or more episodes of hypoglycemia was 19.6% for the PLGS periods and 27.8% for the non-PLGS periods. Based on the pooled estimate, PLGS overnight periods were associated with an 8.8% lower risk of hypoglycemia (risk difference -0.088; 95% CI -0.119 to -0.056, I² = 67.4%, τ² = 0.0006, 4 studies). PLGS systems can reduce nocturnal hypoglycemic events in patients with DM1.

Long-term body weight trajectories and metabolic control in type 1 diabetes patients on insulin pump or multiple daily injections: A 10-year retrospective controlled study

BACKGROUND AND AIMS

Overweight/obesity is a clinical concern also in patients with Type 1 diabetes (T1DM). These patients' body weight may vary depending on whether treatment consists in continuous subcutaneous insulin infusion (CSII) or multiple daily injections (MDI), as these treatments lead to different blood glucose control, insulin doses, and eating behaviors. We compared long-term body weight trajectories in persons with diabetes on CSII or MDI regimens.

METHODS AND RESULTS

Annual changes in body weight, HbA1c, and daily insulin doses over 6-10 years were retrospectively analyzed in T1DM adult patients on CSII (n = 90) or MDI (n = 90), strictly matched for sex, age, BMI, and diabetes duration. Mean follow-up was 9.1 ± 1.4 years. Body weight increased linearly (∼0.5 kg per year) throughout the observation period (p = 0.001, repeated measures ANOVA) with no significant difference between the CSII and MDI cohorts (p = 0.74), in either normal-weight or overweight/obese patients. HbA1c over follow-up was lower with CSII than with MDI (p = 0.037), maintaining the initial reduction after starting pump therapy. Insulin doses over follow-up were stably lower than baseline (∼20%) with CSII, while linearly increasing (∼20% from baseline to the end of observation) with MDI (p = 0.002). Mean annual weight changes correlated directly with total insulin dose changes (r = 0.191; p = 0.011) and baseline HbA1c level (r = 0.267; p = 0.001), and inversely with HbA1c changes (-0.173; p = 0.021) and baseline age (r = -0.254; p = 0.001).

CONCLUSION

T1DM patients on CSII or MDI showed comparable body weight gain over a 10-year follow-up, despite improved glycemic control and decreased insulin doses with CSII.

MiniMed 670G hybrid closed loop artificial pancreas system for the treatment of type 1 diabetes mellitus: overview of its safety and efficacy

Introduction: Automated insulin delivery for people with type 1 diabetes has been a major goal in the diabetes technology field for many years. While a fully automated system has not yet been accomplished, the MiniMed™ 670G artificial pancreas (AP) system is the first commercially available insulin pump that automates basal insulin delivery, while still requiring user input for insulin boluses. Determining the safety and efficacy of this system is essential to the development of future devices striving for more automation. Areas Covered: This review will provide an overview of how the MiniMed 670G system works including its safety and efficacy, how it compares to similar devices, and anticipated future advances in diabetes technology currently under development. Expert Opinion: The ultimate goal of advanced diabetes technologies is to reduce the burden and amount of management required of patients with diabetes. In addition to reducing patient workload, achieving better glucose control and improving hemoglobin A1c (HbA1c) values are essential for reducing the threat of diabetes-related complications further down the road. Current devices come close to reaching these goals, but understanding the unmet needs of patients with diabetes will allow future technologies to achieve these goals more quickly.

Diabetes Technology: Review of the 2019 American Diabetes Association Standards of Medical Care in Diabetes

DESCRIPTION

The American Diabetes Association (ADA) annually updates its Standards of Medical Care in Diabetes to provide clinicians, patients, researchers, payers, and other interested parties with evidence-based recommendations for the diagnosis and management of patients with diabetes.

METHODS

The ADA Professional Practice Committee comprises physicians, adult and pediatric endocrinologists, diabetes educators, registered dietitians, epidemiologists, pharmacists, and public health experts. To develop the 2019 standards, the committee continuously searched MEDLINE through November 2018 to consider and review studies, particularly high-quality trials including persons with diabetes, for potential incorporation into recommendations. It also solicited feedback from the larger clinical community.

