Emerging Landscape of Continuous Glucose Monitoring

Hybrid closed loop insulin therapy versus standard therapy in pregnant women with type 1 diabetes: A systematic review and meta-analysis of randomized controlled trials

OBJECTIVE

We aimed to explore the efficacy and safety of hybrid closed loop (HCL) systems compared to standard care (SC) in pregnant women with Type 1 Diabetes Mellitus (T1DM), pooling results from randomized controlled trials (RCTs).

DATA SOURCES

We searched through multiple databases like PubMed, Cochrane, Embase, Web of Science, and Clinicaltrials.gov etc. from inception to September 2024 and found six relevant studies after screening.

STUDY ELIGIBILITY CRITERIA

We included studies that were (1) RCTs; with patient population (2) pregnant patients with type 1 diabetes; intervention group receiving (3) HCL and control group receiving (4) SC; while reporting (5) outcomes of interest (endpoints). We pooled results pertaining to primary outcomes; time in range (TIR), nocturnal time in range (nTIR), and HbA1c; and relevant secondary outcomes.

STUDY APPRAISAL AND SYNTHESIS METHODS

We used Rob 2: A revised Cochrane risk-of-bias tool for randomized trials for quality assessment of the included RCTs. We employed the DerSimonian-Laird random effects model using review manager 5.4 to analyze the pooled estimates and reported results as risk ratio; for dichotomous outcomes; or mean difference; for continuous outcomes.

RESULTS

Five RCTs (n = 274) with disparate populations were narrowed down for analysis. Pooled estimates for TIR (MD 4.95 %;-0.56 to 10.49)and HbA1c% (MD 0.09; -0.44 to 0.63) were statistically non-significant, while estimates for nTIR (MD 11.16 %; 7.15 to 15.15), % time < 63 mg/dL (MD -0.78; -1.36 to -0.20), % of time < 54 mg/dL (MD -0.22; -0.40 to -0.03), low blood glucose index (LBGI) (MD -0.30; -0.54 to -0.06), and glucose standard deviation (MD -3.05; -6.06 to -0.04) favored HCL over SC. No significant between-group differences were found in other secondary outcomes: % of time >140 mg/dL, % of time >180 mg/dL, mean glucose level, rate of serious adverse events, cesarian delivery, and severe hypoglycemia.

CONCLUSIONS

HCL systems can improve glycemic control in pregnant women with T1DM with a tolerable adverse event profile, however more research is needed to draw a definitive conclusion.

The Development and Potential Applications of an Automated Method for Detecting and Classifying Continuous Glucose Monitoring Patterns

INTRODUCTION

Continuous glucose monitoring (CGM) is emerging as a transformative tool for helping people with diabetes self-manage their glucose and supporting clinicians in effective treatment. Unfortunately, many CGM users, and clinicians, find interpreting the large volume of CGM data to be overwhelming and complex. To address this challenge, an efficient, intelligent method for detecting and classifying discernable patterns in CGM data was desired.

METHODS

We developed an automated artificial intelligence (AI)-driven method to detect and classify different discernable CGM patterns which called "CGM events." We trained different models using 60 days of CGM data from 27 individuals with diabetes from a publicly available data set and then evaluated model performance using separate test data from the same group. Each event is classified according to clinical significance based on three parameters: (1) glucose category at or near the beginning of the CGM event; (2) a calculated severity score that encompasses both signal shape and temporal characteristics (e.g., how high the CGM curve goes (measured in mg/dL) and how long it stays above target (as established by published consensus guidelines); and (3) the glucose category at or near the end of the event.

RESULTS

The system accurately detected and classified events from actual CGM data. This was also validated with expert diabetes clinicians.

CONCLUSIONS

Advanced pattern recognition methods can be used to detect and classify CGM events of interest and may be used to provide actionable insights and self-management support to CGM users and decision support to the clinicians caring for them.

Hypoglycaemia symptom frequency, severity, burden, and utility among adults with type 1 diabetes and impaired awareness of hypoglycaemia: Baseline and 24-week findings from the HypoCOMPaSS study

AIMS

To determine the frequency, severity, burden, and utility of hypoglycaemia symptoms among adults with type 1 diabetes (T1D) and impaired awareness of hypoglycaemia (IAH) at baseline and week 24 following the HypoCOMPaSS awareness restoration intervention.

