Background:
Either under standard basal-bolus treatment or hybrid closed-loop control, subjects with type 1 diabetes are required to count carbohydrates (CHOs). However, CHO counting is not only ...burdensome but also prone to errors. Recently, an artificial pancreas algorithm that does not require premeal insulin boluses—the so-called automatic regulation of glucose (ARG)—was introduced. In its first pilot clinical study, although the exact CHO counting was not required, subjects still needed to announce the meal time and classify the meal size.
Method:
An automatic switching signal generator (SSG) is proposed in this work to remove the manual mealtime announcement from the control strategy. The SSG is based on a Kalman filter and works with continuous glucose monitoring readings only.
Results:
The ARG algorithm with unannounced meals (ARGum) was tested in silico under the effect of different types of mixed meals and intrapatient variability, and contrasted with the ARG algorithm with announced meals (ARGam). Simulations reveal that, for slow-absorbing meals, the time in the euglycemic range, 70-180 mg/dL, increases using the unannounced strategy (ARGam: 78.1 68.6-80.2% (median IQR) and ARGum: 87.8 84.5-90.6%), while similar results were found with fast-absorbing meals (ARGam: 87.4 86.0-88.9% and ARGum: 87.6 86.1-88.8%). On the other hand, when intrapatient variability is considered, time in euglycemia is also comparable (ARGam: 81.4 75.4-83.5% and ARGum: 80.9 77.0-85.1%).
Conclusion:
In silico results indicate that it is feasible to perform an in vivo evaluation of the ARG algorithm with unannounced meals.
•Faster insulin action is hypothesized to improve closed-loop system results with insulin dependent Diabetes.•We studied using Medtronic MiniMedTM novel simulation environment the benefits of ...switching between NovoLog® to Fiasp® insulin (Novo Nordisk A/S, Bagsværd, Denmark) with the MiniMedTM 670G system (Medtronic, Northridge, CA, USA).•The simulation studies were compared with clinical results.
Medtronic has developed a virtual patient simulator for modeling and predicting insulin therapy outcomes for people with type 1 diabetes (T1D). An enhanced simulator was created to estimate outcomes when using the MiniMedTM 670G system with standard NovoLog® (EU: NovoRapid, US: NovoLog) versus Fiasp ® by using clinical data.
Sixty-seven participants' PK profiles were generated per type of insulin (Total of 134 PK profiles). 7,485 virtual patients' PK measurements was matched with one of the 67 NovoLog® PK Tmax values. These 7,485 virtual patients were then simulated using the Medtronic MiniMed™ 670G algorithm following an in-silico protocol of 90 days: 14 days in open loop (Manual Mode) followed by 76 days in closed loop (Auto Mode). Simulation study was repeated with each NovoLog® PK profile being replaced by its corresponding Fiasp® PK profile in the same virtual patient. To validate our in-silico analysis, we compared the results of “actual” 19 “real life” patients from a clinical study
Simulated overall and postprandial glycemic outcomes improved in all age groups with Fiasp®. The percentage of time in the euglycemic range increased by about ~2.2% with Fiasp®, in all age groups (p < 0.01). The percentage of time spent at <70 mg/dL was reduced by about ~0.6% with insulin Fiasp® (p < 0.01) and the mean glucose was reduced by about 1.3 mg/dL (p < 0.01), excluding those age <7 years. The simulated mean postprandial SG was reduced by ~5 mg/dL, a significant difference for all age groups. A clinical study results showed similar improvements with MiniMedTM 670G system when switching from NovoLog® to Fiasp®.
The simulation studies indicate that using Fiasp® in place of NovoLog® with the MiniMedTM 670G system will significantly improve the time spent in the healthy, euglycemic range and reduce exposure to hyperglycemia and hypoglycemia in most patients.