RECOMMENDATIONS

This synopsis focuses on selected guidance relating to use of diabetes technology in adults with diabetes. Recommendations address self-monitoring of blood glucose, continuous glucose monitors, and automated insulin delivery systems.

Predicting Quality of Overnight Glycaemic Control in Type 1 Diabetes Using Binary Classifiers

In type 1 diabetes management, maintaining nocturnal blood glucose within target range can be challenging. Although semi-automatic systems to modulate insulin pump delivery, such as low-glucose insulin suspension and the artificial pancreas, are starting to become a reality, their elevated cost and performance below user expectations is hindering their adoption. Hence, a decision support system that helps people with type 1 diabetes, on multiple daily injections or insulin pump therapy, to avoid undesirable overnight blood glucose fluctuations (hyper- or hypoglycaemic) is an attractive alternative. In this paper, we introduce a novel data-driven approach to predict the quality of overnight glycaemic control in people with type 1 diabetes by analyzing commonly gathered data during the day-time period (continuous glucose monitoring data, meal intake and insulin boluses). The proposed approach is able to predict whether overnight blood glucose concentrations are going to remain within or outside the target range, and therefore allows the user to take the appropriate preventive action (snack or change in basal insulin). For this purpose, a number of popular established machine learning algorithms for binary classification were evaluated and compared on a publicly available clinical dataset (i.e., OhioT1DM). Although there is no clearly superior classification algorithm, this study indicates that, by using commonly gathered data in type 1 diabetes management, it is possible to predict the quality of overnight glycaemic control with reasonable accuracy (AUC-ROC = 0.7).

Reducing Hypoglycemia in the Real World: A Retrospective Analysis of Predictive Low-Glucose Suspend Technology in an Ambulatory Insulin-Dependent Cohort

Objective: Analyze real-world usage and impact of a predictive low-glucose suspend (PLGS) insulin delivery system for maintenance of euglycemia and prevention of hypoglycemic events in people with insulin-dependent diabetes. Methods: Retrospective analysis of Tandem Basal-IQ users who uploaded at least 21 days of PLGS usage data between August 31, 2018, and March 14, 2019 (N = 8132). Insulin delivery and sensor-glucose concentrations were analyzed. The times spent below 70 mg/dL, between 70 and 180 mg/dL, and above 180 mg/dL were assessed. Subgroup analyses were conducted to examine matched pre-/postoutcomes with experienced users (n = 1371) and performance over time for a mixed subgroup with >9 weeks of data (n = 3563). Results: The mean age of patients was 32.4 years, 52% were female, 96% had type 1 diabetes, and 4% had type 2 diabetes. Mean duration on PLGS was 65 days. Algorithm introduction led to a 45% median relative risk reduction in sensor time <70 mg/dL, pre/post (% <70:2.0, 1.1), while the mean glucose remained stable (168 and 168 mg/dL). Mean frequency of hypoglycemic events decreased from one every 9 days to one every 30 days. Total daily insulin dose decreased from 43.4 to 42.3 U in the pre/post subgroup. Manual override of the system was low (4.5%). The number of daily suspensions remained stable (4.9). Conclusions: Introduction of PLGS resulted in effective and sustained prevention of hypoglycemia without a significant increase in mean blood glucose and may be considered for people with type 1 diabetes at risk for hypoglycemia.

Advances in Type 1 Diabetes Technology Over the Last Decade

The past 10 years have witnessed rapid advances in the technology used to treat patients with type 1 diabetes (T1D). While the disease burden is still high, these advances have contributed to improvements in both glycaemic control and quality of life for many of those affected. New technologies allow for individualisation of care, as patients are able to work with their providers to determine which systems best fit their lifestyle and needs. In addition, thanks to improved glucose monitoring technologies, patients can now simultaneously improve glycaemic control and reduce hypoglycaemia, thereby mitigating risk for acute and chronic complications. Technological advances in T1D care are rapidly moving us toward increasingly automated devices, which offer the promise of reduced disease burden. In this article, we review advances in glucose monitoring, insulin and glucagon delivery, and the applications and algorithms seeking to integrate novel technologies.

