1
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Alamir M. Learning-based sensitivity analysis and feedback design for drug delivery of mixed therapy of cancer in the presence of high model uncertainties. J Theor Biol 2023; 568:111508. [PMID: 37148964 DOI: 10.1016/j.jtbi.2023.111508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 03/29/2023] [Accepted: 04/21/2023] [Indexed: 05/08/2023]
Abstract
In this paper, a methodology is proposed that enables to analyze the sensitivity of the outcome of a therapy to unavoidable high dispersion of the patient specific parameters on one hand and to the choice of the parameters that define the drug delivery feedback strategy on the other hand. More precisely, a method is given that enables to extract and rank the most influent parameters that determine the probability of success/failure of a given feedback therapy for a given set of initial conditions over a cloud of realizations of uncertainties. Moreover predictors of the expectations of the amounts of drugs being used can also be derived. This enables to design an efficient stochastic optimization framework that guarantees safe contraction of the tumor while minimizing a weighted sum of the quantities of the different drugs being used. The framework is illustrated and validated using the example of a mixed therapy of cancer involving three combined drugs namely: a chemotherapy drug, an immunology vaccine and an immunotherapy drug. Finally, in this specific case, it is shown that dash-boards can be built in the 2D-space of the most influent state components that summarize the outcomes' probabilities and the associated drug usage as iso-values curves in the reduced state space.
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Affiliation(s)
- Mazen Alamir
- Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-lab, 38000 Grenoble, France.
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2
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Sala-Mira I, Garcia P, Díez JL, Bondia J. Internal model control based module for the elimination of meal and exercise announcements in hybrid artificial pancreas systems. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107061. [PMID: 36116400 DOI: 10.1016/j.cmpb.2022.107061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 07/15/2022] [Accepted: 08/07/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVES Hybrid artificial pancreas systems outperform current insulin pump therapies in blood glucose regulation in type 1 diabetes. However, subjects still have to inform the system about meals intake and exercise to achieve reasonable control. These patient announcements may result in overburden and compromise controller performance if not provided timely and accurately. Here, a hybrid artificial pancreas is extended with an add-on module that releases subjects from meals and exercise announcements. METHODS The add-on module consists of an internal-model controller that generates a "virtual" control action to compensate for disturbances. This "virtual" action is converted into insulin delivery, rescue carbohydrates suggestions, or insulin-on-board limitations, depending on a switching logic based on glucose measurements and predictions. The controller parameters are tuned by optimization and then related to standard parameters from the open-loop therapy. This module is implemented in a hybrid artificial pancreas system proposed by our research group for validation. This hybrid system extended with the add-on module is compared with the hybrid controller with carbohydrate counting errors (hybrid) and the hybrid controller with an alternative unannounced meal compensation module based on a meal detection algorithm (meal detector). The validation used the educational version of the UVa/Padova simulator to simulate the three controllers under two scenarios: one with only meals and another with meals and exercise. The exercise was modeled as a temporal increase of the insulin sensitivity resulting in the glucose drop usually related to an aerobic exercise. RESULTS For the scenario with only meals, the three controllers achieved similar time in range (proposed: 85.1 [77.9,88.1]%, hybrid: 84.0 [75.9,86.4]%, meal detector: 81.9 [79.3,83.8]%, median [interquartile range]) with low time in moderate hypoglycemia. Under the scenario with meals and exercise, the proposed module reduces 4.61% the time in hypoglycemia achieved with the other controllers, suggesting an acceptable amount of rescues (27.2 [23.7, 31.0] g). CONCLUSIONS The proposed add-on module achieved promising results: it outperformed the meal-detector-based controller, even achieving a postprandial performance as good as the hybrid controller (with carbohydrate counting errors). Also, the rescue suggestion feature of the module mitigated exercise-induced hypoglycemia with admissible rescue amounts.
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Affiliation(s)
- Iván Sala-Mira
- Instituto Universitario de Automática e Informática Industrial, Universitat Politécnica de Valéncia, Valencia 46022, Spain
| | - Pedro Garcia
- Instituto Universitario de Automática e Informática Industrial, Universitat Politécnica de Valéncia, Valencia 46022, Spain
| | - José-Luis Díez
- Instituto Universitario de Automática e Informática Industrial, Universitat Politécnica de Valéncia, Valencia 46022, Spain; Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, Spain
| | - Jorge Bondia
- Instituto Universitario de Automática e Informática Industrial, Universitat Politécnica de Valéncia, Valencia 46022, Spain; Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, Spain.
