Predictive Low Glucose Management Research Articles

Characteristics Associated With Elevated Time Below Range in Elderly Patients With Type 1 Diabetes Using an Automated Insulin Delivery System.

This study investigated the characteristics associated with an increased risk of hypoglycemia, in elderly patients with type 1 diabetes mellitus (T1D) using automated insulin delivery (AID) systems. Cross-sectional observational study including patients >60 years, using sensor-augmented insulin pump therapy with predictive low-glucose management (SAPT-PLGM), hybrid closed-loop (HCL), and advanced hybrid closed-loop (AHCL), for more than three months. A geriatric assessment was performed, and body composition was determined to investigate its association with achieving time below range (TBR) <70 mg/dL goals. The study included 59 patients (47.5% of men, mean age of 67.6 years, glycated hemoglobin [HbA1c] of 7.5 ± 0.6%, time in range (TIR) 77.8 ± 9.9%). Time below range <70 and <54 mg/dL were 2.2 ± 2.3% and 0.4 ± 0.81%, respectively. Patients with elevated TBR <70 mg/dL (>1%) had higher HbA1c levels, lower TIR, elevated time above range (TAR), and high glycemic variability. Regarding body composition, greater muscle mass, grip strength, and visceral fat were associated with a lower TBR <70 mg/dL. These factors were independent of the type of technology used, but TIR was higher when using AHCL systems compared with SAPT-PLGM and HCL systems. In elderly patients treated with AID systems with good functional status, lower lean mass, lower grip strength, and lower visceral fat percentage were associated with TBR greater than 1%, regardless of the device used. A similar finding along was found with CGM indicators such as higher HbA1c levels, lower TIR, higher TAR, and higher CV. Geriatric assessment is crucial for personalizing patient management.

Comparison of the Sensor-Augmented Pump System with the Advanced Hybrid Closed-Loop Delivery System: Quality of Life, Diabetes Distress, and Glycaemic Outcomes in a Real-Life Context.

Type 1 diabetes mellitus (T1D) is a chronic disease that requires exogenous insulin administration and intensive management to prevent any complications. Recent innovations in T1D management technologies include the Advanced Hybrid Closed-Loop delivery system (AHCL). The pioneer AHCL system provides automated basal and automated bolus corrections when needed. This study aimed to compare the Advanced Hybrid Closed-Loop (AHCL) system and the Sensor-Augmented Pump (SAP) with Predictive Low Glucose Management (PLGM) system, in relation to glycaemic outcomes, general and diabetes-related Quality of Life (QoL), and diabetes distress. General and diabetes-related QoL were assessed with the Diabetes Quality of Life Brief Clinical Inventory (DQOL-BCI) and the World Health Organization Quality of Life-BREF (WHOQOL-BREF), respectively. Diabetes distress was assessed with the Diabetes Distress Scale for Type 1 diabetes (T1-DDS). Eighty-nine T1D adults participated in the study, mostly females (65.2%), with a mean age of 39.8 (± 11.5 years). They had on average 23 years of diabetes (± 10.7) and they were on continuous subcutaneous insulin infusion therapy. Significant differences favoring the AHCL over the SAP + PLGM system were demonstrated by lower mean glucose levels, less time above range, lower scores on DQOL-BCI, T1-DDS, and higher scores on WHOQOL-BREF. Finally, the linear regression models revealed the association of time in range in most of the above aspects. This study highlighted the advantages of the AHCL system over the SAP + PLGM system in the real-world setting in relation to general and diabetes-related QoL, diabetes distress, and glycaemic outcomes.

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

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). 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. 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. 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.

Safety and glycemic outcomes during the MiniMed™ advanced hybrid closed-loop (AHCL) system pivotal trial in children and adolescents with type 1 diabetes.

