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Young G, Dodier R, Youssef JE, Castle JR, Wilson L, Riddell MC, Jacobs PG. Design and In Silico Evaluation of an Exercise Decision Support System Using Digital Twin Models. J Diabetes Sci Technol 2024; 18:324-334. [PMID: 38390855 DOI: 10.1177/19322968231223217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
Abstract
BACKGROUND Managing glucose levels during exercise is challenging for individuals with type 1 diabetes (T1D) since multiple factors including activity type, duration, intensity and other factors must be considered. Current decision support tools lack personalized recommendations and fail to distinguish between aerobic and resistance exercise. We propose an exercise-aware decision support system (exDSS) that uses digital twins to deliver personalized recommendations to help people with T1D maintain safe glucose levels (70-180 mg/dL) and avoid low glucose (<70 mg/dL) during and after exercise. METHODS We evaluated exDSS using various exercise and meal scenarios recorded from a large, free-living study of aerobic and resistance exercise. The model inputs were heart rate, insulin, and meal data. Glucose responses were simulated during and after 30-minute exercise sessions (676 aerobic, 631 resistance) from 247 participants. Glucose outcomes were compared when participants followed exDSS recommendations, clinical guidelines, or did not modify behavior (no intervention). RESULTS exDSS significantly improved mean time in range for aerobic (80.2% to 92.3%, P < .0001) and resistance (72.3% to 87.3%, P < .0001) exercises compared with no intervention, and versus clinical guidelines (aerobic: 82.2%, P < .0001; resistance: 80.3%, P < .0001). exDSS reduced time spent in low glucose for both exercise types compared with no intervention (aerobic: 15.1% to 5.1%, P < .0001; resistance: 18.2% to 6.6%, P < .0001) and was comparable with following clinical guidelines (aerobic: 4.5%, resistance: 8.1%, P = N.S.). CONCLUSIONS The exDSS tool significantly improved glucose outcomes during and after exercise versus following clinical guidelines and no intervention providing motivation for clinical evaluation of the exDSS system.
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Affiliation(s)
- Gavin Young
- School of Medicine, Oregon Health & Science University, Portland, OR, USA
- Artificial Intelligence for Medical Systems Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Robert Dodier
- Artificial Intelligence for Medical Systems Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Joseph El Youssef
- Harold Schnitzer Diabetes Health Center, Division of Endocrinology, Oregon Health & Science University, Portland, OR, USA
| | - Jessica R Castle
- Harold Schnitzer Diabetes Health Center, Division of Endocrinology, Oregon Health & Science University, Portland, OR, USA
| | - Leah Wilson
- Harold Schnitzer Diabetes Health Center, Division of Endocrinology, Oregon Health & Science University, Portland, OR, USA
| | - Michael C Riddell
- School of Kinesiology & Health Science and The Muscle Health Research Centre, York University, Toronto, ON, Canada
| | - Peter G Jacobs
- Artificial Intelligence for Medical Systems Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
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2
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Jacobs PG, Herrero P, Facchinetti A, Vehi J, Kovatchev B, Breton MD, Cinar A, Nikita KS, Doyle FJ, Bondia J, Battelino T, Castle JR, Zarkogianni K, Narayan R, Mosquera-Lopez C. Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities. IEEE Rev Biomed Eng 2024; 17:19-41. [PMID: 37943654 DOI: 10.1109/rbme.2023.3331297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
OBJECTIVE Artificial intelligence and machine learning are transforming many fields including medicine. In diabetes, robust biosensing technologies and automated insulin delivery therapies have created a substantial opportunity to improve health. While the number of manuscripts addressing the topic of applying machine learning to diabetes has grown in recent years, there has been a lack of consistency in the methods, metrics, and data used to train and evaluate these algorithms. This manuscript provides consensus guidelines for machine learning practitioners in the field of diabetes, including best practice recommended approaches and warnings about pitfalls to avoid. METHODS Algorithmic approaches are reviewed and benefits of different algorithms are discussed including importance of clinical accuracy, explainability, interpretability, and personalization. We review the most common features used in machine learning applications in diabetes glucose control and provide an open-source library of functions for calculating features, as well as a framework for specifying data sets using data sheets. A review of current data sets available for training algorithms is provided as well as an online repository of data sources. SIGNIFICANCE These consensus guidelines are designed to improve performance and translatability of new machine learning algorithms developed in the field of diabetes for engineers and data scientists.
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3
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Palumbo MC, de Graaf AA, Morettini M, Tieri P, Krishnan S, Castiglione F. A computational model of the effects of macronutrients absorption and physical exercise on hormonal regulation and metabolic homeostasis. Comput Biol Med 2023; 163:107158. [PMID: 37390762 DOI: 10.1016/j.compbiomed.2023.107158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 05/19/2023] [Accepted: 06/07/2023] [Indexed: 07/02/2023]
Abstract
Regular physical exercise and appropriate nutrition affect metabolic and hormonal responses and may reduce the risk of developing chronic non-communicable diseases such as high blood pressure, ischemic stroke, coronary heart disease, some types of cancer, and type 2 diabetes mellitus. Computational models describing the metabolic and hormonal changes due to the synergistic action of exercise and meal intake are, to date, scarce and mostly focussed on glucose absorption, ignoring the contribution of the other macronutrients. We here describe a model of nutrient intake, stomach emptying, and absorption of macronutrients in the gastrointestinal tract during and after the ingestion of a mixed meal, including the contribution of proteins and fats. We integrated this effort to our previous work in which we modeled the effects of a bout of physical exercise on metabolic homeostasis. We validated the computational model with reliable data from the literature. The simulations are overall physiologically consistent and helpful in describing the metabolic changes due to everyday life stimuli such as multiple mixed meals and variable periods of physical exercise over prolonged periods of time. This computational model may be used to design virtual cohorts of subjects differing in sex, age, height, weight, and fitness status, for specialized in silico challenge studies aimed at designing exercise and nutrition schemes to support health.
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Affiliation(s)
- Maria Concetta Palumbo
- Institute for Applied Computing (IAC) "Mauro Picone", National Research Council of Italy, via dei Taurini 19, Rome, 00185, Italy.
| | - Albert A de Graaf
- Department Healthy Living, Nederlandse Organisatie voor Toegepast Natuurwetenschappelijk Onderzoek (TNO), Sylviusweg 71, Leiden, 2333 BE, The Netherlands.
| | - Micaela Morettini
- Department of Information Engineering, Università Politecnica delle Marche, via Brecce Bianche 12, Ancona, 60131, Italy.
| | - Paolo Tieri
- Institute for Applied Computing (IAC) "Mauro Picone", National Research Council of Italy, via dei Taurini 19, Rome, 00185, Italy.
| | - Shaji Krishnan
- Department Healthy Living, Nederlandse Organisatie voor Toegepast Natuurwetenschappelijk Onderzoek (TNO), Princetonlaan 6, Utrecht, 3584 BE, The Netherlands.
| | - Filippo Castiglione
- Institute for Applied Computing (IAC) "Mauro Picone", National Research Council of Italy, via dei Taurini 19, Rome, 00185, Italy.
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4
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Gomez LA, Toye AA, Hum RS, Kleinberg S. Simulating Realistic Continuous Glucose Monitor Time Series By Data Augmentation. J Diabetes Sci Technol 2023:19322968231181138. [PMID: 37350111 DOI: 10.1177/19322968231181138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/24/2023]
Abstract
BACKGROUND Simulated data are a powerful tool for research, enabling benchmarking of blood glucose (BG) forecasting and control algorithms. However, expert created models provide an unrealistic view of real-world performance, as they lack the features that make real data challenging, while black-box approaches such as generative adversarial networks do not enable systematic tests to diagnose model performance. METHODS To address this, we propose a method that learns missingness and error properties of continuous glucose monitor (CGM) data collected from people with type 1 diabetes (OpenAPS, OhioT1DM, RCT, and Racial-Disparity), and then augments simulated BG data with these properties. On the task of BG forecasting, we test how well our method brings performance closer to that of real CGM data compared with current simulation practices for missing data (random dropout) and error (Gaussian noise, CGM error model). RESULTS Our methods had the smallest performance difference versus real data compared with random dropout and Gaussian noise when individually testing the effects of missing data and error on simulated BG in most cases. When combined, our approach was significantly better than Gaussian noise and random dropout for all data sets except OhioT1DM. Our error model significantly improved results on diverse data sets. CONCLUSIONS We find a significant gap between BG forecasting performance on simulated and real data, and our method can be used to close this gap. This will enable researchers to rigorously test algorithms and provide realistic estimates of real-world performance without overfitting to real data or at the expense of data collection.
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Affiliation(s)
| | | | - R Stanley Hum
- The Montreal Children's Hospital, McGill University Health Centre, Montreal, QC, Canada
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5
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Mosquera-Lopez C, Ramsey KL, Roquemen-Echeverri V, Jacobs PG. Modeling risk of hypoglycemia during and following physical activity in people with type 1 diabetes using explainable mixed-effects machine learning. Comput Biol Med 2023; 155:106670. [PMID: 36803791 DOI: 10.1016/j.compbiomed.2023.106670] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 01/19/2023] [Accepted: 02/10/2023] [Indexed: 02/13/2023]
Abstract
BACKGROUND Physical activity (PA) can cause increased hypoglycemia (glucose <70 mg/dL) risk in people with type 1 diabetes (T1D). We modeled the probability of hypoglycemia during and up to 24 h following PA and identified key factors associated with hypoglycemia risk. METHODS We leveraged a free-living dataset from Tidepool comprised of glucose measurements, insulin doses, and PA data from 50 individuals with T1D (6448 sessions) for training and validating machine learning models. We also used data from the T1Dexi pilot study that contains glucose management and PA data from 20 individuals with T1D (139 session) for assessing the accuracy of the best performing model on an independent test dataset. We used mixed-effects logistic regression (MELR) and mixed-effects random forest (MERF) to model hypoglycemia risk around PA. We identified risk factors associated with hypoglycemia using odds ratio and partial dependence analysis for the MELR and MERF models, respectively. Prediction accuracy was measured using the area under the receiver operating characteristic curve (AUROC). RESULTS The analysis identified risk factors significantly associated with hypoglycemia during and following PA in both MELR and MERF models including glucose and body exposure to insulin at the start of PA, low blood glucose index 24 h prior to PA, and PA intensity and timing. Both models showed overall hypoglycemia risk peaking 1 h after PA and again 5-10 h after PA, which is consistent with the hypoglycemia risk pattern observed in the training dataset. Time following PA impacted hypoglycemia risk differently across different PA types. Accuracy of hypoglycemia prediction using the fixed effects of the MERF model was highest when predicting hypoglycemia during the first hour following the start of PA (AUROCVALIDATION = 0.83 and AUROCTESTING = 0.86) and decreased when predicting hypoglycemia in the 24 h after PA (AUROCVALIDATION = 0.66 and AUROCTESTING = 0.68). CONCLUSION Hypoglycemia risk after the start of PA can be modeled using mixed-effects machine learning to identify key risk factors that may be used within decision support and insulin delivery systems. We published the population-level MERF model online for others to use.