People with diabetes have been experimenting and self-modifying diabetes devices and technologies for many decades, in order to achieve the best possible quality of life and improving their long-term ...outcomes. There are now hundreds of individuals using DIY closed loop systems globally. They work similarly to commercial systems at a basic level, automatically adjusting and controlling insulin dosing, but are different in terms of transparency, access, customization, and usability. The potential downsides to DIY closed looping include varying responses from individual HCPs, who may be concerned about their own liability. However, initial outcomes from this self-selected community (including adult and pediatric populations globally) have been positive. There have now been several studies documenting improvements in A1c, time in range, and other outcomes such as quality-of-life benefits. More studies on quality-of-life improvements and more collaboration between companies and the community are recommended.
We report a real‐world evaluation of the first commercially approved automated insulin delivery (AID) system, MiniMed 670G (670G), and open source‐automated insulin delivery (OS‐AID) systems. This ...was undertaken as a retrospective observational study in adults with type 1 diabetes using AID systems for 6 months or longer in a publicly funded health service using clinically validated data. Sixty‐eight adults (38 670G, 30 OS‐AID systems) were included. OS‐AID system users were younger, had a shorter diabetes duration and a higher education status. OS‐AID systems displayed a significantly better change in HbA1c (median −0.9% −0.4%, −1.1% vs. −0.1% IQR −0.7%, 0.2%, P = .004) and time in range 3.9‐10 mmol/L (mean 78.5%, SD ± 12.0% vs. 68.2% ± 14.7%, P = .024) compared with 670G. Both systems showed minimal hypoglycaemia, with OS‐AID systems revealing significantly improved secondary outcomes of mean glucose and percentage of time more than 10 mmol/L, with a higher percentage of time of less than 3 mmol/L. OS‐AID system users displayed improved glycaemic outcomes with no clinical safety concerns compared with 670G, although higher weight‐adjusted insulin dose and weight gain were noted. The study highlights key differences in OS‐AID system user characteristics that are important for interpreting real‐world findings from recent OS‐AID system studies.
Background:
Recent development of automated closed-loop (CL) insulin delivery systems, the so-called artificial pancreas (AP), improved the quality of type 1 diabetes (T1D) therapy. As new ...technologies emerge, patients put increasing trust in their therapeutic devices; therefore, it becomes increasingly important to detect malfunctioning affecting such devices. In this work, we explore a new paradigm to detect insulin pump faults (IPFs) that use unsupervised anomaly detection.
Methods:
We generated CL data corrupted with IPFs using the latest version of the T1D Padova/UVA simulator. From the data, we extracted several features capable to describe the patient dynamics and making more apparent suspicious data portions. Then, a feature selection is performed to determine the optimal feature set. Finally, the performance of several popular unsupervised anomaly detection algorithms is analyzed and compared on the identified optimal feature set.
Results:
Using the identified optimal configuration, the best performance is obtained by the Histogram-Based Outlier Score (HBOS) algorithm, which detected 87% of the IPF with only 0.08 false positives per day on average. Isolation forest is the best algorithm that offers more conservative performances, detection of 85% of the faults but only 0.06 false positives per day on average.
Conclusion:
Unsupervised anomaly detection algorithms can be used effectively to detect IPFs and improve the safety of the AP. Future studies will be dedicated to test the presented method inside dedicated clinical trials.
Inpatient diabetes management of those on hemodialysis poses a major challenge. In a post hoc analysis of a randomized controlled clinical trial, we compared the efficacy of fully automated ...closed-loop insulin delivery vs. usual care in patients undergoing hemodialysis while in hospital. Compared to control patients receiving conventional subcutaneous insulin therapy, those patients receiving closed-loop insulin delivery significantly increased the proportion of time when a continuous glucose monitor was in the target range of 5.6-10.0 mmol/l by 37.6 percent without increasing the risk of hypoglycemia. Thus, closed-loop insulin delivery offers a novel way to achieve effective and safe glucose control in this vulnerable patient population.
