Reduction In Hypoglycemia Research Articles

A Real-Time Continuous Glucose Monitoring-Based Algorithm to Trigger Hypotreatments to Prevent/Mitigate Hypoglycemic Events.

Background: The standard treatment for hypoglycemia recommended by the American Diabetes Association (ADA) suggests patients with diabetes to take small amounts of carbohydrates, the so-called hypotreatments (HTs), as soon as blood glucose concentration goes below 70 mg/dL. However, prevention, or at least mitigation, of hypoglycemic events could be achieved by triggering HTs ahead of time thanks to the use of the predictive capabilities of suitable real-time algorithms fed by continuous glucose monitoring (CGM) sensor data. Materials and Methods: The algorithm proposed in this article to trigger HTs for preventing forthcoming hypoglycemic events is based on the computation of the "dynamic risk", there is a nonlinear function combining current glycemia with its rate-of-change, both provided by CGM. A comparison of performance of the proposed algorithm against the ADA guidelines is made, in silico, on datasets of 100 virtual patients undergoing a single-meal experiment, with induced postmeal hypoglycemia, generated by the UVA/Padova type 1 diabetes simulator. Results: On noise-free CGM data, the proposed algorithm reduces the time spent in hypoglycemia, on median [25th-75th percentiles] from 36 [29-43] to 0 [0-11] min (P < 0.0001), with a concomitant decrease of the post-treatment rebound (PTR) in glucose concentration, on median [25th-75th percentiles] from 136 [121-148] to 121 [116-127] mg/dL (P < 0.0001). On noisy CGM data, there is still a reduction of both time spent in hypoglycemia from 41 [28-49] min to 25 [0-41] min (P < 0.0001) and PTR from 174 [146-189] mg/dL to 137 [123-151] mg/dL (P < 0.0001). Conclusions: The potentiality of the new algorithm in generating preventive HTs, which can allow significant reduction of hypoglycemia without concomitant increase of hyperglycemia, suggests its further development and test in silico, for example, simulating both insulin pump and multiple-daily-injection therapies.

Flash Glucose Monitoring in Reduction of Hypoglycaemia in Diabetic Patients With Chronic Kidney Disease

Flash Glucose Monitoring in Reduction of Hypoglycaemia in Diabetic Patients With Chronic Kidney Disease

Minimizing postprandial hypoglycemia in Type 1 diabetes patients using multiple insulin injections and capillary blood glucose self-monitoring with machine learning techniques

Background: Diabetic patients treated with intensive insulin therapies require a tight glycemic control and may benefit from advanced tools to predict blood glucose (BG) concentration levels and hypo/hyperglycemia events. Prediction systems using machine learning techniques have mainly focused on applications for sensor augmented pump (SAP) therapy. In contrast, insulin bolus calculators that rely on BG prediction for multiple daily insulin (MDI) injections for patients under self-monitoring blood glucose (SMBG) are scarce because of insufficient data sources and limited prediction capability of forecasting models.Methods: We trained individualized models that can predict postprandial hypoglycemia via different machine learning algorithms using retrospective data from 10 real patients. In addition, we designed and tested a hypoglycemia reduction strategy for a similar in silico population. The system generates a bolus reduction suggestion as the scaled weighted sum of the predictions. We evaluated the general and postprandial glycemic outcomes of the in silico population to assess the systems capability of avoiding hypoglycemias.Results: The median [IQR] sensitivity and specificity for hypoglycemia cases where the BG level was below 70 mg/dL were 0.49 [0.2–0.5] and 0.74 [0.7–0.9], respectively. For hypoglycemia cases where the BG level was below 54 mg/dL, the median [IQR] sensitivity and specificity were 0.51 [0.4–0.6] and 0.74 [0.7–0.8], respectively.Conclusions: The results indicated a decrease of 37% in the median number of postprandial hypoglycemias median decrease of 44% for hypoglycemias of 70 mg/dL and 54 mg/dL, respectively. This dramatic reduction makes this method a good candidate to be integrated into any Decision Support System for diabetes management.

