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Yuan Y, Shi G, Chen H, Wang M, Liu H, Zhang X, Wang B, Zhang G, Sun L. Effects of preoperative oral enzyme-hydrolyzed rice flour solution on gastric emptying and insulin resistance in patients undergoing laparoscopic cholecystectomy: a prospective randomized controlled trial. BMC Anesthesiol 2023; 23:52. [PMID: 36782111 PMCID: PMC9923920 DOI: 10.1186/s12871-023-02012-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 02/06/2023] [Indexed: 02/15/2023] Open
Abstract
BACKGROUND The effect of preoperative oral carbohydrates (POC) on insulin resistance (IR) of laparoscopic cholecystectomy (LC) remains debatable. Enzyme-hydrolyzed rice flour (EHR) is a kind of water-soluble micromolecular carbohydrates. This study aimed to investigate the impact of preoperative oral EHR solution on gastric emptying and IR in patients undergoing LC. METHODS Patients (n = 100) undergoing LC were divided into oral-water group (group C) or oral-EHR solution (group E) randomly (n = 50 each), and the patients drank 300 ml water or EHR solution 2-3 h before surgery respectively. Gastric emptying which was quantized by gastric volume (GV) from antrum ultrasonography, IR indicators, subjective comfort indicators, handgrip strength, postoperative recovery indexes, and complications were recorded. RESULTS There were no differences in GV between the two groups before oral administration (V0), immediately after oral administration (V1) and before anesthesia induction(V2). The GV at V2 (GV2) reduced to the level of V0 (GV0) in the two groups. Fasting glucose (FG), fasting insulin (FINS) and Homa-IR in the two groups increased at postoperative day 1 (Pos 1d) compared with those at preoperative day 1(Pre 1d). Homa-IS and Homa-β in the two groups decreased at Pos 1d compared with those at Pre 1d. FG, FINS and Homa-IR in group E were lower than those in group C at Pos 1d, and Homa-IS and Homa-β were higher in group E than those in group C at Pos 1d. Subjective comfort indictors (hunger, fatigue and anxiety) in group E were lower than those in group C at preoperative 15 min (Pre 15 min) and postoperative 1 h (Pos 1 h). Handgrip strength in group E was raised compared with that in group C at Pre 15 min, Pos 1 h and Pos 1d. There was a lower incidence of nausea and earlier exhaust time in group E. CONCLUSION Oral 300 ml EHR solution 2-3 h before LC surgery did not increase the occurrence of reflux and aspiration during anesthesia induction with a normal gastric emptying, ameliorated postoperative IR, improved subjective comfort, and promoted postoperative gastrointestinal function recovery. TRIAL REGISTRATION Prospectively registered at the China Clinical Trial Registry, registration number: ChiCTR2000039939, date of registration:14/11/2020.
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Affiliation(s)
- Yang Yuan
- grid.415468.a0000 0004 1761 4893Department of Anesthesiology, Qingdao Municipal Hospital, University of Health and Rehabilitation Sciences, Qingdao, 266071 Shandong People’s Republic of China
| | - Guangjun Shi
- grid.415468.a0000 0004 1761 4893Department of Hepatobiliary Pancreatic Surgery, Qingdao Municipal Hospital, University of Health and Rehabilitation Sciences, Qingdao, 266071 Shandong People’s Republic of China
| | - Huailong Chen
- Department of Anesthesiology, Qingdao Eight People’s Hospital, Qingdao, 266041 Shandong People’s Republic of China
| | - Mingshan Wang
- grid.415468.a0000 0004 1761 4893Department of Anesthesiology, Qingdao Municipal Hospital, University of Health and Rehabilitation Sciences, Qingdao, 266071 Shandong People’s Republic of China
| | - Haofei Liu
- grid.410645.20000 0001 0455 0905 Graduate School, Qingdao University, Qingdao, 266071 Shandong People’s Republic of China
| | - Xiao Zhang
- grid.415468.a0000 0004 1761 4893Department of Anesthesiology, Qingdao Municipal Hospital, University of Health and Rehabilitation Sciences, Qingdao, 266071 Shandong People’s Republic of China
| | - Bin Wang
- grid.415468.a0000 0004 1761 4893Department of Anesthesiology, Qingdao Municipal Hospital, University of Health and Rehabilitation Sciences, Qingdao, 266071 Shandong People’s Republic of China
| | - Gaofeng Zhang
- Department of Anesthesiology, Qingdao Municipal Hospital, University of Health and Rehabilitation Sciences, Qingdao, 266071, Shandong, People's Republic of China.
| | - Lixin Sun
- Department of Anesthesiology, Qingdao Municipal Hospital, University of Health and Rehabilitation Sciences, Qingdao, 266071, Shandong, People's Republic of China.
