An analysis of Medtronic MiniMed 670G insulin pump use in clinical practice and the impact on glycemic control, quality of life, and compliance

Horowitz, M; Kaye, William A.; Pepper, Gary M.; Reynolds, Kathryn; Patel, S. R.; Knudson, Kort C.; Kale, Geetanjali; Gutiérrez, María Eugenia Hernández; Cotto, Leslie; Horowitz, Barry

doi:10.1016/j.diabres.2021.108876

Cited by 12 publications

(9 citation statements)

References 8 publications

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“…In recent years, the number of people with diabetes has grown rapidly around the world, reaching pandemic levels [2,3]. Advances in continuous glucose monitoring (CGM) systems, insulin pump and insulin pen technologies, and in novel insulin formulations has enabled many powerful treatment options [4][5][6][7][8][9]. The current treatment options available to people with T1D range from manual insulin injections to AID.…”

Section: Introductionmentioning

confidence: 99%

Meal and Physical Activity Detection from Free-Living Data for Discovering Disturbance Patterns of Glucose Levels in People with Diabetes

et al. 2022

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Objective: The interpretation of time series data collected in free-living has gained importance in chronic disease management. Some data are collected objectively from sensors and some are estimated and entered by the individual. In type 1 diabetes (T1D), blood glucose concentration (BGC) data measured by continuous glucose monitoring (CGM) systems and insulin doses administered can be used to detect the occurrences of meals and physical activities and generate the personal daily living patterns for use in automated insulin delivery (AID). Methods: Two challenges in time-series data collected in daily living are addressed: data quality improvement and the detection of unannounced disturbances of BGC. CGM data have missing values for varying periods of time and outliers. People may neglect reporting their meal and physical activity information. In this work, novel methods for preprocessing real-world data collected from people with T1D and the detection of meal and exercise events are presented. Four recurrent neural network (RNN) models are investigated to detect the occurrences of meals and physical activities disjointly or concurrently. Results: RNNs with long short-term memory (LSTM) with 1D convolution layers and bidirectional LSTM with 1D convolution layers have average accuracy scores of 92.32% and 92.29%, and outperform other RNN models. The F1 scores for each individual range from 96.06% to 91.41% for these two RNNs. Conclusions: RNNs with LSTM and 1D convolution layers and bidirectional LSTM with 1D convolution layers provide accurate personalized information about the daily routines of individuals. Significance: Capturing daily behavior patterns enables more accurate future BGC predictions in AID systems and improves BGC regulation.

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Section: Introductionmentioning

confidence: 99%

Meal and Physical Activity Detection from Free-Living Data for Discovering Disturbance Patterns of Glucose Levels in People with Diabetes

et al. 2022

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“…Although most gastrointestinal symptoms reported are mild or moderate and may be alleviated by some treatments, this adverse effect is a potential limitation when using DH in the real world 36 . Improving compliance is important for use of artificial pancreas therapy in a real‐world setting 40 …”

Section: Discussionmentioning

confidence: 99%

“…36 Improving compliance is important for use of artificial pancreas therapy in a real-world setting. 40 This study has several strengths. The meta-analysis was performed according to the PRISMA guidelines and a protocol registered in PROSPERO.…”

Section: MD Mean Differencementioning

confidence: 98%

Dual‐hormone artificial pancreas for glucose control in type 1 diabetes: A meta‐analysis

Zeng

Jia

Gao

et al. 2022

Diabetes Obesity Metabolism

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Aim: To evaluate the efficacy and safety of a dual-hormone artificial pancreas (DH) in type 1 diabetes.Material and Methods: PubMed, Embase, the Cochrane Library and ClinicalTrials.gov were searched for studies published up to February 16, 2022. We included randomized controlled trials that compared DH with single-hormone artificial pancreas (SH), continuous subcutaneous insulin infusion (CSII) or sensor-augmented pumps (SAP), and predictive low glucose suspend systems (PLGS) in type 1 diabetes. The primary outcome was percent time in target (3.9-10 mmol/L [70-180 mg/dL]). Data were summarized as mean differences (MDs) or risk differences (RDs).Results: A total of 17 randomized crossover trials (438 participants) were included.There were nine trials of DH versus SH, 13 trials of DH versus SAP/CSII, and two trials of DH versus PLGS. For time in target, DH showed no significant difference in time in target compared with SH (MD 2.69%, 95% confidence interval [CI] À0.38 to 5.76) but resulted in 16.05% (95% CI 12.06 to 20.05) and 6.89% (95% CI 2.63 to 11.14) more time in target range compared with SAP/CSII and PLGS, respectively. DH slightly reduced time in hypoglycaemia (MD À1.20%, 95% CI À1.85 to À0.56) but increased the risk of gastrointestinal symptoms (RD 0.18, 95% CI 0.08 to 0.27) compared with SH. Conclusions:The results of this study suggest that DH has a comparable effect on time in target compared with SH, but is associated with a longer time in target range compared with SAP/CSII and PLGS. The DH slightly reduced time in hypoglycaemia but may increase the risk of gastrointestinal symptoms compared with the SH.

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“…For this reason, controlling glucose without the help of manual bolus control remains difficult in situations where blood glucose changes rapidly, such as after meals or during exercise. In addition, as shown by real-world evidence, improving compliance is an important issue because of the difficulty in wearing current systems [ 158 ].…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

Current Advances of Artificial Pancreas Systems: A Comprehensive Review of the Clinical Evidence

2021

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Since Banting and Best isolated insulin in the 1920s, dramatic progress has been made in the treatment of type 1 diabetes mellitus (T1DM). However, dose titration and timely injection to maintain optimal glycemic control are often challenging for T1DM patients and their families because they require frequent blood glucose checks. In recent years, technological advances in insulin pumps and continuous glucose monitoring systems have created paradigm shifts in T1DM care that are being extended to develop artificial pancreas systems (APSs). Numerous studies that demonstrate the superiority of glycemic control offered by APSs over those offered by conventional treatment are still being published, and rapid commercialization and use in actual practice have already begun. Given this rapid development, keeping up with the latest knowledge in an organized way is confusing for both patients and medical staff. Herein, we explore the history, clinical evidence, and current state of APSs, focusing on various development groups and the commercialization status. We also discuss APS development in groups outside the usual T1DM patients and the administration of adjunct agents, such as amylin analogues, in APSs.

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An analysis of Medtronic MiniMed 670G insulin pump use in clinical practice and the impact on glycemic control, quality of life, and compliance

Cited by 12 publications

References 8 publications

Meal and Physical Activity Detection from Free-Living Data for Discovering Disturbance Patterns of Glucose Levels in People with Diabetes

Meal and Physical Activity Detection from Free-Living Data for Discovering Disturbance Patterns of Glucose Levels in People with Diabetes

Dual‐hormone artificial pancreas for glucose control in type 1 diabetes: A meta‐analysis

Current Advances of Artificial Pancreas Systems: A Comprehensive Review of the Clinical Evidence

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