Adaptive tuning of basal and bolus insulin to reduce postprandial hypoglycemia in a hybrid artificial pancreas

ObjectiveWe introduce an adaptive learning algorithm to better adjust postprandial basal and pre-meal bolus insulin for reducing postprandial hypoglycemia in a hybrid artificial pancreas (AP). An AP uses a control algorithm and sensed glucose to automate the delivery of insulin to people with type 1 diabetes (T1D). A hybrid AP requires the person to dose insulin in advance of a meal. Insulin sensitivity is dynamic in people with T1D, making it challenging for an AP to maintain euglycemia. Adaptive approaches to meal dosing can help prevent postprandial hypoglycemia. MethodsAn adaptive learning postprandial hypoglycemia-prevention algorithm (ALPHA) is introduced. One implementation of ALPHA adjusts the rate of postprandial insulin (ALPHA-BR) proportionally in response to prior postprandial episodes. This is achieved by an adaptive aggressiveness factor applied to postprandial basal insulin. The second implementation adaptively updates the pre-meal bolus insulin by changing the insulin-to-carbohydrate ratio (ALPHA-ICR), also proportionally in response to prior postprandial hypoglycemia. Both implementations were evaluated within an AP on an in-silico T1D virtual population of 99 subjects with circadian insulin sensitivity variations and 30% errors on meal estimations. Twenty real-world 4-day meal scenarios were given and glycemic outcomes were compared with an AP with no adaptation. ResultsOut of the 99 in-silico subjects, 23 of them experienced postprandial hypoglycemia leading to greater than 1% overall time in hypoglycemia. Of these 23 subjects, we evaluated the benefit of using ALPHA-BR and ALPHA-ICR to prevent postprandial hypoglycemia. ALPHA-BR yielded substantially fewer percent time in hypoglycemia compared to AP (0.54% vs 1.92%, p < 0.001) and fewer rescue carbs per day (0.36 vs. 1.29, p < 0.001). For the control algorithm evaluated, it yielded an average aggressiveness factor of 0.72 for reducing postprandial basal insulin. ALPHA-ICR slightly reduced time in hypoglycemia compared to AP (1.77% vs. 1.92%, p = 0.09). ConclusionIncorporating adaptive meal dosing into an AP can help reduce postprandial hypoglycemia, and the reduction is primarily due to changes in postprandial insulin delivery rather than pre-meal bolus. SignificanceAdapting postprandial insulin can lead to substantial reduction in postprandial hypoglycemia and the adaptive algorithm presented can be used both to tune an algorithm prior to a study and to adapt to individuals during real-time usage.

Long-Term Effectiveness of Continuous Subcutaneous Insulin Infusion in the Prevention of Hypoglycemia in Adults with Type 1 Diabetes

Background: Reducing hyperglycemia while avoiding hypoglycemia is the key clinical goal in managing people with type 1 diabetes. Insulin delivery techniques and regimens are constantly evolving to achieve these goals. At present, use of multiple daily injections (MDI) is the standard of care, but there is increasing interest in continuous subcutaneous insulin infusion (CSII). There is a deficit of studies comparing long-term glycemic control and hypoglycemia outcomes between these therapeutic options. Methods: This was a single-center, retrospective cohort study of adults with type 1 diabetes. Data were derived from electronic medical records and included demographic and clinical factors. Participants had all undergone intensive diabetes education, followed by CSII or continued MDI. The primary outcome was difference in hypoglycemia, defined as the percentage of self-monitoring blood glucose levels less than 3.9 mmol/L. Up to 10 years of follow-up data were available, between 2000 and 2016. Results: There were 69 participants using CSII and 78 using MDI. Self-monitoring blood glucose data showed significantly less hypoglycemia with CSII by over 30%, occurring as early as the first year and sustained throughout the follow-up period (P < 0.001). This benefit of CSII on reducing hypoglycemia was independent of more frequent hypoglycemia and higher body weight at baseline, factors that were also independently associated with reduced hypoglycemia. Conclusions: In selected adults with type 1 diabetes, long-term CSII can provide long-term clinically relevant and sustained reductions in hypoglycemia, particularly in those with greater initial risk of hypoglycemia and higher body weight, and improved glycemic control compared with MDI.