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Nimri R, Phillip M, Kovatchev B. Decision Support Systems and Closed-Loop. Diabetes Technol Ther 2022; 24:S58-S75. [PMID: 35475696 DOI: 10.1089/dia.2022.2504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Revital Nimri
- Diabetes Technology Center, Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, Schneider Children's Medical Center of Israel, Petah Tikva, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Moshe Phillip
- Diabetes Technology Center, Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, Schneider Children's Medical Center of Israel, Petah Tikva, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Boris Kovatchev
- University of Virginia Center for Diabetes Technology, University of Virginia School of Medicine, Charlottesville, VA
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Hettiarachchi C, Daskalaki E, Desborough J, Nolan CJ, O'Neal D, Suominen H. Integrating Multiple Inputs Into an Artificial Pancreas System: Narrative Literature Review. JMIR Diabetes 2022; 7:e28861. [PMID: 35200143 PMCID: PMC8914747 DOI: 10.2196/28861] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 06/07/2021] [Accepted: 01/01/2022] [Indexed: 12/02/2022] Open
Abstract
Background Type 1 diabetes (T1D) is a chronic autoimmune disease in which a deficiency in insulin production impairs the glucose homeostasis of the body. Continuous subcutaneous infusion of insulin is a commonly used treatment method. Artificial pancreas systems (APS) use continuous glucose level monitoring and continuous subcutaneous infusion of insulin in a closed-loop mode incorporating a controller (or control algorithm). However, the operation of APS is challenging because of complexities arising during meals, exercise, stress, sleep, illnesses, glucose sensing and insulin action delays, and the cognitive burden. To overcome these challenges, options to augment APS through integration of additional inputs, creating multi-input APS (MAPS), are being investigated. Objective The aim of this survey is to identify and analyze input data, control architectures, and validation methods of MAPS to better understand the complexities and current state of such systems. This is expected to be valuable in developing improved systems to enhance the quality of life of people with T1D. Methods A literature survey was conducted using the Scopus, PubMed, and IEEE Xplore databases for the period January 1, 2005, to February 10, 2020. On the basis of the search criteria, 1092 articles were initially shortlisted, of which 11 (1.01%) were selected for an in-depth narrative analysis. In addition, 6 clinical studies associated with the selected studies were also analyzed. Results Signals such as heart rate, accelerometer readings, energy expenditure, and galvanic skin response captured by wearable devices were the most frequently used additional inputs. The use of invasive (blood or other body fluid analytes) inputs such as lactate and adrenaline were also simulated. These inputs were incorporated to switch the mode of the controller through activity detection, directly incorporated for decision-making and for the development of intermediate modules for the controller. The validation of the MAPS was carried out through the use of simulators based on different physiological models and clinical trials. Conclusions The integration of additional physiological signals with continuous glucose level monitoring has the potential to optimize glucose control in people with T1D through addressing the identified limitations of APS. Most of the identified additional inputs are related to wearable devices. The rapid growth in wearable technologies can be seen as a key motivator regarding MAPS. However, it is important to further evaluate the practical complexities and psychosocial aspects associated with such systems in real life.
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Affiliation(s)
- Chirath Hettiarachchi
- School of Computing, College of Engineering and Computer Science, The Australian National University, Canberra, Australia
| | - Elena Daskalaki
- School of Computing, College of Engineering and Computer Science, The Australian National University, Canberra, Australia
| | - Jane Desborough
- Department of Health Services Research and Policy, Research School of Population Health, College of Health and Medicine, The Australian National University, Canberra, Australia
| | - Christopher J Nolan
- Australian National University Medical School, College of Health and Medicine, The Australian National University, Canberra, Australia.,John Curtin School of Medical Research, College of Health and Medicine, The Australian National University, Canberra, Australia
| | - David O'Neal
- Department of Medicine, University of Melbourne, Melbourne, Australia.,Department of Endocrinology and Diabetes, St Vincent's Hospital Melbourne, Melbourne, Australia
| | - Hanna Suominen
- School of Computing, College of Engineering and Computer Science, The Australian National University, Canberra, Australia.,Data61, Commonwealth Industrial and Scientific Research Organisation, Canberra, Australia.,Department of Computing, University of Turku, Turku, Finland
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Cescon M, Choudhary D, Pinsker JE, Dadlani V, Church MM, Kudva YC, Doyle Iii FJ, Dassau E. Activity detection and classification from wristband accelerometer data collected on people with type 1 diabetes in free-living conditions. Comput Biol Med 2021; 135:104633. [PMID: 34346318 DOI: 10.1016/j.compbiomed.2021.104633] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 06/18/2021] [Accepted: 07/04/2021] [Indexed: 10/20/2022]
Abstract
This paper introduces methods to estimate aspects of physical activity and sedentary behavior from three-axis accelerometer data collected with a wrist-worn device at a sampling rate of 32 [Hz] on adults with type 1 diabetes (T1D) in free-living conditions. In particular, we present two methods able to detect and grade activity based on its intensity and individual fitness as sedentary, mild, moderate or vigorous, and a method that performs activity classification in a supervised learning framework to predict specific user behaviors. Population results for activity level grading show multi-class average accuracy of 99.99%, precision of 98.0 ± 2.2%, recall of 97.9 ± 3.5% and F1 score of 0.9 ± 0.0. As for the specific behavior prediction, our best performing classifier, gave population multi-class average accuracy of 92.43 ± 10.32%, precision of 92.94 ± 9.80%, recall of 92.20 ± 10.16% and F1 score of 92.56 ± 9.94%. Our investigation showed that physical activity and sedentary behavior can be detected, graded and classified with good accuracy and precision from three-axial accelerometer data collected in free-living conditions on people with T1D. This is particularly significant in the context of automated glucose control systems for diabetes, in that the methods we propose have the potential to inform changes in treatment parameters in response to the intensity of physical activity, allowing patients to meet their glycemic targets.
