Novel Insulin Delivery Profiles for Mixed Meals for Sensor-Augmented Pump and Closed-Loop Artificial Pancreas Therapy for Type 1 Diabetes Mellitus

Srinivasan, Asavari; Lee, Joon Bok; Dassau, Eyal; Doyle, Francis J.

doi:10.1177/1932296814543660

Cited by 18 publications

(8 citation statements)

References 32 publications

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“…Some subjects showed prolonged periods of hyperglycemia, most likely related to their consumption of meals with high fat content. This delayed hyperglycemia, seen after consumption of a high-fat meal, may improve with the introduction of automated boluses using biphasic or multiphasic patterns, as previously suggested (36). The AP system used in this study did not support the use of extended boluses, explaining the difficulties some subjects faced after consuming high–fat content meals.…”

Section: Discussionmentioning

confidence: 63%

Randomized Crossover Comparison of Personalized MPC and PID Control Algorithms for the Artificial Pancreas

et al. 2016

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OBJECTIVETo evaluate two widely used control algorithms for an artificial pancreas (AP) under nonideal but comparable clinical conditions.RESEARCH DESIGN AND METHODSAfter a pilot safety and feasibility study (n = 10), closed-loop control (CLC) was evaluated in a randomized, crossover trial of 20 additional adults with type 1 diabetes. Personalized model predictive control (MPC) and proportional integral derivative (PID) algorithms were compared in supervised 27.5-h CLC sessions. Challenges included overnight control after a 65-g dinner, response to a 50-g breakfast, and response to an unannounced 65-g lunch. Boluses of announced dinner and breakfast meals were given at mealtime. The primary outcome was time in glucose range 70–180 mg/dL.RESULTSMean time in range 70–180 mg/dL was greater for MPC than for PID (74.4 vs. 63.7%, P = 0.020). Mean glucose was also lower for MPC than PID during the entire trial duration (138 vs. 160 mg/dL, P = 0.012) and 5 h after the unannounced 65-g meal (181 vs. 220 mg/dL, P = 0.019). There was no significant difference in time with glucose <70 mg/dL throughout the trial period.CONCLUSIONSThis first comprehensive study to compare MPC and PID control for the AP indicates that MPC performed particularly well, achieving nearly 75% time in the target range, including the unannounced meal. Although both forms of CLC provided safe and effective glucose management, MPC performed as well or better than PID in all metrics.

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Section: Discussionmentioning

confidence: 63%

Randomized Crossover Comparison of Personalized MPC and PID Control Algorithms for the Artificial Pancreas

et al. 2016

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“…We have also found several studies that validated their approach using the UVA/Padova patient simulator. Srinivasan et al proposed the use of a set of insulin delivery profiles optimized by a PSO to find the optimal open- and closed-loop profiles for various meal compositions [ 91 ]. More recently, another study [ 92 ] presented an approach based on ANN to optimize bolus calculation by patients using CGM.…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Contreras¹,

Vehı́²

2018

J Med Internet Res

360

207

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BackgroundArtificial intelligence methods in combination with the latest technologies, including medical devices, mobile computing, and sensor technologies, have the potential to enable the creation and delivery of better management services to deal with chronic diseases. One of the most lethal and prevalent chronic diseases is diabetes mellitus, which is characterized by dysfunction of glucose homeostasis.ObjectiveThe objective of this paper is to review recent efforts to use artificial intelligence techniques to assist in the management of diabetes, along with the associated challenges.MethodsA review of the literature was conducted using PubMed and related bibliographic resources. Analyses of the literature from 2010 to 2018 yielded 1849 pertinent articles, of which we selected 141 for detailed review.ResultsWe propose a functional taxonomy for diabetes management and artificial intelligence. Additionally, a detailed analysis of each subject category was performed using related key outcomes. This approach revealed that the experiments and studies reviewed yielded encouraging results.ConclusionsWe obtained evidence of an acceleration of research activity aimed at developing artificial intelligence-powered tools for prediction and prevention of complications associated with diabetes. Our results indicate that artificial intelligence methods are being progressively established as suitable for use in clinical daily practice, as well as for the self-management of diabetes. Consequently, these methods provide powerful tools for improving patients’ quality of life.

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“…23,45 Nevertheless, the optimal time to administer the insulin bolus is usually at mealtime or even 10-15 minutes before, except for meals with high fat contents. 46 …”

Section: Discussionmentioning

confidence: 99%

An Adaptive Nonlinear Basal-Bolus Calculator for Patients With Type 1 Diabetes

Boiroux¹,

Aradóttir²,

Nørgaard

et al. 2016

J Diabetes Sci Technol

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Background: Bolus calculators help patients with type 1 diabetes to mitigate the effect of meals on their blood glucose by administering a large amount of insulin at mealtime. Intraindividual changes in patients physiology and nonlinearity in insulinglucose dynamics pose a challenge to the accuracy of such calculators. Method:We propose a method based on a continuous-discrete unscented Kalman filter to continuously track the postprandial glucose dynamics and the insulin sensitivity. We augment the Medtronic Virtual Patient (MVP) model to simulate noise-corrupted data from a continuous glucose monitor (CGM). The basal rate is determined by calculating the steady state of the model and is adjusted once a day before breakfast. The bolus size is determined by optimizing the postprandial glucose values based on an estimate of the insulin sensitivity and states, as well as the announced meal size. Following meal announcements, the meal compartment and the meal time constant are estimated, otherwise insulin sensitivity is estimated. Results:We compare the performance of a conventional linear bolus calculator with the proposed bolus calculator. The proposed basal-bolus calculator significantly improves the time spent in glucose target (P < .01) compared to the conventional bolus calculator. Conclusion:An adaptive nonlinear basal-bolus calculator can efficiently compensate for physiological changes. Further clinical studies will be needed to validate the results.

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Novel Insulin Delivery Profiles for Mixed Meals for Sensor-Augmented Pump and Closed-Loop Artificial Pancreas Therapy for Type 1 Diabetes Mellitus

Cited by 18 publications

References 32 publications

Randomized Crossover Comparison of Personalized MPC and PID Control Algorithms for the Artificial Pancreas

Randomized Crossover Comparison of Personalized MPC and PID Control Algorithms for the Artificial Pancreas

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

An Adaptive Nonlinear Basal-Bolus Calculator for Patients With Type 1 Diabetes

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