391-P: Hypoglycemia Reductions with the Dexcom G6 CGM System’s Predictive Alert

Background: The Dexcom G6 continuous glucose monitoring (CGM) system differs from Dexcom’s earlier G5 system in that G6 includes a predictive “Urgent Low Soon” (ULS) alert that notifies users when an estimated glucose value (EGV) ≤55 mg/dL is predicted in the next 20 minutes. The effect of the ULS alert on the distribution of EGVs was evaluated in the context of traditional threshold alerts. Methods: We identified individuals who had used G5 and transitioned to G6 between 5/1/2018 and 10/31/2018. Evaluable data were from a subset of 4083 users who had maintained low threshold alerts at either 70 or 80 mg/dL, who used a smartphone to view and upload ≥30 days of data from each system, and who maintained the G6 ULS alert in its default (enabled) state. Results: Use of the 80 mg/dL low threshold alert was associated with higher mean EGVs, less hypoglycemia, and more hyperglycemia than use of the 70 mg/dL low threshold alert (Table). Over 97% of the G6 systems were used with the ULS feature enabled, and the ULS alert was activated less than once daily. At either low threshold setting, the transition to G6 was associated with reductions in hypoglycemia (&amp;lt;55 and &amp;lt;70 mg/dL) and severe hyperglycemia (&amp;gt;250 mg/dL). Conclusions: When used in the context of traditional low threshold alerts, the predictive ULS alert may enable timely and appropriate interventions that contribute to further reductions in hypoglycemia among CGM-experienced users. Disclosure M. Derdzinski: Employee; Self; Dexcom, Inc. J. Welsh: Employee; Self; Dexcom, Inc. S. Puhr: None. T.C. Walker: Employee; Self; Dexcom, Inc. A. Parker: Employee; Self; Dexcom, Inc. A. Jimenez: Employee; Self; Dexcom, Inc.

921-P: Managing Trend Arrows with FGM: Effects on Glycemic Control in Type 1 Diabetic (T1DM) Subjects

Background: The use of flash glucose monitoring (FGM) has expanded widely among diabetic patients. Large randomized controlled trials demonstrated that use of FGM resulted in significant reductions in hypoglycemia, increased time in target range, reduced glycemic variability, and greater patient satisfaction compared with finger stick but no significant effects on glycemic control. Nonetheless, there is sparse guidance for what actions individuals should take based on the trend arrow data. Aim of the Study: To verify the impact of published recommendation (Bianchi C., Aragona M. J Diabetes Complications. 2018 Oct 24) on glycemic control in type 1 diabetic subjects (T1DM). Patients and Methods: Since January 2018, 145 T1DM patients initiated FGM, after a period of education on the system and how to adjust insulin doses based on trend arrow (Trained Group - TG) and compared to a matched group of 204 patients who started FMG before recommendations were elaborated and therefore not being trained (Not Trained Group - NTG). The two groups showed no significant difference in terms of age, sex, duration of disease, type of treatment, BMI and insulin requirements. Results: In TG HbA1c at baseline was 59±14 mmol/mol to decline 55±12 mmol/mol at month 6 (p&amp;lt;0.00001). In NTG HbA1c at baseline was 58±14 mmol/mol with minimal though statistically significant reduction after 6 months (56±12 mmol/mol; p&amp;lt;0.04). In both groups there was no change in BMI and Insulin Requirements. Conclusions and Comments: In our clinical practice, FGM implementation was associated with a small but significant reduction in HbA1c, particularly in trained patients. Prospective and larger studies are needed to validate international guidelines for the education of diabetic patients adopting these new technologies. Disclosure C. Rodia: None. C. Bianchi: None. A. Bertolotto: Speaker's Bureau; Self; Abbott, Lilly Diabetes. R. Giannarelli: None. F. Campi: None. S. Gennai: None. S. Del Prato: Advisory Panel; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company, GlaxoSmithKline plc., Merck Sharp &amp; Dohme Corp., Novartis Pharmaceuticals Corporation, Novo Nordisk A/S, Sanofi, Servier, Takeda Pharmaceutical Company Limited. Board Member; Self; AstraZeneca. Research Support; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc. Speaker's Bureau; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Takeda Pharmaceutical Company Limited. M. Aragona: None.

