Virtual training on the hybrid close loop system in people with type 1 diabetes (T1D) during the COVID-19 pandemic

Virtual insulin pump initiation is safe effective in children adolescents with type 1 diabetes

Objective

There is no head-to-head comparison of the safety and efficacy of virtual versus in-office insulin pump initiation for youth with type 1 diabetes in the US. The study's aim was to determine the safety and efficacy of virtual versus in-office pump initiation in pediatric type 1 diabetes.

Research design and methods

A longitudinal retrospective study of 112 subjects: 65% (n=73), ages 11.2 ± 3.8 years(y), received in-office training; and 35% (n=39), ages 12.0 ± 4.0y, received virtual training. The number of White subjects was 40 (55%) in the in-office group, and 25 (66%) in the remote group; while Black subjects were 11 (15%) in the in-office group and 4 (10%) in the virtual group. Data were collected at pump initiation, 3 and 6 months.

Results

There were no significant differences in sex, race, height, weight, BMI, and the duration of diabetes between the groups at baseline. There was no significant difference in A1c between the groups at 0, 3, and 6 months. A1c correlated significantly with the glucose management indicator at 0, 3, and 6 months: baseline: r=0.49, p<0.0001; 3 months: r=0.77, p<0.0001; and 6 months: r=0.71, p<0.0001. There was no relationship between A1c or TIR and pubertal status, BMI, sex, or race. A1c was significantly elevated in the non-White individuals at 6 months only: 57.9 mmol/mol (50.8-69.4) versus 51.9 mmol/mol (46.5-59.6)], p=0.007.

Conclusion

Virtual insulin pump initiation is safe and effective in children with type 1 diabetes. This approach could accelerate the adoption of the use of diabetes technology in minority populations in the US.

Results From a Virtual Clinic for the Follow-up of Patients Using the Advanced Hybrid Closed-Loop System

BACKGROUND

Evidence regarding the implementation of medium-term strategies in advanced hybrid closed-loop (AHCL) system users is limited. Therefore, this study aimed to describe the efficacy and safety of the AHCL system in patients with type 1 diabetes (T1D) on a six-month follow-up in a virtual diabetes clinic (VDC).

METHOD

A prospective cohort of adult patients with T1D treated using the AHCL system (Mini Med 780G; Medtronic, Northridge, California) in a VDC follow-up. Standardized training and follow-up were conducted virtually. Clinical data and metabolic control outcomes were reported at baseline, and at three and six months.

RESULTS

Sixty-four patients (mean age = 42 ± 14.6 years, 65% men, 54% with graduate education) were included. Percentage time in range (%TIR) increased significantly regardless of prior therapy with intermittently scanned continuous glucose monitoring + multiple daily injections and sensor-augmented pump therapy with predictive low-glucose management after starting AHCL and persisted during the follow-up period with no hypoglycemic events. The %TIR 70 to 180 mg/dL according to socioeconomic strata was 73.4% ± 5.3%, 78.1% ± 8.1%, and 84.2% ± 7.5% for the lower, middle, and upper strata, respectively. The sensor was used more frequently in the population with a higher education level. Adherence to sensor use and SmartGuard retention were higher in patients who underwent the VDC follow-up.

CONCLUSIONS

Medium-term follow-up of users of AHCL systems in a VDC contributes to safely achieving %TIR goals. Virtual diabetes clinic follow-up favored adherence to sensor use and continuous SmartGuard use. Socioeconomic strata were associated with a better glycemic profile and education level was associated with better adherence to sensor use.

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.

Innovations in Diabetes Device Training: A Scoping Review

OBJECTIVE

The coronavirus disease 2019 pandemic highlighted a pre-existing need for alternatives to traditional in-person diabetes device trainings. Barriers to care, which include the heavy burden of training, pose a threat to optimal adoption and utilization of these devices. We searched the literature for alternative methods of training, evaluated user satisfaction, and compared short-term clinical outcomes with guideline-based glucometric targets and historical training results.