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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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Raafat SM, Abd-AL Amear BK, Al-Khazraji A. Multiple model adaptive postprandial glucose control of type 1 diabetes. ENGINEERING SCIENCE AND TECHNOLOGY, AN INTERNATIONAL JOURNAL 2021; 24:83-91. [DOI: 10.1016/j.jestch.2020.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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Detection and Characterization of Physical Activity and Psychological Stress from Wristband Data. SIGNALS 2020. [DOI: 10.3390/signals1020011] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Wearable devices continuously measure multiple physiological variables to inform users of health and behavior indicators. The computed health indicators must rely on informative signals obtained by processing the raw physiological variables with powerful noise- and artifacts-filtering algorithms. In this study, we aimed to elucidate the effects of signal processing techniques on the accuracy of detecting and discriminating physical activity (PA) and acute psychological stress (APS) using physiological measurements (blood volume pulse, heart rate, skin temperature, galvanic skin response, and accelerometer) collected from a wristband. Data from 207 experiments involving 24 subjects were used to develop signal processing, feature extraction, and machine learning (ML) algorithms that can detect and discriminate PA and APS when they occur individually or concurrently, classify different types of PA and APS, and estimate energy expenditure (EE). Training data were used to generate feature variables from the physiological variables and develop ML models (naïve Bayes, decision tree, k-nearest neighbor, linear discriminant, ensemble learning, and support vector machine). Results from an independent labeled testing data set demonstrate that PA was detected and classified with an accuracy of 99.3%, and APS was detected and classified with an accuracy of 92.7%, whereas the simultaneous occurrences of both PA and APS were detected and classified with an accuracy of 89.9% (relative to actual class labels), and EE was estimated with a low mean absolute error of 0.02 metabolic equivalent of task (MET).The data filtering and adaptive noise cancellation techniques used to mitigate the effects of noise and artifacts on the classification results increased the detection and discrimination accuracy by 0.7% and 3.0% for PA and APS, respectively, and by 18% for EE estimation. The results demonstrate the physiological measurements from wristband devices are susceptible to noise and artifacts, and elucidate the effects of signal processing and feature extraction on the accuracy of detection, classification, and estimation of PA and APS.
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Francescato MP, Ajčević M, Accardo A. Carbohydrate Requirement for Exercise in Type 1 Diabetes: Effects of Insulin Concentration. J Diabetes Sci Technol 2020; 14:1116-1121. [PMID: 30767503 PMCID: PMC7645145 DOI: 10.1177/1932296819826962] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Physical activity is a keystone of a healthy lifestyle as well as of management of patients with type 1 diabetes. The risk of exercise-induced hypoglycemia, however, is a great challenge for these patients. The glycemic response to exercise depends upon several factors concerning the patient him/herself (eg, therapy, glycemic control, training level) and the characteristics of the exercise performed. Only in-depth knowledge of these factors will allow to develop individualized strategies minimizing the risk of hypoglycemia. The main factors affecting the exercise-induced hypoglycemia in patients with T1D have been analyzed, including the effects of insulin concentration. A model is discussed, which has the potential to become the basis for providing patients with individualized suggestions to keep constant glucose levels on each exercise occasion.
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Affiliation(s)
- Maria Pia Francescato
- Department of Medicine, University of Udine, Udine, Italy
- Maria Pia Francescato, MD, Department of Medicine, University of Udine, p. le M. Kolbe 4, 33100 Udine, Italy.
| | - Miloš Ajčević
- Department of Engineering and Architecture, University of Trieste, Trieste, Italy
| | - Agostino Accardo
- Department of Engineering and Architecture, University of Trieste, Trieste, Italy
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Al-Matouq AA, Laleg-Kirati TM, Novara C, Rabbone I, Vincent T. Sparse Reconstruction of Glucose Fluxes Using Continuous Glucose Monitors. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:1797-1809. [PMID: 30892232 DOI: 10.1109/tcbb.2019.2905198] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
A new technique for estimating postprandial glucose flux profiles without the use of glucose tracers is proposed. A sparse vector space representation is first found for the space of plausible glucose flux profiles using sparse encoding. A Lasso formulation is then used to estimate the glucose fluxes that combines (1) known patient model parameters; (2) the vector space of plausible glucose flux profiles; (3) continuous glucose monitor measurements taken during the meal; (4) amount of insulin injected; (5) amount of meal carbohydrates; and (6) an estimate of the initial conditions. Three glucose fluxes are then estimated, namely; glucose rate of appearance from the intestine; endogenous glucose production from the liver; insulin dependent glucose utilization; and other important state variables. The simulation results show that the technique is capable of estimating the glucose fluxes with high accuracy, even for complex meal scenarios. The experimental results indicate that the technique is capable of reproducing the triple tracer measurements for three T1DM undergoing the triple tracer protocol while estimating the missing measurements for a certain model parameter selection.