259-LB: Stable Graft Function and Glycemic Control after Clinical Islet Transplantation on the Omentum

We have shown safety and efficacy of islet transplantation (ITx) in the omentum using a biological scaffold in 3 subjects with type 1 diabetes (T1D). Two subjects (#1 and #3) achieved primary outcome at 1 year (A1c ≤6.5%; No severe hypoglycemia). Subject 1 developed progressive decline in C-peptide after 15 months likely attributed to change in immunosuppression. Subject 3 has had stable graft function for over 2 years. We report on 18 month follow-up data for subject 3. A 46 y/o woman with T1D of 26 years duration underwent single ITx on the omentum (12,648 IEQ/Kg) using a biologic scaffold under ATG induction and combination mycophenolate sodium and tacrolimus maintenance. Pre-ITx insulin dose was 0.45 units/Kg/day and A1c 6.3%. At 18 months, 90 min C-peptide was 1.49 ng/mL, A1c 5.7%, and insulin dose 0.18 units/Kg/day; 14-day continuous glucose monitoring (Guardian Sensor 3, Medtronic) showed glucose 109±28mg/dL (mean±SD), 92% time in glucose range 70-180 mg/dL, 6% time in &amp;lt;70mg/dL, and 0% time in &amp;lt;54 mg/dL. Mixed meal tolerance test data is shown in Table 1. Decline in BETA-2 score was observed by 1 year followed by stabilization. ITx resulted in stable graft function, excellent glycemic control, minimal glycemic variability, and reduction in hypoglycemia over 2 years. Strategies to improve oxygen delivery and neo-vascularization and minimize immunosuppression are needed to improve long-term outcomes at this site. Disclosure D. Baidal: None. D.M. Berman: None. C. Ricordi: Advisory Panel; Self; Zone Labs. A.M. Alvarez Gil: None. N. Padilla: None. G. Ciancio: None. E. Linetsky: None. R. Alejandro: None. Funding JDRF; The Leona M. and Harry B. Helmsley Charitable Trust; Diabetes Research Institute Foundation, State of Florida; University of Miami Clinical and Translational Science Institute; Sanofi Genzyme

Flash Glucose Monitoring Accepted in Daily Life of Children and Adolescents with Type 1 Diabetes and Reduction of Severe Hypoglycemia in Real-Life Use.

Background: Flash glucose monitoring (FGM) is covered by the Belgian public health insurance for type 1 diabetes since 2016. The objective of this study was to describe the use of FGM and diabetes outcomes in type 1 diabetic children and adolescents 1 year after reimbursement. Methods: All patients had the choice to convert to FGM or to continue with self-monitoring of blood glucose (SMBG). Clinical data were collected at baseline, at the next visit, and after 12 months; glucose profiles at next visit and after 12 months. Regression analyses were performed to identify predictors of FGM acceptance and changes in metabolic control. Results: A total of 334 subjects were included, of whom 278 (83.2%) switched to FGM. They were younger (13.6 vs. 15.2 years; P = 0.012) and performed more SMBG testing at baseline than patients who did not switch (4.3 vs. 4.1 tests daily; P = 0.008). At the end of follow-up, the rate of severe hypoglycemia decreased by 53% in the group of FGM users (P = 0.012) while it remained stable in SMBG users. Median glycated hemoglobin did not change significantly in both groups. Among subjects who switched to FGM, 15.8% reverted to SMBG after a median use of 5.3 months. Adverse events, diabetes duration, and FGM utilization were independent predictors of the risk for reverting. FGM-related adverse events were associated with a fivefold increased risk to revert to SMBG (hazard ratio = 5.12; P < 0.0001). Conclusions: FGM is relatively well accepted and decreases the risk of severe hypoglycemic events in our pediatric population. FGM is more often discontinued in patients experiencing adverse events and with longer diabetes duration.

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

Frequent real-time continuous glucose monitoring (rtCGM) data viewing has been associated with reduced mean glucose and frequent scanning of an intermittently scanned continuous glucose monitoring (isCGM) system has been associated with reduced hypoglycemia for patients with diabetes. However, requiring patients to frequently interact with their glucose monitoring devices to detect actual or impending hypoglycemia is burdensome. We hypothesized that a predictive low glucose alert, which forecasts glucose ≤55 mg/dL within 20 minutes and is included in a new rtCGM system, could mitigate hypoglycemia without requiring frequent device interaction. We analyzed estimated glucose values (EGVs) from an anonymized convenience sample of 15,000 patients who used Dexcom G6 (Dexcom, Inc, San Diego, CA, USA) and its mobile app for at least 30 days with or without the "Urgent Low Soon" alert (ULS) enabled. Screen view frequency was determined as the frequency with which the trend screen was accessed on the app. Multiple screen views within any 5-minute interval were counted as one. Hypoglycemia exposure for patients in the top and bottom quartiles of screen view frequency (>8.25 and <3.30 per day, respectively) was calculated as the percentage of EGVs below various thresholds. Over 93% of users enabled the ULS alert; its use was associated with significantly reduced hypoglycemia <55 and <70 mg/dL, independent of screen view frequency. Use of the G6 ULS alert may disencumber rtCGM users by promoting significant reductions in hypoglycemia without requiring frequent device interactions.