METHODS

A scoping review of Embase articles from 2019 to 2021 was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines using key words relevant to diabetes technologies. Original full-text articles investigating training of new users on devices were included. Titles and abstracts were screened for eligibility by 2 independent reviewers, and results were summarized.

RESULTS

Of 25 articles retrieved from the database, 11 met the criteria. Alternative training strategies included video conferencing, phone calls, mobile applications, and hybrids with traditional trainings. Overall, there was a high degree of user satisfaction with virtual visits, with a preference for hybrid approaches (6 articles). Although glucometrics varied between articles, short-term glucometrics were satisfactory overall (8 articles), including improved glycated hemoglobin measurements and time in range. Two articles compared time in range over various time points after traditional and remote training. One found equivalency, and the other identified a 5% improvement with remote training.

CONCLUSION

Alternative training approaches are a viable option to reduce the barriers to care and to alleviate training burden. Intentional implementation of alternatives should be considered a solution to address current barriers.

Early and sustained increase in time in range 1 year after initiation of hybrid close loop therapy via telemedicine in type 1 diabetes patients

BACKGROUND AND AIMS

Evidence supports the efficacy and safety of the Hybrid Close loop (HCL) system in patients with type 1 diabetes (T1D). However, limited data are available on the long-term outcomes of patients on HCL with telemedicine follow-up.

METHODS

A prospective observational cohort study including T1D patients, who were upgrading to HCL system. Virtual training and follow-up were done through telemedicine. CGM data were analyzed to compare the baseline time in range (TIR), time below range (TBR), glycemic variability and auto mode (AM), with measurements performed at 3, 6 and 12 months.

RESULTS

134 patients were included with baseline A1c 7.6% ± 1.1. 40.5% had a severe hypoglycemia event in the last year. Baseline TIR, measured two weeks after starting AM was 78.6 ± 9.94%. No changes were evident at three (Mean difference - 0.15;CI-2.47,2.17;p = 0.96), six (MD-1.09;CI-3.42,1.24;p = 0.12) and 12 months (MD-1.30;CI-3.64,1.04;p = 0.08). No significant changes were found in TBR or glycemic variability throughout the follow-up. Use of AM was 85.6 ± 17.5% and percentage of use of sensor was 88.75 ± 9.5% at 12 months. No severe hypoglycemic (SH) events were reported.

CONCLUSIONS

HCL systems allow to improve TIR, TBR and glycemic variability safely, early and sustained up to 1 year of follow-up in patients with T1D and high risk of hypoglycemia followed through telemedicine.

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.

The future of telehealth in type 1 diabetes

PURPOSE OF REVIEW

Although telemedicine and telehealth services have been a part of type 1 diabetes (T1D) clinical care for several decades, the expansion of in-home telemedicine during the COVID-19 pandemic significantly increased interest in long-term use as part of routine care. This review highlights the current literature regarding telemedicine in T1D care as well as the benefits and barriers to use in a postpandemic world.

RECENT FINDINGS

Telemedicine has increased patient contact with healthcare providers, allowing for more frequent insulin dose adjustments and improvements in glycemic outcomes. In addition to routine clinical care, T1D device training and mental healthcare have been successful through telemedicine. Significant barriers to continued telemedicine care exist, including patient access and technology knowledge, language, and loss of face-to-face interaction. Healthcare providers additionally face unpredictable reimbursement and loss of continuity across state lines, and lack of resources and training for device downloads and telemedicine software.

SUMMARY

Telemedicine can be successfully used in T1D care and has the potential to significantly impact glycemic and long-term outcomes. Due to continued interest for in-person visits by people with T1D and providers, it is likely that long-term telemedicine use will include a hybrid format.

Current provision and HCP experiences of remote care delivery and diabetes technology training for people with type 1 diabetes in the UK during the COVID-19 pandemic

BACKGROUND

The COVID-19 pandemic has led to the rapid implementation of remote care delivery in type 1 diabetes. We studied current modes of care delivery, healthcare professional experiences and impact on insulin pump training in type 1 diabetes care in the United Kingdom (UK).