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Affiliation(s)
- Clara Mosquera-Lopez
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA.
| | - Katrina L Ramsey
- Biostatistics and Design Program, Oregon Health & Science University, Portland, Oregon, USA
| | - Valentina Roquemen-Echeverri
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA
| | - Peter G Jacobs
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA
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6
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Deichmann J, Bachmann S, Burckhardt MA, Pfister M, Szinnai G, Kaltenbach HM. New model of glucose-insulin regulation characterizes effects of physical activity and facilitates personalized treatment evaluation in children and adults with type 1 diabetes. PLoS Comput Biol 2023; 19:e1010289. [PMID: 36791144 PMCID: PMC9974135 DOI: 10.1371/journal.pcbi.1010289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 02/28/2023] [Accepted: 01/16/2023] [Indexed: 02/16/2023] Open
Abstract
Accurate treatment adjustment to physical activity (PA) remains a challenging problem in type 1 diabetes (T1D) management. Exercise-driven effects on glucose metabolism depend strongly on duration and intensity of the activity, and are highly variable between patients. In-silico evaluation can support the development of improved treatment strategies, and can facilitate personalized treatment optimization. This requires models of the glucose-insulin system that capture relevant exercise-related processes. We developed a model of glucose-insulin regulation that describes changes in glucose metabolism for aerobic moderate- to high-intensity PA of short and prolonged duration. In particular, we incorporated the insulin-independent increase in glucose uptake and production, including glycogen depletion, and the prolonged rise in insulin sensitivity. The model further includes meal absorption and insulin kinetics, allowing simulation of everyday scenarios. The model accurately predicts glucose dynamics for varying PA scenarios in a range of independent validation data sets, and full-day simulations with PA of different timing, duration and intensity agree with clinical observations. We personalized the model on data from a multi-day free-living study of children with T1D by adjusting a small number of model parameters to each child. To assess the use of the personalized models for individual treatment evaluation, we compared subject-specific treatment options for PA management in replay simulations of the recorded data with altered meal, insulin and PA inputs.
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Affiliation(s)
- Julia Deichmann
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Switzerland
- Life Science Zurich Graduate School, Zurich, Switzerland
| | - Sara Bachmann
- Pediatric Endocrinology and Diabetology, University Children’s Hospital Basel, Basel, Switzerland
- Department of Clinical Research, University Hospital Basel, Basel, Switzerland
| | - Marie-Anne Burckhardt
- Pediatric Endocrinology and Diabetology, University Children’s Hospital Basel, Basel, Switzerland
- Department of Clinical Research, University Hospital Basel, Basel, Switzerland
| | - Marc Pfister
- Department of Clinical Research, University Hospital Basel, Basel, Switzerland
- Pediatric Pharmacology and Pharmacometrics, University Children’s Hospital Basel, Basel, Switzerland
| | - Gabor Szinnai
- Pediatric Endocrinology and Diabetology, University Children’s Hospital Basel, Basel, Switzerland
- Department of Clinical Research, University Hospital Basel, Basel, Switzerland
| | - Hans-Michael Kaltenbach
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Switzerland
- * E-mail:
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7
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Hobbs N, Samadi S, Rashid M, Shahidehpour A, Askari MR, Park M, Quinn L, Cinar A. A physical activity-intensity driven glycemic model for type 1 diabetes. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107153. [PMID: 36183639 DOI: 10.1016/j.cmpb.2022.107153] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 06/21/2022] [Accepted: 09/21/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVE The glucose response to physical activity for a person with type 1 diabetes (T1D) depends upon the intensity and duration of the physical activity, plasma insulin concentrations, and the individual physical fitness level. To accurately model the glycemic response to physical activity, these factors must be considered. METHODS Several physiological models describing the glycemic response to physical activity are proposed by incorporating model terms proportional to the physical activity intensity and duration describing endogenous glucose production (EGP), glucose utilization, and glucose transfer from the plasma to tissues. Leveraging clinical data of T1D during physical activity, each model fit is assessed. RESULTS The proposed model with terms accommodating EGP, glucose transfer, and insulin-independent glucose utilization allow for an improved simulation of physical activity glycemic responses with the greatest reduction in model error (mean absolute percentage error: 16.11 ± 4.82 vs. 19.49 ± 5.87, p = 0.002). CONCLUSIONS The development of a physiologically plausible model with model terms representing each major contributor to glucose metabolism during physical activity can outperform traditional models with physical activity described through glucose utilization alone. This model accurately describes the relation of plasma insulin and physical activity intensity on glucose production and glucose utilization to generate the appropriately increasing, decreasing or stable glucose response for each physical activity condition. The proposed model will enable the in silico evaluation of automated insulin dosing algorithms designed to mitigate the effects of physical activity with the appropriate relationship between the reduction in basal insulin and the corresponding glycemic excursion.
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Affiliation(s)
- Nicole Hobbs
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Sediqeh Samadi
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Perlstein Hall, Suite 127, 10 W. 33rd St., Chicago, IL 60616, USA
| | - Mudassir Rashid
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Perlstein Hall, Suite 127, 10 W. 33rd St., Chicago, IL 60616, USA
| | - Andrew Shahidehpour
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Perlstein Hall, Suite 127, 10 W. 33rd St., Chicago, IL 60616, USA
| | - Mohammad Reza Askari
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Perlstein Hall, Suite 127, 10 W. 33rd St., Chicago, IL 60616, USA
| | - Minsun Park
- College of Nursing, University of Illinois at Chicago. Chicago, IL, USA
| | - Laurie Quinn
- College of Nursing, University of Illinois at Chicago. Chicago, IL, USA
| | - Ali Cinar
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA; Department of Chemical and Biological Engineering, Illinois Institute of Technology, Perlstein Hall, Suite 127, 10 W. 33rd St., Chicago, IL 60616, USA.
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8
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Faulds ER, Rayo M, Lewis C, Noble CW, Gifford R, Happ MB, Joyce L, Dungan K. Simulation Platform Development for Diabetes and Technology Self-Management. J Diabetes Sci Technol 2022; 16:1451-1460. [PMID: 34293963 PMCID: PMC9631530 DOI: 10.1177/19322968211029303] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Specialized education is critical for optimal insulin pump use but is not widely utilized or accessible. We aimed to (1) test the usability and acceptability of A1Control, a simulation platform supporting insulin pump education, and (2) determine predictors of performance. METHOD Rural adult insulin pump users with type 1 diabetes (T1D) participated in a mixed methods usability study in 2 separate rounds. Participants navigated 3 simulations (ie, infusion site occlusion, hypoglycemia, exercise). Net Promoter Score (NPS) and Systems Usability Scale (SUS) were administered. Semi-structured interviews and direct observation were used to assess perceived usability, acceptability and performance. Synthetic Minority Oversampling Technique was used to fit predictive models for visualization of patterns leading to good or poor A1Control performance. RESULTS Participants (N = 13) were 28-70 years old, 10 used automated insulin delivery and 12 used continuous glucose monitoring (CGM). Mean NPS was 9.5 (range 9-10) and positive sentiment during interviews indicated very high acceptability. SUS (mean 88.5, range 70-100) indicted a high perceived usability. CGM percent wear ≥ 94%, time spent in hypoglycemia ≤ 54 mg/dl of <0.01%, and <70 mg/dl of 0.5% predicted successful site-occlusion scenario performance with 100% accuracy. BOLUS score ≥ 2, TDD ≥ 34, and technology brand predicted exercise scenario success with 100% accuracy. There were an insufficient number of failed hypoglycemia scenarios to assess predictors. CONCLUSION A1Control shows potential to increase access and frequency of self-management and technology education. Additional study is needed to determine sustained engagement and benefit.
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Affiliation(s)
- Eileen R. Faulds
- The Ohio State University College of
Nursing, Columbus, OH, USA
- The Ohio State University Medical
Center, Columbus, OH, USA
- Eileen R. Faulds, The Ohio State University
College of Nursing, 560 McCampbell Hall, 5 South, 1581 Dodd Dr. Columbus, OH
43210, USA.
| | - Michael Rayo
- The Ohio State University College of
Engineering, Columbus, OH, USA
| | - Claudia Lewis
- Wake Forest School of Medicine,
Winston-Salem, NC, USA
| | - Carl W Noble
- University of Cincinnati Medical
Center, Cincinnati, OH, USA
| | - Ryan Gifford
- The Ohio State University College of
Engineering, Columbus, OH, USA
| | - Mary Beth Happ
- The Ohio State University College of
Nursing, Columbus, OH, USA
| | - Lilly Joyce
- The Ohio State University College of
Nursing, Columbus, OH, USA
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9
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Tyler NS, Mosquera-Lopez C, Young GM, El Youssef J, Castle JR, Jacobs PG. Quantifying the impact of physical activity on future glucose trends using machine learning. iScience 2022; 25:103888. [PMID: 35252806 PMCID: PMC8889374 DOI: 10.1016/j.isci.2022.103888] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 11/19/2021] [Accepted: 02/04/2022] [Indexed: 01/21/2023] Open
Abstract
Prevention of hypoglycemia (glucose <70 mg/dL) during aerobic exercise is a major challenge in type 1 diabetes. Providing predictions of glycemic changes during and following exercise can help people with type 1 diabetes avoid hypoglycemia. A unique dataset representing 320 days and 50,000 + time points of glycemic measurements was collected in adults with type 1 diabetes who participated in a 4-arm crossover study evaluating insulin-pump therapies, whereby each participant performed eight identically designed in-clinic exercise studies. We demonstrate that even under highly controlled conditions, there is considerable intra-participant and inter-participant variability in glucose outcomes during and following exercise. Participants with higher aerobic fitness exhibited significantly lower minimum glucose and steeper glucose declines during exercise. Adaptive, personalized machine learning (ML) algorithms were designed to predict exercise-related glucose changes. These algorithms achieved high accuracy in predicting the minimum glucose and hypoglycemia during and following exercise sessions, for all fitness levels.