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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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Seo W, Lee YB, Lee S, Jin SM, Park SM. A machine-learning approach to predict postprandial hypoglycemia. BMC Med Inform Decis Mak 2019; 19:210. [PMID: 31694629 PMCID: PMC6833234 DOI: 10.1186/s12911-019-0943-4] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Accepted: 10/21/2019] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND For an effective artificial pancreas (AP) system and an improved therapeutic intervention with continuous glucose monitoring (CGM), predicting the occurrence of hypoglycemia accurately is very important. While there have been many studies reporting successful algorithms for predicting nocturnal hypoglycemia, predicting postprandial hypoglycemia still remains a challenge due to extreme glucose fluctuations that occur around mealtimes. The goal of this study is to evaluate the feasibility of easy-to-use, computationally efficient machine-learning algorithm to predict postprandial hypoglycemia with a unique feature set. METHODS We use retrospective CGM datasets of 104 people who had experienced at least one hypoglycemia alert value during a three-day CGM session. The algorithms were developed based on four machine learning models with a unique data-driven feature set: a random forest (RF), a support vector machine using a linear function or a radial basis function, a K-nearest neighbor, and a logistic regression. With 5-fold cross-subject validation, the average performance of each model was calculated to compare and contrast their individual performance. The area under a receiver operating characteristic curve (AUC) and the F1 score were used as the main criterion for evaluating the performance. RESULTS In predicting a hypoglycemia alert value with a 30-min prediction horizon, the RF model showed the best performance with the average AUC of 0.966, the average sensitivity of 89.6%, the average specificity of 91.3%, and the average F1 score of 0.543. In addition, the RF showed the better predictive performance for postprandial hypoglycemic events than other models. CONCLUSION In conclusion, we showed that machine-learning algorithms have potential in predicting postprandial hypoglycemia, and the RF model could be a better candidate for the further development of postprandial hypoglycemia prediction algorithm to advance the CGM technology and the AP technology further.
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Affiliation(s)
- Wonju Seo
- Department of Creative IT engineering, POSTECH, 77, Cheongam-Ro, Nam-Gu, Pohang, 37673, Republic of Korea
| | - You-Bin Lee
- Division of Endocrinology and Metabolism, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Seoul, 06351, Republic of Korea
| | - Seunghyun Lee
- Department of Creative IT engineering, POSTECH, 77, Cheongam-Ro, Nam-Gu, Pohang, 37673, Republic of Korea
| | - Sang-Man Jin
- Division of Endocrinology and Metabolism, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Seoul, 06351, Republic of Korea.
| | - Sung-Min Park
- Department of Creative IT engineering, POSTECH, 77, Cheongam-Ro, Nam-Gu, Pohang, 37673, Republic of Korea.
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Moscardó V, Herrero P, Díez JL, Giménez M, Rossetti P, Georgiou P, Bondia J. Coordinated dual-hormone artificial pancreas with parallel control structure. Comput Chem Eng 2019. [DOI: 10.1016/j.compchemeng.2019.06.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Abstract
Over the past 50 years, the diabetes technology field progressed remarkably through self-monitoring of blood glucose (SMBG), continuous subcutaneous insulin infusion (CSII), risk and variability analysis, mathematical models and computer simulation of the human metabolic system, real-time continuous glucose monitoring (CGM), and control algorithms driving closed-loop control systems known as the "artificial pancreas" (AP). This review follows these developments, beginning with an overview of the functioning of the human metabolic system in health and in diabetes and of its detailed quantitative network modeling. The review continues with a brief account of the first AP studies that used intravenous glucose monitoring and insulin infusion, and with notes about CSII and CGM-the technologies that made possible the development of contemporary AP systems. In conclusion, engineering lessons learned from AP research, and the clinical need for AP systems to prove their safety and efficacy in large-scale clinical trials, are outlined.
