Effectiveness and safety of a model predictive control (MPC) algorithm for an artificial pancreas system in outpatients with type 1 diabetes (T1D): systematic review and meta-analysis

Kang, Su Lim; Hwang, Yoo Na; Kwon, Ji Yean; Kim, Sung Min

doi:10.1186/s13098-022-00962-2

Cited by 9 publications

(6 citation statements)

References 64 publications

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Section: Resultsmentioning

confidence: 99%

“…The trial was conducted again with a conventional Proportional-Integral-Derivative (PID) insulin pump being tested. PID works by analyzing deviations in blood sugar levels to calculate insulin doses and is one of the main types of control algorithms (Kang, S.L., et al, 2022). As depicted in Figures 11 and 12, patients using the insulin-glucagon system compared to the patients using the PID system on average, spent (a) 38% more time in the normal blood sugar range, (b) 21.89% less time in the dangerous blood sugar range, (c) 9.56% less time in the low blood sugar range, and (d) 6.71% less time in the high blood sugar range.…”

Section: Simulated Clinical Trials Overview and Resultsmentioning

confidence: 99%

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Optimizing Glycemic Control in Type 1 Diabetic Patients using a Deep Learning-Based Artificial Pancreas with a Secure Glucagon and Insulin Delivery System

Singh,

Rao

2023

Preprint

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Type 1 diabetes impacts millions worldwide, with some patients facing rapid fluctuations in their blood sugar levels. These fluctuations can negatively impact an individual’s quality of life and if untreated, can lead to nerve damage, coma, and death. While current methods have helped address hyperglycemia (high blood sugar), there has been less success with hypoglycemia (low blood sugar) and glucagon administration. To bridge this gap, an artificial pancreas with a novel insulin and glucagon pump was developed. Initiating the system, a personalized mobile app enables users to input meal carbohydrate and insulin bolus data. The data is then transmitted to a deep learning model that incorporates Continuous Glucose Monitor readings, along with carbohydrate and insulin data from the app. The two-layer Long Short-Term Memory network, developed in Python, accurately forecasts blood sugar levels on Ohio University’s OhioT1DM patient dataset and the UVa/Padova simulation’s data for a 30 minute-interval. An algorithm then utilizes the predictions to calculate optimal insulin and glucagon doses using metabolization formulas. To ensure system security, data is transmitted through a cloud-based MQ Telemetry Transport server and secured with industry-standard authentication and encryption methods. Finally, a microcontroller-based prototype accurately dispenses insulin and glucagon doses. The system kept in-silico patients at optimal levels for 38% longer and reduced dangerous levels by 22% compared to conventional controllers on an FDA-approved preclinical trial alternative simulation. By addressing both hypo and hyperglycemia, this real-time medical device can be a transformative tool for individuals with diabetes, enabling them to live healthier and more fulfilling lives.

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Section: Resultsmentioning

confidence: 99%

Section: Simulated Clinical Trials Overview and Resultsmentioning

confidence: 99%

Optimizing Glycemic Control in Type 1 Diabetic Patients using a Deep Learning-Based Artificial Pancreas with a Secure Glucagon and Insulin Delivery System

Singh,

Rao

2023

Preprint

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“…7 8 New randomised controlled trials (RCTs) with larger sample sizes and longer intervention duration have been published since then. [9][10][11][12][13][14] In addition, recent systematic reviews did not include NMAs, were conducted without patient or public involvement, [15][16][17][18][19] were limited to only a few outcomes without psychosocial patientreported outcomes [15][16][17][18] and did not include a grading of the evidence. 15 17 19…”

Section: Strengths and Limitations Of This Studymentioning

confidence: 99%

Efficacy of automated insulin delivery (AID) systems in type 1 diabetes: protocol of a systematic review and network meta-analysis of outpatient randomised controlled trials