METHODS

The UK Diabetes Technology Network designed a 48-question survey aimed at healthcare professionals providing care in type 1 diabetes.

RESULTS

One hundred and forty-three healthcare professionals (48% diabetes physicians, 52% diabetes educators and 88% working in adult services) from approximately 75 UK centres (52% university hospitals, 46% general and community hospitals), responded to the survey. Telephone consultations were the main modality of care delivery. There was a higher reported time taken for video consultations versus telephone (p < 0.001). Common barriers to remote consultations were patient familiarity with technology (72%) and access to patient device data (67%). We assessed the impact on insulin pump training. A reduction in total new pump starts (73%) and renewals (61%) was highlighted. Common barriers included patient digital literacy (61%), limited healthcare professional experience (46%) and time required per patient (44%). When grouped according to size of insulin pump service, pump starts and renewals in larger services were less impacted by the pandemic compared to smaller services.

CONCLUSION

This survey highlights UK healthcare professional experiences of remote care delivery. While supportive of virtual care models, a number of factors highlighted, especially patient digital literacy, need to be addressed to improve virtual care delivery and device training.

Six-Month Glycemic Control with a Hybrid Closed-Loop System in Type 1 Diabetes Patients in a Latin American Country

The goal of this study was to assess the 6-month effectiveness of hybrid closed loop on glycemic control in type 1 diabetes (T1D) patients in Latin America. An exploratory analysis of data prospectively collected from non-selected consecutive patients with T1D who initiated treatment with the MiniMed™ 670G system in Argentina was conducted. Baseline and follow-up visits at days 7, 28, 90, and 180 were carried out and data were downloaded at each visit. A total of 30 patients (age range 9-57 years, female 63.3%), 73.3% (n = 22) of whom previously used sensor augmented pump-predictive low glucose management (SAP-PLGM), with baseline glycated hemoglobin 7.4% ± 1% were included. Time in range between 70 and 180 mg/dL significantly increased from 65.1% at baseline to 77.3%, 76.2%, 75.7%, and 75.2% at days 7, 28, 90 and 180, respectively. Time above range (>180 mg/dL) significantly decreased from 33% to 22.5% (P < 0.001), while time below range (<70 mg/dL) did not change. Mean glucose levels were reduced from 163.5 mg/dL at baseline to 150.9 mg/dL (P = 0.001) at last visit. The Auto Mode feature was used > 90% of the time. Virtual training was successfully completed with a Net Promoter Score^® (NPS^®) of 87%. This analysis confirms that MiniMed 670G system use allowed successful achievement of glycemic control within recommended targets in a non-selected Latin American patient population who underwent virtual system training.

Trends in Glycemic Control Among Youth and Young Adults With Diabetes: The SEARCH for Diabetes in Youth Study

OBJECTIVE

To describe temporal trends and correlates of glycemic control in youth and young adults (YYA) with youth-onset diabetes.

RESEARCH DESIGN AND METHODS

The study included 6,369 participants with type 1 or type 2 diabetes from the SEARCH for Diabetes in Youth study. Participant visit data were categorized into time periods of 2002-2007, 2008-2013, and 2014-2019, diabetes durations of 1-4, 5-9, and ≥10 years, and age groups of 1-9, 10-14, 15-19, 20-24, and ≥25 years. Participants contributed one randomly selected data point to each duration and age group per time period. Multivariable regression models were used to test differences in hemoglobin A1c (HbA1c) over time by diabetes type. Models were adjusted for site, age, sex, race/ethnicity, household income, health insurance status, insulin regimen, and diabetes duration, overall and stratified for each diabetes duration and age group.