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3
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Camerlingo N, Vettoretti M, Del Favero S, Facchinetti A, Choudhary P, Sparacino G. Generation of post-meal insulin correction boluses in type 1 diabetes simulation models for in-silico clinical trials: More realistic scenarios obtained using a decision tree approach. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 221:106862. [PMID: 35597208 DOI: 10.1016/j.cmpb.2022.106862] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 04/19/2022] [Accepted: 05/07/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE In type 1 diabetes (T1D) research, in-silico clinical trials (ISCTs) notably facilitate the design/testing of new therapies. Published simulation tools embed mathematical models of blood glucose (BG) and insulin dynamics, continuous glucose monitoring (CGM) sensors, and insulin treatments, but lack a realistic description of some aspects of patient lifestyle impacting on glucose control. Specifically, to effectively simulate insulin correction boluses, required to treat post-meal hyperglycemia (BG > 180 mg/dL), the timing of the bolus may be influenced by subjects' behavioral attitudes. In this work, we develop an easily interpretable model of the variability of correction bolus timing observed in real data, and embed it into a popular simulation tool for ISCTs. METHODS Using data collected in 196 adults with T1D monitored in free-living conditions, we trained a decision tree (DT) model to classify whether a correction bolus is injected in a future time window, based on predictors collected back in time, related to CGM data, previous insulin boluses and subject's characteristics. The performance was compared to that of a logistic regression classifier with LASSO regularization (LC), trained on the same dataset. After validation, the DT was embedded within a popular T1D simulation tool and an ISCT was performed to compare the simulated correction boluses against those observed in a subset of data not used for model training. RESULTS The DT provided better classification performance (accuracy: 0.792, sensitivity: 0.430, specificity: 0.878, precision: 0.455) than the LC and presented good interpretability. The most predictive features were related to CGM (and its temporal variations), time since the last insulin bolus, and time of the day. The correction boluses simulated by the DT, after implementation in the simulation tool, showed a good agreement with real-world data. CONCLUSIONS The DT developed in this work represents a simple set of rules to mimic the same timing of correction boluses observed on real data. The inclusion of the model in simulation tools allows investigators to perform ISCTs that more realistically represent the patient behavior in taking correction boluses and the post-prandial BG response. In the future, more complex models can be investigated.
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Affiliation(s)
- N Camerlingo
- Department of Information Engineering, University of Padova, Via G. Gradenigo 6B, Padova 35131, Italy
| | - M Vettoretti
- Department of Information Engineering, University of Padova, Via G. Gradenigo 6B, Padova 35131, Italy
| | - S Del Favero
- Department of Information Engineering, University of Padova, Via G. Gradenigo 6B, Padova 35131, Italy
| | - A Facchinetti
- Department of Information Engineering, University of Padova, Via G. Gradenigo 6B, Padova 35131, Italy
| | - P Choudhary
- Department of Diabetes, Leicester Diabetes Centre, University of Leicester, Gwendolen Rd, Leicester LE5 4PW, United Kingdom
| | - G Sparacino
- Department of Information Engineering, University of Padova, Via G. Gradenigo 6B, Padova 35131, Italy.
