Predictive Low Glucose Management System Research Articles

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.

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.

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.

The Predictive Low Glucose Management System in Prevention of Clinically Significant Hypoglycemia in Type 1 Diabetes. A Preliminary Study Identifying the Most Common Events Leading Up to Hypoglycemia During Insulin Pump Therapy.

Prevention of hypoglycemia remains a major challenge in diabetic management, despite the introduction of modern insulin pumps in daily clinical practice. The Low Glucose Suspend (LGS) and the newer Predictive Low Glucose Management (PLGM) systems incorporated in the Medtronic insulin pumps have shown promising results in prevention of hypoglycemia. Our aim was to evaluate the effect of the 2 systems relative to the frequency of clinically significant hypoglycemia in Type 1 diabetes (T1DM). In addition, we investigated the events preceding clinically significant hypoglycemia episodes. A cross-sectional study was conducted in 30 T1DM patients using the MiniMed 640G vs. 30 using the MiniMed Veo sensor-augmented insulin pump. All data was recorded during patients' normal daily activity and living conditions. The patients were matched for age and duration of diabetes. PLGM use was associated with lower incidence of clinically significant hypoglycemia (1.9±1.4 vs. 3.6±1.9 episodes per week), along with reduced exposure to hypoglycemia. The data indicated that both pump systems are effective in preventing severe hypoglycemic episodes. In both groups the most common events preceding hypoglycemic episodes included adjustment of hyperglycemia, basal rate increase and miscalculation of carbohydrates. The results indicated that the use of the Minimed 640G pump system can help reduce the frequency of clinically significant hypoglycemia. Management of hyperglycemia must be addressed in diabetes education programs in order to encourage proper adjustment of high blood glucose levels. Future studies would be useful in exploring the details of the events preceding hypoglycemia episodes in insulin pump users.

Characteristics of Automated Insulin Suspension and Glucose Responses with the Predictive Low-Glucose Management System.

The Predictive Low-Glucose Management (PLGM) system suspends basal insulin when hypoglycemia is predicted and reduces hypoglycemia. The aim of this analysis was to explore the characteristics of automated insulin suspension and sensor glucose (SG) responses following PLGM-initiated pump suspension. Children and adolescents with type 1 diabetes used the Medtronic MiniMed™ 640G pump as part of a randomized controlled trial. Data collected on a subgroup of participants on PLGM (suspend before low enabled) from CareLink® Therapy Management Software were analyzed to explore the time and duration of PLGM-initiated pump suspension. Day and nighttime were defined as 06:00 am to 10:00 pm and 10:00 pm to 6:00 am, respectively. There were 20,183 suspend before low events in 8523 days (2.37 events/day). The mean suspend duration was 55.0 ± 32.7 min (day 50.0 ± 30.1, night 71.7 ± 35.1; P < 0.001). Although a 2-h pump suspension was more frequent at night (day 5%, night 18%), a patient-initiated resumption occurred more during day (day 34%, night 12%). SG values did not reach <3.5 and <3 mmol/L in 79% and 91% of the events, respectively. The 2-h SG following pump resumption was higher following autoresumption during the day (day vs. night 9.3 mmol/L vs. 8.4 mmol/L; P < 0.001). Longer suspends and fewer glycemic excursions occur at night compared with day. The higher glycemic daytime excursions could be due to carbohydrate consumption to increase glucose levels and highlights the need for health care professionals to educate patients about carbohydrate intake around pump suspension.

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

To assess the optimal setting of the predictive low glucose management (PLGM) algorithm for preventing exercise-induced hypoglycemia in adolescents with type 1 diabetes. Thirty-four adolescents, 15 to 20 years, wearing PLGM system, were followed during 3 days exercise during a diabetes camp. PLGM threshold was set at 70 mg/dL between 8 am and 10 pm and 90 mg/dL during 10 pm and 8 am Adolescents were divided into group A and B, with PLGM threshold at 90 and 70 mg/dL, respectively, during exercise. Time spent in hypoglycemia and AUC for time slots 8 am to 1 pm, 1 to 4 pm, 4 to 11 pm, 11 pm to 3 am, 3 to 8 am, in 3 days were compared between groups by Wilcoxon rank sum test. We analyzed 31 patients (median age 15.0 years, 58.1% males, median diabetes duration 7.0 years, hemoglobin A1c [HbA1c] 7.1%). No significant difference has been observed in time spent in hypoglycemia between groups using threshold 70 or 90. Time spent in target was similar in both groups, as well as time spent in hypo or hyperglycemia. The trends of blood glucose over the 3 days in the 2 groups over-lapped without significant differences. A PLGM threshold of 90 mg/dL during the night was associated with reduced time in hypoglycemia in adolescents doing frequent physical exercise, while maintaining 65.1% time in range during the day. However, a threshold of 70 mg/dL seems to be safe in the duration of the physical exercise. PLGM system in adolescents with type 1 diabetes was effective to prevent hypoglycemia during and after exercise, irrespective of the PLGM thresholds used.