During MiniMed™ advanced hybrid closed-loop (AHCL) use by adolescents and adults in the pivotal trial, A1C was significantly reduced, time in range (TIR) was significantly increased, and there were no episodes of severe hypoglycemia or diabetic ketoacidosis (DKA). The present study investigated the same primary safety and effectiveness endpoints during AHCL use by a younger cohort with T1D. An intention to treat population (N=160, aged 7-17 years) with T1D was enrolled in a single-arm study at 13 investigational centers. There was a baseline run-in (~25 days) using HCL or sensor-augmented pump with/without predictive low glucose management, followed by a three-month study period with AHCL activated at two glucose targets (100mg/dL and 120mg/dL) for ~45 days each. The mean±SD of A1C, TIR, mean sensor glucose (SG), coefficient of variation (CV) of SG, time at SG ranges, and insulin delivered between baseline or run-in and study were analyzed (Wilcoxon signed-rank test or t-test). Compared to baseline, AHCL use was associated with reduced A1C from 7.9±0.9% (N=160) to 7.4±0.7% (N=136) (p<0.001) and overall TIR increased from the run-in 59.4±11.8% to 70.3±6.5% by end of study (p<0.001), without change in CV, TBR <70mg/dL or TBR <54mg/dL. Relative to longer active insulin time (AIT) settings (N=52), an AIT of 2 hours (N=10) with the 100mg/dL glucose target (GT) increased mean TIR to 73.4%, reduced TBR <70mg/dL from 3.5% to 2.2% and reduced TAR >180mg/dL from 28.7% to 24.4%. During AHCL use, there was no severe hypoglycemia or DKA. In children and adolescents with T1D, MiniMed™ AHCL system use was safe, A1C was lower, and TIR was increased. The lowest GT and shortest AIT was associated with the highest TIR and lowest TBR and TAR, all of which met recommended glycemic targets.

IoMT-Enabled Real-Time Blood Glucose Prediction With Deep Learning and Edge Computing

Blood glucose (BG) prediction is essential to the success of glycemic control in type 1 diabetes (T1D) management. Empowered by the recent development of the Internet of Medical Things (IoMT), continuous glucose monitoring (CGM) and deep learning technologies have been demonstrated to achieve the state of the art in BG prediction. However, it is challenging to implement such algorithms in actual clinical settings to provide persistent decision support due to the high demand for computational resources, while smartphone-based implementations are limited by short battery life and require users to carry the device. In this work, we propose a new deep learning model using an attention-based evidential recurrent neural network and design an IoMT-enabled wearable device to implement the embedded model, which comprises a low-cost and low-power system on a chip to perform Bluetooth connectivity and edge computing for real-time BG prediction and predictive hypoglycemia detection. In addition, we developed a smartphone app to visualize BG trajectories and predictions, and desktop and cloud platforms to backup data and fine-tune models. The embedded model was evaluated on three clinical data sets including 47 T1D subjects. The proposed model achieved superior performance of root mean square error (RMSE), mean absolute error, and glucose-specific RMSE, and obtained the best accuracy for hypoglycemia detection when compared with a group of machine learning baseline methods. Moreover, we performed hardware-in-the-loop in silico trials with ten virtual T1D adults to test the whole IoMT system with predictive low-glucose management, which significantly reduced hypoglycemia and improved BG control.

Insulin pump therapy and glucose control during Ramadan fasting in an adolescent with type 1 diabetes: from an open-loop sensor-augmented pump therapy with predictive low-glucose management to an advanced hybrid closed-loop system.

Insulin pump therapy and glucose control during Ramadan fasting in an adolescent with type 1 diabetes: from an open-loop sensor-augmented pump therapy with predictive low-glucose management to an advanced hybrid closed-loop system.

Long-term efficacy of sensor-augmented pump therapy (Minimed 640G system) combined with a telemedicine follow-up in patients with type 1 diabetes: A real life study