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4
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Scharbarg E, Greck J, Le Carpentier E, Chaillous L, Moog CH. A metamodel-based flexible insulin therapy for type 1 diabetes patients subjected to aerobic physical activity. Sci Rep 2022; 12:8017. [PMID: 35577814 PMCID: PMC9110411 DOI: 10.1038/s41598-022-11772-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 04/26/2022] [Indexed: 11/09/2022] Open
Abstract
Patients with type 1 diabetes are subject to exogenous insulin injections, whether manually or through (semi)automated insulin pumps. Basic knowledge of the patient's characteristics and flexible insulin therapy (FIT) parameters are then needed. Specifically, artificial pancreas-like closed-loop insulin delivery systems are some of the most promising devices for substituting for endogenous insulin secretion in type 1 diabetes patients. However, these devices require self-reported information such as carbohydrates or physical activity from the patient, introducing potential miscalculations and delays that can have life-threatening consequences. Here, we display a metamodel for glucose-insulin dynamics that is subject to carbohydrate ingestion and aerobic physical activity. This metamodel incorporates major existing knowledge-based models. We derive comprehensive and universal definitions of the underlying FIT parameters to form an insulin sensitivity factor (ISF). In addition, the relevance of physical activity modelling is assessed, and the FIT is updated to take physical exercise into account. Specifically, we cope with physical activity by using heart rate sensors (watches) with a fully automated closed insulin loop, aiming to maximize the time spent in the glycaemic range (75.5% in the range and 1.3% below the range for hypoglycaemia on a virtual patient simulator).These mathematical parameter definitions are interesting on their own, may be new tools for assessing mathematical models and can ultimately be used in closed-loop artificial pancreas algorithms or to extend distinguished FIT.
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Affiliation(s)
- Emeric Scharbarg
- Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, Nantes, F-44000, France.
- Nantes Université, CHU Nantes, Department of Endocrinology, l'Institut du Thorax, Nantes, F-44000, France.
| | - Joachim Greck
- Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, Nantes, F-44000, France
| | - Eric Le Carpentier
- Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, Nantes, F-44000, France
| | - Lucy Chaillous
- Nantes Université, CHU Nantes, Department of Endocrinology, l'Institut du Thorax, Nantes, F-44000, France
| | - Claude H Moog
- Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, Nantes, F-44000, France
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5
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Garcia-Tirado J, Lv D, Corbett JP, Colmegna P, Breton MD. Advanced hybrid artificial pancreas system improves on unannounced meal response - In silico comparison to currently available system. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 211:106401. [PMID: 34560603 DOI: 10.1016/j.cmpb.2021.106401] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 09/01/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Glycemic control, especially meal-related disturbance rejection, has proven to be a major challenge for people with type 1 diabetes. In this manuscript, we introduce a novel, personalized, advanced hybrid insulin infusion system (a.k.a. artificial pancreas) based on the Model Predictive Control (MPC) methodology to adjust insulin infusion while automatically rejecting uninformed meals. METHODS The proposed advanced hybrid closed-loop system relies on the integration of three key elements: (i) an adaptive personalized MPC control law that modulates the control strength depending on recent past control actions, glucose measurements, and its derivative, (ii) an automatic Bolus Priming System (BPS) that commands additional insulin injections safely upon the detection of enabling metabolic conditions (e.g., an unacknowledged meal), and (iii) a new hyperglycemia mitigation system to avoid prevailing hyperglycemia. The benefits of the proposed system are demonstrated through simulations and tests using the most up-to-date Type 1 UVA/Padova simulator as preclinical stage prior to in vivo clinical tests. We used a legacy algorithm (USS Virginia), currently used in clinical care, as a benchmark controller. RESULTS Overall, the proposed control strategy enhanced by an automatic BPS improves glycemic control when compared with an available system. When a large meal is not announced (80g CHO), the proposed controller outperformed the legacy controller in time-in-target-range TIR (postprandial and overnight) and time-in-tight-range TTR (overall, postprandial, and overnight). CONCLUSION The integration of a novel BPS into an advanced control system allowed to automatically reject unannounced meals. Exhaustive simulation studies indicated the safety and feasibility of the proposed controller to be deployed in human clinical trials.
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Affiliation(s)
- Jose Garcia-Tirado
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA.
| | - Dayu Lv
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA.
| | - John P Corbett
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA; Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA, USA.
| | - Patricio Colmegna
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA.
| | - Marc D Breton
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA.