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Affiliation(s)
- Nichole S. Tyler
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering Oregon Health & Science University Portland, OR 97232, USA
| | - Clara Mosquera-Lopez
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering Oregon Health & Science University Portland, OR 97232, USA
| | - Gavin M. Young
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering Oregon Health & Science University Portland, OR 97232, USA
| | - Joseph El Youssef
- Harold Schnitzer Diabetes Health Center, Division of Endocrinology Oregon Health & Science University Portland, OR 97239, USA
| | - Jessica R. Castle
- Harold Schnitzer Diabetes Health Center, Division of Endocrinology Oregon Health & Science University Portland, OR 97239, USA
| | - Peter G. Jacobs
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering Oregon Health & Science University Portland, OR 97232, USA
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10
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Islam MJ, Hoque ASML. Virtual diabetic patient with physical activity dynamics. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 213:106485. [PMID: 34752961 DOI: 10.1016/j.cmpb.2021.106485] [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: 03/01/2021] [Accepted: 10/17/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Diabetes is a disease of impaired blood glucose regulation due to the absence or insufficient secretion of insulin hormone or insulin resistance induced in the human body. In literature, the impact of exercise is considered in few models based on the minimal representation of glucose dynamics along with the assumption that no endogenous insulin is produced in the body. Hence these models are not capable of describing diabetic behavior which is independent of exogenous insulin. This type of diabetes, known as type-2, affects almost 90% of the total diabetes population. In this article, a constraint-based comprehensive physiological model of blood glucose dynamics is aimed to build for filling up the gap in the literature. METHODS For physiological comprehensiveness, the model is considered to consist of several compartments separately connected with a common compartment named 'plasma'. Plasma is the only accessible compartment and contains the state variables. Plasma variables are the integrated result of the net change in rates of metabolic processes and basal rates are influenced between two saturation constraints for an operating range of each plasma variable. The influence of a plasma variable on a metabolic rate is represented using a form of the hyperbolic tangent function. Validation is done by fitting the model with clinical experiments and continuous glucose monitoring data of a free-living environment. RESULTS The proposed model generates an average correlation coefficient of 0.85 ± 0.13 on all simulated responses with the target in the fitting experiments. Besides this, the model can produce a spectrum of metabolic effects of plasma variables for showing more insight into glucose metabolism. CONCLUSIONS A constraint-based comprehensive glucose regulation with exercise dynamics for modeling diabetes is pursued. The model doesn't consider age, gender, physical, and mental condition of the human body but can be applied in operation research by mathematical programming.
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Affiliation(s)
- Md Jahirul Islam
- Department of Computer Science & Engineering, Bangladesh University of Engineering & Technology, Dhaka, Bangladesh.
| | - Abu Sayed Md Latiful Hoque
- Department of Computer Science & Engineering, Bangladesh University of Engineering & Technology, Dhaka, Bangladesh
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11
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Ahdab MA, Leth J, Knudsen T, Vestergaard P, Clausen HG. Glucose-insulin mathematical model for the combined effect of medications and life style of Type 2 diabetic patients. Biochem Eng J 2021. [DOI: 10.1016/j.bej.2021.108170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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12
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A Hybrid Automata Approach for Monitoring the Patient in the Loop in Artificial Pancreas Systems. SENSORS 2021; 21:s21217117. [PMID: 34770425 PMCID: PMC8587755 DOI: 10.3390/s21217117] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 10/18/2021] [Accepted: 10/23/2021] [Indexed: 11/16/2022]
Abstract
The use of automated insulin delivery systems has become a reality for people with type 1 diabetes (T1D), with several hybrid systems already on the market. One of the particularities of this technology is that the patient is in the loop. People with T1D are the plant to control and also a plant operator, because they may have to provide information to the control loop. The most immediate information provided by patients that affects performance and safety are the announcement of meals and exercise. Therefore, to ensure safety and performance, the human factor impact needs to be addressed by designing fault monitoring strategies. In this paper, a monitoring system is developed to diagnose potential patient modes and faults. The monitoring system is based on the residual generation of a bank of observers. To that aim, a linear parameter varying (LPV) polytopic representation of the system is adopted and a bank of Kalman filters is designed using linear matrix inequalities (LMI). The system uncertainty is propagated using a zonotopic-set representation, which allows determining confidence bounds for each of the observer outputs and residuals. For the detection of modes, a hybrid automaton model is generated and diagnosis is performed by interpreting the events and transitions within the automaton. The developed system is tested in simulation, showing the potential benefits of using the proposed approach for artificial pancreas systems.
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13
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Zhang M, Flores KB, Tran HT. Deep learning and regression approaches to forecasting blood glucose levels for type 1 diabetes. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102923] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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14
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Sevil M, Rashid M, Hajizadeh I, Park M, Quinn L, Cinar A. Physical Activity and Psychological Stress Detection and Assessment of Their Effects on Glucose Concentration Predictions in Diabetes Management. IEEE Trans Biomed Eng 2021; 68:2251-2260. [PMID: 33400644 PMCID: PMC8265613 DOI: 10.1109/tbme.2020.3049109] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
OBJECTIVE Continuous glucose monitoring (CGM) enables prediction of the future glucose concentration (GC) trajectory for making informed diabetes management decisions. The glucose concentration values are affected by various physiological and metabolic variations, such as physical activity (PA) and acute psychological stress (APS), in addition to meals and insulin. In this work, we extend our adaptive glucose modeling framework to incorporate the effects of PA and APS on the GC predictions. METHODS A wristband conducive of use by free-living ambulatory people is used. The measured physiological variables are analyzed to generate new quantifiable input features for PA and APS. Machine learning techniques estimate the type and intensity of the PA and APS when they occur individually and concurrently. Variables quantifying the characteristics of both PA and APS are integrated as exogenous inputs in an adaptive system identification technique for enhancing the accuracy of GC predictions. Data from clinical experiments illustrate the improvement in GC prediction accuracy. RESULTS The average mean absolute error (MAE) of one-hour-ahead GC predictions with testing data decreases from 35.1 to 31.9 mg/dL (p-value = 0.01) with the inclusion of PA information, and it decreases from 16.9 to 14.2 mg/dL (p-value = 0.006) with the inclusion of PA and APS information. CONCLUSION The first-ever glucose prediction model is developed that incorporates measures of physical activity and acute psychological stress to improve GC prediction accuracy. SIGNIFICANCE Modeling the effects of physical activity and acute psychological stress on glucose concentration values will improve diabetes management and enable informed meal, activity and insulin dosing decisions.
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15
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De Paoli B, D’Antoni F, Merone M, Pieralice S, Piemonte V, Pozzilli P. Blood Glucose Level Forecasting on Type-1-Diabetes Subjects during Physical Activity: A Comparative Analysis of Different Learning Techniques. Bioengineering (Basel) 2021; 8:bioengineering8060072. [PMID: 34073433 PMCID: PMC8229703 DOI: 10.3390/bioengineering8060072] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 05/17/2021] [Accepted: 05/22/2021] [Indexed: 01/26/2023] Open
Abstract
Background: Type 1 Diabetes Mellitus (T1DM) is a widespread chronic disease in industrialized countries. Preventing blood glucose levels from exceeding the euglycaemic range would reduce the incidence of diabetes-related complications and improve the quality of life of subjects with T1DM. As a consequence, in the last decade, many Machine Learning algorithms aiming to forecast future blood glucose levels have been proposed. Despite the excellent performance they obtained, the prediction of abrupt changes in blood glucose values produced during physical activity (PA) is still one of the main challenges. Methods: A Jump Neural Network was developed in order to overcome the issue of predicting blood glucose values during PA. Three learning configurations were developed and tested: offline training, online training, and online training with reinforcement. All configurations were tested on six subjects suffering from T1DM that held regular PA (three aerobic and three anaerobic) and exploited Continuous Glucose Monitoring (CGM). Results: The forecasting performance was evaluated in terms of the Root-Mean-Squared-Error (RMSE), according to a paradigm of Precision Medicine. Conclusions: The online learning configurations performed better than the offline configuration in total days but not on the only CGM associated with the PA; thus, the results do not justify the increased computational burden because the improvement was not significant.
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Affiliation(s)
- Benedetta De Paoli
- Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; (B.D.P.); (F.D.)
| | - Federico D’Antoni
- Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; (B.D.P.); (F.D.)
| | - Mario Merone
- Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; (B.D.P.); (F.D.)
- Correspondence: ; Tel.: +39-06-225-419-622
| | - Silvia Pieralice
- Unit of Diabetology and Endocrinology, Department of Medicine, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; (S.P.); (P.P.)
| | - Vincenzo Piemonte
- Unit of Chemical Engineering, Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy;
| | - Paolo Pozzilli
- Unit of Diabetology and Endocrinology, Department of Medicine, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; (S.P.); (P.P.)
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16
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Modelling glucose dynamics during moderate exercise in individuals with type 1 diabetes. PLoS One 2021; 16:e0248280. [PMID: 33770092 PMCID: PMC7996980 DOI: 10.1371/journal.pone.0248280] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 02/24/2021] [Indexed: 12/17/2022] Open
Abstract
The artificial pancreas is a closed-loop insulin delivery system that automatically regulates glucose levels in individuals with type 1 diabetes. In-silico testing using simulation environments accelerates the development of better artificial pancreas systems. Simulation environments need an accurate model that captures glucose dynamics during exercise to simulate real-life scenarios. We proposed six variations of the Bergman Minimal Model to capture the physiological effects of moderate exercise on glucose dynamics in individuals with type 1 diabetes. We estimated the parameters of each model with clinical data using a Bayesian approach and Markov chain Monte Carlo methods. The data consisted of measurements of plasma glucose, plasma insulin, and oxygen consumption collected from a study of 17 adults with type 1 diabetes undergoing aerobic exercise sessions. We compared the models based on the physiological plausibility of their parameters estimates and the deviance information criterion. The best model features (i) an increase in glucose effectiveness proportional to exercise intensity, and (ii) an increase in insulin action proportional to exercise intensity and duration. We validated the selected model by reproducing results from two previous clinical studies. The selected model accurately simulates the physiological effects of moderate exercise on glucose dynamics in individuals with type 1 diabetes. This work offers an important tool to develop strategies for exercise management with the artificial pancreas.
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17
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Zheng M, Ni B, Kleinberg S. Automated meal detection from continuous glucose monitor data through simulation and explanation. J Am Med Inform Assoc 2021; 26:1592-1599. [PMID: 31562509 PMCID: PMC6857509 DOI: 10.1093/jamia/ocz159] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 07/09/2019] [Accepted: 08/14/2019] [Indexed: 01/01/2023] Open
Abstract
Background Artificial pancreas systems aim to reduce the burden of type 1 diabetes by automating insulin dosing. These systems link a continuous glucose monitor (CGM) and insulin pump with a control algorithm, but require users to announce meals, without which the system can only react to the rise in blood glucose. Objective We investigate whether CGM data can be used to automatically infer meals in daily life even in the presence of physical activity, which can raise or lower blood glucose. Materials and Methods We propose a novel meal detection algorithm that combines simulations with CGM, insulin pump, and heart rate monitor data. When observed and predicted glucose differ, our algorithm uses simulations to test whether a meal may explain this difference. We evaluated our method on simulated data and real-world data from individuals with type 1 diabetes. Results In simulated data, we detected meals earlier and with higher accuracy than was found in prior work (25.7 minutes, 1.2 g error; compared with 48.3 minutes, 17.2 g error). In real-world data, we discovered a larger number of plausible meals than was found in prior work (30 meals, 76.7% accepted; compared with 33 meals, 39.4% accepted). Discussion Prior research attempted meal detection from CGM, but had delays and lower accuracy in real data or did not allow for physical activity. Our approach can be used to improve insulin dosing in an artificial pancreas and trigger reminders for missed meal boluses. Conclusions We demonstrate that meal information can be robustly inferred from CGM and body-worn sensor data, even in challenging environments of daily life.