ObjectiveEvaluate the efficacy of a new modality of insulin therapy associating both the sensor-augmented pump therapy with predictive low-glucose management (SAP-PLGM) and a telemedicine follow-up in patients with Type 1 diabetes (T1D) in a real-life setting. MethodsT1D adults under Minimed 640G system with a telemedicine follow-up for glucose management were included in a retrospective study. The primary endpoint was HbA1c while continuous glucose monitoring parameters (CGM) and treatment compliance were the secondary endpoints. These parameters were analyzed according to the therapeutic indication, HbA1c ≥ 8 % (GroupA) or severe hypoglycemic events (GroupB) and in patients switched to SAP-PLGM therapy. Results62patients were analyzed with a 28 ± 12 months of follow-up. In GroupA, HbA1c decreased from 8.3 ± 0.4 % to 7.7 ± 0.7 % (p < 0.05) and to 7.9 ± 0.3 % (p < 0.05) after 2 and 3 years, respectively. In patients switched to SAP-PLGM therapy, HbA1c decreased from 7.7 ± 0.7 % to 7.2 ± 0.8 % (p < 0.05) at 2 years. After 6 months, the time-below-range (<70 mg/dL) decreased from 2.1 % [0.6–4] to 1.1 % [0.3–2.6] (p < 0.05). Severe hypoglycemic events decreased from 1.62 to 0.5events/patient/year in GroupB (p < 0.05). At 3 years, treatment compliance was 92 % [70–97] in the total population. ConclusionsLong-term real-life treatment with the SAP-PLGM therapy combined with telemedicine was associated with improved glycemic control in T1D, along with high treatment compliance.

In-home use of a hybrid closed loop achieves time-in-range targets in preschoolers and school children: Results from a randomized, controlled, crossover trial.

To obtain additional information on the incremental differences between using a sensor-augmented pump (SAP) without automated insulin delivery (AID), using it with predictive low-glucose management (PLGM) or as hybrid closed loop (HCL), in preschool and school children. We conducted a monocentric, randomized, controlled, two-phase crossover study in 38 children aged 2-6 and 7-14 years. The primary endpoint was the percentage of time in range (TIR) of 70-180 mg/dl. Other continuous glucose sensor metrics, HbA1c, patient-related outcomes (DISABKIDS questionnaire, Fear of Hypoglycaemia Survey) and safety events were also assessed. Results from 2 weeks of SAP, 8 weeks of PLGM and 8 weeks of HCL were compared using a paired t-test or Wilcoxon signed-rank test. Overall, we found a high rate of TIR target (>70%) achievement with HCL in preschool (88%) and school children (50%), with average times in Auto Mode of 93% and 87%, respectively. Preschool children achieved a mean TIR of 73% ± 6% (+8% vs. SAP, +6% vs. PLGM) and school children 69% ± 8% (+15% vs. SAP and + 14% vs. PLGM). Overall, HbA1c improved from 7.4% ± 0.9% to 6.9% ± 0.5% (P= .0002). Diabetes burden and worries and fear of hypoglycaemia remained at low levels, without significant changes versus PLGM. No events of severe hypoglycaemia or diabetic ketoacidosis occurred. Preschool children profit from AID at least as much as those aged 7 years and older. To ensure safe use and prescribing modalities, regulatory approval is also required for young children.

Cost-Effectiveness Analysis of an Advanced Hybrid Closed-Loop Insulin Delivery System in People with Type 1 Diabetes in Greece.

AbstractIntroduction: Usage of automated insulin delivery systems is increasing for the treatment of people with type 1 diabetes (T1D). This study compared long-term cost-effectiveness of the Advanced Hybrid Closed Loop MiniMed 780G (AHCL) system versus sensor augmented pump (SAP) system with predictive low glucose management (PLGM) or multiple daily injections (MDI) plus intermittently scanned continuous glucose monitoring (isCGM) in people with T1D in Greece. Methods: Analyses were performed using the IQVIA CORE Diabetes Model, with clinical input data sourced from various studies. In the AHCL versus SAP plus PLGM analysis, patients were assumed to have 7.5% baseline glycated hemoglobin (HbA1c), when comparing AHCL with MDI plus isCGM baseline HbA1c was assumed to be 7.8%. HbA1c was reduced to 7.0% following AHCL treatment initiation but remained at baseline levels in the comparator arms. Analyses were performed from a societal perspective over a lifetime time horizon. Future costs and clinical outcomes were discounted at 1.5% per annum. Results: AHCL was associated with increased quality-adjusted life expectancy of 0.284 quality-adjusted life years (QALYs) and EUR 10,173 lower mean total lifetime costs with SAP plus PLGM. Compared with MDI plus isCGM, AHCL was associated with increased quality-adjusted life expectancy of 2.708 QALYs, EUR 76,396 higher mean total lifetime costs, and an incremental cost-effectiveness ratio of EUR 29,869 per QALY. Extensive sensitivity analysis confirmed the robustness of results. Conclusions: Over patient lifetime, the MiniMed 780G system is likely to be cost saving compared with the SAP plus PLGM system and cost-effective compared with MDI plus isCGM in people with T1D in Greece.