Stahl-Pehe,

Schlesinger,

Kuss

et al. 2023

BMJ Open

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IntroductionAutomated insulin delivery (AID), also known as artificial pancreas system or ‘closed-loop system’, represents a novel option for current treatments for type 1 diabetes (T1D). The objective of this systematic review and meta-analysis is to assess the efficacy of AID systems in comparison with current intensified insulin therapy for glycaemic control and patient-reported outcomes in individuals with T1D.Methods and analysisStudies will be eligible if they are randomised controlled trials (RCTs) in people with T1D of all ages, and if they compare an AID system for self-administration during the day and night period with any other type of insulin therapy for at least 3 weeks. The primary outcome will be time in the glucose target range of 70–180 mg/dL. A systematic review will be conducted in the MEDLINE, Embase, Cochrane Central Register of Controlled Trials and ClinicalTrials.gov registries from their inception dates. Two authors will independently screen all references based on titles and abstracts against the eligibility criteria. For data extraction, standard forms will be developed and tested before extraction. All information will be assessed independently by at least two reviewers. The risk of bias of the included studies will be assessed using the Cochrane Risk of Bias 2 tool. The data synthesis will include a random-effects pairwise and network meta-analysis (NMA) in a frequentist framework. Where applicable and if sufficient RCTs are available, sensitivity analyses will be performed, and heterogeneity and publication bias will be assessed. The certainty of evidence from the NMA will be evaluated following the Grading of Recommendations Assessment, Development, and Evaluation working group guidance.Ethics and disseminationNo ethical approval is needed. The results will be reported to the funder, presented in a peer-reviewed scientific journal and at conferences, and disseminated via press release, social media and public events.PROSPERO registration numberCRD42023395492.

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“…The disease course of diabetes is long, and persistent hyperglycemia, hypoglycemia, and frequent fluctuation of glucose are easy to cause damages to whole body tissues and organs, and even death [2,3]. An artificial pancreas is generally considered the most ideal treatment for diabetes, and can effectively control and stabilize the patient's glucose level [4]. The artificial pancreas generally consists of three parts:…”

Section: Introductionmentioning

confidence: 99%

Research of GPCEMBP Glucose Prediction Algorithm Based on Continuous Glucose Monitoring

Cai,

Ge,

Liu

2023

Journal of Sensors

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In view of the poor timeliness of dynamic blood glucose data and a delay of insulin effect for blood glucose control, and considering the nonlinearity and nonstationarity of the glucose data, a new blood Glucose Prediction algorithm combined Correlation coefficient-based complete ensemble empirical mode decomposition with adaptive noise and back propagation neural network (GPCEMBP) was proposed to increase the prediction time and improve the prediction accuracy. It refined the mode decomposition algorithm and integrated the correlation mode filter function to extract the characteristic intrinsic mode functions from the original signal. A new neural network prediction model was constructed by optimizing the number of hidden layer neurons, the number of hidden layers, activation functions, the number of inputs, structure, and other parameters. Finally, a predicted blood glucose value was calculated by phase space reconstruction technology. Through ablation and comparison experiments, it was demonstrated that the GPCEMBP algorithm had better prediction accuracy, convergence, and robustness in blood glucose prediction within 84 min. In addition, it has good adaptability to deal with different quality glucose data.

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Effectiveness and safety of a model predictive control (MPC) algorithm for an artificial pancreas system in outpatients with type 1 diabetes (T1D): systematic review and meta-analysis

Cited by 9 publications

References 64 publications

Optimizing Glycemic Control in Type 1 Diabetic Patients using a Deep Learning-Based Artificial Pancreas with a Secure Glucagon and Insulin Delivery System

Optimizing Glycemic Control in Type 1 Diabetic Patients using a Deep Learning-Based Artificial Pancreas with a Secure Glucagon and Insulin Delivery System

Efficacy of automated insulin delivery (AID) systems in type 1 diabetes: protocol of a systematic review and network meta-analysis of outpatient randomised controlled trials

Research of GPCEMBP Glucose Prediction Algorithm Based on Continuous Glucose Monitoring

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