METHODS

Adults (N = 96) with T1D (duration: 29 ± 12 years; 64% women) and IAH completed the Hypoglycaemia Burden Questionnaire (HypoB-Q), assessing experience of 20 pre-specified hypoglycaemia symptoms, at baseline and week 24.

RESULTS

At baseline, 93 (97%) participants experienced at least one symptom (mean ± SD 10.6 ± 4.6 symptoms). The proportion recognising each specific symptom ranged from 15% to 83%. At 24 weeks, symptom severity and burden appear reduced, and utility increased.

CONCLUSIONS

Adults with T1D and IAH experience a range of hypoglycaemia symptoms. Perceptions of symptom burden or utility are malleable. Although larger scale studies are needed to confirm, these findings suggest that changing the salience of the symptomatic response may be more important in recovering protection from hypoglycaemia through regained awareness than intensifying symptom frequency or severity.

A Systematic Review Supporting the Endocrine Society Guidelines: Management of Diabetes and High Risk of Hypoglycemia

CONTEXT

Interventions targeting hypoglycemia in people with diabetes are important for improving quality of life and reducing morbidity and mortality.

OBJECTIVE

To support development of the Endocrine Society Clinical Practice Guideline for management of individuals with diabetes at high risk for hypoglycemia.

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 149 studies reporting on 43 344 patients. Continuous glucose monitoring (CGM) reduced episodes of severe hypoglycemia in patients with type 1 diabetes (T1D) and reduced the proportion of patients with hypoglycemia (blood glucose [BG] levels <54 mg/dL). There were no data on use of real-time CGM with algorithm-driven insulin pumps vs multiple daily injections with BG testing in people with T1D. CGM in outpatients with type 2 diabetes taking insulin and/or sulfonylureas reduced time spent with BG levels under 70 mg/dL. Initiation of CGM in hospitalized patients at high risk for hypoglycemia reduced episodes of hypoglycemia with BG levels lower than 54 mg/dL and time spent under 54 mg/dL. The proportion of patients with hypoglycemia with BG levels lower than 70 mg/dL and lower than 54 mg/dL detected by CGM was significantly higher than point-of-care BG testing. We found no data evaluating continuation of personal CGM in the hospital. Use of an inpatient computerized glycemic management program utilizing electronic health record data was associated with fewer patients with and episodes of hypoglycemia with BG levels lower than 70 mg/dL and fewer patients with severe hypoglycemia compared with standard care. Long-acting basal insulin analogs were associated with less hypoglycemia. Rapid-acting insulin analogs were associated with reduced severe hypoglycemia, though there were more patients with mild to moderate hypoglycemia. Structured diabetes education programs reduced episodes of severe hypoglycemia and time below 54 mg/dL in outpatients taking insulin. Glucagon formulations not requiring reconstitution were associated with longer times to recovery from hypoglycemia, although the proportion of patients who recovered completely from hypoglycemia was not different between the 2 groups.

CONCLUSION

This systematic review summarized the best available evidence about several interventions addressing hypoglycemia in people with diabetes. This evidence base will facilitate development of clinical practice guidelines by the Endocrine Society.

Use of Telecommunication and Diabetes-Related Technologies in Older Adults With Type 1 Diabetes During a Time of Sudden Isolation: Mixed Methods Study

BACKGROUND

The COVID-19 lockdown imposed a sudden change in lifestyle with self-isolation and a rapid shift to the use of technology to maintain clinical care and social connections.

OBJECTIVE

In this mixed methods study, we explored the impact of isolation during the lockdown on the use of technology in older adults with type 1 diabetes (T1D).

METHODS

Older adults (aged ≥65 years) with T1D using continuous glucose monitoring (CGM) participated in semistructured interviews during the COVID-19 lockdown. A multidisciplinary team coded the interviews. In addition, CGM metrics from a subgroup of participants were collected before and during the lockdown.