Safety and Glycemic Outcomes During the MiniMed™ Advanced Hybrid Closed-Loop System Pivotal Trial in Adolescents and Adults with Type 1 Diabetes.

Introduction:This trial assessed safety and effectiveness of an advanced hybrid closed-loop (AHCL) system with automated basal (Auto Basal) and automated bolus correction (Auto Correction) in adolescents and adults with type 1 diabetes (T1D).Materials and Methods:This multicenter single-arm study involved an intent-to-treat population of 157 individuals (39 adolescents aged 14–21 years and 118 adults aged ≥22–75 years) with T1D. Study participants used the MiniMed™ AHCL system during a baseline run-in period in which sensor-augmented pump +/− predictive low glucose management or Auto Basal was enabled for ∼14 days. Thereafter, Auto Basal and Auto Correction were enabled for a study phase (∼90 days), with glucose target set to 100 or 120 mg/dL for ∼45 days, followed by the other target for ∼45 days. Study endpoints included safety events and change in mean A1C, time in range (TIR, 70–180 mg/dL) and time below range (TBR, <70 mg/dL). Run-in and study phase values were compared using Wilcoxon signed-rank test or paired t-test.Results:Overall group time spent in closed loop averaged 94.9% ± 5.4% and involved only 1.2 ± 0.8 exits per week. Compared with run-in, AHCL reduced A1C from 7.5% ± 0.8% to 7.0% ± 0.5% (<0.001, Wilcoxon signed-rank test, n = 155), TIR increased from 68.8% ± 10.5% to 74.5% ± 6.9% (<0.001, Wilcoxon signed-rank test), and TBR reduced from 3.3% ± 2.9% to 2.3% ± 1.7% (<0.001, Wilcoxon signed-rank test). Similar benefits to glycemia were observed for each age group and were more pronounced for the nighttime (12 AM–6 AM). The 100 mg/dL target increased TIR to 75.4% (n = 155), which was further optimized at a lower active insulin time (AIT) setting (i.e., 2 h), without increasing TBR. There were no severe hypoglycemic or diabetic ketoacidosis events during the study phase.Conclusions:These findings show that the MiniMed AHCL system is safe and allows for achievement of recommended glycemic targets in adolescents and adults with T1D. Adjustments in target and AIT settings may further optimize glycemia and improve user experience.Clinical Trial Registration number: NCT03959423.

Hybrid closed Loop improved glucose control compared to sensor-augmented pumps in outpatients with type 1 diabetes in real-life conditions with telehealth monitoring.

ObjectivesThe study aims at evaluating glucose metrics and HbA1C values after pump initiation in outpatient settings.Research design and methodsThis single center observational study enrolled 121 subjects with type 1 diabetes between September 2020 and May 2021 initiating sensor-augmented pump therapy with stand-alone CGM (n = 26) or pump users who only changed their device (n = 51), with predictive low glucose management (n = 8) or with Hybrid Closed Loop using Medtronic 780G (n = 36) systems. Changes in HbA1C levels and glucose metrics were analyzed after 3 months. All subjects received diabetes and carbohydrate-counting education if needed at time of initiation and were proposed a telehealth monitoring by a diabetic nurse educator.ResultsThere was no episodes of severe hypoglycemia or diabetic ketoacidosis nor serious pump-related adverse events despite outpatient model of care. While only 18/121 (14.8%) participants reached initially the recommended HbA1C levels, 23/85 (27%) in the conventional group and 33/36 (91%) subjects in the Hybrid Closed Loop group reached target levels after 3 months of follow-up. Time in target range 3.9–10 mmol/L (70–180 mg/dl) also improved and was optimal with closed loop with 30/36 (83%) subjects with time in range above 70%.ConclusionsInitiation of insulin pump therapy for outpatients is safe with a dedicated facility. Telehealth monitoring after outpatient initiation provides tools for improvement in glucose control with an insulin pump. Outpatient pump initiation is compatible with Hybrid Closed Loop systems which provide the largest improvements in glucose control.

