Welcome to the ATTD 2022 Interactive Program
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STENOPOOL: A SYSTEM FOR MANAGING ALL DIABETES DEVICE DATA
Abstract
Background and Aims
Insulin pumps, CGMs and glucose meters are now an integrated part of diabetes management. Most devices have their own software, and clinicians must switch between these during consultations. Our aim was to develop one platform for all diabetes device data independent of manufacturers.
Methods
Using the open source cloud-based diabetes management software from Tidepool.org we developed a stand-alone solution for all diabetes devices used at Steno Diabetes Center Copenhagen, providing service to more than 11.000 people with diabetes. The solution had to be customized to comply with GDPR regulations, hosted on an authorized server, have single sign-on and user administration using Microsoft Active Directory, detailed logging of users and allow home-based access and upload using the national login-system available for all citizens in Denmark.
Results
As of November 2021, we started using the solution in the clinic, and continue further developments including closer integration with our electronic healthcare record (Epic, USA). People using different devices are now able to upload and access their own data in the same program, which also enables closer patient-provider interaction as well as telemedicine consultations. Also, we are now able to collect continuous quality data, do clinical and real-world evidence studies and start the development of more advanced use of diabetes data such as treatment guidance and alerts using AI technology.
Conclusions
By using open-source software, it was feasible to create a single diabetes management platform for all diabetes devices data for benefit of people with diabetes, quality improvement.
REDUCTION IN DIABETES-RELATED HOSPITALIZATION RATES AFTER REAL-TIME CONTINUOUS GLUCOSE MONITOR (RTCGM) INITIATION
Abstract
Background and Aims
Inadequate glycemic control in patients with diabetes can result in diabetes-related hospitalizations. RtCGM helps with glycemic management by providing current glucose level and glucose trends. This study evaluated change in diabetes-related hospitalizations before and after rtCGM initiation.
Methods
A retrospective analysis of administrative claims data from the Optum Clinformatics® Database was conducted. CGM naïve patients with type 1 (T1D) and type 2 diabetes (T2D) initiated rtCGM (Dexcom G6) between 8/1/2018 and 3/31/2020 (index date = earliest observed pharmacy claim). Continuous health plan enrollment of 12-months pre, and 12-months post index date and ≥1 sensor pharmacy claim after index was required for study inclusion. Individuals with evidence of pregnancy were excluded. Diabetes-related ER and inpatient visits were assessed during the 12-months pre- and 12-months post-index periods and expressed as changes in number of visits and days of hospital stay.
Results
A total of 806 T1D (average age= 38.8 (sd=14.2) years, 45% female) and 337 T2D (average age= 52.6(sd=10.5) years, 46% female) rtCGM users on intensive insulin therapy met inclusion criteria. Statistically significant reductions were observed after rtCGM initiation in diabetes-related inpatient stays (T1D= -54%, p<0.001; T2D= -48%, p<0.001). RtCGM initiation resulted in reduced average length of stay (T1D= -0.39 days, p=0.01; T2D= -0.88 days, p=0.02). However, reductions in diabetes-related ER visits did not reach statistical significance (T1D= -29%, p=0.07; T2D= -15%, p=0.52).
Conclusions
These findings provide real-world evidence rtCGM was associated with reduced diabetes-related hospitalizations. Improved access to rtCGM may help more T1D and T2D patients avoid serious glycemic excursions that result in hospitalizations.
PRINCIPAL DIMENSIONS OF GLYCEMIC VARIABILITY AND QUALITY OF GLYCEMIC CONTROL IN DIABETES
Abstract
Background and Aims
Many of the available metrics to quantify glycemic variability (GV) and quality of glycemic control (QGC) derived from continuous glucose monitoring (CGM) data, are highly correlated. The aim of this work is to identify the principal uncorrelated dimensions of GV and QGC, to be considered in the assessment of diabetes management.
Methods
Six widely-used metrics were evaluated on CGM traces generated by 782 participants in 6 studies in type 1 and type 2 diabetes (T1D, T2D): mean blood glucose (MBG); percent time >180 mg/dL (T180), >250 mg/dL (T250), <70 mg/dL (T70), <54 mg/dL (T54); coefficient of variation (CV). Principal component analysis (PCA) was used to identify two principal uncorrelated dimensions of GV and QGC. These principal dimensions were first identified in a training set (550 subjects) and then fixed and validated in an independent test set (232 subjects).
