Closed-loop insulin delivery in inpatients with type 2 diabetes: a randomised, parallel-group trial

Real-Life Use of Automated Insulin Delivery in Individuals With Type 2 Diabetes

BACKGROUND

The objective of this work is to document performance of automated insulin delivery (AID) during real-life use in type 2 diabetes (T2D).

METHODS

A retrospective analysis was performed of continuous glucose monitoring and insulin delivery data from 796 individuals with T2D, who transitioned from 1-month predictive low-glucose suspend (PLGS) use to 3-month AID use, in real-life settings. Primary outcome was change of time in range (TIR = 70-180 mg/dL) from PLGS to AID. Secondary outcomes included time above/below range (TAR/TBR) and total daily insulin (TDI).

RESULTS

Compared with PLGS, AID increased TIR on average from 63.2% to 72.6%, decreased TAR from 36.2% to 26.8%, and increased TDI from 70.2 to 76.3 U (all P < .001), without significant change to TBR. Glycemic improvements were more pronounced in those with worse glycemic control during PLGS use (P < .001).

CONCLUSIONS

Real-life use of AID led to a rapid and sustained improvement of glycemic control in individuals with T2D.

Review Article - Diabetes Technology in the Hospital: An Update

PURPOSE OF REVIEW

There have been many developments in diabetes technology in recent years, with continuous glucose monitoring (CGM), insulin pump therapy (CSII) and automated insulin delivery (AID) becoming progressively accepted in outpatient diabetes care. However, the use of such advanced diabetes technology in the inpatient setting is still limited for several reasons, including logistical challenges and staff training needs. On the other hand, hospital settings with altered diet and stress-induced hyperglycemia often pose challenges to tight glycemic control using conventional treatment tools. Integrating smarter glucose monitoring and insulin delivery devices into the increasingly technical hospital environment could reduce diabetes-related morbidity and mortality. This narrative review describes the most recent literature on the use of diabetes technology in the hospital and suggests avenues for further research.

RECENT FINDINGS

Advanced diabetes technology has the potential to improve glycemic control in hospitalized people with and without diabetes, and could add particular value in certain conditions, such as nutrition therapy or perioperative management. Taken together, CGM allows for more accurate and patient-friendly follow-up and ad hoc titration of therapy. AID may also provide benefits, including improved glycemic control and reduced nursing workload. Before advanced diabetes technology can be used on a large scale in the hospital, further research is needed on efficacy, accuracy and safety, while implementation factors such as cost and staff training must also be overcome.

Technology in the management of diabetes in hospitalised adults

Suboptimal glycaemic management in hospitals has been associated with adverse clinical outcomes and increased financial costs to healthcare systems. Despite the availability of guidelines for inpatient glycaemic management, implementation remains challenging because of the increasing workload of clinical staff and rising prevalence of diabetes. The development of novel and innovative technologies that support the clinical workflow and address the unmet need for effective and safe inpatient diabetes care delivery is still needed. There is robust evidence that the use of diabetes technology such as continuous glucose monitoring and closed-loop insulin delivery can improve glycaemic management in outpatient settings; however, relatively little is known of its potential benefits and application in inpatient diabetes management. Emerging data from clinical studies show that diabetes technologies such as integrated clinical decision support systems can potentially mediate safer and more efficient inpatient diabetes care, while continuous glucose sensors and closed-loop systems show early promise in improving inpatient glycaemic management. This review aims to provide an overview of current evidence related to diabetes technology use in non-critical care adult inpatient settings. We highlight existing barriers that may hinder or delay implementation, as well as strategies and opportunities to facilitate the clinical readiness of inpatient diabetes technology in the future.

The use of technology in type 2 diabetes and prediabetes: a narrative review

The increasing incidence of type 2 diabetes, which represents 90% of diabetes cases globally, is a major public health concern. Improved glucose management reduces the risk of vascular complications and mortality; however, only a small proportion of the type 2 diabetes population have blood glucose levels within the recommended treatment targets. In recent years, diabetes technologies have revolutionised the care of people with type 1 diabetes, and it is becoming increasingly evident that people with type 2 diabetes can also benefit from these advances. In this review, we describe the current knowledge regarding the role of technologies for people living with type 2 diabetes and the evidence supporting their use in clinical practice. We conclude that continuous glucose monitoring systems deliver glycaemic benefits for individuals with type 2 diabetes, whether treated with insulin or non-insulin therapy; further data are required to evaluate the role of these systems in those with prediabetes (defined as impaired glucose tolerance and/or impaired fasting glucose and/or HbA1c levels between 39 mmol/mol [5.7%] and 47 mmol/mol [6.4%]). The use of insulin pumps seems to be safe and effective in people with type 2 diabetes, especially in those with an HbA1c significantly above target. Initial results from studies exploring the impact of closed-loop systems in type 2 diabetes are promising. We discuss directions for future research to fully understand the potential benefits of integrating evidence-based technology into care for people living with type 2 diabetes and prediabetes.

Does fully closed-loop automated insulin delivery improve glycaemic control in patients with type 2 diabetes? A meta-analysis of randomized controlled trials

AIMS

This meta-analysis investigated the efficacy and safety of fully closed-loop automated insulin delivery (AID) in patients with type 2 diabetes.

MATERIALS AND METHODS

We systemically searched PubMed, Scopus, Web of Science, and Cochrane Central from inception until April 26, 2023. We included randomized controlled trials (RCTs) comparing fully closed-loop AID versus conventional insulin therapy. The outcomes were pooled as the mean difference (MD) and risk ratio with 95% confidence interval (CI) in the random effect model. Our primary outcome was the proportion of time in the target glucose range (5.6-10 mmol/L, 3.9-10 mmol/L, or 3.9-8 mmol/L, depending on the study). Key secondary outcomes included the proportion of time spent in hyperglycaemia or hypoglycaemia.

RESULTS

We included seven RCTs (three crossover and four parallel design), compromising 390 patients. Our analysis showed that compared to the control group, fully closed-loop AID increased the proportion of time spent within the target glucose range by additional 337 min per 24 h (MD = 23.39%, 95% CI [16.64%, 30.14%], p < 0.01), additional 108 min overnight (MD = 22.40%, 95% CI [12.88%, 31.91%], p < 0.01), and additional 258 min during the daytime period (MD = 26.85%, 95% CI [21.06%, 32.63%], p < 0.01). Compared to the control group, the overall time in hyperglycaemia was shortened by 326 min per 24 h (MD = -22.67%, 95% CI [-30.87%, -14.46%], p < 0.01). There was no significant difference between the two groups in terms of overall, overnight, and daytime periods spent in hypoglycaemia.

CONCLUSIONS

Our meta-analysis suggests that fully closed-loop AID may improve glycaemic control in patients with type 2 diabetes, particularly for those with more challenging diabetes management. Further research is required to establish the feasibility of implementing these systems in clinical practice. [Correction added on 26 August 2023 after first online publication: Under Results, the first sentence "We included seven RCTs (three crossover and one parallel designs)" has been changed to "We included seven RCTs (three crossover and four parallel designs)".].

Glycemic control metrics in a cohort of hospitalized patients with type 1 diabetes using hybrid closed-loop and advanced hybrid closed-loop systems

AIMS

To describe Hybrid closed-loop (HCL) and advanced hybrid closed-loop (AHCL) performance in the hospital setting based on the continuous glucose monitoring (CGM) metrics description.

METHODS

This was an observational study from a cohort of patients with T1D using HCL/AHCL with history of hospitalization. CGM metrics were analyzed during the hospital stay. CGM metrics sub-analysis of the population with active Automated Mode (AM) and SmartGuard (SG) during hospitalization and/or surgical procedure was performed.