Improved technology satisfaction and sleep quality with Medtronic MiniMed® Advanced Hybrid Closed-Loop delivery compared to predictive low glucose suspend in people with Type 1 Diabetes in a randomized crossover trial.

Automated insulin delivery aims to lower treatment burden and improve quality of life as well as glycemic outcomes. We present sub-study data from a dual-center, randomized, open-label, two-sequence crossover study in automated insulin delivery naïve users, comparing Medtronic MiniMed® Advanced Hybrid Closed-Loop (AHCL) to Sensor Augmented Pump therapy with Predictive Low Glucose Management (SAP + PLGM). At the end of each 4-week intervention, impacts on quality of life, sleep and treatment satisfaction were compared using seven age-appropriate validated questionnaires given to patients or caregivers. 59/60 people completed the study (mean age 23.3 ± 14.4yrs). Statistically significant differences favoring AHCL were demonstrated in several scales (data shown as mean ± SE). In adults (≥ 18yrs), technology satisfaction favored AHCL over PLGM as shown by a higher score in the DTSQs during AHCL (n = 28) vs SAP + PLGM (n = 29) (30.9 ± 0.7 vs 27.9 ± 0.7, p = 0.004) and DTSQc AHCL (n = 29) vs SAP + PLGM (n = 30) (11.7 ± 0.9 vs 9.2 ± 0.8, p = 0.032). Adolescents (aged 13-17yrs) also showed a higher DTSQc score during AHCL (n = 16) versus SAP + PLGM (n = 15) (14.8 ± 0.7 vs 12.1 ± 0.8, p = 0.024). The DTQ "change" score (n = 59) favored AHCL over SAP + PLGM (3.5 ± 0.0 vs 3.3 ± 0.0, p < 0.001). PSQI was completed in those > 16years (n = 36) and demonstrated improved sleep quality during AHCL vs SAP + PLGM (4.8 ± 0.3 vs 5.7 ± 0.3, p = 0.048) with a total score > 5 indicating poor quality sleep. These data suggest that AHCL compared to SAP + PLGM mode has the potential to increase treatment satisfaction and improve subjective sleep quality in adolescents and adults with T1D.

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.

Improved Glycemic Outcomes With Medtronic MiniMed Advanced Hybrid Closed-Loop Delivery: Results From a Randomized Crossover Trial Comparing Automated Insulin Delivery With Predictive Low Glucose Suspend in People With Type 1 Diabetes.

To study the MiniMed Advanced Hybrid Closed-Loop (AHCL) system, which includes an algorithm with individualized basal target set points, automated correction bolus function, and improved Auto Mode stability. This dual-center, randomized, open-label, two-sequence crossover study in automated-insulin-delivery-naive participants with type 1 diabetes (aged 7-80 years) compared AHCL to sensor-augmented pump therapy with predictive low glucose management (SAP + PLGM). Each study phase was 4 weeks, preceded by a 2- to 4-week run-in and separated by a 2-week washout. The study was completed by 59 of 60 people (mean age 23.3 ± 14.4 years). Time in target range (TIR) 3.9-10 mmol/L (70-180 mg/dL) favored AHCL over SAP + PLGM (70.4 ± 8.1% vs. 57.9 ± 11.7%) by 12.5 ± 8.5% (P < 0.001), with greater improvement overnight (18.8 ± 12.9%, P < 0.001). All age-groups (children [7-13 years], adolescents [14-21 years], and adults [>22 years]) demonstrated improvement, with adolescents showing the largest improvement (14.4 ± 8.4%). Mean sensor glucose (SG) at run-in was 9.3 ± 0.9 mmol/L (167 ± 16.2 mg/dL) and improved with AHCL (8.5 ± 0.7 mmol/L [153 ± 12.6 mg/dL], P < 0.001), but deteriorated during PLGM (9.5 ± 1.1 mmol/L [17 ± 19.8 mg/dL], P < 0.001). TIR was optimal when the algorithm set point was 5.6 mmol/L (100 mg/dL) compared with 6.7 mmol/L (120 mg/dL), 72.0 ± 7.9% vs. 64.6 ± 6.9%, respectively, with no additional hypoglycemia. Auto Mode was active 96.4 ± 4.0% of the time. The percentage of hypoglycemia at baseline (<3.9 mmol/L [70 mg/dL] and ≤3.0 mmol/L [54 mg/dL]) was 3.1 ± 2.1% and 0.5 ± 0.6%, respectively. During AHCL, the percentage time at <3.9 mmol/L (70 mg/dL) improved to 2.1 ± 1.4% (P = 0.034) and was statistically but not clinically reduced for ≤3.0 mmol/L (54 mg/dL) (0.5 ± 0.5%; P = 0.025). There was one episode of mild diabetic ketoacidosis attributed to an infusion set failure in combination with an intercurrent illness, which occurred during the SAP + PLGM arm. AHCL with automated correction bolus demonstrated significant improvement in glucose control compared with SAP + PLGM. A lower algorithm SG set point during AHCL resulted in greater TIR, with no increase in hypoglycemia.