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Background:
The first two studies of an artificial pancreas (AP) system carried out in Latin America took place in 2016 (phase 1) and 2017 (phase 2). They evaluated a hybrid algorithm from the ...University of Virginia (UVA) and the automatic regulation of glucose (ARG) algorithm in an inpatient setting using an AP platform developed by the UVA. The ARG algorithm does not require carbohydrate (CHO) counting and does not deliver meal priming insulin boluses. Here, the first outpatient trial of the ARG algorithm using an own AP platform and doubling the duration of previous phases is presented.
Method:
Phase 3 involved the evaluation of the ARG algorithm in five adult participants (n = 5) during 72 hours of closed-loop (CL) and 72 hours of open-loop (OL) control in an outpatient setting. This trial was performed with an own AP and remote monitoring platform developed from open-source resources, called InsuMate. The meals tested ranged its CHO content from 38 to 120 g and included challenging meals like pasta. Also, the participants performed mild exercise (3-5 km walks) daily. The clinical trial is registered in ClinicalTrials.gov with identifier: NCT04793165.
Results:
The ARG algorithm showed an improvement in the time in hyperglycemia (52.2% 16.3% OL vs 48.0% 15.4% CL), time in range (46.9% 15.6% OL vs 50.9% 14.4% CL), and mean glucose (188.9 25.5 mg/dl OL vs 186.2 24.7 mg/dl CL) compared with the OL therapy. No severe hyperglycemia or hypoglycemia episodes occurred during the trial. The InsuMate platform achieved an average of more than 95% of the time in CL.
Conclusion:
The results obtained demonstrated the feasibility of outpatient full CL regulation of glucose levels involving the ARG algorithm and the InsuMate platform.
Background:
Despite recent advances in closed-loop control of blood glucose concentration (BGC) in people with type 1 diabetes (T1D), online performance assessment and modification of artificial ...pancreas (AP) control systems remain a challenge as the metabolic characteristics of users change over time.
Methods:
A controller performance assessment and modification system (CPAMS) analyzes the glucose concentration variations and controller behavior, and modifies the parameters of the control system used in the multivariable AP system. Various indices are defined to quantitatively evaluate the controller performance in real time. Controller performance assessment and modification system also incorporates online learning from historical data to anticipate impending disturbances and proactively counteract their effects.
Results:
Using a multivariable simulation platform for T1D, the CPAMS is used to enhance the BGC regulation in people with T1D by means of automated insulin delivery with an adaptive learning predictive controller. Controller performance assessment and modification system increases the percentage of time in the target range (70-180) mg/dL by 52.3% without causing any hypoglycemia and hyperglycemia events.
Conclusions:
The results demonstrate a significant improvement in the multivariable AP controller performance by using CPAMS.
Background:
The objective of this research is to show the effectiveness of individualized hypoglycemia predictive alerts (IHPAs) based on patient-tailored glucose-insulin models (PTMs) for different ...subjects. Interpatient variability calls for PTMs that have been identified from data collected in free-living conditions during a one-month trial.
Methods:
A new impulse-response (IR) identification technique has been applied to free-living data in order to identify PTMs that are able to predict the future glucose trends and prevent hypoglycemia events. Impulse response has been applied to seven patients with type 1 diabetes (T1D) of the University of Amsterdam Medical Centre. Individualized hypoglycemia predictive alert has been designed for each patient thanks to the good prediction capabilities of PTMs.
Results:
The PTMs performance is evaluated in terms of index of fitting (FIT), coefficient of determination, and Pearson’s correlation coefficient with a population FIT of 63.74%. The IHPAs are evaluated on seven patients with T1D with the aim of predicting in advance (between 45 and 10 minutes) the unavoidable hypoglycemia events; these systems show better performance in terms of sensitivity, precision, and accuracy with respect to previously published results.
Conclusion:
The proposed work shows the successful results obtained applying the IR to an entire set of patients, participants of a one-month trial. Individualized hypoglycemia predictive alerts are evaluated in terms of hypoglycemia prevention: the use of a PTM allows to detect 84.67% of the hypoglycemia events occurred during a one-month trial on average with less than 0.4% of false alarms. The promising prediction capabilities of PTMs can be a key ingredient for new generations of individualized model predictive control for artificial pancreas.
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