RESULTS

Twenty-four patients were included (50 % women; mean age, 49 years [inter-quartile range (IQR), 39-62 years]). During hospitalization 70.8 % patients achieved %Time in Range (TIR) between 70 and 180 mg/dL ≥ 70 %. The overall %TIR was 75.5 % (IQR, 67.3-81.5 %), % time below range (TBR) < 70 mg/dL was 2.1 % (IQR, 0.7-5.4 %) and %TBR < 54 mg/dL was 0 % (IQR, 0-5.4 %). Users of the AHCL with active SG achieved a non-significant higher %TIR during hospitalization (79 % [73.8.88 %] vs. 76 % [72.81 %], p = 0.312) and had a shorter stay (3[IQR, 2.4] vs. 6 days[IQR, 5.7], p = 0.045) compared to the users of the HCL with AM active. No device-related serious adverse events occurred for users of either system.

CONCLUSIONS

HCL/AHCL systems with active AM/SG in patients with T1D in the hospital environment leads to %TIR > 70 % in ranges of 70-180 mg/dL in patients without increasing hypoglycemia.

Automated Insulin Delivery with Remote Real-Time Continuous Glucose Monitoring for Hospitalized Patients with Diabetes: A Multicenter, Single-Arm, Feasibility Trial

Introduction: Multiple daily injection insulin therapy frequently fails to meet hospital glycemic goals and is prone to hypoglycemia. Automated insulin delivery (AID) with remote glucose monitoring offers a solution to these shortcomings. Research Design and Methods: In a single-arm multicenter pilot trial, we tested the feasibility, safety, and effectiveness of the Omnipod 5 AID System with real-time continuous glucose monitoring (CGM) for up to 10 days in hospitalized patients with insulin-requiring diabetes on nonintensive care unit medical-surgical units. Primary endpoints included the proportion of time in automated mode and percent time-in-range (TIR 70-180 mg/dL) among participants with >48 h of CGM data. Safety endpoints included incidence of severe hypoglycemia and diabetes-related ketoacidosis (DKA). Additional glycemic endpoints, CGM accuracy, and patient satisfaction were also explored. Results: Twenty-two participants were enrolled; 18 used the system for a total of 96 days (mean 5.3 ± 3.1 days per patient), and 16 had sufficient CGM data required for analysis. Median percent time in automated mode was 95% (interquartile range 92%-98%) for the 18 system users, and the 16 participants with >48 h of CGM data achieved an overall TIR of 68% ± 16%, with 0.17% ± 0.3% time <70 mg/dL and 0.06% ± 0.2% time <54 mg/dL. Sensor mean glucose was 167 ± 21 mg/dL. There were no DKA or severe hypoglycemic events. All participants reported satisfaction with the system at study end. Conclusions: The use of AID with a disposable tubeless patch-pump along with remote real-time CGM is feasible in the hospital setting. These results warrant further investigation in randomized trials.

An Adaptive Model Predictive Controller to Address the Biovariability in Blood Clotting Response During Therapy With Warfarin

OBJECTIVE

Effective dosing of anticoagulants aims to prevent blood clot formation while avoiding hemorrhages. This complex task is challenged by several disturbing factors and drug-effect uncertainties, requesting frequent monitoring and adjustment. Biovariability in drug absorption and action further complicates titration and calls for individualized strategies. In this paper, we propose an adaptive closed-loop control algorithm to assist in warfarin therapy management.

METHODS

The controller was designed and tested in silico using an established pharmacometrics model of warfarin, which accounts for inter-subject variability. The control algorithm is an adaptive Model Predictive Control (a-MPC) that leverages a simplified patient model, whose parameters are updated with a Bayesian strategy. Performance was quantitatively evaluated in simulations performed on a population of virtual subjects against an algorithm reproducing medical guidelines (MG) and an MPC controller available in the literature (l-MPC).

RESULTS

The proposed a-MPC significantly (p 0.05) lowers rising time (2.8 vs. 4.4 and 11.2 days) and time out of range (3.3 vs. 7.2 and 12.9 days) with respect to both MG and l-MPC, respectively. Adaptivity grants a significantly (p 0.05) lower number of subjects reaching unsafe INR values compared to when this feature is not present (8.9% vs.15% of subjects presenting an overshoot outside the target range and 0.08% vs. 0.28% of subjects reaching dangerous INR values).

CONCLUSION

The a-MPC algorithm improve warfarin therapy compared to the benchmark therapies.

SIGNIFICANCE

This in-silico validation proves effectiveness of the a-MPC algorithm for anticoagulant administration, paving the way for clinical testing.

Efficacy and safety of Android artificial pancreas system use at home among adults with type 1 diabetes mellitus in China: protocol of a 26-week, free-living, randomised, open-label, two-arm, two-phase, crossover trial

INTRODUCTION

Do-it-yourself artificial pancreas system (DIY APS) is built using commercially available insulin pump, continuous glucose monitoring (CGM) and an open-source algorithm. Compared with commercial products, DIY systems are affordable, allow personalised settings and provide updated algorithms, making them a more promising therapy for most patients with type 1 diabetes mellitus (T1DM). Many small and self-reported observational studies have found that their real-world use was associated with potential metabolic and psychological benefits. However, rigorous-designed studies are urgently needed to confirm its efficacy and safety.

METHODS AND ANALYSIS

In this 26-week randomised, open-label, two-arm, two-phase, crossover trial, participants aged 18-75 years, with T1DM and glycated haemoglobin (HbA1c) 7-11%, will use AndroidAPS during one 12-week period and sensor-augmented pump during another 12-week period. This study will recruit at least 24 randomised participants. AndroidAPS consists of three components: (1) real-time CGM; (2) insulin pump; (3) AndroidAPS algorithm implemented in Android smartphone. The primary endpoint is time in range (3.9-10.0 mmol/L) derived from CGM. The main secondary endpoints include percentage of sensor glucose values below, within and above target range; mean sensor glucose value; measures of glycaemic variability and centralised HbA1c. Safety endpoints mainly include the frequency of hypoglycaemia events, diabetic ketoacidosis and other serious adverse events.

ETHICS AND DISSEMINATION

This study has been approved by the Ethics Committee of the Third Affiliated Hospital of Sun Yat-sen University. There will be verbal and written information regarding the trial given to each participant. The study will be disseminated through peer-reviewed publications and conference presentations.

OVERALL STATUS

Recruiting.

STUDY START

11 February 2023.

PRIMARY COMPLETION

31 July 2024.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov Registry (NCT05726461).

Implementation of fully closed-loop insulin delivery for inpatients with diabetes: Real-world outcomes

AIMS

Fully closed-loop insulin delivery has been shown in clinical trials to be safe and improve glucose control compared with standard insulin therapy in the inpatient setting. We investigated the feasibility of implementing the approved CamAPS HX fully closed-loop system in a hospital setting.

METHODS

This implementation project was conducted in a large teaching hospital in Cambridge, UK. Healthcare professional training was multimodal including face-to-face workshops, online learning modules and supported by standard operating procedures. Set-up and maintenance of closed-loop devices were undertaken by the inpatient diabetes team. Selection of suitable patients was multidisciplinary and prioritised those with more challenging diabetes management. Demographic and clinical data were collected from electronic health records and diabetes data management platforms.