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Affiliation(s)
- Boris Kovatchev
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia 22908
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Abstract
The number of patients living with diabetes has increased significantly in recent years due to several factors. Many of these patients are choosing to use insulin pumps for their treatment, artificial systems that administer their insulin and consist of a glucometer and an automatic insulin supply working in an open loop. Currently, only a few closed-loop insulin delivery devices are commercially available. The most widespread systems among patients are what have been called the “Do-It-Yourself Hybrid Closed-Loop systems.” These systems require the use of platforms with high computing power. In this paper, we will present a novel wearable system for insulin delivery that reduces the energy and computing consumption of the platform without affecting the computation requirements. Patients’ information is obtained from a commercial continuous glucose sensor and a commercial insulin pump operating in a conventional manner. An ad-hoc embedded system will connect with the pump and the sensor to collect the glucose data and process it. That connection is accomplished through a radiofrequency channel that provides a suitable system for the patient. Thus, this system does not require to be connected to any other processor, which increases the overall stability. Using parameters configured by the patient, the control system will make automatic adjustments in the basal insulin infusion thereby bringing the patient’s glycaemia to the target set by a doctor’s prescription. The results obtained will be satisfactory as long as the configured parameters faithfully match the specific characteristics of the patient. Results from the simulation of 30 virtual patients (10 adolescents, 10 adults, and 10 children), using a python implementation of the FDA-approved (Food and Drug Administration) UVa (University of Virginia)/Padova Simulator and a python implementation of the proposed algorithm, are presented.
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Leelarathna L, Thabit H. THE MINIMED ™ 670G HYBRID AUTOMATED INSULIN DELIVERY SYSTEM: SETTING THE RIGHT EXPECTATIONS. Endocr Pract 2019; 24:698-700. [PMID: 30048172 DOI: 10.4158/ep-2018-0242] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Kolle K, Biester T, Christiansen S, Fougner AL, Stavdahl O. Pattern Recognition Reveals Characteristic Postprandial Glucose Changes: Non-Individualized Meal Detection in Diabetes Mellitus Type 1. IEEE J Biomed Health Inform 2019; 24:594-602. [PMID: 30951481 DOI: 10.1109/jbhi.2019.2908897] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Accurate continuous glucose monitoring (CGM) is essential for fully automated glucose control in diabetes mellitus type 1. State-of-the-art glucose control systems automatically regulate the basal insulin infusion. Users still need to manually announce meals to dose the prandial insulin boluses. An automated meal detection could release the user and improve the glucose regulation. In this study, patterns in the postprandial CGM data are exploited for meal detection. Binary classifiers are trained to recognize the postprandial pattern in horizons of the estimated glucose rate of appearance and in CGM data. The appearance rate is determined by moving horizon estimation based on a simple model. Linear discriminant analysis (LDA) is used for classification. The proposed method is compared to methods that detect meals when thresholds are violated. Diabetes care data from 12 free-living pediatric patients was downloaded during regular screening. Experts identified meals and their start by retrospective evaluation. The classification was tested by cross-validation. Compared to the threshold-based methods, LDA showed higher sensitivity to meals with a low rate of false alarms. Classifying horizons outperformed the other methods also with respect to time of detection. The onset of meals can be detected by pattern recognition based on estimated model states and consecutive CGM measurements. No individual tuning is necessary. This makes the method easily adopted in the clinical practice.
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Navigating Two Roads to Glucose Normalization in Diabetes: Automated Insulin Delivery Devices and Cell Therapy. Cell Metab 2019; 29:545-563. [PMID: 30840911 DOI: 10.1016/j.cmet.2019.02.007] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 02/12/2019] [Accepted: 02/13/2019] [Indexed: 12/23/2022]
Abstract
Incredible strides have been made since the discovery of insulin almost 100 years ago. Insulin formulations have improved dramatically, glucose levels can be measured continuously, and recently first-generation biomechanical "artificial pancreas" systems have been approved by regulators around the globe. However, still only a small fraction of patients with diabetes achieve glycemic goals. Replacement of insulin-producing cells via transplantation shows significant promise, but is limited in application due to supply constraints (cadaver-based) and the need for chronic immunosuppression. Over the past decade, significant progress has been made to address these barriers to widespread implementation of a cell therapy. Can glucose levels in people with diabetes be normalized with artificial pancreas systems or via cell replacement approaches? Here we review the road ahead, including the challenges and opportunities of both approaches.
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Kovatchev B. Automated closed-loop control of diabetes: the artificial pancreas. Bioelectron Med 2018; 4:14. [PMID: 32232090 PMCID: PMC7098217 DOI: 10.1186/s42234-018-0015-6] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Accepted: 10/08/2018] [Indexed: 12/28/2022] Open
Abstract
The incidence of Diabetes Mellitus is on the rise worldwide, which exerts enormous health toll on the population and enormous pressure on the healthcare systems. Now, almost hundred years after the discovery of insulin in 1921, the optimization problem of diabetes is well formulated as maintenance of strict glycemic control without increasing the risk for hypoglycemia. External insulin administration is mandatory for people with type 1 diabetes; various medications, as well as basal and prandial insulin, are included in the daily treatment of type 2 diabetes. This review follows the development of the Diabetes Technology field which, since the 1970s, progressed remarkably through continuous subcutaneous insulin infusion (CSII), mathematical models and computer simulation of the human metabolic system, real-time continuous glucose monitoring (CGM), and control algorithms driving closed-loop control systems known as the "artificial pancreas" (AP). All of these developments included significant engineering advances and substantial bioelectronics progress in the sensing of blood glucose levels, insulin delivery, and control design. The key technologies that enabled contemporary AP systems are CSII and CGM, both of which became available and sufficiently portable in the beginning of this century. This powered the quest for wearable home-use AP, which is now under way with prototypes tested in outpatient studies during the past 6 years. Pivotal trials of new AP technologies are ongoing, and the first hybrid closed-loop system has been approved by the FDA for clinical use. Thus, the closed-loop AP is well on its way to become the digital-age treatment of diabetes.