Factors associated with clinically significant hypoglycemia in patients with type 1 diabetes using sensor-augmented pump therapy with predictive low-glucose management: A multicentric study on iberoamerica

Background and aimsDespite using sensor-augmented pump therapy (SAPT) with predictive low-glucose management (PLGM), hypoglycemia is still an issue in patients with type 1 Diabetes (T1D). Our aim was to determine factors associated with clinically significant hypoglycemia (<54 mg/dl) in persons with T1D treated with PLGM-SAPT. Methodology: This is a multicentric prospective real-life study performed in Colombia, Chile and Spain. Patients with T1D treated with PLGM-SAPT, using sensor ≥70% of time, were included. Data regarding pump and sensor use patterns and carbohydrate intake from 28 consecutive days were collected. A bivariate and multivariate Poisson regression analysis was carried out, to evaluate the association between the number of events of <54 mg/dl with the clinical variables and patterns of sensor and pump use. Results188 subjects were included (41 ± 13.8 years-old, 23 ± 12 years disease duration, A1c 7.2% ± 0.9). The median of events <54 mg/dl was four events/patient/month (IQR 1–10), 77% of these events occurred during day time. Multivariate analysis showed that the number of events of hypoglycemia were higher in patients with previous severe hypoglycemia (IRR1.38; 95% CI 1.19–1.61; p < 0.001), high glycemic variability defined as Coefficient of Variation (CV%) > 36% (IRR 2.09; 95%CI 1.79–2.45; p < 0.001) and hypoglycemia unawareness. A protector effect was identified for adequate sensor calibration (IRR 0.77; 95%CI 0.66–0.90; p:0.001), and the use of bolus wizard >60% (IRR 0.74; 95%CI 0.58–0.95; p:0.017). ConclusionIn spite of using advanced SAPT, clinically significant hypoglycemia is still a non-negligible risk. Only the identification and intervention of modifiable factors could help to prevent and reduce hypoglycemia in clinical practice.

Optimal predictive low glucose management settings during physical exercise in adolescents with type 1 diabetes

Pediatric DiabetesVolume 21, Issue 5 p. 917-917 CORRECTIONFree Access Optimal predictive low glucose management settings during physical exercise in adolescents with type 1 diabetes This article corrects the following: Optimal predictive low glucose management settings during physical exercise in adolescents with type 1 diabetes Valentino Cherubini, Rosaria Gesuita, Edlira Skrami, Ivana Rabbone, Riccardo Bonfanti, Claudia Arnaldi, Giuseppe D'Annunzio, Annapaola Frongia, Fortunato Lombardo, Elvira Piccinno, Riccardo Schiaffini, Sonia Toni, Stefano Tumini, Davide Tinti, Paola Cipriano, Nicola Minuto, Lorenzo Lenzi, Lucia Ferrito, Claudia Ventrici, Federica Ortolani, Ohad Cohen, Andrea Scaramuzza, Volume 20Issue 1Pediatric Diabetes pages: 107-112 First Published online: November 22, 2018 First published: 03 July 2020 https://doi.org/10.1111/pedi.13060AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat In the article by Cherubini et al. (2019), the authors would like to correct the affiliation of Dr. Nicola Minuto to “Department of Pediatrics, Istituto ‘G. Gaslini’ Children's Hospital, Genoa, Italy” instead of “Institute of Endocrinology, Sheba Medical Center, Ramat Gan, Israel”. The authors apologize for this error. REFERENCE 1Cherubini V, Gesuita R, Skrami E, et al. Optimal predictive low glucose management settings during physical exercise in adolescents with type 1 diabetes. Pediatr Diabetes. 2019; 20: 107- 112. https://doi.org/10.1111/pedi.12792. Wiley Online LibraryCASPubMedWeb of Science®Google Scholar Volume21, Issue5August 2020Pages 917-917 ReferencesRelatedInformation