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

Improvement of Glycemic Control after Three Months’ Use of the Predictive Low-Glucose Management System and Persisting for 18 Months in Patients with Diabetes Mellitus Type 1

Aim: To study the efficacy of the Medtronic MiniMed 640G insulin pump (CSII with Suspend before low (Predictive Low-Glucose Management [PLGM]) on glycemic control in patients with diabetes mellitus type 1 (DMT1) at baseline, after the first trimester and after 18 months’ use. Patients/Methods: Thirteen patients with DMT1 participated who were on PLGM system (Minimed 640G system-Medtronic), 8 females, age 45.8±12.5 years, DM duration 28.1±8.5 years and PLGM duration 17.5±7.4 months. HbA1c, anthropometric measurements and medical history were recorded at baseline, after 3 months and after 18 months’ use of the PLGM system. The continuous glucose measurements (CGM) data of the last two weeks of each interval were downloaded using the software Carelink Pro and Personal. Results: The glycemic control after using PLGM system was significantly better compared with baseline at 3 and 18 months’ use (HbA1c 7.2±0.7% vs. 6.6±0.5 vs. 6.7±0.5, p=0.006). There was an improvement at first trimester compared with baseline (HbA1c 6.6±0.5 vs. 7.2±0.7, p=0.027) and at 18 months’ use compared with baseline (HbA1c 6.7±0.5 vs. 7.2±0.7, p=0.046). There was no difference in glycemic control between 3 and 18 months’ use. There was no difference of time duration &amp;lt; 3.9 mmol/l (min/day), hypoglycemic episodes and AUC &amp;lt; 3.9 mmol/l compared with baseline at 3 and 18 months’ use. There was a reduction of the time duration &amp;gt; 9.9 mmol/l (min/day) between baseline and after 18 months’ use (3281.3±2175.9 vs. 1891.6±555.7, p=0.037). Hyperglycemic events were reduced significantly after 18 months compared to baseline (median 28.5, interquartile range (IQR) 14 vs. median 39.5, IQR 19, p=0.045). Conclusion: Patients with DMT1 using CSII with PLGM system for 18 months improved their glycemic control due to the reduction of hyperglycemic fluctuation. Glycemic control improved after 3 months and that improvement persisted for 18 months of follow-up. Disclosure T. Didandelos: None. P. Giannoulaki: None. E. Karlafti: None. E. Kotzakioulafi: None. Z. Kontoninas: None. K. Tziomalos: None. A.I. Hatzitolios: None.

Predictive Low-Glucose Suspend to Prevent Hypoglycemia.

Diabetes Technology & TherapeuticsVol. 19, No. 5 CommentariesPredictive Low-Glucose Suspend to Prevent HypoglycemiaJordan E. Pinsker and Eyal DassauJordan E. PinskerWilliam Sansum Diabetes Center, Santa Barbara, California.Search for more papers by this author and Eyal DassauWilliam Sansum Diabetes Center, Santa Barbara, California.Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts.Search for more papers by this authorPublished Online:1 May 2017https://doi.org/10.1089/dia.2017.0064AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetailsCited byPredictive Low-Glucose Suspend Necessitates Less Carbohydrate Supplementation to Rescue Hypoglycemia: Need to Revisit Current Hypoglycemia Treatment Guidelines Jordan E. Pinsker, Amy Bartee, Michelle Katz, Amy Lalonde, Richard Jones, Eyal Dassau, and Howard Wolpert29 June 2021 | Diabetes Technology & Therapeutics, Vol. 23, No. 7The Predictive Low Glucose Management System in Prevention of Clinically Significant Hypoglycemia in Type 1 Diabetes. A Preliminary Study Identifying the Most Common Events Leading Up to Hypoglycemia During Insulin Pump Therapy15 April 2019 | Experimental and Clinical Endocrinology & Diabetes, Vol. 129, No. 05Accuracy of CGM systemsA Review of Predictive Low Glucose Suspend and Its Effectiveness in Preventing Nocturnal Hypoglycemia Ethan Chen, Fraya King, Michael A. Kohn, Elias K. Spanakis, Marc Breton, and David C. Klonoff6 September 2019 | Diabetes Technology & Therapeutics, Vol. 21, No. 10Meso-Cellular Silicate Foam-Modified Reduced Graphene Oxide with a Sandwich Structure for Enzymatic Immobilization and Bioelectrocatalysis26 July 2019 | ACS Applied Materials & Interfaces, Vol. 11, No. 33Optimal predictive low glucose management settings during physical exercise in adolescents with type 1 diabetes22 November 2018 | Pediatric Diabetes, Vol. 20, No. 1 Volume 19Issue 5May 2017 InformationCopyright 2017, Mary Ann Liebert, Inc.To cite this article:Jordan E. Pinsker and Eyal Dassau.Predictive Low-Glucose Suspend to Prevent Hypoglycemia.Diabetes Technology & Therapeutics.May 2017.271-276.http://doi.org/10.1089/dia.2017.0064Published in Volume: 19 Issue 5: May 1, 2017Online Ahead of Print:April 20, 2017KeywordsHypoglycemiaType 1 diabetesLow glucose suspendPredictive low glucose suspendAutomated insulin deliveryArtificial pancreasPDF download