RESULTS

In the 12 months since the closed-loop system was implemented, 32 inpatients (mean ± SD age 61 ± 16 years, 8 females, 24 males) used closed-loop insulin delivery during their admission, across medical and surgical wards in the hospital with a total of 555 days of closed-loop glucose control (median [IQR]: 14 [6, 22] days per inpatient). The time spent in target glucose range 3.9-10.0 mmol/L was 53.3 ± 18.3%. Mean glucose was 10.7 ± 1.9 mmol/L with 46.0 ± 18.2% of time spent with glucose >10.0 mmol/L. Time spent with sensor glucose below 3.9 mmol/L was low (median [IQR]: 0.38 [0.00, 0.85]). There were no episodes of severe hypoglycaemia or diabetic ketoacidosis during closed-loop use.

CONCLUSIONS

We have demonstrated that the fully closed-loop system can be safely and effectively implemented by a diabetes outreach team in complex medical and surgical inpatients with challenging glycaemic control.

The Clinical Impact of Flash Glucose Monitoring-a Digital Health App and Smartwatch Technology in Patients With Type 2 Diabetes: Scoping Review

BACKGROUND

Type 2 diabetes has a growing prevalence and confers significant cost burden to the health care system, raising the urgent need for cost-effective and easily accessible solutions. The management of type 2 diabetes requires significant commitment from the patient, caregivers, and the treating team to optimize clinical outcomes and prevent complications. Technology and its implications for the management of type 2 diabetes is a nascent area of research. The impact of some of the more recent technological innovations in this space, such as continuous glucose monitoring, flash glucose monitoring, web-based applications, as well as smartphone- and smart watch-based interactive apps has received limited attention in the research literature.

OBJECTIVE

This scoping review aims to explore the literature available on type 2 diabetes, flash glucose monitoring, and digital health technology to improve diabetic clinical outcomes and inform future research in this area.

METHODS

A scoping review was undertaken by searching Ovid MEDLINE and CINAHL databases. A second search using all identified keywords and index terms was performed on Ovid MEDLINE (January 1966 to July 2021), EMBASE (January 1980 to July 2021), Cochrane Central Register of Controlled Trials (CENTRAL; the Cochrane Library, latest issue), CINAHL (from 1982), IEEE Xplore, ACM Digital Libraries, and Web of Science databases.

RESULTS

There were very few studies that have explored the use of mobile health and flash glucose monitoring in type 2 diabetes. These studies have explored somewhat disparate and limited areas of research, and there is a distinct lack of methodological rigor in this area of research. The 3 studies that met the inclusion criteria have addressed aspects of the proposed research question.

CONCLUSIONS

This scoping review has highlighted the lack of research in this area, raising the opportunity for further research in this area, focusing on the clinical impact and feasibility of the use of multiple technologies, including flash glucose monitoring in the management of patients with type 2 diabetes.

Consensus Recommendations for the Use of Automated Insulin Delivery Technologies in Clinical Practice

The significant and growing global prevalence of diabetes continues to challenge people with diabetes (PwD), healthcare providers, and payers. While maintaining near-normal glucose levels has been shown to prevent or delay the progression of the long-term complications of diabetes, a significant proportion of PwD are not attaining their glycemic goals. During the past 6 years, we have seen tremendous advances in automated insulin delivery (AID) technologies. Numerous randomized controlled trials and real-world studies have shown that the use of AID systems is safe and effective in helping PwD achieve their long-term glycemic goals while reducing hypoglycemia risk. Thus, AID systems have recently become an integral part of diabetes management. However, recommendations for using AID systems in clinical settings have been lacking. Such guided recommendations are critical for AID success and acceptance. All clinicians working with PwD need to become familiar with the available systems in order to eliminate disparities in diabetes quality of care. This report provides much-needed guidance for clinicians who are interested in utilizing AIDs and presents a comprehensive listing of the evidence payers should consider when determining eligibility criteria for AID insurance coverage.

Automated Insulin Delivery Systems as a Treatment for Type 2 Diabetes Mellitus: A Review

OBJECTIVE

Approximately 6.3% of the worldwide population has type 2 diabetes mellitus (T2DM), and the number of people requiring insulin is increasing. Automated insulin delivery (AID) systems integrate continuous subcutaneous insulin infusion and continuous glucose monitoring with a predictive control algorithm to provide more physiologic glycemic control. Personalized glycemic targets are recommended in T2DM owing to the heterogeneity of the disease. Based on the success of hybrid closed-loop systems in improving glycemic control and safety in type 1 diabetes mellitus, there has been further interest in the use of these systems in people with T2DM.

METHODS

We performed a review of AID systems with a focus on the T2DM population.

RESULTS

In 5 randomized controlled trials, AID systems improve time in range and reduce glycemic variability, without increasing insulin requirements or the risk of hypoglycemia.

CONCLUSION

AID systems in T2DM are safe and effective in hospitalized and closely monitored settings. Home studies of longer duration are required to assess for long-term benefit and identify target populations of benefit.

[Hospital diabetes management (Update 2023)]

This position statement presents the recommendations of the Austrian Diabetes Association for diabetes management of adult patients during inpatient stay. It is based on the current evidence with respect to blood glucose targets, insulin therapy and treatment with oral/injectable antidiabetic drugs during inpatient hospitalization. Additionally, special circumstances such as intravenous insulin therapy, concomitant therapy with glucocorticoids and use of diabetes technology during hospitalization are discussed.

Fully automated closed-loop insulin delivery in adults with type 2 diabetes: an open-label, single-center, randomized crossover trial

In adults with type 2 diabetes, the benefits of fully closed-loop insulin delivery, which does not require meal bolusing, are unclear. In an open-label, single-center, randomized crossover study, 26 adults with type 2 diabetes (7 women and 19 men; (mean ± s.d.) age, 59 ± 11 years; baseline glycated hemoglobin (HbA1c), 75 ± 15 mmol mol^-1 (9.0% ± 1.4%)) underwent two 8-week periods to compare the CamAPS HX fully closed-loop app with standard insulin therapy and a masked glucose sensor (control) in random order, with a 2-week to 4-week washout between periods. The primary endpoint was proportion of time in target glucose range (3.9-10.0 mmol l^-1). Analysis was by intention to treat. Thirty participants were recruited between 16 December 2020 and 24 November 2021, of whom 28 were randomized to two groups (14 to closed-loop therapy first and 14 to control therapy first). Proportion of time in target glucose range (mean ± s.d.) was 66.3% ± 14.9% with closed-loop therapy versus 32.3% ± 24.7% with control therapy (mean difference, 35.3 percentage points; 95% confidence interval (CI), 28.0-42.6 percentage points; P < 0.001). Time > 10.0 mmol l^-1 was 33.2% ± 14.8% with closed-loop therapy versus 67.0% ± 25.2% with control therapy (mean difference, -35.2 percentage points; 95% CI, -42.8 to -27.5 percentage points; P < 0.001). Mean glucose was lower during the closed-loop therapy period than during the control therapy period (9.2 ± 1.2 mmol l^-1 versus 12.6 ± 3.0 mmol l^-1, respectively; mean difference, -3.6 mmol l^-1; 95% CI, -4.6 to -2.5 mmol l^-1; P < 0.001). HbA1c was lower following closed-loop therapy (57 ± 9 mmol mol^-1 (7.3% ± 0.8%)) than following control therapy (72 ± 13 mmol mol^-1 (8.7% ± 1.2%); mean difference, -15 mmol mol^-1; 95% CI, -11 to -20 mmol l^-1 (mean difference, -1.4%; 95% CI, -1.0 to -1.8%); P < 0.001). Time < 3.9 mmol l^-1 was similar between treatments (a median of 0.44% (interquartile range, 0.19-0.81%) during the closed-loop therapy period versus a median of 0.08% (interquartile range, 0.00-1.05%) during the control therapy period; P = 0.43). No severe hypoglycemia events occurred in either period. One treatment-related serious adverse event occurred during the closed-loop therapy period. Fully closed-loop insulin delivery improved glucose control without increasing hypoglycemia compared with standard insulin therapy and may represent a safe and efficacious method to improve outcomes in adults with type 2 diabetes. This study is registered with ClinicalTrials.gov (NCT04701424).