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Affiliation(s)
- Boris Kovatchev
- Center for Diabetes Technology, University of Virginia, P.O. Box 400888, Charlottesville, VA 22908 USA
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Dadlani V, Pinsker JE, Dassau E, Kudva YC. Advances in Closed-Loop Insulin Delivery Systems in Patients with Type 1 Diabetes. Curr Diab Rep 2018; 18:88. [PMID: 30159816 DOI: 10.1007/s11892-018-1051-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
To provide a current review of closed-loop insulin delivery or artificial pancreas (AP) as therapy for people with type 1 diabetes mellitus (T1D) RECENT FINDINGS: The Medtronic Minimed 670G AP system has been in use in clinical practice since March 2017. Currently, Medtronic is conducting a large randomized clinical trial to evaluate its efficacy further in T1D. Simultaneously, the NIH has funded four research consortia to accelerate progress to approval of other AP and decision support systems. Several research groups are currently developing next-generation AP systems, with a number of companies moving toward releasing closed-loop systems in the future. AP systems are also being tested in select populations such as hypoglycemia-unaware T1D and pregnant T1D. AP research is rapidly advancing. The clinical range of AP will be expanded in the next decade.
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Affiliation(s)
- Vikash Dadlani
- Endocrine Research Unit, Mayo Clinic, 200 First Street SW, Rochester, MN, 55902, USA
| | - Jordan E Pinsker
- Sansum Diabetes Research Institute, 2219 Bath Street, Santa Barbara, CA, 93105, USA
| | - Eyal Dassau
- Sansum Diabetes Research Institute, 2219 Bath Street, Santa Barbara, CA, 93105, USA
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, 29 Oxford St, Cambridge, MA, USA
- Joslin Diabetes Center, Boston, MA, USA
| | - Yogish C Kudva
- Endocrine Research Unit, Mayo Clinic, 200 First Street SW, Rochester, MN, 55902, USA.
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Rollins DK, Mei Y. A new feedback predictive control approach for processes with time delay in the manipulated variable. Chem Eng Res Des 2018. [DOI: 10.1016/j.cherd.2018.06.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Colmegna P, Sánchez-Peña R, Gondhalekar R. Linear parameter-varying model to design control laws for an artificial pancreas. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2017.09.021] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Affiliation(s)
- Revital Nimri
- 1 Diabetes Technology Center, Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, Schneider Children's Medical Center of Israel, Petah Tikva, Israel
| | - Pearl Audon
- 2 William Sansum Diabetes Center, Santa Barbara, CA
| | | | - Eyal Dassau
- 2 William Sansum Diabetes Center, Santa Barbara, CA
- 3 Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
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Bertachi A, Ramkissoon CM, Bondia J, Vehí J. Automated blood glucose control in type 1 diabetes: A review of progress and challenges. ACTA ACUST UNITED AC 2017; 65:172-181. [PMID: 29279252 DOI: 10.1016/j.endinu.2017.10.011] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2017] [Revised: 10/11/2017] [Accepted: 10/21/2017] [Indexed: 12/27/2022]
Abstract
Since the 2000s, research teams worldwide have been working to develop closed-loop (CL) systems able to automatically control blood glucose (BG) levels in patients with type 1 diabetes. This emerging technology is known as artificial pancreas (AP), and its first commercial version just arrived in the market. The main objective of this paper is to present an extensive review of the clinical trials conducted since 2011, which tested various implementations of the AP for different durations under varying conditions. A comprehensive table that contains key information from the selected publications is provided, and the main challenges in AP development and the mitigation strategies used are discussed. The development timelines for different AP systems are also included, highlighting the main evolutions over the clinical trials for each system.
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Affiliation(s)
- Arthur Bertachi
- Institute of Informatics and Applications, University of Girona, Campus de Montilivi, s/n, Edifici P4, 17071 Girona, Spain; Federal University of Technology - Paraná (UTFPR), Guarapuava, Avenida Professora Laura Pacheco Bastos 800, 85053-525 Guarapuava, Paraná, Brazil
| | - Charrise M Ramkissoon
- Institute of Informatics and Applications, University of Girona, Campus de Montilivi, s/n, Edifici P4, 17071 Girona, Spain
| | - Jorge Bondia
- Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, Camino de Vera, s/n, Edificio 8G, 46022 Valencia, Spain
| | - Josep Vehí
- Institute of Informatics and Applications, University of Girona, Campus de Montilivi, s/n, Edifici P4, 17071 Girona, Spain.