Effect of real-life insulin pump with predictive low-glucose management use for 3months: Analysis of the patients treated in a Japanese center.

Aims/IntroductionIn Japan, an insulin pump with predictive low‐glucose management (PLGM) was launched in 2018. It automatically suspends insulin delivery when the sensor detects or predicts low glucose values. The aim of this study was to analyze the safety and efficacy of PLGM in patients treated in a Japanese center.Materials and MethodsWe carried out a retrospective observational analysis of 16 patients with type 1 diabetes mellitus and one patient after pancreatectomy. They switched from the MiniMed 620G device to the 640G device with PLGM. The primary outcome was the change in the percentage of time in hypoglycemia. The secondary outcome was the change in HbA1c (%) over a period of 3 months. We also explored the presence of “post‐suspend hyperglycemia” with the 640G device.ResultsAfter changing to the 640G device, the percentage of time in hypoglycemia (glucose <50 mg/dL) significantly decreased from 0.39% (0–1.51%) to 0% (0–0.44%; P = 0.0407). The percentage of time in hyperglycemia (glucose >180 mg/dL) significantly increased from 25.53% (15.78–44.14%) to 32.9% (24.71–45.49%; P = 0.0373). HbA1c significantly increased from 7.6 ± 1.0% to 7.8 ± 1.1% (P = 0.0161). From 1.5 to 4.5 h after the resumption of insulin delivery, the percentage of time in hyperglycemia was 32.23% (24.2–53.75%), but it was significantly lower, 2.78% (0–21.6%), when patients manually restarted the pump within 30 min compared with automatic resumption 31.2% (20–61.66%; P = 0.0063).ConclusionsPredictive low‐glucose management is an effective tool for reducing hypoglycemia, but possibly elicits “post‐suspend hyperglycemia.” This information is useful for achieving better blood glucose control in the patients treated with PLGM.

199-OR: Improved Glycemic Outcomes with Medtronic Minimed Advanced Hybrid Closed-Loop Delivery: Results from a Randomized Crossover Trial Comparing Automated Insulin Delivery with Predictive Low Glucose Suspend in People with Type 1 Diabetes

Background: The Medtronic Minimed® Advanced Hybrid Closed-Loop system (AHCL) includes an individualised algorithm with optional set points, automated correction bolus, and improved SmartGuard™ Auto Mode stability. Methods: This dual-centre, randomized, open-label, two-sequence cross-over study in automated insulin delivery naïve participants (aged 7-80yrs), compared AHCL to Sensor Augmented Pump therapy with Predictive Low Glucose Management (SAP+PLGM). Each study phase was 4 weeks, preceded by a 2-4 week run-in, and separated by 2-week washout. Results: 59/60 people completed the study (mean age 23.3±14.4yrs). Overall time in target range (TIR) (70-180mg/dL) favoured AHCL over SAP+PLGM by 12.5±8.5% (p&amp;lt;0.001), with greater improvement overnight (15.7±12.0%, p&amp;lt;0.001) (Table 1). This was primarily due to reduction in hyperglycemia (-12.1±9.0% time spent &amp;gt;180mg/dL). Mean SG improved in AHCL by 10.0±7.4mg/dL (p&amp;lt;0.001). For each 1% lower baseline TIR, there was a 0.5% greater TIR improvement with AHCL (p&amp;lt;0.001). Auto Mode was active for 95.3±3.6% of the time. Conclusion: AHCL with automated correction bolus showed significant improvement in glucose control compared to PLGM, in a population with a younger mean age reflecting a challenging demographic. Disclosure O. Collyns: None. R. Meier: None. Z. Betts: None. D. Chan: None. C. Frampton: None. C.M. Frewen: None. B. Grosman: Employee; Self; Medtronic. N. Hewapathirana: Other Relationship; Self; Sanofi. S. Jones: None. N. Kurtz: Employee; Self; Medtronic. A. Roy: Employee; Self; Medtronic. J. Shin: Employee; Self; Medtronic. R. Vigersky: Employee; Self; Medtronic. B.J. Wheeler: Research Support; Self; Medtronic. M. de Bock: None.