Results
PCA identified two principal dimensions explaining >90% of the original variance in the testing data, irrespective of treatment modality, age range, and diabetes type. These dimensions represent exposure to hyperglycemia, or therapy efficacy, as indicated by a combination of MBG, T180, and T250 (Dimension 1), and risk for hypoglycemia, or therapy safety, as indicated by T70, T54, and CV (Dimension 2). A graphical representation of the two dimensions is shown in the figure.
Conclusions
Two uncorrelated dimensions are sufficient to characterize GV and QGC in diabetes, and to explain over 90% of the variance carried by common metrics. Thus, quantitatively, treatment optimization is reduced to a 2-dimensional problem.
ACTIVE & PASSIVE SHARING OF DIABETES DEVICE DATA TO CLINICS IS ASSOCIATED WITH REDUCED A1C AND DECREASED DKA RATES
Abstract
Background and Aims
Reviewing device data is an integral part of routine diabetes care. Streaming or uploading this data prior to in-person or telehealth clinic visits may indicate increased engagement in self-management behaviors. This study aimed to evaluate if having streaming/uploaded data at the start of a clinic visit was associated with improvements in diabetes outcomes.
Methods
Individuals with T1D, aged < 23 years, who received care from a single network of pediatric diabetes clinics in the Midwest USA from 3/2020 to 11/2021 were included. Uploading prior to the start of a clinic visit or having streaming data from at least one diabetes device defined the “connected” group. Sharing classification was recorded by CDE as part of routine visit documentation.
Results
Observations from 2116 unique individuals living with T1D were included in the analysis. Of which, 1063 had shared data at the time of their visit (50.2%). Mean A1c was statistically lower in those who were actively or passive sharing data (8.3% vs 9.3%, p < 0.001). Mean episodes of DKA were also lower (0.16 episodes/patient vs 0.05 episodes/patient, p < 0.05).
Conclusions
Passively or actively sharing data for clinic visits may be considered an adjunct measure of engagement in self-management. Our data suggest that an association exists between sharing data and decreased HbA1c and decreased incidence of DKA events. As technologies continue to advance, efforts to passively connect these data to diabetes clinics will become increasingly important.
RATES OF SENSOR DETECTED HYPOGLYCAEMIA AND PATIENT REPORTED HYPOGLYCAEMIA; PRELIMINARY DATA FROM THE HYPO-METRICS TRIAL
Abstract
Background and Aims
Many hypoglycaemic episodes detected by continuous glucose monitoring (CGM) are asymptomatic. The HypoMETRICS study aims to understand the impact of symptomatic and asymptomatic sensor-detected hypoglycaemia (SDH). We report preliminary study data on rates of SDH and patient-reported hypoglycaemia (PRH).
Methods
We recruited people with insulin-treated diabetes who had experienced >1 hypoglycaemic episode in the last month and were hypoglycaemia aware by Gold score. Participants continued their usual method of glucose monitoring, with blinded CGM and recorded episodes of PRH in real-time through a purpose-built smartphone app for 10 weeks. PRH was defined as symptomatic episodes that resolved on carbohydrate ingestion, or a self-measured glucose <4 mmol/l (72mg/dl).
Results
The present analysis includes 105 participants (81 type 1 diabetes, 24 type 2 diabetes), mean (SD) age 49.1(15.9) years, diabetes duration 20.8(13.3) years, 63 using Flash and 4 using CGM. Mean time in range was 60(14.4) %, with time below 3.9mmol (70mg/dl) 4.7(3.9) %; time below 3mmol(54mg/dl) at 1.1(1.5) %. There were 7132 and 1931 level 1 and level 2 hypoglycaemic episodes with a mean rate 6.8(4.1) and 1.8(1.8) episodes/week respectively. Prolonged hypoglycaemia (below 3mmol for >2hours) accounted for 8% of level 2 hypoglycaemia, with 0.2 (0.4) episodes/week. Participants recorded 3,967 PRHs at 3.8(3.1) episodes/week.