Automated insulin delivery: benefits, challenges, and recommendations. A Consensus Report of the Joint Diabetes Technology Working Group of the European Association for the Study of Diabetes and the American Diabetes Association

A technological solution for the management of diabetes in people who require intensive insulin therapy has been sought for decades. The last 10 years have seen substantial growth in devices that can be integrated into clinical care. Driven by the availability of reliable systems for continuous glucose monitoring, we have entered an era in which insulin delivery through insulin pumps can be modulated based on sensor glucose data. Over the past few years, regulatory approval of the first automated insulin delivery (AID) systems has been granted, and these systems have been adopted into clinical care. Additionally, a community of people living with type 1 diabetes has created its own systems using a do-it-yourself approach by using products commercialised for independent use. With several AID systems in development, some of which are anticipated to be granted regulatory approval in the near future, the joint Diabetes Technology Working Group of the European Association for the Study of Diabetes and the American Diabetes Association has created this consensus report. We provide a review of the current landscape of AID systems, with a particular focus on their safety. We conclude with a series of recommended targeted actions. This is the fourth in a series of reports issued by this working group. The working group was jointly commissioned by the executives of both organisations to write the first statement on insulin pumps, which was published in 2015. The original authoring group was comprised by three nominated members of the American Diabetes Association and three nominated members of the European Association for the Study of Diabetes. Additional authors have been added to the group to increase diversity and range of expertise. Each organisation has provided a similar internal review process for each manuscript prior to submission for editorial review by the two journals. Harmonisation of editorial and substantial modifications has occurred at both levels. The members of the group have selected the subject of each statement and submitted the selection to both organisations for confirmation.

A system theory based digital model for predicting the cumulative fluid balance course in intensive care patients

Background: Surgical interventions can cause severe fluid imbalances in patients undergoing cardiac surgery, affecting length of hospital stay and survival. Therefore, appropriate management of daily fluid goals is a key element of postoperative intensive care in these patients. Because fluid balance is influenced by a complex interplay of patient-, surgery- and intensive care unit (ICU)-specific factors, fluid prediction is difficult and often inaccurate. Methods: A novel system theory based digital model for cumulative fluid balance (CFB) prediction is presented using recorded patient fluid data as the sole parameter source by applying the concept of a transfer function. Using a retrospective dataset of n = 618 cardiac intensive care patients, patient-individual models were created and evaluated. RMSE analyses and error calculations were performed for reasonable combinations of model estimation periods and clinically relevant prediction horizons for CFB. Results: Our models have shown that a clinically relevant time horizon for CFB prediction with the combination of 48 h estimation time and 8-16 h prediction time achieves high accuracy. With an 8-h prediction time, nearly 50% of CFB predictions are within ±0.5 L, and 77% are still within the clinically acceptable range of ±1.0 L. Conclusion: Our study has provided a promising proof of principle and may form the basis for further efforts in the development of computational models for fluid prediction that do not require large datasets for training and validation, as is the case with machine learning or AI-based models. The adaptive transfer function approach allows estimation of CFB course on a dynamically changing patient fluid balance system by simulating the response to the current fluid management regime, providing a useful digital tool for clinicians in daily intensive care.

Insulin Pumps and Hybrid Close Loop Systems Within Hospital: A Scoping Review and Practical Guidance From the Joint British Diabetes Societies for Inpatient Care

This article is the second of a two-part series providing a scoping review and summary of the Joint British Diabetes Societies for Inpatient Care (JBDS-IP) guidelines on the use of diabetes technology in people with diabetes admitted to hospital. The first part reviewed the use of continuous glucose monitoring (CGM) in hospital. In this article, we focus on the use of continuous subcutaneous insulin infusion (CSII; insulin pumps) and hybrid closed-loop systems in hospital. JBDS-IP advocates enabling people who can self-manage and are willing and capable of using CSII to continue doing so as they would do out of hospital. CSII should be discontinued if the individual is critically ill or hemodynamically unstable. For individuals on hybrid closed-loop systems, the system should be discontinued from auto-mode, and may be used individually (as CGM only or CSII only, if criteria are met). Continuing in closed-loop mode may only be done so under specialist guidance from the Diabetes Team, where the diabetes teams are comfortable and knowledgeable about the specific devices used. Health care organizations need to have clear local policies and guidance to support individuals using these wearable technologies, and ensure the relevant workforce is capable and skilled enough to ensure their safe use within the hospital setting.

Automated Insulin Delivery: Benefits, Challenges, and Recommendations. A Consensus Report of the Joint Diabetes Technology Working Group of the European Association for the Study of Diabetes and the American Diabetes Association

A technological solution for the management of diabetes in people who require intensive insulin therapy has been sought for decades. The last 10 years have seen substantial growth in devices that can be integrated into clinical care. Driven by the availability of reliable systems for continuous glucose monitoring, we have entered an era in which insulin delivery through insulin pumps can be modulated based on sensor glucose data. Over the past few years, regulatory approval of the first automated insulin delivery (AID) systems has been granted, and these systems have been adopted into clinical care. Additionally, a community of people living with type 1 diabetes has created its own systems using a do-it-yourself approach by using products commercialized for independent use. With several AID systems in development, some of which are anticipated to be granted regulatory approval in the near future, the joint Diabetes Technology Working Group of the European Association for the Study of Diabetes and the American Diabetes Association has created this consensus report. We provide a review of the current landscape of AID systems, with a particular focus on their safety. We conclude with a series of recommended targeted actions. This is the fourth in a series of reports issued by this working group. The working group was jointly commissioned by the executives of both organizations to write the first statement on insulin pumps, which was published in 2015. The original authoring group was comprised by three nominated members of the American Diabetes Association and three nominated members of the European Association for the Study of Diabetes. Additional authors have been added to the group to increase diversity and range of expertise. Each organization has provided a similar internal review process for each manuscript prior to submission for editorial review by the two journals. Harmonization of editorial and substantial modifications has occurred at both levels. The members of the group have selected the subject of each statement and submitted the selection to both organizations for confirmation.

CGM in the Hospital: Is It Ready for Prime Time?

PURPOSE OF REVIEW

The use of continuous glucose monitoring (CGM) in the hospital setting is growing with more patients using these devices at home and when admitted to the hospital, especially during the COVID-19 pandemic.

RECENT FINDINGS

Historically, most evidence for CGM use in the inpatient setting was limited to small studies utilizing outdated CGM technology and analyzing accuracy of sensor measurements. Previous studies have shown reduced sensor accuracy during extreme hypo- or hyperglycemia, rapid fluctuations of glucose, compression of the sensor itself, and in those who are critically ill. Studies that are more recent have shown CGM to have adequate accuracy and may be effective in reducing hypoglycemia in hospitalized patients; some studies have also showed improvement in time in target glycemic range. Furthermore, CGM may reduce nursing workload, cost of inpatient care, and use of personal protective equipment and face-to-face patient care especially for patients during the COVID-19 pandemic. This review will describe the evidence for use of CGM in hospitalized critically ill or non-critically ill patients, address accuracy and safety considerations, and outline paths for future implementation.

Management of Hyperglycemia in Hospitalized Adult Patients in Non-Critical Care Settings: An Endocrine Society Clinical Practice Guideline

BACKGROUND

Adult patients with diabetes or newly recognized hyperglycemia account for over 30% of noncritically ill hospitalized patients. These patients are at increased risk for adverse clinical outcomes in the absence of defined approaches to glycemic management.