Implications of Predictive Low-Glucose Management System in Hybrid of Full Closed Loop System.

Diabetes Technology & TherapeuticsVol. 18, No. 7 CommentariesImplications of Predictive Low-Glucose Management System in Hybrid of Full Closed Loop SystemPratik ChoudharyPratik ChoudharySearch for more papers by this authorPublished Online:5 Jul 2016https://doi.org/10.1089/dia.2016.0227AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View article"Implications of Predictive Low-Glucose Management System in Hybrid of Full Closed Loop System." Diabetes Technology & Therapeutics, 18(7), pp. 408–409FiguresReferencesRelatedDetailsCited by“Let the Algorithm Do the Work”: Reduction of Hypoglycemia Using Sensor-Augmented Pump Therapy with Predictive Insulin Suspension (SmartGuard) in Pediatric Type 1 Diabetes Patients Torben Biester, Olga Kordonouri, Martin Holder, Kerstin Remus, Dorothee Kieninger-Baum, Tanja Wadien, and Thomas Danne1 March 2017 | Diabetes Technology & Therapeutics, Vol. 19, No. 3Minimally Disruptive Diabetes Care for the Elderly Gabriela Spencer-Bonilla, Rene Rodriguez-Gutierrez, and Victor M. Montori1 December 2016 | Diabetes Technology & Therapeutics, Vol. 18, No. 12 Volume 18Issue 7Jul 2016 InformationCopyright 2016, Mary Ann Liebert, Inc.To cite this article:Pratik Choudhary.Implications of Predictive Low-Glucose Management System in Hybrid of Full Closed Loop System.Diabetes Technology & Therapeutics.Jul 2016.408-409.http://doi.org/10.1089/dia.2016.0227Published in Volume: 18 Issue 7: July 5, 2016PDF download

Prevention of Insulin-Induced Hypoglycemia in Type 1 Diabetes with Predictive Low Glucose Management System.

Sensor-augmented pump therapy (SAPT) with algorithms to predict impending low blood glucose and suspend insulin delivery has the potential to reduce hypoglycemia exposure. The aim of this study was to determine whether predictive low glucose management (PLGM) system is effective in preventing insulin-induced hypoglycemia in controlled experiments. Two protocols were used to induce hypoglycemia in an in-clinic environment. (A) Insulin bolus: Insulin was administered as a manual bolus through the pump. (B) Increased basal insulin: Hypoglycemia was induced by increasing basal rates overnight to 180%. For both protocols, participants were randomized and studied on 2 separate days; a control day with SAPT alone and an intervention day with SAPT and PLGM activated. The predictive algorithm was programmed to suspend basal insulin infusion when sensor glucose was predicted to be <80 mg/dL in 30 min. The primary outcome was the requirement for hypoglycemia treatment (symptomatic hypoglycemia or plasma glucose <50 mg/dL) and was compared in both control and intervention arms. With insulin bolus, 24/28 participants required hypoglycemia treatment with SAPT alone compared to 5/28 participants when PLGM was activated (P ≤ 0.001). With increased basal rates, all the eight SAPT-alone participants required treatment for hypoglycemia compared to only one with SAPT and PLGM. There was no post pump-suspend hyperglycemia with insulin bolus (P = 0.4) or increased basal rates (P = 0.69) in participants with 2-h pump suspension on intervention days. SAPT with PLGM reduced the requirement for hypoglycemia treatment following insulin-induced hypoglycemia in an in-clinic setting.

Safety and efficacy of the predictive low glucose management system in the prevention of hypoglycaemia: protocol for randomised controlled home trial to evaluate the Suspend before low function

IntroductionInnovations with sensor-augmented pump therapy (SAPT) to reduce hypoglycaemia in patients with type 1 diabetes are an ongoing area of research. The predictive low glucose management (PLGM) system incorporates continuous...