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Affiliation(s)
- Min Zheng
- Computer Science, Stevens Institute of Technology, Hoboken, New Jersey, USA
| | - Baohua Ni
- Electronic Engineering, Tsinghua University, Beijing, China
| | - Samantha Kleinberg
- Computer Science, Stevens Institute of Technology, Hoboken, New Jersey, USA
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18
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Frank S, Jbaily A, Hinshaw L, Basu R, Basu A, Szeri AJ. Modeling the acute effects of exercise on glucose dynamics in healthy nondiabetic subjects. J Pharmacokinet Pharmacodyn 2021; 48:225-239. [PMID: 33394220 DOI: 10.1007/s10928-020-09726-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Accepted: 11/04/2020] [Indexed: 11/25/2022]
Abstract
To shed light on how acute exercise affects blood glucose (BG) concentrations in nondiabetic subjects, we develop a physiological pharmacokinetic/pharmacodynamic model of postprandial glucose dynamics during exercise. We unify several concepts of exercise physiology to derive a multiscale model that includes three important effects of exercise on glucose dynamics: increased endogenous glucose production (EGP), increased glucose uptake in skeletal muscle (SM), and increased glucose delivery to SM by capillary recruitment (i.e. an increase in surface area and blood flow in capillary beds). We compare simulations to experimental observations taken in two cohorts of healthy nondiabetic subjects (resting subjects (n = 12) and exercising subjects (n = 12)) who were each given a mixed-meal tolerance test. Metabolic tracers were used to quantify the glucose flux. Simulations reasonably agree with postprandial measurements of BG concentration and EGP during exercise. Exercise-induced capillary recruitment is predicted to increase glucose transport to SM by 100%, causing hypoglycemia. When recruitment is blunted, as in those with capillary dysfunction, the opposite occurs and higher than expected BG levels are predicted. Model simulations show how three important exercise-induced phenomena interact, impacting BG concentrations. This model describes nondiabetic subjects, but it is a first step to a model that describes glucose dynamics during exercise in those with type 1 diabetes (T1D). Clinicians and engineers can use the insights gained from the model simulations to better understand the connection between exercise and glucose dynamics and ultimately help patients with T1D make more informed insulin dosing decisions around exercise.
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Affiliation(s)
- Spencer Frank
- Department of Mechanical Engineering at the University of California Berkeley, Berkeley, USA.
- Dexcom in San Diego, San Diego, CA, USA.
| | - Abdulrahman Jbaily
- Department of Mechanical Engineering at the University of California Berkeley, Berkeley, USA
- Dexcom in San Diego, San Diego, CA, USA
| | - Ling Hinshaw
- Division of Endocrinology at Mayo Clinic, Rochester, USA
| | - Rita Basu
- Division of Endocrinology at the University of Virginia School of Medicine, Charlottesville, USA
| | - Ananda Basu
- Division of Endocrinology at the University of Virginia School of Medicine, Charlottesville, USA
| | - Andrew J Szeri
- Department of Mechanical Engineering at the University of California Berkeley, Berkeley, USA
- Department of Mechanical Engineering at the University of British Columbia, Vancouver, Canada
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19
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Deichmann J, Bachmann S, Burckhardt MA, Szinnai G, Kaltenbach HM. Simulation-Based Evaluation of Treatment Adjustment to Exercise in Type 1 Diabetes. Front Endocrinol (Lausanne) 2021; 12:723812. [PMID: 34489869 PMCID: PMC8417413 DOI: 10.3389/fendo.2021.723812] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 07/26/2021] [Indexed: 01/26/2023] Open
Abstract
Regular exercise is beneficial and recommended for people with type 1 diabetes, but increased glucose demand and changes in insulin sensitivity require treatment adjustments to prevent exercise-induced hypoglycemia. Several different adjustment strategies based on insulin bolus reductions and additional carbohydrate intake have been proposed, but large inter- and intraindividual variability and studies using different exercise duration, intensity, and timing impede a direct comparison of their effects. In this study, we use a mathematical model of the glucoregulatory system and implement published guidelines and strategies in-silico to provide a direct comparison on a single 'typical' person on a standard day with three meals. We augment this day by a broad range of exercise scenarios combining different intensity and duration of the exercise session, and different timing with respect to adjacent meals. We compare the resulting blood glucose trajectories and use summary measures to evaluate the time-in-range and risk scores for hypo- and hyperglycemic events for each simulation scenario, and to determine factors that impede prevention of hypoglycemia events. Our simulations suggest that the considered strategies and guidelines successfully minimize the risk for acute hypoglycemia. At the same time, all adjustments substantially increase the risk of late-onset hypoglycemia compared to no adjustment in many cases. We also find that timing between exercise and meals and additional carbohydrate intake during exercise can lead to non-intuitive behavior due to superposition of meal- and exercise-related glucose dynamics. Increased insulin sensitivity appears as a major driver of non-acute hypoglycemic events. Overall, our results indicate that further treatment adjustment might be required both immediately following exercise and up to several hours later, but that the intricate interplay between different dynamics makes it difficult to provide generic recommendations. However, our simulation scenarios extend substantially beyond the original scope of each model component and proper model validation is warranted before applying our in-silico results in a clinical setting.
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Affiliation(s)
- Julia Deichmann
- Department of Biosystems Science and Engineering and Swiss Institute of Bioinformatics (SIB), ETH Zurich, Basel, Switzerland
- Life Science Zurich Graduate School, Zurich, Switzerland
| | - Sara Bachmann
- Pediatric Endocrinology and Diabetology, University Children’s Hospital Basel, and Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Marie-Anne Burckhardt
- Pediatric Endocrinology and Diabetology, University Children’s Hospital Basel, and Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Gabor Szinnai
- Pediatric Endocrinology and Diabetology, University Children’s Hospital Basel, and Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Hans-Michael Kaltenbach
- Department of Biosystems Science and Engineering and Swiss Institute of Bioinformatics (SIB), ETH Zurich, Basel, Switzerland
- *Correspondence: Hans-Michael Kaltenbach,
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20
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Tyler NS, Jacobs PG. Artificial Intelligence in Decision Support Systems for Type 1 Diabetes. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3214. [PMID: 32517068 PMCID: PMC7308977 DOI: 10.3390/s20113214] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 05/29/2020] [Accepted: 06/02/2020] [Indexed: 12/16/2022]
Abstract
Type 1 diabetes (T1D) is a chronic health condition resulting from pancreatic beta cell dysfunction and insulin depletion. While automated insulin delivery systems are now available, many people choose to manage insulin delivery manually through insulin pumps or through multiple daily injections. Frequent insulin titrations are needed to adequately manage glucose, however, provider adjustments are typically made every several months. Recent automated decision support systems incorporate artificial intelligence algorithms to deliver personalized recommendations regarding insulin doses and daily behaviors. This paper presents a comprehensive review of computational and artificial intelligence-based decision support systems to manage T1D. Articles were obtained from PubMed, IEEE Xplore, and ScienceDirect databases. No time period restrictions were imposed on the search. After removing off-topic articles and duplicates, 562 articles were left to review. Of those articles, we identified 61 articles for comprehensive review based on algorithm evaluation using real-world human data, in silico trials, or clinical studies. We grouped decision support systems into general categories of (1) those which recommend adjustments to insulin and (2) those which predict and help avoid hypoglycemia. We review the artificial intelligence methods used for each type of decision support system, and discuss the performance and potential applications of these systems.
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Affiliation(s)
| | - Peter G. Jacobs
- Artificial Intelligence for Medical Systems Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA;
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21
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Xie J, Wang Q. A Data-Driven Personalized Model of Glucose Dynamics Taking Account of the Effects of Physical Activity for Type 1 Diabetes: An In Silico Study. J Biomech Eng 2020; 141:2703963. [PMID: 30458503 DOI: 10.1115/1.4041522] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Indexed: 12/17/2022]
Abstract
This paper aims to develop a data-driven model for glucose dynamics taking into account the effects of physical activity (PA) through a numerical study. It intends to investigate PA's immediate effect on insulin-independent glucose variation and PA's prolonged effect on insulin sensitivity. We proposed a nonlinear model with PA (NLPA), consisting of a linear regression of PA and a bilinear regression of insulin and PA. The model was identified and evaluated using data generated from a physiological PA-glucose model by Dalla Man et al. integrated with the uva/padova Simulator. Three metrics were computed to compare blood glucose (BG) predictions by NLPA, a linear model with PA (LPA), and a linear model with no PA (LOPA). For PA's immediate effect on glucose, NLPA and LPA showed 45-160% higher mean goodness of fit (FIT) than LOPA under 30 min-ahead glucose prediction (P < 0.05). For the prolonged PA effect on glucose, NLPA showed 87% higher FIT than LPA (P < 0.05) for simulations using no previous measurements. NLPA had 25-37% and 31-54% higher sensitivity in predicting postexercise hypoglycemia than LPA and LOPA, respectively. This study demonstrated the following qualitative trends: (1) for moderate-intensity exercise, accuracy of BG prediction was improved by explicitly accounting for PA's effect; and (2) accounting for PA's prolonged effect on insulin sensitivity can increase the chance of early prediction of postexercise hypoglycemia. Such observations will need to be further evaluated through human subjects in the future.
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Affiliation(s)
- Jinyu Xie
- Mechanical and Nuclear Engineering, 315 Leonhard Building, Penn State University, University Park, PA 16802 e-mail:
| | - Qian Wang
- Mem. ASME Professor Mechanical Engineering, 325 Leonhard Building, Penn State University, University Park, PA 16802 e-mail:
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22
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Fabris C, Ozaslan B, Breton MD. Continuous Glucose Monitors and Activity Trackers to Inform Insulin Dosing in Type 1 Diabetes: The University of Virginia Contribution. SENSORS 2019; 19:s19245386. [PMID: 31817678 PMCID: PMC6961036 DOI: 10.3390/s19245386] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 11/19/2019] [Accepted: 11/21/2019] [Indexed: 01/11/2023]
Abstract
Objective: Suboptimal insulin dosing in type 1 diabetes (T1D) is frequently associated with time-varying insulin requirements driven by various psycho-behavioral and physiological factors influencing insulin sensitivity (IS). Among these, physical activity has been widely recognized as a trigger of altered IS both during and following the exercise effort, but limited indication is available for the management of structured and (even more) unstructured activity in T1D. In this work, we present two methods to inform insulin dosing with biosignals from wearable sensors to improve glycemic control in individuals with T1D. Research Design and Methods: Continuous glucose monitors (CGM) and activity trackers are leveraged by the methods. The first method uses CGM records to estimate IS in real time and adjust the insulin dose according to a person’s insulin needs; the second method uses step count data to inform the bolus calculation with the residual glucose-lowering effects of recently performed (structured or unstructured) physical activity. The methods were tested in silico within the University of Virginia/Padova T1D Simulator. A standard bolus calculator and the proposed “smart” systems were deployed in the control of one meal in presence of increased/decreased IS (Study 1) and following a 1-hour exercise bout (Study 2). Postprandial glycemic control was assessed in terms of time spent in different glycemic ranges and low/high blood glucose indices (LBGI/HBGI), and compared between the dosing strategies. Results: In Study 1, the CGM-informed system allowed to reduce exposure to hypoglycemia in presence of increased IS (percent time < 70 mg/dL: 6.1% versus 9.9%; LBGI: 1.9 versus 3.2) and exposure to hyperglycemia in presence of decreased IS (percent time > 180 mg/dL: 14.6% versus 18.3%; HBGI: 3.0 versus 3.9), tending toward optimal control. In Study 2, the step count-informed system allowed to reduce hypoglycemia (percent time < 70 mg/dL: 3.9% versus 13.4%; LBGI: 1.7 versus 3.2) at the cost of a minor increase in exposure to hyperglycemia (percent time > 180 mg/dL: 11.9% versus 7.5%; HBGI: 2.4 versus 1.5). Conclusions: We presented and validated in silico two methods for the smart dosing of prandial insulin in T1D. If seen within an ensemble, the two algorithms provide alternatives to individuals with T1D for improving insulin dosing accommodating a large variety of treatment options. Future work will be devoted to test the safety and efficacy of the methods in free-living conditions.