OBJECTIVE

To review and update the 2012 Management of Hyperglycemia in Hospitalized Patients in Non-Critical Care Settings: An Endocrine Society Clinical Practice Guideline and to address emerging areas specific to the target population of noncritically ill hospitalized patients with diabetes or newly recognized or stress-induced hyperglycemia.

METHODS

A multidisciplinary panel of clinician experts, together with a patient representative and experts in systematic reviews and guideline development, identified and prioritized 10 clinical questions related to inpatient management of patients with diabetes and/or hyperglycemia. The systematic reviews queried electronic databases for studies relevant to the selected questions. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology was used to assess the certainty of evidence and make recommendations.

RESULTS

The panel agreed on 10 frequently encountered areas specific to glycemic management in the hospital for which 15 recommendations were made. The guideline includes conditional recommendations for hospital use of emerging diabetes technologies including continuous glucose monitoring and insulin pump therapy; insulin regimens for prandial insulin dosing, glucocorticoid, and enteral nutrition-associated hyperglycemia; and use of noninsulin therapies. Recommendations were also made for issues relating to preoperative glycemic measures, appropriate use of correctional insulin, and diabetes self-management education in the hospital. A conditional recommendation was made against preoperative use of caloric beverages in patients with diabetes.

CONCLUSION

The recommendations are based on the consideration of important outcomes, practicality, feasibility, and patient values and preferences. These recommendations can be used to inform system improvement and clinical practice for this frequently encountered inpatient population.

A Systematic Review Supporting the Endocrine Society Clinical Practice Guideline for the Management of Hyperglycemia in Adults Hospitalized for Noncritical Illness or Undergoing Elective Surgical Procedures

CONTEXT

Individuals with diabetes or newly recognized hyperglycemia account for over 30% of noncritically ill hospitalized patients. Management of hyperglycemia in these patients is challenging.

OBJECTIVE

To support development of the Endocrine Society Clinical Practice Guideline for management of hyperglycemia in adults hospitalized for noncritical illness or undergoing elective surgical procedures.

METHODS

We searched several databases for studies addressing 10 questions provided by a guideline panel from the Endocrine Society. Meta-analysis was conducted when feasible. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology was used to assess certainty of evidence.

RESULTS

We included 94 studies reporting on 135 553 patients. Compared with capillary blood glucose, continuous glucose monitoring increased the number of patients identified with hypoglycemia and decreased mean daily blood glucose (BG) (very low certainty). Data on continuation of insulin pump therapy in hospitalized adults were sparse. In hospitalized patients receiving glucocorticoids, combination neutral protamine hagedorn (NPH) and basal-bolus insulin was associated with lower mean BG compared to basal-bolus insulin alone (very low certainty). Data on NPH insulin vs basal-bolus insulin in hospitalized adults receiving enteral nutrition were inconclusive. Inpatient diabetes education was associated with lower HbA1c at 3 and 6 months after discharge (moderate certainty) and reduced hospital readmissions (very low certainty). Preoperative HbA1c level < 7% was associated with shorter length of stay, lower postoperative BG and a lower number of neurological complications and infections, but a higher number of reoperations (very low certainty). Treatment with glucagon-like peptide-1 agonists or dipeptidyl peptidase-4 inhibitors in hospitalized patients with type 2 diabetes and mild hyperglycemia was associated with lower frequency of hypoglycemic events than insulin therapy (low certainty). Caloric oral fluids before surgery in adults with diabetes undergoing surgical procedures did not affect outcomes (very low certainty). Counting carbohydrates for prandial insulin dosing did not affect outcomes (very low certainty). Compared with scheduled insulin (basal-bolus or basal insulin + correctional insulin), correctional insulin was associated with higher mean daily BG and fewer hypoglycemic events (low certainty).

CONCLUSION

The certainty of evidence supporting many hyperglycemia management decisions is low, emphasizing importance of shared decision-making and consideration of other decisional factors.

The impact of nursing staff education on diabetes inpatient glucose management: a pilot cluster randomised controlled trial

BACKGROUND

An increasing number of patients in hospital have diabetes, with most of them cared for by non-specialist staff. The effect of diabetes education for staff on patient outcomes, as well as the most effective method of staff education is unclear. Therefore, the aim of this study was to compare diabetes outcomes in medical wards where nursing staff were offered one face-to-face (F2F) session followed by access to online education (online), F2F education only, or standard care (control).

METHODS

We conducted a pilot cluster randomised controlled trial involving 16-weeks baseline/rollout followed by a 28-week post-intervention period across three medical wards (clusters) in a Sydney Teaching Hospital. The online ward provided an online competency-based diabetes education program and 1-h F2F teaching from a diabetes nurse educator (DNE), the F2F ward provided four separate 1-h teaching sessions by a DNE, with no additional sessions in the control ward. The primary outcome was length of stay (LOS); secondary outcomes included good diabetes days (GDD), hypoglycaemia and medication errors. Poisson and binary logistic regression were used to compare clusters.

RESULTS

Staff attendance/completion of ≥ 2 topics was greater with online than F2F education [39/48 (81%) vs 10/33 (30%); p < 0.001]. Among the 827/881 patients, there was no difference in LOS change between online [Median(IQR) 5(2-8) to 4(2-7) days], F2F [7(4-14) to 5(3-13) days] or control wards [5(3-9) to 5(3-7) days]. GDD improved only in the online ward 4.7(2.7-7.0) to 6.0(2.3-7.0) days; p = 0.038. Total patients with hypoglycaemia and appropriately treated hypoglycaemia increased in the online ward.

CONCLUSIONS

The inclusion of online education increased diabetes training uptake among nursing staff. GDD and appropriate hypoglycaemia management increased in the online education wards.

TRIAL REGISTRATION

Prospectively registered on the Australia New Zealand Clinical Trials Registry (ANZCTR) on 24/05/2017: ACTRN12617000762358 .

Open-source automated insulin delivery: international consensus statement and practical guidance for health-care professionals

Open-source automated insulin delivery systems, commonly referred to as do-it-yourself automated insulin delivery systems, are examples of user-driven innovations that were co-created and supported by an online community who were directly affected by diabetes. Their uptake continues to increase globally, with current estimates suggesting several thousand active users worldwide. Real-world user-driven evidence is growing and provides insights into safety and effectiveness of these systems. The aim of this consensus statement is two-fold. Firstly, it provides a review of the current evidence, description of the technologies, and discusses the ethics and legal considerations for these systems from an international perspective. Secondly, it provides a much-needed international health-care consensus supporting the implementation of open-source systems in clinical settings, with detailed clinical guidance. This consensus also provides important recommendations for key stakeholders that are involved in diabetes technologies, including developers, regulators, and industry, and provides medico-legal and ethical support for patient-driven, open-source innovations.