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Abstract
PURPOSE OF REVIEW The review summarizes the current state of the artificial pancreas (AP) systems and introduces various new modules that should be included in future AP systems. RECENT FINDINGS A fully automated AP must be able to detect and mitigate the effects of meals, exercise, stress and sleep on blood glucose concentrations. This can only be achieved by using a multivariable approach that leverages information from wearable devices that provide real-time streaming data about various physiological variables that indicate imminent changes in blood glucose concentrations caused by meals, exercise, stress and sleep. The development of a fully automated AP will necessitate the design of multivariable and adaptive systems that use information from wearable devices in addition to glucose sensors and modify the models used in their model-predictive alarm and control systems to adapt to the changes in the metabolic state of the user. These AP systems will also integrate modules for controller performance assessment, fault detection and diagnosis, machine learning and classification to interpret various signals and achieve fault-tolerant control. Advances in wearable devices, computational power, and safe and secure communications are enabling the development of fully automated multivariable AP systems.
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Affiliation(s)
- Ali Cinar
- Department of Chemical and Biological Engineering and Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA.
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Weisman A, Bai JW, Cardinez M, Kramer CK, Perkins BA. Effect of artificial pancreas systems on glycaemic control in patients with type 1 diabetes: a systematic review and meta-analysis of outpatient randomised controlled trials. Lancet Diabetes Endocrinol 2017; 5:501-512. [PMID: 28533136 DOI: 10.1016/s2213-8587(17)30167-5] [Citation(s) in RCA: 304] [Impact Index Per Article: 43.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Revised: 04/11/2017] [Accepted: 04/11/2017] [Indexed: 12/27/2022]
Abstract
BACKGROUND Closed-loop artificial pancreas systems have been in development for several years, including assessment in numerous varied outpatient clinical trials. We aimed to summarise the efficacy and safety of artificial pancreas systems in outpatient settings and explore the clinical and technical factors that can affect their performance. METHODS We did a systematic review and meta-analysis of randomised controlled trials comparing artificial pancreas systems (insulin only or insulin plus glucagon) with conventional pump therapy (continuous subcutaneous insulin infusion [CSII] with blinded continuous glucose monitoring [CGM] or unblinded sensor-augmented pump [SAP] therapy) in adults and children with type 1 diabetes. We searched Medline, Embase, and the Cochrane Central Register of Controlled Trials for studies published from 1946, to Jan 1, 2017. We excluded studies not published in English, those involving pregnant women or participants who were in hospital, and those testing adjunct medications other than glucagon. The primary outcome was the mean difference in percentage of time blood glucose concentration remained in target range (3·9-10 mmol/L or 3·9-8 mmol/L, depending on the study), assessed by random-effects meta-analysis. This study is registered with PROSPERO, number 2015:CRD42015026854. FINDINGS We identified 984 reports; after exclusions, 27 comparisons from 24 studies (23 crossover and one parallel design) including a total of 585 participants (219 in adult studies, 265 in paediatric studies, and 101 in combined studies) were eligible for analysis. Five comparisons assessed dual-hormone (insulin and glucagon), two comparisons assessed both dual-hormone and single-hormone (insulin only), and 20 comparisons assessed single-hormone artificial pancreas systems. Time in target was 12·59% higher with artificial pancreas systems (95% CI 9·02-16·16; p<0·0001), from a weighted mean of 58·21% for conventional pump therapy (I2=84%). Dual-hormone artificial pancreas systems were associated with a greater improvement in time in target range compared with single-hormone systems (19·52% [95% CI 15·12-23·91] vs 11·06% [6·94 to 15·18]; p=0·006), although six of seven comparisons compared dual-hormone systems to CSII with blinded CGM, whereas 21 of 22 single-hormone comparisons had SAP as the comparator. Single-hormone studies had higher heterogeneity than dual-hormone studies (I2 79% vs 66%). Bias assessment characteristics were incompletely reported in 12 of 24 studies, no studies masked participants to the intervention assignment, and masking of outcome assessment was not done in 12 studies and was unclear in 12 studies. INTERPRETATION Artificial pancreas systems uniformly improved glucose control in outpatient settings, despite heterogeneous clinical and technical factors. FUNDING None.
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Affiliation(s)
- Alanna Weisman
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada; Division of Endocrinology and Metabolism, Department of Medicine, University of Toronto, Toronto, ON, Canada.
| | - Johnny-Wei Bai
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada
| | - Marina Cardinez
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada
| | - Caroline K Kramer
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada; Division of Endocrinology and Metabolism, Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Bruce A Perkins
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada; Division of Endocrinology and Metabolism, Department of Medicine, University of Toronto, Toronto, ON, Canada
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Oviedo S, Vehí J, Calm R, Armengol J. A review of personalized blood glucose prediction strategies for T1DM patients. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2017; 33:e2833. [PMID: 27644067 DOI: 10.1002/cnm.2833] [Citation(s) in RCA: 107] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Revised: 09/15/2016] [Accepted: 09/16/2016] [Indexed: 06/06/2023]
Abstract
This paper presents a methodological review of models for predicting blood glucose (BG) concentration, risks and BG events. The surveyed models are classified into three categories, and they are presented in summary tables containing the most relevant data regarding the experimental setup for fitting and testing each model as well as the input signals and the performance metrics. Each category exhibits trends that are presented and discussed. This document aims to be a compact guide to determine the modeling options that are currently being exploited for personalized BG prediction.