Are newer insulins always the better option?

Since its discovery almost a century ago, there have been numerous advancements in the formulations of insulin. The newer insulin analogs have structural modifications with the goal of altering pharmacokinetics to achieve either quick onset and offset of action (mealtime bolus analogs), or a prolonged steady action (basal analogs). These analogs offer many advantages over older human insulins but are several-fold more expensive. The aim of this review is to evaluate reasons for the exorbitant price of the newer insulins, to examine the evidence regarding their clinical advantages and to make value-based prescribing recommendations. The higher cost of newer insulins cannot be justified based on drug development or manufacturing costs. Compared with older insulins, newer analogs do not offer significant advantage in achieving hemoglobin A1c targets, but they reduce risk of hypoglycemia. The reductions in hypoglycemia are relatively modest and most apparent in those with type 1 diabetes, possibly because these individuals are more prone to hypoglycemia. When cost considerations are important, the older insulins (regular and NPH insulin) can be used safely and effectively for most individuals with type 2 diabetes who have a low risk of hypoglycemia.

Detection and Control of Unannounced Exercise in the Artificial Pancreas Without Additional Physiological Signals

The purpose of this study was to develop an algorithm that detects aerobic exercise and triggers disturbance rejection actions to prevent exercise-induced hypoglycemia. This approach can provide a solution to poor glycemic control during and after aerobic exercise, a major hindrance in the participation of exercise by patients with type 1 diabetes. This novel exercise-induced hypoglycemia reduction algorithm (EHRA) detects exercise using a threshold on a disturbance term, a parameter estimated from an augmented minimal model using an unscented Kalman filter. After detection, the EHRA triggers the following three actions: First, a carbohydrate suggestion, second, a reduction in basal insulin and the insulin-on-board maximum limit, and finally, a 30% reduction of the next insulin meal bolus. The EHRA was tested in silico using a 15-day scenario with 8 exercise sessions of 50min at [Formula: see text] on alternating days. The EHRA was able to obtain improved results when compared to strategies with and without exercise announcement. The unannounced, announced, and EHRA strategies all obtained an overall percentage of time in range (70-180 mg/dl) of 94% and a percentage of time 70mg/dl of 2%, 0%, and 0%, respectively. The EHRA was tested for robustness during exercise sessions of +25% and -25% intensity and results suggest that the EHRA is able to account for variability in exercise intensity, duration, and patient dynamics such as glucose uptake rate and insulin sensitivity.

Sydney Diabetes centre's experience of the Australian Government's roll out of subsidised continuous glucose monitoring for children with type 1 diabetes mellitus.

To determine patient/carer expectations of continuous glucose monitoring (CGM) and short-term satisfaction, to assess the efficacy of CGM in improving: fear of hypoglycaemia and glycaemic control (HbA1c , ketosis, hypoglycaemia) and to determine time requirements of diabetes clinic staff in commencing and administering CGM. We assessed CGM-naïve patients starting on CGM at a Sydney Diabetes Centre following the introduction of a nationwide government subsidy for CGM. A standardised questionnaire was administered collecting demographic and glycaemic information in addition to Likert scale assessment of expectations and satisfaction. Clinic staff reported time dedicated to CGM education, commencement and follow-up. A total of 55 patients or parents/carers completed baseline questionnaires, with 37 completing a 3-month follow-up questionnaire. There were high expectations of CGM prior to commencement and high satisfaction ratings on follow-up. CGM improved fear of hypoglycaemia, and total daily insulin dose increased after commencement of CGM. There was a trend towards lower HbA1c that was not statistically significant and no statistically significant reduction in ketosis or hypoglycaemia. Comments were mostly positive, with some concern raised regarding technical issues and a lack of subsidy after 21 years of age. Staff time requirements were substantial, with an estimated average of 7.7 h per patient per year. Patients and families have high expectations of CGM, and satisfaction levels are high in the short term. Total insulin delivery increased after CGM commencement. Time requirements by staff are substantial but are worthwhile if families' overall satisfaction levels are high.