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Garcia-Tirado J, Diaz JL, Esquivel-Zuniga R, Koravi CLK, Corbett JP, Dawson M, Wakeman C, Barnett CL, Oliveri MC, Myers H, Krauthause K, Breton MD, DeBoer MD. Advanced Closed-Loop Control System Improves Postprandial Glycemic Control Compared With a Hybrid Closed-Loop System Following Unannounced Meal. Diabetes Care 2021; 44:dc210932. [PMID: 34400480 DOI: 10.2337/dc21-0932] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 07/16/2021] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Meals are a major hurdle to glycemic control in type 1 diabetes (T1D). Our objective was to test a fully automated closed-loop control (CLC) system in the absence of announcement of carbohydrate ingestion among adolescents with T1D, who are known to commonly omit meal announcement. RESEARCH DESIGN AND METHODS Eighteen adolescents with T1D (age 15.6 ± 1.7 years; HbA1c 7.4 ± 1.5%; 9 females/9 males) participated in a randomized crossover clinical trial comparing our legacy hybrid CLC system (Unified Safety System Virginia [USS]-Virginia) with a novel fully automated CLC system (RocketAP) during two 46-h supervised admissions (each with one announced and one unannounced dinner), following 2 weeks of data collection. Primary outcome was the percentage time-in-range 70-180 mg/dL (TIR) following the unannounced meal, with secondary outcomes related to additional continuous glucose monitoring-based metrics. RESULTS Both TIR and time-in-tight-range 70-140 mg/dL (TTR) were significantly higher using RocketAP than using USS-Virginia during the 6 h following the unannounced meal (83% [interquartile range 64-93] vs. 53% [40-71]; P = 0.004 and 49% [41-59] vs. 27% [22-36]; P = 0.002, respectively), primarily driven by reduced time-above-range (TAR >180 mg/dL: 17% [1.3-34] vs. 47% [28-60]), with no increase in time-below-range (TBR <70 mg/dL: 0% median for both). RocketAP also improved control following the announced meal (mean difference TBR: -0.7%, TIR: +7%, TTR: +6%), overall (TIR: +5%, TAR: -5%, TTR: +8%), and overnight (TIR: +7%, TTR: +19%, TAR: -5%). RocketAP delivered less insulin overall (78 ± 23 units vs. 85 ± 20 units, P = 0.01). CONCLUSIONS A new fully automated CLC system with automatic prandial dosing was proven to be safe and feasible and outperformed our legacy USS-Virginia in an adolescent population with and without meal announcement.
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Affiliation(s)
- Jose Garcia-Tirado
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | - Jenny L Diaz
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | | | | | - John P Corbett
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | - Martha Dawson
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | - Christian Wakeman
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | | | - Mary C Oliveri
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | - Helen Myers
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | | | - Marc D Breton
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | - Mark D DeBoer
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
- Department of Pediatrics, University of Virginia, Charlottesville, VA
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7
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Garcia-Tirado J, Brown SA, Laichuthai N, Colmegna P, Koravi CL, Ozaslan B, Corbett JP, Barnett CL, Pajewski M, Oliveri MC, Myers H, Breton MD. Anticipation of Historical Exercise Patterns by a Novel Artificial Pancreas System Reduces Hypoglycemia During and After Moderate-Intensity Physical Activity in People with Type 1 Diabetes. Diabetes Technol Ther 2021; 23:277-285. [PMID: 33270531 PMCID: PMC7994426 DOI: 10.1089/dia.2020.0516] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Objective: Physical activity is a major challenge to glycemic control for people with type 1 diabetes. Moderate-intensity exercise often leads to steep decreases in blood glucose and hypoglycemia that closed-loop control systems have so far failed to protect against, despite improving glycemic control overall. Research Design and Methods: Fifteen adults with type 1 diabetes (42 ± 13.5 years old; hemoglobin A1c 6.6% ± 1.0%; 10F/5M) participated in a randomized crossover clinical trial comparing two hybrid closed-loop (HCL) systems, a state-of-the-art hybrid model predictive controller and a modified system designed to anticipate and detect unannounced exercise (APEX), during two 32-h supervised admissions with 45 min of planned moderate activity, following 4 weeks of data collection. Primary outcome was the number of hypoglycemic episodes during exercise. Continuous glucose monitor (CGM)-based metrics and hypoglycemia are also reported across the entire admissions. Results: The APEX system reduced hypoglycemic episodes overall (9 vs. 33; P = 0.02), during exercise (5 vs. 13; P = 0.04), and in the 4 h following (2 vs. 11; P = 0.02). Overall CGM median percent time <70 mg/dL decreased as well (0.3% vs. 1.6%; P = 0.004). This protection was obtained with no significant increase in time >180 mg/dL (18.5% vs. 16.6%, P = 0.15). Overnight control was notable for both systems with no hypoglycemia, median percent in time 70-180 mg/dL at 100% and median percent time 70-140 mg/dL at ∼96% for both. Conclusions: A new closed-loop system capable of anticipating and detecting exercise was proven to be safe and feasible and outperformed a state-of-the-art HCL, reducing participants' exposure to hypoglycemia during and after moderate-intensity physical activity. ClinicalTrials.gov NCT03859401.