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23
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Rashid M, Samadi S, Sevil M, Hajizadeh I, Kolodziej P, Hobbs N, Maloney Z, Brandt R, Feng J, Park M, Quinn L, Cinar A. Simulation Software for Assessment of Nonlinear and Adaptive Multivariable Control Algorithms: Glucose - Insulin Dynamics in Type 1 Diabetes. Comput Chem Eng 2019; 130:106565. [PMID: 32863472 PMCID: PMC7449052 DOI: 10.1016/j.compchemeng.2019.106565] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
A simulator for testing automatic control algorithms for nonlinear systems with time-varying parameters, variable time delays, and uncertainties is developed. It is based on simulation of virtual patients with Type 1 diabetes (T1D). Nonlinear models are developed to describe glucose concentration (GC) variations based on user-defined scenarios for meal consumption, insulin administration, and physical activity. They compute GC values and physiological variables, such as heart rate, skin temperature, accelerometer, and energy expenditure, that are indicative of physical activities affecting GC dynamics. This is the first simulator designed for assessment of multivariable controllers that consider supplemental physiological variables in addition to GC measurements to improve glycemic control. Virtual patients are generated from distributions of identified model parameters using clinical data. The simulator will enable testing and evaluation of new control algorithms proposed for automated insulin delivery as well as various control algorithms for nonlinear systems with uncertainties, time-varying parameters and delays.
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Affiliation(s)
- Mudassir Rashid
- Dept of Chemical and Biological Engineering, Illinois Institute of Technology, 10 W 33rd Street, Chicago, IL, USA, 60616
| | - Sediqeh Samadi
- Dept of Chemical and Biological Engineering, Illinois Institute of Technology, 10 W 33rd Street, Chicago, IL, USA, 60616
| | - Mert Sevil
- Dept of Biomedical Engineering, Illinois Institute of Technology, 10 W 33rd Street, Chicago, IL, USA, 606016
| | - Iman Hajizadeh
- Dept of Chemical and Biological Engineering, Illinois Institute of Technology, 10 W 33rd Street, Chicago, IL, USA, 60616
| | - Paul Kolodziej
- Dept of Chemical and Biological Engineering, Illinois Institute of Technology, 10 W 33rd Street, Chicago, IL, USA, 60616
| | - Nicole Hobbs
- Dept of Biomedical Engineering, Illinois Institute of Technology, 10 W 33rd Street, Chicago, IL, USA, 606016
| | - Zacharie Maloney
- Dept of Biomedical Engineering, Illinois Institute of Technology, 10 W 33rd Street, Chicago, IL, USA, 606016
| | - Rachel Brandt
- Dept of Biomedical Engineering, Illinois Institute of Technology, 10 W 33rd Street, Chicago, IL, USA, 606016
| | - Jianyuan Feng
- Dept of Chemical and Biological Engineering, Illinois Institute of Technology, 10 W 33rd Street, Chicago, IL, USA, 60616
| | - Minsun Park
- College of Nursing, University of Illinois at Chicago, Chicago, IL, USA, 60612
| | - Laurie Quinn
- College of Nursing, University of Illinois at Chicago, Chicago, IL, USA, 60612
| | - Ali Cinar
- Dept of Chemical and Biological Engineering, Illinois Institute of Technology, 10 W 33rd Street, Chicago, IL, USA, 60616
- Dept of Biomedical Engineering, Illinois Institute of Technology, 10 W 33rd Street, Chicago, IL, USA, 606016
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Liu C, Vehí J, Avari P, Reddy M, Oliver N, Georgiou P, Herrero P. Long-Term Glucose Forecasting Using a Physiological Model and Deconvolution of the Continuous Glucose Monitoring Signal. SENSORS (BASEL, SWITZERLAND) 2019; 19:E4338. [PMID: 31597288 PMCID: PMC6806292 DOI: 10.3390/s19194338] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 10/03/2019] [Accepted: 10/05/2019] [Indexed: 11/29/2022]
Abstract
(1) Objective: Blood glucose forecasting in type 1 diabetes (T1D) management is a maturing field with numerous algorithms being published and a few of them having reached the commercialisation stage. However, accurate long-term glucose predictions (e.g., >60 min), which are usually needed in applications such as precision insulin dosing (e.g., an artificial pancreas), still remain a challenge. In this paper, we present a novel glucose forecasting algorithm that is well-suited for long-term prediction horizons. The proposed algorithm is currently being used as the core component of a modular safety system for an insulin dose recommender developed within the EU-funded PEPPER (Patient Empowerment through Predictive PERsonalised decision support) project. (2) Methods: The proposed blood glucose forecasting algorithm is based on a compartmental composite model of glucose-insulin dynamics, which uses a deconvolution technique applied to the continuous glucose monitoring (CGM) signal for state estimation. In addition to commonly employed inputs by glucose forecasting methods (i.e., CGM data, insulin, carbohydrates), the proposed algorithm allows the optional input of meal absorption information to enhance prediction accuracy. Clinical data corresponding to 10 adult subjects with T1D were used for evaluation purposes. In addition, in silico data obtained with a modified version of the UVa-Padova simulator was used to further evaluate the impact of accounting for meal absorption information on prediction accuracy. Finally, a comparison with two well-established glucose forecasting algorithms, the autoregressive exogenous (ARX) model and the latent variable-based statistical (LVX) model, was carried out. (3) Results: For prediction horizons beyond 60 min, the performance of the proposed physiological model-based (PM) algorithm is superior to that of the LVX and ARX algorithms. When comparing the performance of PM against the secondly ranked method (ARX) on a 120 min prediction horizon, the percentage improvement on prediction accuracy measured with the root mean square error, A-region of error grid analysis (EGA), and hypoglycaemia prediction calculated by the Matthews correlation coefficient, was 18.8 % , 17.9 % , and 80.9 % , respectively. Although showing a trend towards improvement, the addition of meal absorption information did not provide clinically significant improvements. (4) Conclusion: The proposed glucose forecasting algorithm is potentially well-suited for T1D management applications which require long-term glucose predictions.
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Affiliation(s)
- Chengyuan Liu
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK;
| | - Josep Vehí
- Department of Electrical and Electronic Engineering, Universitat de Girona and with CIBERDEM, Girona 17004, Spain;
| | - Parizad Avari
- Department of Medicine, Imperial College Healthcare NHS Trust, London W12 0HS, UK; (P.A.); (M.R.); (N.O.)
| | - Monika Reddy
- Department of Medicine, Imperial College Healthcare NHS Trust, London W12 0HS, UK; (P.A.); (M.R.); (N.O.)
| | - Nick Oliver
- Department of Medicine, Imperial College Healthcare NHS Trust, London W12 0HS, UK; (P.A.); (M.R.); (N.O.)
| | - Pantelis Georgiou
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK;
| | - Pau Herrero
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK;
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25
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Ramkissoon CM, Bertachi A, Beneyto A, Bondia J, Vehi J. Detection and Control of Unannounced Exercise in the Artificial Pancreas Without Additional Physiological Signals. IEEE J Biomed Health Inform 2019; 24:259-267. [PMID: 30763250 DOI: 10.1109/jbhi.2019.2898558] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The purpose of this study was to develop an algorithm that detects aerobic exercise and triggers disturbance rejection actions to prevent exercise-induced hypoglycemia. This approach can provide a solution to poor glycemic control during and after aerobic exercise, a major hindrance in the participation of exercise by patients with type 1 diabetes. This novel exercise-induced hypoglycemia reduction algorithm (EHRA) detects exercise using a threshold on a disturbance term, a parameter estimated from an augmented minimal model using an unscented Kalman filter. After detection, the EHRA triggers the following three actions: First, a carbohydrate suggestion, second, a reduction in basal insulin and the insulin-on-board maximum limit, and finally, a 30% reduction of the next insulin meal bolus. The EHRA was tested in silico using a 15-day scenario with 8 exercise sessions of 50 min at [Formula: see text] on alternating days. The EHRA was able to obtain improved results when compared to strategies with and without exercise announcement. The unannounced, announced, and EHRA strategies all obtained an overall percentage of time in range (70-180 mg/dl) of 94% and a percentage of time 70 mg/dl of 2%, 0%, and 0%, respectively. The EHRA was tested for robustness during exercise sessions of +25% and -25% intensity and results suggest that the EHRA is able to account for variability in exercise intensity, duration, and patient dynamics such as glucose uptake rate and insulin sensitivity.
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Palumbo MC, Morettini M, Tieri P, Diele F, Sacchetti M, Castiglione F. Personalizing physical exercise in a computational model of fuel homeostasis. PLoS Comput Biol 2018; 14:e1006073. [PMID: 29698395 PMCID: PMC5919631 DOI: 10.1371/journal.pcbi.1006073] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Accepted: 03/07/2018] [Indexed: 11/18/2022] Open
Abstract
The beneficial effects of physical activity for the prevention and management of several chronic diseases are widely recognized. Mathematical modeling of the effects of physical exercise in body metabolism and in particular its influence on the control of glucose homeostasis is of primary importance in the development of eHealth monitoring devices for a personalized medicine. Nonetheless, to date only a few mathematical models have been aiming at this specific purpose. We have developed a whole-body computational model of the effects on metabolic homeostasis of a bout of physical exercise. Built upon an existing model, it allows to detail better both subjects' characteristics and physical exercise, thus determining to a greater extent the dynamics of the hormones and the metabolites considered.