A Clinical Overview of Insulin Pump Therapy for the Management of Diabetes: Past, Present, and Future of Intensive Therapy

IN BRIEF Insulin pump therapy is advancing rapidly. This article summarizes the variety of insulin pump technologies available to date and discusses important clinical considerations for each type of technology.

A Technological Revolution: The Integration of New Treatments to Manage Type 1 Diabetes

Intensive insulin treatment and frequent self-monitoring of blood glucose (SMBG) have been recognized as pillars of diabetes treatment. Many patients with type 1 diabetes (T1D) struggle to achieve targeted glycemic control. Technology has vastly changed how these tenets to treatment can occur. Continuous subcutaneous insulin infusion (CSII) pumps and continuous glucose monitoring (CGM) can be used in place of their counterparts, multiple daily injections and SMBG. We present a review of CSII, CGM, and of different levels of integration among these two therapies, ranging from low glucose suspension devices to hybrid closed loop insulin delivery. Analysis of the various tools, their effect on glycemic control, and a guide to integrate them into pediatric clinical practice is presented. Although a cure for T1D remains the ultimate goal, technology holds the promise of keeping youth with T1D in targeted control and minimize the burden of this chronic medical condition. [Pediatr Ann. 2019;48(8):e311-e318.].

A Review of Continuous Glucose Monitoring Data Interpretation in the Age of Automated Insulin Delivery

Using a continuous glucose monitor (CGM) improves glycemic control in patients with type 1 diabetes. The ambulatory glucose profile (AGP) has been recommended as a standard method for reporting CGM data. However, in recently developed automated insulin delivery (AID) systems, a standard format for reporting data has not yet been developed. Instead, reports are specific to each system being used. Currently, the only FDA approved AID system is a hybrid closed-loop insulin pump. In these systems, the patient is still required to announce a meal, respond to alerts, and keep the system in automated insulin delivery. The integrated pump and sensor information provides insights into how the system is performing, and how to make changes to tunable parameters, such as carbohydrate to insulin ratios. The reports also offer a window into human behavior related to performing diabetes tasks, responding to alarms, reasons for exiting HCL, and how glycemic goals are being met. This article reviews the pump and CGM data provided by several of the current closed-loop systems with a focus on systems that are currently approved in the United States (MiniMed™ 670G, Tandem Basal:IQ) and those used by patients using do-it-yourself systems. A step-wise approach to reviewing the nuances of these systems is provided. The comparison may reinforce the importance of the continued need for streamlining a standard report for providers to be able to interpret the CGM data of these systems.

Information and communication technology enabling partnership in person-centred diabetes management: building a theoretical framework from an inductive case study in The Netherlands

OBJECTIVES

The aim of this paper is to construct a theoretical framework for information and communication technology (ICT)-enabled partnership towards diabetes management.

DESIGN

We conducted an inductive case study and held interviews on the development and use of an artificial pancreas (AP) system for diabetes management.

SETTING

The study was carried out in the Netherlands with users of an AP system.

PARTICIPANTS

We interviewed six patients with type 1 diabetes, five healthcare professionals (two medical specialists and three diabetes nurses), and one policy advisor from the Ministry of Health, Welfare and Sport.

RESULTS

We built a new theoretical framework for ICT-enabled person-centred diabetes management, covering the central themes of self-managing the disease, shared analysing of (medical) data and experiencing the partnership. We found that ICT yielded new activities of data sharing and a new role for data professionals in the provision of care as well as contributed to carefree living thanks to the semiautomated management enabled by the device. Our data suggested that to enable the partnership through ICT, organisational adjustments need to be made such as the development of new ICT services and a viable financial model to support these services.

CONCLUSION

The management of diabetes through ICT requires an adjustment of the partnership between persons with the chronic condition and the healthcare professional(s) in such a way that the potential for self-managing the condition by analysing the newly available (medical) data (from the AP system) together leads to an experience of partnership between patients and healthcare professionals.