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Affiliation(s)
- Jose Garcia-Tirado
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA
| | - Sue A. Brown
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA
- Division of Endocrinology and Metabolism, Department of Medicine, University of Virginia, Charlottesville, Virginia, USA
| | - Nitchakarn Laichuthai
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA
- Excellence Center in Diabetes, Hormone, and Metabolism, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, and Division of Endocrinology and Metabolism, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Patricio Colmegna
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA
| | - Chaitanya L.K. Koravi
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA
| | - Basak Ozaslan
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA
| | - John P. Corbett
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA
| | - Charlotte L. Barnett
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA
| | - Michael Pajewski
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA
| | - Mary C. Oliveri
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA
| | - Helen Myers
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA
| | - Marc D. Breton
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA
- Address correspondence to: Marc D. Breton, PhD, Center for Diabetes Technology, University of Virginia, PO Box 400888, Charlottesville, VA 22904-4888, USA
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Camerlingo N, Vettoretti M, Del Favero S, Facchinetti A, Sparacino G. Mathematical Models of Meal Amount and Timing Variability With Implementation in the Type-1 Diabetes Patient Decision Simulator. J Diabetes Sci Technol 2021; 15:346-359. [PMID: 32940087 PMCID: PMC7925444 DOI: 10.1177/1932296820952123] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND In type 1 diabetes (T1D) research, in-silico clinical trials (ISCTs) have proven effective in accelerating the development of new therapies. However, published simulators lack a realistic description of some aspects of patient lifestyle which can remarkably affect glucose control. In this paper, we develop a mathematical description of meal carbohydrates (CHO) amount and timing, with the aim to improve the meal generation module in the T1D Patient Decision Simulator (T1D-PDS) published in Vettoretti et al. METHODS Data of 32 T1D subjects under free-living conditions for 4874 days were used. Univariate probability density function (PDF) parametric models with different candidate shapes were fitted, individually, against sample distributions of: CHO amounts of breakfast (CHOB), lunch (CHOL), dinner (CHOD), and snack (CHOS); breakfast timing (TB); and time between breakfast-lunch (TBL) and between lunch-dinner (TLD). Furthermore, a support vector machine (SVM) classifier was developed to predict the occurrence of a snack in future fixed-length time windows. Once embedded inside the T1D-PDS, an ISCT was performed. RESULTS Resulting PDF models were: gamma (CHOB, CHOS), lognormal (CHOL, TB), loglogistic (CHOD), and generalized-extreme-values (TBL, TLD). The SVM showed a classification accuracy of 0.8 over the test set. The distributions of simulated meal data were not statistically different from the distributions of the real data used to develop the models (α = 0.05). CONCLUSIONS The models of meal amount and timing variability developed are suitable for describing real data. Their inclusion in modules that describe patient behavior in the T1D-PDS can permit investigators to perform more realistic, reliable, and insightful ISCTs.