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Affiliation(s)
- Maria Concetta Palumbo
- Institute for Applied Computing (IAC) “Mauro Picone”, National Research Council of Italy, Rome, Italy
| | - Micaela Morettini
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
| | - Paolo Tieri
- Institute for Applied Computing (IAC) “Mauro Picone”, National Research Council of Italy, Rome, Italy
| | - Fasma Diele
- Institute for Applied Computing (IAC) “Mauro Picone”, National Research Council of Italy, Rome, Italy
| | - Massimo Sacchetti
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, Rome, Italy
| | - Filippo Castiglione
- Institute for Applied Computing (IAC) “Mauro Picone”, National Research Council of Italy, Rome, Italy
- * E-mail:
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Bertachi A, Beneyto A, Ramkissoon CM, Vehí J. Assessment of Mitigation Methods to Reduce the Risk of Hypoglycemia for Announced Exercise in a Uni-hormonal Artificial Pancreas. Diabetes Technol Ther 2018; 20:285-295. [PMID: 29608335 DOI: 10.1089/dia.2017.0392] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
BACKGROUND Moderate physical activity improves overall health conditions in subjects with type 1 diabetes. However, insulin management during and after exercise is challenging due to the effects of exercise on glycemic control. Artificial pancreas (AP) systems aim to automatically control blood glucose levels, but exercise-induced hypoglycemia is a major challenge for these systems, especially in uni-hormonal configurations. The aim of this work was to evaluate the ability of several feed-forward (FF) actions to prevent exercise-induced hypoglycemia in a closed-loop setting. METHODS A closed-loop control algorithm combined with FF actions aimed at eliminating exercise-induced hypoglycemia was evaluated in silico using the UVa/Padova type 1 diabetes simulator. The simulator was modified with an exercise model fitted to clinical data. The FF actions were evaluated in two scenarios: (1) exercise sessions during postprandial period and (2) exercise sessions during fasting period. RESULTS The mitigation methods proposed in this work were able to minimize the occurrence of hypoglycemic events related with exercise in both scenarios. The time spent in hypoglycemic range in the 2-h period after exercise decreased from 33.3% to 0.0% (P < 0.01) and from 41.3% to 0.0% (P < 0.01) in both scenarios tested. Besides that, the occurrence of hypoglycemic events after exercise sessions was also reduced. CONCLUSIONS The combination of the FF actions presented in this article within an AP system showed to be an effective strategy to mitigate the risk of hypoglycemia in front of aerobic exercise.
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Affiliation(s)
- Arthur Bertachi
- 1 Institute of Informatics and Applications, University of Girona , Girona, Spain
- 2 Federal University of Technology - Paraná (UTFPR) , Guarapuava, Brazil
| | - Aleix Beneyto
- 1 Institute of Informatics and Applications, University of Girona , Girona, Spain
| | | | - Josep Vehí
- 1 Institute of Informatics and Applications, University of Girona , Girona, Spain
- 3 Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM) , Madrid, Spain
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Chase JG, Preiser JC, Dickson JL, Pironet A, Chiew YS, Pretty CG, Shaw GM, Benyo B, Moeller K, Safaei S, Tawhai M, Hunter P, Desaive T. Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them. Biomed Eng Online 2018; 17:24. [PMID: 29463246 PMCID: PMC5819676 DOI: 10.1186/s12938-018-0455-y] [Citation(s) in RCA: 84] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Accepted: 02/12/2018] [Indexed: 01/17/2023] Open
Abstract
Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current "one size fits all" protocolised care to adaptive, model-based "one method fits all" personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care.
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Affiliation(s)
- J. Geoffrey Chase
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Jean-Charles Preiser
- Department of Intensive Care, Erasme University of Hospital, 1070 Brussels, Belgium
| | - Jennifer L. Dickson
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Antoine Pironet
- GIGA In Silico Medicine, University of Liege, 4000 Liege, Belgium
| | - Yeong Shiong Chiew
- Department of Mechanical Engineering, School of Engineering, Monash University Malaysia, 47500 Selangor, Malaysia
| | - Christopher G. Pretty
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Geoffrey M. Shaw
- Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand
| | - Balazs Benyo
- Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Budapest, Hungary
| | - Knut Moeller
- Department of Biomedical Engineering, Institute of Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany
| | - Soroush Safaei
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Merryn Tawhai
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Peter Hunter
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Thomas Desaive
- GIGA In Silico Medicine, University of Liege, 4000 Liege, Belgium
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A system model of the effects of exercise on plasma Interleukin-6 dynamics in healthy individuals: Role of skeletal muscle and adipose tissue. PLoS One 2017; 12:e0181224. [PMID: 28704555 PMCID: PMC5507524 DOI: 10.1371/journal.pone.0181224] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2016] [Accepted: 06/27/2017] [Indexed: 12/25/2022] Open
Abstract
Interleukin-6 (IL-6) has been recently shown to play a central role in glucose homeostasis, since it stimulates the production and secretion of Glucagon-like Peptide-1 (GLP-1) from intestinal L-cells and pancreas, leading to an enhanced insulin response. In resting conditions, IL-6 is mainly produced by the adipose tissue whereas, during exercise, skeletal muscle contractions stimulate a marked IL-6 secretion as well. Available mathematical models describing the effects of exercise on glucose homeostasis, however, do not account for this IL-6 contribution. This study aimed at developing and validating a system model of exercise’s effects on plasma IL-6 dynamics in healthy humans, combining the contributions of both adipose tissue and skeletal muscle. A two-compartment description was adopted to model plasma IL-6 changes in response to oxygen uptake’s variation during an exercise bout. The free parameters of the model were estimated by means of a cross-validation procedure performed on four different datasets. A low coefficient of variation (<10%) was found for each parameter and the physiologically meaningful parameters were all consistent with literature data. Moreover, plasma IL-6 dynamics during exercise and post-exercise were consistent with literature data from exercise protocols differing in intensity, duration and modality. The model successfully emulated the physiological effects of exercise on plasma IL-6 levels and provided a reliable description of the role of skeletal muscle and adipose tissue on the dynamics of plasma IL-6. The system model here proposed is suitable to simulate IL-6 response to different exercise modalities. Its future integration with existing models of GLP-1-induced insulin secretion might provide a more reliable description of exercise’s effects on glucose homeostasis and hence support the definition of more tailored interventions for the treatment of type 2 diabetes.
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Mansell EJ, Docherty PD, Chase JG. Shedding light on grey noise in diabetes modelling. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2016.06.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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Contreras I, Quirós C, Giménez M, Conget I, Vehi J. Profiling intra-patient type I diabetes behaviors. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 136:131-141. [PMID: 27686710 DOI: 10.1016/j.cmpb.2016.08.022] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2016] [Revised: 07/22/2016] [Accepted: 08/29/2016] [Indexed: 06/06/2023]
Abstract
BACKGROUND The large intra-patient variability in type 1 diabetic patients dramatically reduces the ability to achieve adequate blood glucose control. A novel methodology to identify different blood glucose dynamics profiles will allow therapies to be more accurate and tailored according to patient's conditions and to the situations faced by patients (exercise, week-ends, holidays, menstruation, etc). MATERIALS AND METHODS A clustering methodology based on the normalized compression distance is applied to identify different profiles for diabetic patients. First, the methodology is validated using "in silico" data from 10 patients in 3 different scenarios: days without exercise, poor controlled exercise days and days with well-controlled exercise. Second, we perform a series of in vivo experiments using data from 10 patients assessing the ability of the proposed methodology in real scenarios. RESULTS In silico experiments show that the methodology is able to identify poor and well-controlled days in theoretical scenarios. In vivo experiments present meaningful profiles for working days, bank days and other situations, where different insulin requirements were detected. CONCLUSIONS A tool for profiling blood glucose dynamics of patients can be implemented in a short term to enhance existing analysis platforms using combined CGM-CSII systems. Besides coping with the information overload, the tool will assist physicians to adjust and improve insulin therapy and patients in the self-management of the disease.
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Affiliation(s)
- Iván Contreras
- Institut d'Informática i Aplicacions, Universitat de Girona, Campus de Montilivi, Girona, Spain.
| | - Carmen Quirós
- Diabetes Unit, Endocrinology and Nutrition Department, Hospital Clínic i Universitari, Barcelona, Spain
| | - Marga Giménez
- Diabetes Unit, Endocrinology and Nutrition Department, Hospital Clínic i Universitari, Barcelona, Spain
| | - Ignacio Conget
- Diabetes Unit, Endocrinology and Nutrition Department, Hospital Clínic i Universitari, Barcelona, Spain
| | - Josep Vehi
- Institut d'Informática i Aplicacions, Universitat de Girona, Campus de Montilivi, Girona, Spain
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Colmegna PH, Sánchez-Peña RS, Gondhalekar R, Dassau E, Doyle FJ. Reducing Glucose Variability Due to Meals and Postprandial Exercise in T1DM Using Switched LPV Control: In Silico Studies. J Diabetes Sci Technol 2016; 10:744-53. [PMID: 27022097 PMCID: PMC5038547 DOI: 10.1177/1932296816638857] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Time-varying dynamics is one of the main issues for achieving safe blood glucose control in type 1 diabetes mellitus (T1DM) patients. In addition, the typical disturbances considered for controller design are meals, which increase the glucose level, and physical activity (PA), which increases the subject's sensitivity to insulin. In previous works the authors have applied a linear parameter-varying (LPV) control technique to manage unannounced meals. METHODS A switched LPV controller that switches between 3 LPV controllers, each with a different level of aggressiveness, is designed to further cope with both unannounced meals and postprandial PA. Thus, the proposed control strategy has a "standard" mode, an "aggressive" mode, and a "conservative" mode. The "standard" mode is designed to be applied most of the time, while the "aggressive" mode is designed to deal only with hyperglycemia situations. On the other hand, the "conservative" mode is focused on postprandial PA control. RESULTS An ad hoc simulator has been developed to test the proposed controller. This simulator is based on the distribution version of the UVA/Padova model and includes the effect of PA based on Schiavon.(1) The test results obtained when using this simulator indicate that the proposed control law substantially reduces the risk of hypoglycemia with the conservative strategy, while the risk of hyperglycemia is scarcely affected. CONCLUSIONS It is demonstrated that the announcement, or anticipation, of exercise is indispensable for letting a mono-hormonal artificial pancreas deal with the consequences of postprandial PA. In view of this the proposed controller allows switching into a conservative mode when notified of PA by the user.
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Affiliation(s)
- Patricio H Colmegna
- National Scientific and Technical Research Council, Buenos Aires, Argentina Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, Buenos Aires, Argentina
| | - Ricardo S Sánchez-Peña
- National Scientific and Technical Research Council, Buenos Aires, Argentina Centro de Sistemas y Control, Instituto Tecnológico de Buenos Aires, Buenos Aires, Argentina
| | - Ravi Gondhalekar
- Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Eyal Dassau
- John A. Paulson School of Engineering & Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Francis J Doyle
- John A. Paulson School of Engineering & Applied Sciences, Harvard University, Cambridge, MA, USA
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Ding S, Schumacher M. Sensor Monitoring of Physical Activity to Improve Glucose Management in Diabetic Patients: A Review. SENSORS 2016; 16:s16040589. [PMID: 27120602 PMCID: PMC4851102 DOI: 10.3390/s16040589] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 02/17/2016] [Revised: 04/14/2016] [Accepted: 04/21/2016] [Indexed: 12/11/2022]
Abstract
Diabetic individuals need to tightly control their blood glucose concentration. Several methods have been developed for this purpose, such as the finger-prick or continuous glucose monitoring systems (CGMs). However, these methods present the disadvantage of being invasive. Moreover, CGMs have limited accuracy, notably to detect hypoglycemia. It is also known that physical exercise, and even daily activity, disrupt glucose dynamics and can generate problems with blood glucose regulation during and after exercise. In order to deal with these challenges, devices for monitoring patients’ physical activity are currently under development. This review focuses on non-invasive sensors using physiological parameters related to physical exercise that were used to improve glucose monitoring in type 1 diabetes (T1DM) patients. These devices are promising for diabetes management. Indeed they permit to estimate glucose concentration either based solely on physical activity parameters or in conjunction with CGM or non-invasive CGM (NI-CGM) systems. In these last cases, the vital signals are used to modulate glucose estimations provided by the CGM and NI-CGM devices. Finally, this review indicates possible limitations of these new biosensors and outlines directions for future technologic developments.