The Rowan Hillson Inpatient Diabetes Safety Award 2017 for the best digital initiative

Introduction: The annual National Diabetes Inpatient Audit (NaDIA) in the UK continues to show a high incidence of errors in patients admitted to hospital with diabetes. It is clear that new initiatives are urgently required to address this risk.Methods: The Joint British Diabetes Societies for Inpatient Care (JBDS-IP) organised the fourth national Rowan Hillson Insulin Safety Award on the theme of the best digital initiative to improve insulin and prescribing safety in hospital.Results: The winner was Western General Hospital team (Edinburgh). The team created diabetes dashboards which helped collect high quality accurate data on capillary blood glucose from patients admitted to the hospital and deliver specific and concise messages to various wards. The team from King’s College Hospital secured runner-up prize for their initiative which enabled reduction in hypoglycaemia during treatment of hyperkalaemia by adding a personalised dose of dextrose to insulin infusion.Conclusions: These and similar innovations need to be developed, promoted and shared to improve patient safety in hospitalised patients with diabetes.

Reduction in hypoglycemia with the predictive low-glucose management system: a long-term randomized controlled trial in adolescents with T1DM

Reduction in hypoglycemia with the predictive low-glucose management system: a long-term randomized controlled trial in adolescents with T1DM

Intensive Glycemic Treatment During Type 1 Diabetes Pregnancy: A Story of (Mostly) Sweet Success!

Studies from Scotland and Canada confirm large increases in the incidence of pregnancies complicated by pregestational type 1 diabetes (T1D). With this increased antenatal workload comes more specialization and staff expertise, which may be important as diabetes technology use increases. While euglycemia remains elusive and obstetrical intervention (earlier delivery, increased operative deliveries) is increasing, there have been some notable successes in the past 5-10 years. These include a decline in the rates of congenital anomaly (Canada) and stillbirths (U.K.) and substantial reductions in both maternal hypoglycemia (both moderate and severe) across many countries. However, pregnant women with T1D still spend ∼30-45% of the time (8-11 h/day) hyperglycemic during the second and third trimesters. The duration of maternal hyperglycemia appears unchanged in routine clinical care over the past decade. This ongoing fetal exposure to maternal hyperglycemia likely explains the persistent rates of large for gestational age (LGA), neonatal hypoglycemia, and neonatal intensive care unit (NICU) admissions in T1D offspring. The Continuous Glucose Monitoring in Women With Type 1 Diabetes in Pregnancy Trial (CONCEPTT) found that pregnant women using real-time continuous glucose monitoring (CGM) spent 5% less time (1.2 h/day) hyperglycemic during the third trimester, with clinically relevant reductions in LGA, neonatal hypoglycemia, and NICU admissions. This article will review the progress in our understanding of the intensive glycemic treatment of T1D pregnancy, focusing in particular on the recent technological advances in CGM and automated insulin delivery. It suggests that even with advanced diabetes technology, optimal maternal dietary intake is needed to minimize the neonatal complications attributed to postprandial hyperglycemia.

Differential Effects of the Insulin-Only and Bihormonal Configurations of the Bionic Pancreas on Mean Glucose and Hypoglycemia during the Daytime and Nighttime

The bihormonal bionic pancreas (BP) reduced both mean CGM glucose (CGMG) and time in the hypoglycemic range relative to usual care in outpatient studies. We recently performed an outpatient study in which we compared insulin-only (IOBP) and bihormonal (BHBP) configurations of the BP with usual care (UC; conventional or sensor-augmented insulin pump therapy) both with and without remote monitoring. We previously reported that monitoring did not affect the mean CGMG or hypoglycemia with either configuration of the BP. The current analysis focuses on the data collected from arms without remote monitoring (how the BP is eventually meant to be used) to determine how the effects of the BP differ between the day and night. As shown in the Table, both the IOBP (with glucose target of 110 mg/dl) and BHBP (with glucose target of 100 mg/dl) significantly reduced mean CGMG vs. UC during both the daytime and nighttime. In contrast, only the BHBP significantly reduced the percent of time spent with CGMG &amp;lt;60 mg/dl vs. UC. The BHBP similarly reduced percent of time &amp;lt;60 mg/dl vs. IOBP. The significant reductions in hypogycemia associated with the BHBP were driven primarily by reductions in hypoglycemia at night. Disclosure J. Sherwood: None. C.A. Balliro: None. R.Z. Jafri: None. F. El-Khatib: Stock/Shareholder; Self; Beta Bionics. Employee; Self; Beta Bionics. M. Maheno: None. M.A. Hillard: None. A.J. O'Donovan: None. R. Selagamsetty: None. H. Zheng: None. E. Damiano: Other Relationship; Self; Beta Bionics. S.J. Russell: Other Relationship; Self; Beta Bionics, Novo Nordisk Inc.. Advisory Panel; Self; Companion Medical, Tandem Diabetes Care, Inc., Unomedical a/s. Research Support; Self; Beta Bionics, Zealand Pharma A/S, MITRE Corporation.