The automated pancreas: A review of technologies and clinical practice

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

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

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

Fully automated closed-loop glucose control compared with standard insulin therapy in adults with type 2 diabetes requiring dialysis: an open-label, randomized crossover trial

We evaluated the safety and efficacy of fully closed-loop insulin therapy compared with standard insulin therapy in adults with type 2 diabetes requiring dialysis. In an open-label, multinational, two-center, randomized crossover trial, 26 adults with type 2 diabetes requiring dialysis (17 men, 9 women, average age 68 ± 11 years (mean ± s.d.), diabetes duration of 20 ± 10 years) underwent two 20-day periods of unrestricted living, comparing the Cambridge fully closed-loop system using faster insulin aspart (‘closed-loop’) with standard insulin therapy and a masked continuous glucose monitor (‘control’) in random order. The primary endpoint was time in target glucose range (5.6–10.0 mmol l⁻¹). Thirteen participants received closed-loop first and thirteen received control therapy first. The proportion of time in target glucose range (5.6–10.0 mmol l⁻¹; primary endpoint) was 52.8 ± 12.5% with closed-loop versus 37.7 ± 20.5% with control; mean difference, 15.1 percentage points (95% CI 8.0–22.2; P < 0.001). Mean glucose was lower with closed-loop than control (10.1 ± 1.3 versus 11.6 ± 2.8 mmol l⁻¹; P = 0.003). Time in hypoglycemia (<3.9 mmol l⁻¹) was reduced with closed-loop versus control (median (IQR) 0.1 (0.0–0.4%) versus 0.2 (0.0–0.9%); P = 0.040). No severe hypoglycemia events occurred during the control period, whereas one severe hypoglycemic event occurred during the closed-loop period, but not during closed-loop operation. Fully closed-loop improved glucose control and reduced hypoglycemia compared with standard insulin therapy in adult outpatients with type 2 diabetes requiring dialysis. The trial registration number is NCT04025775.

A new randomized, crossover clinical trial testing rapid-acting insulin aspart delivered in a closed-loop system compared with standard care demonstrates increased time in range in patients with type 2 diabetes who require dialysis in the outpatient setting.

Day-to-day variability of insulin requirements in the inpatient setting: Observations during fully closed-loop insulin delivery

The aim of this study was to characterize the variability of exogenous insulin requirements during fully closed-loop insulin delivery in hospitalized patients with type 2 diabetes or new-onset hyperglycaemia, and to determine patient-related characteristics associated with higher variability of insulin requirements. We retrospectively analysed data from two fully closed-loop inpatient studies involving adults with type 2 diabetes or new-onset hyperglycaemia requiring insulin therapy. The coefficient of variation quantified day-to-day variability of exogenous insulin requirements during up to 15 days using fully automated closed-loop insulin delivery. Data from 535 days in 67 participants were analysed. The coefficient of variation of day-to-day exogenous insulin requirements was 30% ± 16%, and was higher between nights than between any daytime period (56% ± 29% overnight [11:00 pm to 4:59 am] compared with 41% ± 21% in the morning [5:00 am to 10:59 am], 39% ± 15% in the afternoon [11:00 am to 4:59 pm] and 45% ± 19% during the evening [5:00 pm to 10:59 pm]; all P < 0.01). There is high day-to-day variability of exogenous insulin requirements in inpatients, particularly overnight, and diabetes management approaches should account for this variability.

Technology in the management of type 2 diabetes: Present status and future prospects

The growing incidence of type 2 diabetes (T2D) is a significant health concern, representing 90% of diabetes cases worldwide. As the disease progresses, resultant insulin deficiency and hyperglycaemia necessitates insulin therapy in many cases. It has been recognized that a significant number of people who have a clinical requirement for insulin therapy, as well as their healthcare professionals, are reluctant to intensify treatment with insulin due to fear of hypoglycaemia, poor understanding of treatment regimens or lack of engagement, and are therefore at higher risk of developing complications from poor glycaemic control. Over the past decade, the rise of diabetes technologies, including dosing advisors, continuous glucose monitoring systems, insulin pumps and automated insulin delivery systems, has led to great improvements in the therapies available, particularly to those requiring insulin. Although the focus has largely been on delivering these therapies to the type 1 diabetes population, it is becoming increasingly recognized that people with T2D face similar challenges to achieve recommended glycaemic standards and also have the potential to benefit from these advances. In this review, we discuss diabetes technologies that are currently available for people with T2D and the evidence supporting their use, as well as future prospects. We conclude that there is a clinical need to extend the use of these technologies to the T2D population to curb the consequences of suboptimal disease management in this group.

Insulin Pump Therapy for Patients With Type 2 Diabetes Mellitus: Evidence, Current Barriers, and New Technologies

An increasing number of patients with type 2 diabetes mellitus (T2DM) use insulin pumps. The first insulin pumps especially designed for patients with T2DM have recently become available. However, national guidelines do not primarily recommend the use of continuous subcutaneous insulin infusion (CSII) for this patient group. The effectiveness of CSII in T2DM has not yet been convincingly demonstrated, despite some positive evidence. An overview and an assessment of various studies to date will be given. T2DM is a heterogeneous disease with a substantial phenotypic variability; therefore, it is difficult to provide general conclusions about the effectiveness of CSII in T2DM therapy. The pump types, characteristics, and associated barriers may play a relevant role for therapy outcome. Most advanced functions like various bolus dosages offered by conventional insulin pumps are not needed for T2DM treatment and complicate the device handling for this subject group. Additionally, new technologies like increased connectivity, advanced software features, and interoperability are currently becoming available representing further barriers. The implementation of this technological progress might be a benefit for pumps for T2DM as well. However, these have not been sufficiently examined either and increased security challenges due to integrated peripheral components should not be neglected in terms of a sound cybersecurity. Pump features and handling for patients with T2DM should be as easy as possible, indicating a need for insulin pumps specially designed for patients with T2DM. However, it has to be investigated if pumps designed for T2DM are more effective than other intensified insulin regimens.

Managing Patients with Insulin Pumps and Continuous Glucose Monitors in the Hospital: to Wear or Not to Wear

PURPOSE OF REVIEW

As the prevalence of diabetes mellitus in the USA continues to rise, so does the popularity of diabetes management devices such as continuous glucose monitors (CGMs) and insulin pumps. The use of this technology has been shown to improve outpatient glycemic outcomes and quality of life and oftentimes may be continued in the hospital setting. Our aim is to review the current guidelines and available evidence on the continuation of insulin pumps and CGMs in the inpatient setting.

RECENT FINDINGS

Patients with diabetes are at higher risk for hospitalizations and complications due to hyper- or hypoglycemia, metabolic co-morbidities, or as seen recently, more severe illness from infections such as SARS-CoV-2. The maintenance of euglycemia is important to decrease both morbidity and mortality in the hospital setting. There is consensus among experts and medical societies that inpatient use of diabetes technology in carefully selected patients with proper institutional protocols is safe and can improve inpatient glycemic outcomes and reduce hypoglycemia. During the COVID-19 pandemic, CGMs played a vital role in managing hyperglycemia in some hospitalized patients. Insulin pumps and CGMs have the potential to transform glycemic management in hospitalized patients. In order for institutions to safely and effectively incorporate these technologies on their inpatient units, hospital-based providers will need to be able to understand how to manage and utilize these devices in their practice in conjunction with diabetes experts.

Efficacy of Hybrid Closed-Loop Insulin Delivery System in a Hospital Setting: A Case Series

Objective

We report a case series of 4 patients with type 1 diabetes who used hybrid closed-loop insulin pumps (Medtronic MiniMed 670 G) during hospitalization.

Methods

Clinical data and point-of-care glucose values are presented for each patient. Glucose values are shown graphically while in manual mode as well as in auto mode.

Results

The first case was a 30-year-old man admitted for pancreatitis. Mean point-of-care blood glucose was 165.7 mg/dL while in auto mode, without hypoglycemia, compared with 221 mg/dL while in manual mode. The second case was a 28-year-old woman who was admitted for a laparoscopic cholecystectomy. Mean point-of-care blood glucose in auto mode was 131.3 mg/dL, without hypoglycemia, compared with 117.6 mg/dL while in manual mode. The third case was a 46-year-old man admitted to the intensive care unit for influenzal pneumonia. Mean point-of-care blood glucose in auto mode was 159.1 mg/dL without hypoglycemia, compared with 218.5 mg/dL while in manual mode. The fourth case was a 60-year-old man who remained in auto mode throughout his hospitalization except for a period when he removed his pump for an endoscopic retrograde cholangiopancreatography and endoscopic ultrasound. His mean point-of-care blood glucose while in auto mode was 156.8 mg/dL without hypoglycemia.