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Affiliation(s)
- Sandrine Ding
- HESAV, University of Applied Sciences and Arts Western Switzerland (HES-SO), Av. Beaumont 21, Lausanne 1011, Switzerland.
| | - Michael Schumacher
- Institute of Information Systems, University of Applied Sciences and Arts Western Switzerland (HES-SO), Techno-Pôle 3, Sierre 3960, Switzerland.
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Merck CA, Kleinberg S. Causal Explanation Under Indeterminism: A Sampling Approach. PROCEEDINGS OF THE ... AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE. AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE 2016; 2016:1037-1043. [PMID: 31001456 PMCID: PMC6465960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
One of the key uses of causes is to explain why things happen. Explanations of specific events, like an individual's heart attack on Monday afternoon or a particular car accident, help assign responsibility and inform our future decisions. Computational methods for causal inference make use of the vast amounts of data collected by individuals to better understand their behavior and improve their health. However, most methods for explanation of specific events have provided theoretical approaches with limited applicability. In contrast we make two main contributions: an algorithm for explanation that calculates the strength of token causes, and an evaluation based on simulated data that enables objective comparison against prior methods and ground truth. We show that the approach finds the correct relationships in classic test cases (causal chains, common cause, and backup causation) and in a realistic scenario (explaining hyperglycemic episodes in a simulation of type 1 diabetes).
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Li P, Yu L, Fang Q, Lee SY. A simplification of Cobelli's glucose-insulin model for type 1 diabetes mellitus and its FPGA implementation. Med Biol Eng Comput 2016; 54:1563-77. [PMID: 26718555 DOI: 10.1007/s11517-015-1436-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2015] [Accepted: 12/11/2015] [Indexed: 11/24/2022]
Abstract
Cobelli's glucose-insulin model is the only computer simulator of glucose-insulin interactions accepted by Food Drug Administration as a substitute to animal trials. However, it consists of multiple differential equations that make it hard to be implemented on a hardware platform. In this investigation, the Cobelli's model is simplified by Padé approximant method and implemented on a field-programmable gate array-based platform as a hardware model for predicting glucose changes in subjects with type 1 diabetes mellitus. Compared with the original Cobelli's model, the implemented hardware model provides a nearly perfect approximation in predicting glucose changes with rather small root-mean-square errors and maximum errors. The RMSE results for 30 subjects show that the method for simplifying and implementing Cobelli's model has good robustness and applicability. The successful hardware implementation of Cobelli's model will promote a wider adoption of this model that can substitute animal trials, provide fast and reliable glucose and insulin estimation, and ultimately assist the further development of an artificial pancreas system.
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Affiliation(s)
- Peng Li
- Department of Medical Electronics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China. .,Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, China. .,University of Chinese Academy of Sciences, Beijing, China.
| | - Lei Yu
- Department of Medical Electronics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Qiang Fang
- School of Electrical and Computer Engineering, RMIT University, Melbourne, Australia
| | - Shuenn-Yuh Lee
- Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan
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Dasanayake IS, Seborg DE, Pinsker JE, Doyle FJ, Dassau E. Empirical Dynamic Model Identification for Blood-Glucose Dynamics in Response to Physical Activity. PROCEEDINGS OF THE ... IEEE CONFERENCE ON DECISION & CONTROL. IEEE CONFERENCE ON DECISION & CONTROL 2015; 2015:3834-3839. [PMID: 26997750 PMCID: PMC4794272 DOI: 10.1109/cdc.2015.7402815] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2023]
Abstract
In this paper, the dynamic response of blood glucose concentration in response to physical activity of people with Type 1 Diabetes Mellitus (T1DM) is captured by subspace identification methods. Activity (input) and subcutaneous blood glucose measurements (output) are employed to construct a personalized prediction model through semi-definite programming. The model is calibrated and subsequently validated with non-overlapping data sets from 15 T1DM subjects. This preliminary clinical evaluation reveals the underlying linear dynamics between blood glucose concentration and physical activity. These types of models can enhance our capabilities of achieving tighter blood glucose control and early detection of hypoglycemia for people with T1DM.
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Affiliation(s)
- Isuru S. Dasanayake
- Department of Chemical Engineering, University of California
Santa Barbara, Santa Barbara, CA 93106-5080, USA
- William Sansum Diabetes Center, Santa Barbara, CA 93105,
USA
| | - Dale E. Seborg
- Department of Chemical Engineering, University of California
Santa Barbara, Santa Barbara, CA 93106-5080, USA
- William Sansum Diabetes Center, Santa Barbara, CA 93105,
USA
| | | | - Francis J. Doyle
- William Sansum Diabetes Center, Santa Barbara, CA 93105,
USA
- John A. Paulson School of Engineering and Applied Sciences,
Harvard University, Cambridge, MA 02138, USA
| | - Eyal Dassau
- William Sansum Diabetes Center, Santa Barbara, CA 93105,
USA
- John A. Paulson School of Engineering and Applied Sciences,
Harvard University, Cambridge, MA 02138, USA
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Rahman SA, Huang Y, Claassen J, Heintzman N, Kleinberg S. Combining Fourier and lagged k-nearest neighbor imputation for biomedical time series data. J Biomed Inform 2015; 58:198-207. [PMID: 26477633 DOI: 10.1016/j.jbi.2015.10.004] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2015] [Revised: 09/29/2015] [Accepted: 10/05/2015] [Indexed: 01/23/2023]
Abstract
Most clinical and biomedical data contain missing values. A patient's record may be split across multiple institutions, devices may fail, and sensors may not be worn at all times. While these missing values are often ignored, this can lead to bias and error when the data are mined. Further, the data are not simply missing at random. Instead the measurement of a variable such as blood glucose may depend on its prior values as well as that of other variables. These dependencies exist across time as well, but current methods have yet to incorporate these temporal relationships as well as multiple types of missingness. To address this, we propose an imputation method (FLk-NN) that incorporates time lagged correlations both within and across variables by combining two imputation methods, based on an extension to k-NN and the Fourier transform. This enables imputation of missing values even when all data at a time point is missing and when there are different types of missingness both within and across variables. In comparison to other approaches on three biological datasets (simulated and actual Type 1 diabetes datasets, and multi-modality neurological ICU monitoring) the proposed method has the highest imputation accuracy. This was true for up to half the data being missing and when consecutive missing values are a significant fraction of the overall time series length.
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Affiliation(s)
- Shah Atiqur Rahman
- Department of Computer Science, Stevens Institute of Technology, NJ, United States.
| | - Yuxiao Huang
- Department of Computer Science, Stevens Institute of Technology, NJ, United States.
| | - Jan Claassen
- Division of Critical Care Neurology, Department of Neurology, Columbia University, College of Physicians and Surgeons, New York, NY, United States.
| | | | - Samantha Kleinberg
- Department of Computer Science, Stevens Institute of Technology, NJ, United States.
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Mansell EJ, Docherty PD, Fisk LM, Chase JG. Estimation of secondary effect parameters in glycaemic dynamics using accumulating data from a virtual type 1 diabetic patient. Math Biosci 2015; 266:108-17. [DOI: 10.1016/j.mbs.2015.06.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2014] [Revised: 06/08/2015] [Accepted: 06/09/2015] [Indexed: 11/25/2022]
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Fang Q, Yu L, Li P. A new insulin-glucose metabolic model of type 1 diabetes mellitus: An in silico study. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2015; 120:16-26. [PMID: 25896293 DOI: 10.1016/j.cmpb.2015.03.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2014] [Revised: 03/12/2015] [Accepted: 03/30/2015] [Indexed: 06/04/2023]
Abstract
Diabetes mellitus is a serious metabolic disease that threatens people's health. The artificial pancreas system (APS) has been generally considered as the ultimate cure of type 1 diabetes mellitus (T1DM). The simulation model of insulin-glucose metabolism is an essential part of an APS as it processes the measured glucose level and generates control signal to the insulin infusion system. This paper presents a new insulin-glucose metabolic model using model reduction methods applied to the popular but complex Cobelli's model. The performances of three different model reduction methods, namely Padé approximation, Routh approximation and system identification, are compared. The results of in silico simulation based on 30 virtual patients of three groups for adults, adolescents, and children show that the approximation error between this new model and the original Cobelli's model is so small that can be neglected. It can be concluded that the proposed simplified model can describe the insulin-glucose metabolism process rather accurately as well as can be easily implemented and integrated into an APS to make the APS technology more mature and closer to clinical use. The FPGA implementation, testing and further simplification possibility will be explored in the next stage of research.
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Affiliation(s)
- Qiang Fang
- School of Electrical and Computing Engineering, RMIT University, Melbourne, VIC 3000, Australia.
| | - Lei Yu
- University of Chinese Academy of Sciences, Beijing, China
| | - Peng Li
- University of Chinese Academy of Sciences, Beijing, China
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Balakrishnan NP, Samavedham L, Rangaiah GP. Personalized mechanistic models for exercise, meal and insulin interventions in children and adolescents with type 1 diabetes. J Theor Biol 2014; 357:62-73. [DOI: 10.1016/j.jtbi.2014.04.038] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2013] [Revised: 03/29/2014] [Accepted: 04/30/2014] [Indexed: 11/15/2022]
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Liu SW, Huang HP, Lin CH, Chien IL. Modified control algorithms for patients with type 1 diabetes mellitus undergoing exercise. J Taiwan Inst Chem Eng 2014. [DOI: 10.1016/j.jtice.2014.06.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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Peyser T, Dassau E, Breton M, Skyler JS. The artificial pancreas: current status and future prospects in the management of diabetes. Ann N Y Acad Sci 2014; 1311:102-23. [PMID: 24725149 DOI: 10.1111/nyas.12431] [Citation(s) in RCA: 107] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Recent advances in insulins, insulin pumps, continuous glucose-monitoring systems, and control algorithms have resulted in an acceleration of progress in the development of artificial pancreas devices. This review discusses progress in the development of external systems that are based on subcutaneous drug delivery and subcutaneous continuous glucose monitoring. There are two major system-level approaches to achieving closed-loop control of blood glucose in diabetic individuals. The unihormonal approach uses insulin to reduce blood glucose and relies on complex safety mitigation algorithms to reduce the risk of hypoglycemia. The bihormonal approach uses both insulin to lower blood glucose and glucagon to raise blood glucose, and also relies on complex algorithms to provide for safety of the user. There are several major strategies for the design of control algorithms and supervision control for application to the artificial pancreas: proportional-integral-derivative, model predictive control, fuzzy logic, and safety supervision designs. Advances in artificial pancreas research in the first decade of this century were based on the ongoing computer revolution and miniaturization of electronic technology. The advent of modern smartphones has created the ability to utilize smartphone technology as the engineering centerpiece of an artificial pancreas. With these advances, an artificial or bionic pancreas is within reach.