RESULTS

We evaluated 34 participants (mean age 71, SD 5 years). Three themes related to technology use emerged from the thematic analysis regarding the impact of isolation on (1) insulin pump and CGM use to manage diabetes, including timely access to supplies, and changing Medicare eligibility regulations; (2) technology use for social interaction; and (3) telehealth use to maintain medical care. The CGM data from a subgroup (19/34, 56%; mean age 74, SD 5 years) showed an increase in time in range (mean 57%, SD 17% vs mean 63%, SD 15%; P=.001), a decrease in hyperglycemia (>180 mg/dL; mean 41%, SD 19% vs mean 35%, SD 17%; P<.001), and no change in hypoglycemia (<70 mg/dL; median 0.7%, IQR 0%-2% vs median 1.1%, IQR 0%-4%; P=.40) during the lockdown compared to before the lockdown.

CONCLUSIONS

These findings show that our cohort of older adults successfully used technology during isolation. Participants provided the positive and negative perceptions of technology use. Clinicians can benefit from our findings by identifying barriers to technology use during times of isolation and developing strategies to overcome these barriers.

A model to design financially sustainable algorithm-enabled remote patient monitoring for pediatric type 1 diabetes care

Introduction

Population-level algorithm-enabled remote patient monitoring (RPM) based on continuous glucose monitor (CGM) data review has been shown to improve clinical outcomes in diabetes patients, especially children. However, existing reimbursement models are geared towards the direct provision of clinic care, not population health management. We developed a financial model to assist pediatric type 1 diabetes (T1D) clinics design financially sustainable RPM programs based on algorithm-enabled review of CGM data.

Methods

Data were gathered from a weekly RPM program for 302 pediatric patients with T1D at Lucile Packard Children's Hospital. We created a customizable financial model to calculate the yearly marginal costs and revenues of providing diabetes education. We consider a baseline or status quo scenario and compare it to two different care delivery scenarios, in which routine appointments are supplemented with algorithm-enabled, flexible, message-based contacts delivered according to patient need. We use the model to estimate the minimum reimbursement rate needed for telemedicine contacts to maintain revenue-neutrality and not suffer an adverse impact to the bottom line.

Results

The financial model estimates that in both scenarios, an average reimbursement rate of roughly $10.00 USD per telehealth interaction would be sufficient to maintain revenue-neutrality. Algorithm-enabled RPM could potentially be billed for using existing RPM CPT codes and lead to margin expansion.

Conclusion

We designed a model which evaluates the financial impact of adopting algorithm-enabled RPM in a pediatric endocrinology clinic serving T1D patients. This model establishes a clear threshold reimbursement value for maintaining revenue-neutrality, as well as an estimate of potential RPM reimbursement revenue which could be billed for. It may serve as a useful financial-planning tool for a pediatric T1D clinic seeking to leverage algorithm-enabled RPM to provide flexible, more timely interventions to its patients.

Building the 2022 Diabetes Technology Practice Competencies Using Modified Delphi Methodology

PURPOSE

The purpose of this study was to construct professional competencies for diabetes technology use in various care settings reflecting the mission of the Association of Diabetes Care & Education Specialists (ADCES).

METHOD

ADCES convened a core team of nationally representative diabetes technology experts to develop professional competencies specifically related to diabetes technology use. A modified Delphi methodology, which comprised 4 rounds, was used for consensus development among these experts. First, experts developed and arrived at a consensus on the initial draft of competencies. They also identified health care professionals and staff essential for effective technology integration in various diabetes care settings. A survey was completed by diabetes technology experts that are members of ADCES. Next, a multidisciplinary focus group was conducted to gain feedback. Finally, the edited competencies were distributed via survey for feedback by diabetes technology experts from various disciplines.

RESULTS

One hundred four diabetes technology experts in the United States participated in the final survey, representing various health care professions and clinical settings. A final set of 94 competencies across 7 domains was determined.

CONCLUSION

Modified Delphi methodology is an effective way to utilize multidisciplinary expertise to develop diabetes technology-related competencies for diabetes care professionals and staff in a variety of settings. These competencies align with the mission of ADCES to empower diabetes care and education specialists to expand the horizons of innovative education, management, and support.