Conclusion

These case reports support the use of hybrid closed-loop insulin-pump therapy in the inpatient setting to maintain inpatient glycemic targets and avoid hypoglycemia when part of an institution-sanctioned strategy for safe use of insulin pumps that includes point-of-care blood glucose monitoring.

Effect of nutrition on postprandial glucose control in hospitalized patients with type 2 diabetes receiving fully automated closed-loop insulin therapy

Fully automated closed-loop insulin delivery may offer a novel way to manage diabetes in hospital. However, postprandial glycaemic control remains challenging. We aimed to assess the effect of nutritional intake on postprandial glucose control in hospitalized patients with type 2 diabetes receiving fully closed-loop insulin therapy. The effects of different meal types and macronutrient composition on sensor glucose time-in-target (TIT, 3.9-10.0 mmol/L) and mean sensor glucose were assessed with hierarchical linear models using a Bayesian estimation approach. TIT was lower and the mean sensor glucose slightly higher, after breakfast compared with lunch and dinner, whereas the insulin dose was higher. Across meals, when carbohydrates were replaced by fat, or to a lesser extent by protein, postprandial glucose control improved. For breakfast, a 3.9% improvement in TIT was observed when 10% of the energy from carbohydrates was replaced by fat. Improvements were slightly lower during lunch and dinner (3.2% and 3.4%) or when carbohydrates were replaced by protein (2.2 and 2.7%, respectively). We suggest that reducing carbohydrate at the expense of fat or protein, could further improve glucose control during fully closed-loop insulin therapy in hospital.

Practical implementation of automated closed-loop insulin delivery: A French position statement

Automated closed-loop (CL) insulin therapy has come of age. This major technological advance is expected to significantly improve the quality of care for adults, adolescents and children with type 1 diabetes. To improve access to this innovation for both patients and healthcare professionals (HCPs), and to promote adherence to its requirements in terms of safety, regulations, ethics and practice, the French Diabetes Society (SFD) brought together a French Working Group of experts to discuss the current practical consensus. The result is the present statement describing the indications for CL therapy with emphasis on the idea that treatment expectations must be clearly defined in advance. Specifications for expert care centres in charge of initiating the treatment were also proposed. Great importance was also attached to the crucial place of high-quality training for patients and healthcare professionals. Long-term follow-up should collect not only metabolic and clinical results, but also indicators related to psychosocial and human factors. Overall, this national consensus statement aims to promote the introduction of marketed CL devices into standard clinical practice.

Continuous Glucose Monitors and Automated Insulin Dosing Systems in the Hospital Consensus Guideline

This article is the work product of the Continuous Glucose Monitor and Automated Insulin Dosing Systems in the Hospital Consensus Guideline Panel, which was organized by Diabetes Technology Society and met virtually on April 23, 2020. The guideline panel consisted of 24 international experts in the use of continuous glucose monitors (CGMs) and automated insulin dosing (AID) systems representing adult endocrinology, pediatric endocrinology, obstetrics and gynecology, advanced practice nursing, diabetes care and education, clinical chemistry, bioengineering, and product liability law. The panelists reviewed the medical literature pertaining to five topics: (1) continuation of home CGMs after hospitalization, (2) initiation of CGMs in the hospital, (3) continuation of AID systems in the hospital, (4) logistics and hands-on care of hospitalized patients using CGMs and AID systems, and (5) data management of CGMs and AID systems in the hospital. The panelists then developed three types of recommendations for each topic, including clinical practice (to use the technology optimally), research (to improve the safety and effectiveness of the technology), and hospital policies (to build an environment for facilitating use of these devices) for each of the five topics. The panelists voted on 78 proposed recommendations. Based on the panel vote, 77 recommendations were classified as either strong or mild. One recommendation failed to reach consensus. Additional research is needed on CGMs and AID systems in the hospital setting regarding device accuracy, practices for deployment, data management, and achievable outcomes. This guideline is intended to support these technologies for the management of hospitalized patients with diabetes.

Predicting the Risk of Inpatient Hypoglycemia With Machine Learning Using Electronic Health Records

OBJECTIVE

We analyzed data from inpatients with diabetes admitted to a large university hospital to predict the risk of hypoglycemia through the use of machine learning algorithms.

RESEARCH DESIGN AND METHODS

Four years of data were extracted from a hospital electronic health record system. This included laboratory and point-of-care blood glucose (BG) values to identify biochemical and clinically significant hypoglycemic episodes (BG ≤3.9 and ≤2.9 mmol/L, respectively). We used patient demographics, administered medications, vital signs, laboratory results, and procedures performed during the hospital stays to inform the model. Two iterations of the data set included the doses of insulin administered and the past history of inpatient hypoglycemia. Eighteen different prediction models were compared using the area under the receiver operating characteristic curve (AUROC) through a 10-fold cross validation.

RESULTS

We analyzed data obtained from 17,658 inpatients with diabetes who underwent 32,758 admissions between July 2014 and August 2018. The predictive factors from the logistic regression model included people undergoing procedures, weight, type of diabetes, oxygen saturation level, use of medications (insulin, sulfonylurea, and metformin), and albumin levels. The machine learning model with the best performance was the XGBoost model (AUROC 0.96). This outperformed the logistic regression model, which had an AUROC of 0.75 for the estimation of the risk of clinically significant hypoglycemia.

CONCLUSIONS

Advanced machine learning models are superior to logistic regression models in predicting the risk of hypoglycemia in inpatients with diabetes. Trials of such models should be conducted in real time to evaluate their utility to reduce inpatient hypoglycemia.

Closed-loop management of inpatient hyperglycaemia

The prevalence of diabetes in the inpatient setting is increasing, and suboptimal glucose control in hospital is associated with increased morbidity and mortality. Attaining the recommended glucose levels is challenging with standard insulin therapy. Hypoglycaemia and hyperglycaemia are common and diabetes management in hospital can be a considerable workload burden for health-care professionals. Fully automated insulin delivery (closed-loop) has been shown to be safe, and achieves superior glucose control than standard insulin therapy in the hospital, including in those patients receiving haemodialysis and enteral or parenteral nutrition where glucose control can be particularly challenging. Evidence that the improved glucose control achieved using closed-loop systems can translate into improved clinical outcomes for patients is key to support widespread adoption of this technology. The closed-loop approach has the potential to provide a paradigm shift in the management of inpatient diabetes, particularly in the most challenging inpatient populations, and may reduce staff work burden and the health-care costs associated with inpatient diabetes.

Automated Insulin Delivery in Adults

Hybrid closed-loop (artificial pancreas) systems have recently been introduced into clinical practice for adults with type 1 diabetes. This reflects successful translation from research studies in highly supervised settings to evaluation of the technology in free-living home settings. We review the different closed-loop approaches and the key clinical evidence supporting adoption of hybrid closed-loop systems for adults with type 1 diabetes. We also discuss the growing evidence for automated insulin delivery in pregnant women and in hospitalized patients with hyperglycemia. We consider the psychosocial impact of closed-loop systems and the challenges and potential future advancements for automated insulin delivery.

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.