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Ståhl F, Johansson R, Renard E. Ensemble Glucose Prediction in Insulin-Dependent Diabetes. DATA-DRIVEN MODELING FOR DIABETES 2014. [DOI: 10.1007/978-3-642-54464-4_2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Zecchin C, Facchinetti A, Sparacino G, Dalla Man C, Manohar C, Levine JA, Basu A, Kudva YC, Cobelli C. Physical activity measured by physical activity monitoring system correlates with glucose trends reconstructed from continuous glucose monitoring. Diabetes Technol Ther 2013; 15:836-44. [PMID: 23944973 PMCID: PMC3781118 DOI: 10.1089/dia.2013.0105] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
BACKGROUND In type 1 diabetes mellitus (T1DM), physical activity (PA) lowers the risk of cardiovascular complications but hinders the achievement of optimal glycemic control, transiently boosting insulin action and increasing hypoglycemia risk. Quantitative investigation of relationships between PA-related signals and glucose dynamics, tracked using, for example, continuous glucose monitoring (CGM) sensors, have been barely explored. SUBJECTS AND METHODS In the clinic, 20 control and 19 T1DM subjects were studied for 4 consecutive days. They underwent low-intensity PA sessions daily. PA was tracked by the PA monitoring system (PAMS), a system comprising accelerometers and inclinometers. Variations on glucose dynamics were tracked estimating first- and second-order time derivatives of glucose concentration from CGM via Bayesian smoothing. Short-time effects of PA on glucose dynamics were quantified through the partial correlation function in the interval (0, 60 min) after starting PA. RESULTS Correlation of PA with glucose time derivatives is evident. In T1DM, the negative correlation with the first-order glucose time derivative is maximal (absolute value) after 15 min of PA, whereas the positive correlation is maximal after 40-45 min. The negative correlation between the second-order time derivative and PA is maximal after 5 min, whereas the positive correlation is maximal after 35-40 min. Control subjects provided similar results but with positive and negative correlation peaks anticipated of 5 min. CONCLUSIONS Quantitative information on correlation between mild PA and short-term glucose dynamics was obtained. This represents a preliminary important step toward incorporation of PA information in more realistic physiological models of the glucose-insulin system usable in T1DM simulators, in development of closed-loop artificial pancreas control algorithms, and in CGM-based prediction algorithms for generation of hypoglycemic alerts.
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Affiliation(s)
- Chiara Zecchin
- 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
| | - Chiara Dalla Man
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Chinmay Manohar
- Department of Internal Medicine, Division of Endocrinology and Metabolism, Mayo Clinic, Rochester, Minnesota
| | - James A. Levine
- Department of Internal Medicine, Division of Endocrinology and Metabolism, Mayo Clinic, Rochester, Minnesota
| | - Ananda Basu
- Department of Internal Medicine, Division of Endocrinology and Metabolism, Mayo Clinic, Rochester, Minnesota
| | - Yogish C. Kudva
- Department of Internal Medicine, Division of Endocrinology and Metabolism, Mayo Clinic, Rochester, Minnesota
| | - Claudio Cobelli
- Department of Information Engineering, University of Padova, Padova, Italy
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Manohar C, O'Keeffe DT, Hinshaw L, Lingineni R, McCrady-Spitzer SK, Levine JA, Carter RE, Basu A, Kudva YC. Comparison of physical activity sensors and heart rate monitoring for real-time activity detection in type 1 diabetes and control subjects. Diabetes Technol Ther 2013; 15:751-7. [PMID: 23937615 PMCID: PMC3757536 DOI: 10.1089/dia.2013.0044] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND Currently, patients with type 1 diabetes decide on the amount of insulin to administer based on several factors, including current plasma glucose value, expected meal input, and physical activity (PA). One future therapeutic modality for patients with type 1 diabetes is the artificial endocrine pancreas (AEP). Incorporation of PA could enhance the efficacy of AEP significantly. We compared the main technologies used for PA quantitation. SUBJECTS AND METHODS Data were collected during inpatient studies involving healthy control subjects and type 1 diabetes. We report PA quantified from accelerometers (acceleration units [AU]) and heart rate (HR) monitors during a standardized activity protocol performed after a dinner meal at 7 p.m. from nine control subjects (four were males, 37.4±12.7 years old, body mass index of 24.8±3.8 kg/m(2), and fasting plasma glucose of 4.71±0.63 mmol/L) and eight with type 1 diabetes (six were males, 45.2±13.4 years old, body mass index of 25.1±2.9 kg/m(2), and fasting plasma glucose of 8.44±2.31 mmol/L). RESULTS The patient-to-patient variability was considerably less when examining AU compared with HR monitors. Furthermore, the exercise bouts and rest periods were more evident from the data streams when AUs were used to quantify activity. Unlike the AU, the HR measurements provided little insight for active and rest stages, and HR data required patient-specific standardizations to discern any meaningful pattern in the data. CONCLUSIONS Our results indicated that AU provides a reliable signal in response to PA, including low-intensity activity. Correlation of this signal with continuous glucose monitoring data would be the next step before exploring inclusion as input for AEP control.
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Affiliation(s)
- Chinmay Manohar
- Division of Endocrinology and Metabolism, Mayo Clinic, Rochester, Minnesota
| | - Derek T. O'Keeffe
- Division of Endocrinology and Metabolism, Mayo Clinic, Rochester, Minnesota
| | - Ling Hinshaw
- Division of Endocrinology and Metabolism, Mayo Clinic, Rochester, Minnesota
| | - Ravi Lingineni
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | | | - James A. Levine
- Division of Endocrinology and Metabolism, Mayo Clinic, Rochester, Minnesota
| | - Rickey E. Carter
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Ananda Basu
- Division of Endocrinology and Metabolism, Mayo Clinic, Rochester, Minnesota
| | - Yogish C. Kudva
- Division of Endocrinology and Metabolism, Mayo Clinic, Rochester, Minnesota
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Blood glucose control algorithms for type 1 diabetic patients: A methodological review. Biomed Signal Process Control 2013. [DOI: 10.1016/j.bspc.2012.09.003] [Citation(s) in RCA: 83] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Hughes-Karvetski C, Patek SD, Breton MD, Kovatchev BP. Historical data enhances safety supervision system performance in T1DM insulin therapy risk management. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2013; 109:220-5. [PMID: 22342221 PMCID: PMC3369012 DOI: 10.1016/j.cmpb.2011.12.016] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2011] [Revised: 12/21/2011] [Accepted: 12/24/2011] [Indexed: 05/10/2023]
Abstract
Safety measures to prevent or mitigate hypoglycemia are an important component of open loop, closed loop, and advisory mode insulin therapy control settings in type 1 diabetes. In recent work, we introduce a method for the automatic, gradual attenuation of the insulin pump delivery rate when a risk of hypoglycemia is detected, a method that we refer to as brakes. In the methods presented here, we demonstrate the use of historical glucose measurement data to inform and enhance the ability of the brakes to prevent hypoglycemia in real-time. The updated brakes are based on a patient-specific, time-varying model that reflects the typical trajectory of glycemic fluctuations throughout the day. Historical heightened risk of hypoglycemia throughout the day prompts an increase in the aggressiveness of insulin attenuation as compared to the original brakes that are based on real-time data alone. Through the use of available real-time data supplemented with historical glucose information to assess hypoglycemic risk, we are able to better anticipate and prevent hypoglycemia.
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Manohar C, Levine JA, Nandy DK, Saad A, Dalla Man C, McCrady-Spitzer SK, Basu R, Cobelli C, Carter RE, Basu A, Kudva YC. The effect of walking on postprandial glycemic excursion in patients with type 1 diabetes and healthy people. Diabetes Care 2012; 35:2493-9. [PMID: 22875231 PMCID: PMC3507567 DOI: 10.2337/dc11-2381] [Citation(s) in RCA: 62] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Physical activity (PA), even at low intensity, promotes health and improves hyperglycemia. However, the effect of low-intensity PA captured with accelerometery on glucose variability in healthy individuals and patients with type 1 diabetes has not been examined. Quantifying the effects of PA on glycemic variability would improve artificial endocrine pancreas (AEP) algorithms. RESEARCH DESIGN AND METHODS We studied 12 healthy control subjects (five males, 37.7 ± 13.7 years of age) and 12 patients with type 1 diabetes (five males, 37.4 ± 14.2 years of age) for 88 h. Participants performed PA approximating a threefold increase over their basal metabolic rate. PA was captured using a PA-monitoring system, and interstitial fluid glucose concentrations were captured with continuous glucose monitors. In random order, one meal per day was followed by inactivity, and the other meals were followed by walking. Glucose and PA data for a total of 216 meals were analyzed from 30 min prior to meal ingestion to 270 min postmeal. RESULTS In healthy subjects, the incremental glucose area under the curve was 4.5 mmol/L/270 min for meals followed by walking, whereas it was 9.6 mmol/L/270 min (P = 0.022) for meals followed by inactivity. The corresponding glucose excursions for those with type 1 diabetes were 7.5 mmol/L/270 min and 18.4 mmol/L/270 min, respectively (P < 0.001). CONCLUSIONS Walking significantly impacts postprandial glucose excursions in healthy populations and in those with type 1 diabetes. AEP algorithms incorporating PA may enhance tight glycemic control end points.
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Affiliation(s)
- Chinmay Manohar
- Center for Clinical Investigation, Case Medical School, Case Western Reserve University, Cleveland, Ohio, USA
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Supporting Content, Context and User Awareness in Future Internet Applications. THE FUTURE INTERNET 2012. [DOI: 10.1007/978-3-642-30241-1_14] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Abstract
Automated closed-loop insulin delivery, also referred to as the 'artificial pancreas', has been an important but elusive goal of diabetes treatment for many decades. Research milestones include the conception of continuous glucose monitoring in the early 1960s, followed by the production of the first commercial hospital-based artificial pancreas in the late 1970s that combined intravenous glucose sensing and insulin delivery. In the past 10 years, research into the artificial pancreas has gained substantial momentum and focused on the subcutaneous route for glucose measurement and insulin delivery, which reflects technological advances in interstitial glucose monitoring and the increasing use of the continuous subcutaneous insulin infusion. This Review discusses the design of an artificial pancreas, its components and clinical results, as well as the advantages and disadvantages of different types of automated closed-loop systems and potential future advances. The introduction of the artificial pancreas into clinical practice will probably occur gradually, starting with simpler approaches, such as overnight control of blood glucose concentration and temporary pump shut-off, that are adapted to more complex situations, such as glycemic control during meals and exercise.
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Affiliation(s)
- Roman Hovorka
- Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0QQ, UK.
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