Performance of a predictive algorithm in sensor-augmented pump therapy in the prevention of hypoglycaemia

The Predictive Low Glucose Management (PLGM) system consists of a Medtronic Veo pump, Enlite sensor, MiniLink REAL-Time transmitter, Bluetooth-RF translator and a predictive algorithm operating from a Blackberry smartphone. The system suspended insulin delivery when the pre-set hypoglycaemic threshold of 4.4mmol/L was predicted to be reached in 30 minutes. The aim of this study was to determine the plasma glucose profile with the PLGM system when hypoglycaemia was induced by (a) moderate-intensity exercise, (b) subcutaneous insulin bolus and (c) increasing the overnight basal infusion rate in individuals with type 1 diabetes. The primary outcome was the plasma glucose nadir following each hypoglycaemic stimulus with and without PLGM. Participants performed 30-60 minutes of moderate-intensity exercise or were administered a subcutaneous insulin bolus following a glucose stabilisation period on basal continuous subcutaneous insulin infusion. In participants studied with increased overnight basal rates, hypoglycaemia was induced by increasing basal rates by 180%. They were randomised and studied on 2 separate days; with PLGM off and with PLGM on. On both days, participants were observed until plasma glucose dropped to 2.8mmol/L or were symptomatic.

Abstracts from ATTD 20147th International Conference on AdvancedTechnologies &amp; Treatments for DiabetesVienna, Austria, February 5–8, 2014

Introduction: Currently measures of glycaemic variability (Standard Deviation, Mean and MAGE) all fail to fully highlight the extent of blood glucose variability so there is a need for improved measures of glycaemic variability. Aim: To utilise clinical data produced using CGMS to develop and validate a clinically-relevant novel measure of glycaemic variability and to use it to compare CSII with MDI. Methods: 20 participants (6 months pre and post CSII) with 24 hr CGM data sets were analysed. Glucose variability Rating (GVR) was calculated in three steps including change in sensor reading at 5 minute intervals which were then squared and av- eraged to obtain GVR (mmol/L) 2/hr. GVR was plotted, area under curve (AUC) calculated and pre vs. post-CSII GVR compared. Time series analysis was also investigated to try and identify specific periods with increased variability. A compari- son of the percentage change in GVR vs the change in HbA1c value in pre vs post-CSII was also analysed to identify correla- tion between these two measures. Results: GVR was applied to pre and post-CSII CGM data; 75% of participants showed < 20% reduction of GVR post-CSII vs. MDI. The group also showed a significant reduction in GVR (p < 0.003) post-CSII (vs MDI). Conclusions: Results indicate a reduction in glucose vari- ability with CSII and highlight the limitations of HbA1c, as well as the useful potential of GVR to highlight specific time periods where intervention is required to control blood glucose variability.

The PILGRIM Study: In Silico Modeling of a Predictive Low Glucose Management System and Feasibility in Youth with Type 1 Diabetes During Exercise

Predictive low glucose management (PLGM) may help prevent hypoglycemia by stopping insulin pump delivery based on predicted sensor glucose values. Hypoglycemic challenges were simulated using the Food and Drug Administration-accepted glucose simulator with 100 virtual patients. PLGM was then tested with a system composed of a Paradigm(®) insulin pump (Medtronic, Northridge, CA), an Enlite™ glucose sensor (Medtronic), and a BlackBerry(®) (Waterloo, ON, Canada)-based controller. Subjects (n=22) on continuous subcutaneous insulin infusion (five females, 17 males; median [range] age, 15 [range, 14-20] years; median [range] diabetes duration, 7 [2-14] years; median [range] glycated hemoglobin, 8.0% [6.7-10.4%]) exercised until the PLGM system suspended insulin delivery or until the reference blood glucose value (HemoCue(®); HemoCue GmbH, Großostheim, Germany) reached the predictive suspension threshold setting. PLGM reduced hypoglycemia (<70 mg/dL) in silico by 26.7% compared with no insulin suspension, as opposed to a 5.3% reduction in hypoglycemia with use of low glucose suspend (LGS). The median duration of hypoglycemia (time spent <70 mg/dL) with PLGM was significantly less than with LGS (58 min vs. 101 min, respectively; P<0.001). In the clinical trial the hypoglycemic threshold during exercise was reached in 73% of the patients, and hypoglycemia was prevented in 80% of the successful experiments. The mean (±SD) sensor glucose at predictive suspension was 92±7 mg/dL, resulting in a postsuspension nadir (by HemoCue) of 77±22 mg/dL. The suspension lasted for 90±35 (range, 30-120) min, resulting in a sensor glucose level at insulin resumption of 97±19 mg/dL. In silico modeling and early feasibility data demonstrate that PLGM may further reduce the severity of hypoglycemia beyond that already established for algorithms that use a threshold-based suspension.