Conclusions
As rates of SDH at 3.9mmol were 80% higher than PRH, this would suggest significant asymptomtic hypoglycaemia, even in people with hypoawareness intact. Using sensor data alone to judge awareness should be done with caution.
TYPE 2 DIABETES IMPAIRS ANTIVIRAL IMMUNITY BY PREVENTING THE INDUCTION OF FASTING METABOLISM
Abstract
Background and Aims
Type 2 diabetes (T2D) is a major risk factor for developing severe infectious disease, such as COVID-19. The endocrine and immune system closely interact following viral infection, which is deregulated in T2D. Previously, we showed in humans and mice that viral infection causes transient insulin resistance, which can lead to permanent loss of glycemic control in subjects with pre-diabetes. How changes in systemic glycemia benefit the antiviral response, and how this derails in T2D is mostly unknown.
Methods
Mice were infected with virulent strains of cytomegalovirus or lymphocytic choriomeningitis virus. Glucose-, insulin- and pyruvate-tolerance tests and hyperinsulinemic euglycemic clamping were used to determine the metabolic state of animals. Conditional knock-out models were used to measure the impact of cytokines on metabolism of specific organs. Diet-induced obesity models were used to determine the impact of hyperglycemia on the antiviral response.
Results
Severe viral infection causes pancreatic β-cell hyperfunctionality following their stimulation with the cytokine IFNγ by local T cells. Virus-induced hyperinsulinemia impaired glucose release by the liver and promoted induction of fasting metabolism, because of reduced hepatic glycogenolysis, causing relative, transient hypoglycemia (RHG). RHG was beneficial to the antiviral response by promoting the release of antiviral cytokines by endothelial cells, which impaired viral replication. Obese mice failed to induce fastng metabolsim, resulting in lower antiviral cytokines, higher viral titers and increased pathology.
Conclusions
Metabolic adaptations following infection are of major importance for optimal control of viral replication. In context of T2D, these changes cannot be accomplished, thus leading to more frequent and severe infections.
THE USE OF FLASH GLUCOSE MONITORING REDUCES THE RISK OF HYPOGLYCEMIA IN PEOPLE WITH DIABETES ON MAINTENANCE HEMODIALYSIS
Abstract
Background and Aims
Few prospective studies have examined the clinical accuracy of flash glucose monitoring (FGM) in people with diabetes (DM) on maintenance hemodialysis (HD). Furthermore, in these patients data on the impact of this technology on glycemic control are lacking.
Methods
A 12-week monocentric, pilot study was conducted in 13 DM subjects on HD (11 males; mean age 64±12.6 years; dialysis vintage 2.9±1.4 years). FGM (Freestyle Libre, Abbott) was applied and main traditional glycemic markers (HbA1c and fructosamine) and FGM-derived metrics were evaluated during the study. Paired SMBG-FGM glucose values were analyzed to calculate mean absolute relative difference (MARD).
Results
Overall, the median MARD was 19.2% (IQR, 9.9-29.9). After 12 weeks, a reduction in time below range (TBR) 54-69 mg/dl [2.5% (IQR, 0.2-4.0) vs. 4% (IQR, 1.0-6.5)] and TBR <54 mg/dl [ 0% (IQR, 0-7) vs. 1% (IQR, 0-2)] was observed (Fig.1). The number of hypoglycemic events also improved, from 6 (IQR, 1.5-9.5) to 2.5 (2.0-6.5) events/day after 10 weeks. Conversely, at the end of follow-up, time in range (TIR) [65 (IQR, 45.5-83.5) vs. 65% (IQR, 54-77)], TAR [(23 (11.5-29.5) vs. 22 (10.5-30.5)%)], HbA1c, and fructosamine were not significantly different compared to baseline. In ROC curve analysis, TIR (AUC=0.686;P=0.011) was a better predictor of glucose variability (coefficient of variation >36%) than HbA1c (AUC=0.592; P=0.372).
Conclusions
FGM is a clinically acceptable tool to assess glycemic control in DM on HD. Moreover, it is effective in reducing the time spent in hypoglycemia in this particular population.