Cost Calculation and Adherence to ADA Recommendations Based on a Flash Continuous Glucose Monitoring System for People with T1DM or T2DM Using MDI Therapy

Background: A novel, factory-calibrated, flash continuous glucose monitoring system (flash CGM; FreeStyle Libre™ system) was approved by FDA in September 2017. The clinical benefit of flash CGM as a replacement for routine self-monitoring of blood glucose (SMBG) for people using MDI therapy has been assessed in RCTs in T1DM (Bolinder, 2016) and T2DM (Haak,2017). In both 6-month studies, people using flash CGM achieved a substantial reduction in hypoglycemia compared with those using SMBG, without increasing HbA1c or reducing the dose of insulin. A cost calculation based on ADA recommendations is presented, comparing the acquisition cost of flash CGM with routine SMBG. Methods: The ADA Standards of Medical Care in Diabetes (2017) recommend between 6 to 10 glucose tests per day for people with diabetes who are using MDI therapy. List prices are $1.42 per strip and $36.00 per sensor. Sensor duration is up to 10 days. In the T1DM RCT, people using flash CGM also used 0.5 SMBG tests/day on average; in the T2DM RCT, people using flash CGM also used 0.3 SMBG tests/day. The calculation is based on a 30-day month. Results: Monthly cost of 10 SMBG/day is $426.00 and of 6 SMBG/day is $255.60. This is compared with the monthly sensor cost of $108.00, to which should be added the monthly cost of occasional SMBG based on utilization in the RCTs: $21.30 per person per month (PPPM) for people with T1DM and $12.78 for people with T2DM. For those testing 6 times/day, flash CGM saves over $120 PPPM compared with SMBG and it saves over $290 PPPM for people testing 10 times/day. For people testing more than 3 times/day, flash CGM has a lower acquisition cost than SMBG. Conclusion: For people with T1DM or T2DM who use MDI therapy, flash CGM has demonstrated improved adherence to the ADA recommendation as well as reduced acquisition cost when compared with routine SMBG. Disclosure R. Hellmund: Employee; Self; Abbott. Stock/Shareholder; Self; Abbott.

Impact of Continuous Glucose Monitors’ Accuracy on Their Clinical Utility—A Quantitative Assessment

Objective: Accuracy is recognized as an important attribute of continuous glucose monitors (CGMs). The objective of this research was to understand if improvements in CGM accuracy impact clinical outcomes in patients with T1DM or T2DM. Research Design and Methods: A review of literature published from January 1999 to June 2017 was conducted using Medline. Articles were included if they reported desired outcome measures of clinical efficacy (HbA1c reduction, time-in-range [TIR], hypoglycemia detection or reduction) regardless of patient diabetes type. Simple linear and multiple regressions were utilized to evaluate the relationship between mean absolute relative difference (MARD) and measures of efficacy. Results: The literature search yielded 574 results from which 20 articles met the inclusion criteria. MARD for commercialized CGM devices improved, on average, 0.75% per year (R2=0.87, p&amp;lt;0.0001). There was no significant correlation between HbA1c reduction and MARD (coefficient=0.464, R2=0, p=0.8857). A significant relationship was observed between MARD and the detection of hypoglycemic events (coefficient= -0.33, R2=0.63, p&amp;lt;0.0001). Insufficient data was available to assess the correlation between accuracy and TIR or hypoglycemia reduction. Conclusion: CGM accuracy correlates with an increased detection of hypoglycemic events, but not with HbA1c reduction. Disclosure C.J. Koziel: None. D. Bialonczyk: Employee; Self; Becton, Dickinson and Company. D. Morel: Employee; Self; Becton, Dickinson and Company. J. Petisce: None. D. Saliu: Employee; Self; Becton, Dickinson and Company.