Diabetes Technology in the Inpatient Setting for Management of Hyperglycemia

In past decades, a rapid evolution of diabetes technology led to increased popularity and use of continuous glucose monitoring (CGM) and continuous subcutaneous insulin infusion (CSII) in the ambulatory setting for diabetes management, and recently, the artificial pancreas became available. Efforts to translate this technology to the hospital setting have shown accuracy and reliability of CGM, safety of CSII in appropriate populations, improvement of inpatient glycemic control with computerized glycemic management systems, and feasibility of inpatient CGM-CSII closed-loop systems. Several ongoing studies are focusing on continued translation of this technology to improve glycemic control and outcomes in hospitalized patients.

Recent Avenues in Novel Patient-Friendly Techniques for the Treatment of Diabetes

Background:

Diabetes is one of the most common chronic metabolic disorders which affect the quality of human life worldwide. As per the WHO report, between 1980 to 2014, the number of diabetes patients increases from 108 million to 422 million, with a global prevalence rate of 8.5% per year. Diabetes is the prime reason behind various other diseases like kidney failure, stroke, heart disorders, glaucoma, etc. It is recognized as the seventh leading cause of death throughout the world. The available therapies are painful (insulin injections) and inconvenient due to higher dosing frequency. Thus, to find out a promising and convenient treatment, extensive investigations are carried out globally by combining novel carrier system (like microparticle, microneedle, nanocarrier, microbeads etc.) and delivery devices (insulin pump, stimuli-responsive device, inhalation system, bioadhesive patch, insulin pen etc.) for more precise diagnosis and painless or less invasive treatment of disease.

Objective:

The review article is made with an objective to compile information about various upcoming and existing modern technologies developed to provide greater patient compliance and reduce the undesirable side effect of the drug. These devices evade the necessity of daily insulin injection and offer a rapid onset of action, which sustained for a prolonged duration of time to achieve a better therapeutic effect.

Conclusion:

Despite numerous advantages, various commercialized approaches, like Afrezza (inhalation insulin) have been a failure in recent years. Such results call for more potential work to develop a promising system. The novel approaches range from the delivery of non-insulin blood glucose lowering agents to insulin-based therapy with minimal invasion are highly desirable.

Closed-loop insulin delivery in end-of-life care: a case report

BACKGROUND

Glucose management for people with diabetes approaching the end of life can be very challenging. The aim is to balance a minimally invasive approach with avoidance of symptomatic hypo- and hyperglycaemia.

CASE REPORT

We present a case of a hospitalized individual whose glucose was managed with closed-loop insulin delivery within a randomized controlled trial setting during a period of terminal illness. During the time in which closed-loop insulin delivery was used, glucose control was safe, with no glucose-related harm. The mean ± sd sensor glucose for this individual was 11.3 ± 4.3 mmol/l, percentage of time spent in target glucose range between 6 and 15 mmol/l was 70.5%, time spent in hypoglycaemia was 2.0% and time spent in significant hyperglycaemia >20 mmol/l was 2.6%.

CONCLUSION

Closed-loop systems can accommodate personalized glucose targets and highly variable insulin requirements. Factory-calibrated continuous glucose sensors and insulin pump therapy are less intrusive than finger-stick glucose measurements and insulin injections, respectively. Closed-loop systems may provide a safer and less burdensome approach to glucose management towards the end of life.

A subcutaneous insulin pharmacokinetic model for insulin Detemir

BACKGROUND AND OBJECTIVE

Type 2 diabetes (T2D) is rapidly increasing in incidence and has significant social and economic costs. Given the increasing cost of complications, even relatively short delays in the onset of T2D can significantly reduce long-term complications and costs. Equally, recent studies have shown the onset of T2D can be delayed by use of long-acting insulin, despite the risk and concomitant low adherence. Thus, there is a strong potential motivation to develop models of long-acting insulin analogues to enable safe, effective use in model-based dosing systems. In particular, there are no current models of long-acting insulin Detemir and its unique action for model-based control. The objective of this work is to develop a first model of insulin Detemir and its unique action, and validate it against existing data in the literature.

METHODS

This study develops a detailed compartment model for insulin Detemir. Model specific parameters are identified using data from a range of published clinical studies on the pharmacokinetic of insulin Detemir. Model validity and robustness are assessed by identifying the model for each study and using average identified parameters over several dose sizes and study cohorts. Comparisons to peak concentration, time of peak concentration and overall error versus measured plasma concentrations are used to assess model accuracy and validity.

RESULTS

Almost all studies and cohorts fit literature data to within one standard deviation of error, even when using averaged identified model parameters. However, there appears to be a noticeable dose dependent dynamic not included in this first model, nor reported in the literature studies.

CONCLUSIONS

A first model of insulin Detemir including its unique albumin binding kinetics is derived and provisionally validated against clinical pharmacokinetic data. The pharmacokinetic curves are suitable for model-based control and general enough for use. While there are limitations in the studies used for validation that prevent a more complete understanding, the results provide an effective first model and justify the design and implementation of further, more precise human trials.

Single-Hormone Artificial Pancreas Use in Diabetes: Clinical Efficacy and Remaining Challenges

IN BRIEF Artificial pancreas systems are rapidly developing and constitute the most promising technology for insulin-requiring diabetes management. Single-hormone systems (SH-AP) that deliver only insulin and have a hybrid design that necessitates patients' interventions around meals and exercise are the first to appear on the market. Trials with SH-AP have demonstrated improvement in time spent with blood glucose levels within target ranges, with a concomitant decrease in hypoglycemia. Longer and larger trials involving different patient populations are ongoing to further advance this important technology.

Advancing the Use of CGM Devices in a Non-ICU Setting

Improvements in glycemic control using continuous glucose monitoring (CGM) systems have been demonstrated in the outpatient setting. Among hospitalized patients the use of CGM is largely investigational, particularly in the non-ICU setting. Although there is no commercially available closed-loop system, it has recently been evaluated in the non-critical care setting. Both CGMs and closed-loop systems may lead to improved glycemic control, decreased length of stay, reduced risk of adverse events related to severe hypoglycemia or hyperglycemia. Limitations of inpatient use of CGM and closed-loop systems include lack of FDA approvals, inexperience with this technology, and costs related to supplies. Significant investment may be necessary for hospital staff training and for development of infrastructure to support inpatient use. Additional limitations for CGM systems includes potential inaccuracy of interstitial glucose measurements due to medication interferences, sensor lag, or sensor drift. Limitations for closed-loop systems also includes need for routine monitoring to detect infusion site issues as well as monitoring to ensure adequate insulin supply in reservoir to avoid abrupt cessation of insulin infusion leading to severe hyperglycemia. Hospital staff must be familiar with trouble-shooting and conversion to alternative mode of insulin delivery in the event of insulin pump malfunction. Given these complexities, implementation of closed-loop systems may require involvement of an endocrinology team, limiting widespread adoption. This article reviews current state of CGM and closed-loop system use in the non-ICU setting, available literature, advantages and limitations, as well as suggestions for future CGM design, specifically for the inpatient setting.

A Century of Diabetes Technology: Signals, Models, and Artificial Pancreas Control

Arguably, diabetes mellitus is one of the best-quantified human conditions: elaborate in silico models describe the action of the human metabolic system; real-time signals such as continuous glucose monitoring are readily available; insulin delivery is being automated; and control algorithms are capable of optimizing blood glucose fluctuation in patients' natural environments. The transition of the artificial pancreas (AP) to everyday clinical use is happening now, and is contingent upon seamless concerted work of devices encompassing the patient in a digital treatment ecosystem. This review recounts briefly the story of diabetes technology, which began a century ago with the discovery of insulin, progressed through glucose monitoring and subcutaneous insulin delivery, and is now rapidly advancing towards fully automated clinically viable AP systems.