RESULTS

Adjusted mean HbA1c for the 2014-2019 cohort of YYA with type 1 diabetes was 8.8 ± 0.04%. YYA with type 1 diabetes in the 10-14-, 15-19-, and 20-24-year-old age groups from the 2014-2019 cohort had worse glycemic control than the 2002-2007 cohort. Race/ethnicity, household income, and treatment regimen predicted differences in glycemic control in participants with type 1 diabetes from the 2014-2019 cohort. Adjusted mean HbA1c was 8.6 ± 0.12% for 2014-2019 YYA with type 2 diabetes. Participants aged ≥25 years with type 2 diabetes had worse glycemic control relative to the 2008-2013 cohort. Only treatment regimen was associated with differences in glycemic control in participants with type 2 diabetes.

CONCLUSIONS

Despite advances in diabetes technologies, medications, and dissemination of more aggressive glycemic targets, many current YYA are less likely to achieve desired glycemic control relative to earlier cohorts.

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.

Digital technologies in the care of people with diabetes during the COVID-19 pandemic: a scoping review

OBJECTIVE

To map evidence on the use of digital technologies in the care of people with diabetes during the COVID-19 pandemic.

METHOD

This is a scoping review, based on the JBI manual, which included scientific articles and gray literature from nine primary and seven secondary databases. Articles were independently assessed by two reviewers. Rayyan® was used to select the studies. The description of study characterization is presented in a table and tables, ending in a narrative synthesis.

RESULTS

A total of 1,964 studies were identified and, after selection, 23 publications remained for analysis. It turned out that telemedicine was used in all studies and remote consultation support technologies included continuous glucose monitoring devices, glucose data analysis software, insulin delivery systems, applications, audio and/or voice communication devices, which facilitated remote diabetes mellitus monitoring and management.

CONCLUSION

Telehealth, monitoring technologies, insulin delivery systems and communication devices were tools used to monitor patients with diabetes during the pandemic.

Rapid Improvement in Time in Range After the Implementation of an Advanced Hybrid Closed-Loop System in Adolescents and Adults with Type 1 Diabetes

Background: Advanced hybrid closed-loop (AHCL) systems represent the next step of automation intended to maximize normoglycemia in people with type 1 diabetes (T1D). In the AHCL MiniMed 780G system, different algorithm glucose targets for insulin infusion are available and autocorrection boluses are delivered. The aim was to prospectively evaluate the impact of the implementation of this AHCL system in a clinical setting. Materials and Methods: T1D subjects using a sensor-augmented pump with predictive low-glucose suspend (SAP-PLGS) were upgraded to AHCL. Baseline, every 3 days, 2-week and 1-month sensor and pump data were downloaded. Glucose target was set to 100 mg/dL and active insulin time to 2 h for all the subjects. Time in different glucose ranges was compared. Results: Fifty-two T1D subjects were included (age: 43 ± 12 years, 73% females, diabetes duration: 27 ± 11 years, HbA1c: 7.2% ± 0.9%, time in SAP-PLGS: 5 ± 2 years). Time in range (TIR) 70-180 mg/dL increased from 67.3% ± 13.6% at baseline to 79.6% ± 7.9% at 1 month (P = 0.001). Time in hyperglycemia >180 and >250 mg/dL decreased from 29.4% ± 15.1% to 17.3% ± 8.6% and from 6.9% ± 7.8% to 2.5% ± 2.4%, respectively (P = 0.001). No differences in time in hypoglycemia <70 or <54 mg/dL were found. Time in Auto Mode was 97% ± 4%, and autocorrection insulin was 31% ± 14% of bolus insulin. Four hours postprandial glucose was improved from 162 ± 26 mg/dL at baseline to 142 ± 16 mg/dL at 1 month (P = 0.001). No severe hypoglycemia or diabetic ketoacidosis episodes occurred. Conclusion: AHCL systems allow well-controlled T1D patients to rapidly increase their TIR. The most aggressive settings allow optimal outcomes in TIR, without increasing hypoglycemia frequency.