Switching to Insulin Glargine 300 U/mL (Gla-300) Improves Glycemic Control after Failure of Basal-Bolus Therapy (BBT) with Other Basal Insulins (BI) in Patients (Pts) with Type 1 Diabetes (T1DM)

The prospective observational Toujeo-Neo study investigated effects of switching 397 adult pts with T1DM, uncontrolled on BBT (A1c 7.5-10%) with another BI to Gla-300 in daily clinical practice in Germany. Primary endpoint (EP) was duration of response (fasting plasma glucose (FPG) ≤110 mg/dL or A1c ≤ predefined individual A1c target). Secondary EPs included changes in A1c and FPG, body weight (BW) and insulin doses, hypoglycemia incidence and safety. Here we report on pts with 12 months results available (n=181). Pts baseline characteristics, efficacy and hypoglycemia EPs are shown in Table 1. Main previous BI was insulin glargine 100 U/mL (Gla-100; 35.5%). At 12 months, proportions of pts at FPG target (≤110 mg/dL) were highest in pts previously on insulin detemir (n=38; 44.7%) and NPH insulin (n=27; 36.0%), respectively. Percentages of pts at A1c target were highest in pts previously on NPH insulin (n=27; 30.8%) and Gla-100 (n=62; 27.9%), respectively. Hypoglycemia incidences did not change except for a trend in reduction of nocturnal hypoglycemia by &amp;gt;50%. BW remained stable. In conclusion, switching the BI in BBT to Gla-300 allowed inadequately controlled T1DM pts to reduce their A1c by 0.48% with a trend to lower nocturnal hypoglycemia, minor BI and moderate prandial insulin dose changes after 12 months follow-up. Disclosure A. Fritsche: Advisory Panel; Self; Sanofi-Aventis Deutschland GmbH, Novo Nordisk A/S, Eli Lilly and Company, Boehringer Ingelheim GmbH. S. Pscherer: Advisory Panel; Self; Sanofi-Aventis Deutschland GmbH. H. Anderten: Advisory Panel; Self; Sanofi-Aventis Deutschland GmbH, Merck Sharp &amp; Dohme Corp.. Speaker's Bureau; Self; Sanofi-Aventis Deutschland GmbH, Merck Sharp &amp; Dohme Corp., Eli Lilly and Company, Boehringer Ingelheim GmbH, Pfizer Inc., AstraZeneca, GlaxoSmithKline plc., Novartis AG, Novo Nordisk A/S, Takeda. K. Pegelow: Employee; Self; Sanofi-Aventis Deutschland GmbH. J. Seufert: Speaker's Bureau; Self; AstraZeneca, Berlin-Chemie AG, Boehringer Ingelheim GmbH. Research Support; Self; Boehringer Ingelheim GmbH. Advisory Panel; Self; Boehringer Ingelheim GmbH, GI Dynamics Inc.. Research Support; Self; GI Dynamics Inc., GlaxoSmithKline plc., Intarcia Therapeutics, Inc., Ipsen Biopharmaceuticals, Inc., Janssen Pharmaceuticals, Inc.. Speaker's Bureau; Self; Janssen Pharmaceuticals, Inc.. Advisory Panel; Self; Janssen Pharmaceuticals, Inc.. Speaker's Bureau; Self; Eli Lilly and Company, Merck Sharp &amp; Dohme Corp., Novartis Pharmaceuticals Corporation. Research Support; Self; Novartis Pharmaceuticals Corporation. Advisory Panel; Self; Novo Nordisk A/S. Speaker's Bureau; Self; Novo Nordisk A/S. Research Support; Self; Novo Nordisk A/S. Speaker's Bureau; Self; Sanofi-Aventis Deutschland GmbH. Advisory Panel; Self; Sanofi-Aventis Deutschland GmbH. Research Support; Self; Sanofi-Aventis Deutschland GmbH. Advisory Panel; Self; Sanofi. Research Support; Self; Ypsomed AG. M. Pfohl: Advisory Panel; Self; Eli Lilly and Company, Novo Nordisk A/S, Sanofi. Speaker's Bureau; Self; Sanofi.