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Affiliation(s)
- Nunzio Camerlingo
- Department of Information Engineering,
University of Padova, Padova, Italy
| | - Martina Vettoretti
- Department of Information Engineering,
University of Padova, Padova, Italy
| | - Simone Del Favero
- Department of Information Engineering,
University of Padova, Padova, Italy
| | - Andrea Facchinetti
- Department of Information Engineering,
University of Padova, Padova, Italy
| | - Giovanni Sparacino
- Department of Information Engineering,
University of Padova, Padova, Italy
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9
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Garcia-Tirado J, Colmegna P, Corbett JP, Ozaslan B, Breton MD. In Silico Analysis of an Exercise-Safe Artificial Pancreas With Multistage Model Predictive Control and Insulin Safety System. J Diabetes Sci Technol 2019; 13:1054-1064. [PMID: 31679400 PMCID: PMC6835197 DOI: 10.1177/1932296819879084] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND Maintaining glycemic equilibrium can be challenging for people living with type 1 diabetes (T1D) as many factors (eg, length, type, duration, insulin on board, stress, and training) will impact the metabolic changes triggered by physical activity potentially leading to both hypoglycemia and hyperglycemia. Therefore, and despite the noted health benefits, many individuals with T1D do not exercise as much as their healthy peers. While technology advances have improved glucose control during and immediately after exercise, it remains one of the key limitations of artificial pancreas (AP) systems, largely because stopping insulin at the onset of exercise may not be enough to prevent impending, exercise-induced hypoglycemia. METHODS A hybrid AP algorithm with subject-specific exercise behavior recognition and anticipatory action is designed to prevent hypoglycemic events during and after moderate-intensity exercise. Our approach relies on a number of key innovations, namely, an activity informed premeal bolus calculator, personalized exercise pattern recognition, and a multistage model predictive control (MS-MPC) strategy that can transition between reactive and anticipatory modes. This AP design was evaluated on 100 in silico subjects from the most up-to-date FDA-accepted UVA/Padova metabolic simulator, emulating an outpatient clinical trial setting. Results with a baseline controller, a regular MPC (rMPC), are also included for comparison purposes. RESULTS In silico experiments reveal that the proposed MS-MPC strategy markedly reduces the number of exercise-related hypoglycemic events (8 vs 68). CONCLUSION An anticipatory mode for insulin administration of a monohormonal AP controller reduces the occurrence of hypoglycemia during moderate-intensity exercise.
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Affiliation(s)
- Jose Garcia-Tirado
- University of Virginia, Center for Diabetes Technology, Charlottesville, VA, USA
- Jose Garcia-Tirado, PhD, University of Virginia, Center for Diabetes Technology, 560 Ray C Hunt Dr, Charlottesville, VA 22903, USA.
| | - Patricio Colmegna
- University of Virginia, Center for Diabetes Technology, Charlottesville, VA, USA
- National Scientific and Technical Research Council, Buenos Aires, Argentina
| | - John P. Corbett
- University of Virginia, Center for Diabetes Technology, Charlottesville, VA, USA
- Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA, USA
| | - Basak Ozaslan
- University of Virginia, Center for Diabetes Technology, Charlottesville, VA, USA
- Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA, USA
| | - Marc D. Breton
- University of Virginia, Center for Diabetes Technology, Charlottesville, VA, USA
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10
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Garcia-Tirado J, Corbett JP, Boiroux D, Jørgensen JB, Breton MD. Closed-Loop Control with Unannounced Exercise for Adults with Type 1 Diabetes using the Ensemble Model Predictive Control. JOURNAL OF PROCESS CONTROL 2019; 80:202-210. [PMID: 32831483 PMCID: PMC7437946 DOI: 10.1016/j.jprocont.2019.05.017] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
This paper presents an individualized Ensemble Model Predictive Control (EnMPC) algorithm for blood glucose (BG) stabilization and hypoglycemia prevention in people with type 1 diabetes (T1D) who exercise regularly. The EnMPC formulation can be regarded as a simplified multi-stage MPC allowing for the consideration of N en scenarios gathered from the patient's recent behavior. The patient's physical activity behavior is characterized by an exercise-specific input signal derived from the deconvolution of the patient's continuous glucose monitor (CGM), accounting for known inputs such as meal, and insulin pump records. The EnMPC controller was tested in a cohort of in silico patients with representative inter-subject and intra-subject variability from the FDA-accepted UVA/Padova simulation platform. Results show a significant improvement on hypoglycemia prevention after 30 min of mild to moderate exercise in comparison to a similarly tuned baseline controller (rMPC); with a reduction in hypoglycemia occurrences (< 70 mg/dL), from 3.08% ± 3.55 with rMPC to 0.78% ± 2.04 with EnMPC (P < 0.05).
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Affiliation(s)
- Jose Garcia-Tirado
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | - John P. Corbett
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
- Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA
| | - Dimitri Boiroux
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
- Danish Diabetes Academy, DK-5000 Odense, Denmark
| | - John Bagterp Jørgensen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
| | - Marc D Breton
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
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