Newest Diabetes-Related Technologies for Pediatric Type 1 Diabetes and Its Impact on Routine Care: a Narrative Synthesis of the Literature

Purpose of Review

This review aims to address the actual state of the most advanced diabetes devices, as follows: continuous subcutaneous insulin infusions (CSII), continuous glucose monitoring systems (CGM), hybrid-closed loop (HCL) systems, and “Do-it-yourself” Artificial Pancreas Systems (DIYAPS) in children, adolescents, and young adults. This review has also the objective to assess the use of telemedicine for diabetes care across three different areas: education, social media, and daily care.

Recent Findings

Recent advances in diabetes technology after integration of CSII with CGM have increased the popularity of this treatment modality in pediatric age and shifted the standard diabetes management in many countries. We found an impressive transition from the use of CSII and/or CGM only to integrative devices with automated delivery systems. Although much has changed over the past 5 years, including a pandemic period that precipitated a broader use of telemedicine in diabetes care, some advances in technology may still be an additional burden of care for providers, patients, and caregivers. The extent of a higher rate of “auto-mode” use in diabetes devices while using the HCL/DIYAPS is essential to reduce the burden of diabetes treatment.

Summary

More studies including higher-risk populations are needed, and efforts should be taken to ensure proper access to cost-effective advanced technology on diabetes care.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40124-021-00248-7.

Virtual training on advanced hybrid closed-loop system MiniMed 780G in a teenager with type 1 diabetes previously treated with multiple daily injections: A case report

Virtual pump training program for novel devices in people with type 1 diabetes on multiple daily injections can be an effective tool to initiate an advanced HCL system (MiniMed 780G) and to improve glycemic control in a safe manner without severe hypoglycemia and hyperglycemia.

Has COVID-19 lockdown improved glycaemic control in pediatric patients with type 1 diabetes? An analysis of continuous glucose monitoring metrics

AIMS

Our observational study aimed to evaluate the impact of the lockdown period due to 2019 Coronavirus disease pandemic on glycaemic control in a cohort of paediatric patients with type 1 diabetes (T1D).

METHODS

Eighty-five patients with T1D aged 5-18 years using continuous glucose monitoring (CGM) systems were enrolled. Demographic and clinical data, including glucose metrics generated by CGM-specific web-based cloud platforms, were collected in three different periods (pre-lockdown phase, lockdown phase, and post-lockdown phase) of 90 days each and were statistically analysed.

RESULTS

During the lockdown period, a clear improvement in almost all CGM metrics (time in range, time above range, coefficient of variation, and glucose management indicator) was observed in our study population, regardless of age and insulin type treatment. In the months following lockdown, maintaining satisfactory diabetes outcomes was confirmed only in younger patients (aged 5-9 years) and in those individuals on hybrid closed loop therapy.

CONCLUSIONS

The increasing use of innovative technological devices together with data sharing systems and interaction with multidisciplinary diabetes team through telemedicine allowed paediatric patients with T1D to improve glucose metrics during the lockdown period. However, our findings showed that the achievement of better glycaemic control was transient for most patients.

Efficacy of advanced hybrid closed loop systems for the management of type 1 diabetes in children

Over the last years significant advances have been achieved in the development of technologies for diabetes management. Continuous subcutaneous insulin infusion (CSII), continuous glucose monitoring (CGM), predictive low glucose management (PLGM), hybrid closed loop (HCL) and advanced hybrid closed loop (AHCL) systems allow better diabetes management, thus reducing the burden of the disease and the risk of chronic complications. This review summarizes the main characteristics of the currently available HCL and AHCL systems and their primary effects in children and adolescents with type 1 diabetes (T1D). The findings of trials assessing the glucose control (time in range, HbA1c values, hypoglycemic events), the health-related quality of life and the existing limits of the use of these technologies are reported. The most recent data clearly confirm the ability of the HCL and AHCL insulin delivery systems to safely achieve a significant improvement of glucose control and quality of life in the pediatric population with T1D. Further studies are underway to overcame current barriers and future improvements in the usability of these technologies are awaited to facilitate their use in the routine clinical practice. The HCL and AHCL algorithms are the key features of today's insulin delivery systems that mark a crucial step towards fully automated closed loop systems.