979-P: Improved Technology Satisfaction and Sleep Quality with Medtronic Minimed Advanced Hybrid Closed-Loop Delivery Compared with Predictive Low Glucose Suspend in People with Type 1 Diabetes in a Randomized Crossover Trial

Background: Automated insulin delivery aims to lower treatment burden and improve quality of life as well as glycemic outcomes. Methods: This dual-centre, randomized, open-label, two-sequence cross-over study in automated insulin delivery naïve users, compared Medtronic Minimed® Advanced Hybrid Closed Loop (AHCL) to Sensor Augmented Pump therapy with Predictive Low Glucose Management (SAP+PLGM). At the end of each 4 week intervention impacts on quality of life were compared using age-appropriate scales: Diabetes Treatment Satisfaction Questionnaire status and change (DTSQs and DTSQc), Diabetes Technology Questionnaire (DTQ), Pittsburgh Sleep Quality Index (PSQI), World Health Organisation-Five Well-Being Index (WHO-5), Hypoglycemic Fear Survey (HFS-II) and Hypoglycemia Confidence Scale (HCS). Results: 59/60 people completed the study (mean age 23.3±14.4yrs). Statistically significant differences favouring AHCL were demonstrated in several scales (data shown as mean±SE). Technology satisfaction favoured AHCL over PLGM as shown by a higher score in the DTSQs during AHCL (n=28) vs. SAP+PLGM (n=29) (30.9±0.7 vs. 27.9±0.7, p=0.004) and DTSQc AHCL (n=29) vs. SAP+PLGM (n=30) (11.7±0.9 vs. 9.2±0.8, p=0.032) in adults. Adolescents also showed a higher DTSQc score during AHCL (n=16) vs. SAP+PLGM (n=15) (14.8±0.7 vs. 12.1±0.8, p=0.024). The DTQ “change” score (n=59) favoured AHCL over SAP+ PLGM (3.5±0.0 vs. 3.3±0.0, p&amp;lt;0.001). Improved sleep quality was demonstrated by the lower PSQI in those aged over 16 years (n= 36) during AHCL vs. SAP+PLGM (4.8±0.3 vs. 5.7±0.3, p=0.048) with &amp;gt; 5 indicating poor quality sleep. No differences were found in the other scales. Conclusion: The findings from this RCT suggest AHCL compared to SAP + PLGM mode has the potential to increase treatment satisfaction and improve subjective sleep quality in adolescents and adults with T1D. Disclosure O. Collyns: None. R. Meier: None. Z. Betts: None. D. Chan: None. C. Frampton: None. C.M. Frewen: None. B. Galland: None. N. Hewapathirana: Other Relationship; Self; Sanofi. S. Jones: None. N. Kurtz: Employee; Self; Medtronic. J. Shin: Employee; Self; Medtronic. R. Vigersky: Employee; Self; Medtronic. B.J. Wheeler: Research Support; Self; Medtronic. M. de Bock: None.

Effect of frequency of sensor use on glycaemic control in individuals on sensor-augmented pump therapy with and without Predictive Low Glucose Management System

Improved frequency of sensor use improves glycaemic control. Furthermore, there is no deterioration of glycaemic control with increased sensor use in individuals on Predictive Low Glucose Management (PLGM) system. Younger children are more likely to have better sensor uptake than older children.