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Affiliation(s)
- Silvia Oviedo
- Institut d'Informàtica i Aplicacions, Parc Científic i Tecnològic de la Universitat de Girona, 17003, Girona, Spain
| | - Josep Vehí
- Institut d'Informàtica i Aplicacions, Universitat de Girona, Campus Montilivi, Edifici P4, 17071, Girona, Spain
| | - Remei Calm
- Institut d'Informàtica i Aplicacions, Universitat de Girona, Campus Montilivi, Edifici P4, 17071, Girona, Spain
| | - Joaquim Armengol
- Institut d'Informàtica i Aplicacions, Universitat de Girona, Campus Montilivi, Edifici P4, 17071, Girona, Spain
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Xie J, Wang Q. A Variable State Dimension Approach to Meal Detection and Meal Size Estimation: In Silico Evaluation Through Basal-Bolus Insulin Therapy for Type 1 Diabetes. IEEE Trans Biomed Eng 2017; 64:1249-1260. [PMID: 28541188 DOI: 10.1109/tbme.2016.2599073] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE This paper aims to develop an algorithm that can detect unannounced meals and estimate meal sizes to achieve a robust glucose control. METHODS A variable state dimension (VSD) algorithm is developed to detect unannounced meals and estimate meal sizes, where a Kalman filter operates on a quiescent state model when no meal is detected, and switches to a maneuvering state model to estimate meal information once the meal-induced glucose variability is statistically significant. RESULTS Through evaluation using 30 subjects of the UVa/Padova simulator, a basal-bolus (BB) control using the VSD-estimated meal size for each meal can achieve mean blood glucose (BG) of 142 mg/dl with an average 17.7% of time in hypoglycemia. In terms of 20 Monte-Carlo simulations for each subject over a two-day scenario, where each meal/snack has a probability of 0.5 not to be announced, the BB control using VSD for unannounced meals can achieve an average mean BG of 143 mg/dl with 8% of time in hypoglycemia, in contrast to mean BG of 180 mg/dl with 8% of time in hypoglycemia obtained by BB with missing boluses. Additionally, VSD is able to detect a meal within 45 (±14) min since its start with a 76% success rate and 16% false alarm rate. CONCLUSION The addition of VSD to the BB control improves glucose control when meal announcements are missed. SIGNIFICANCE The VSD can be used as a complementary tool to detect meal and estimate meal size in absence of a meal announcement.
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Nanotechnology and nanocarrier-based approaches on treatment of degenerative diseases. INTERNATIONAL NANO LETTERS 2017. [DOI: 10.1007/s40089-017-0208-0] [Citation(s) in RCA: 90] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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Use of Wearable Sensors and Biometric Variables in an Artificial Pancreas System. SENSORS 2017; 17:s17030532. [PMID: 28272368 PMCID: PMC5375818 DOI: 10.3390/s17030532] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2016] [Revised: 03/02/2017] [Accepted: 03/03/2017] [Indexed: 01/26/2023]
Abstract
An artificial pancreas (AP) computes the optimal insulin dose to be infused through an insulin pump in people with Type 1 Diabetes (T1D) based on information received from a continuous glucose monitoring (CGM) sensor. It has been recognized that exercise is a major challenge in the development of an AP system. The use of biometric physiological variables in an AP system may be beneficial for prevention of exercise-induced challenges and better glucose regulation. The goal of the present study is to find a correlation between biometric variables such as heart rate (HR), heat flux (HF), skin temperature (ST), near-body temperature (NBT), galvanic skin response (GSR), and energy expenditure (EE), 2D acceleration-mean of absolute difference (MAD) and changes in glucose concentrations during exercise via partial least squares (PLS) regression and variable importance in projection (VIP) in order to determine which variables would be most useful to include in a future artificial pancreas. PLS and VIP analyses were performed on data sets that included seven different types of exercises. Data were collected from 26 clinical experiments. Clinical results indicate ST to be the most consistently important (important for six out of seven tested exercises) variable over all different exercises tested. EE and HR are also found to be important variables over several types of exercise. We also found that the importance of GSR and NBT observed in our experiments might be related to stress and the effect of changes in environmental temperature on glucose concentrations. The use of the biometric measurements in an AP system may provide better control of glucose concentration.
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Ramkissoon CM, Aufderheide B, Bequette BW, Vehi J. A Review of Safety and Hazards Associated With the Artificial Pancreas. IEEE Rev Biomed Eng 2017; 10:44-62. [DOI: 10.1109/rbme.2017.2749038] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Strategies used by Patients with Type 1 Diabetes to Avoid Hypoglycemia in a 24×1-Hour Marathon: Comparison with the Amounts of Carbohydrates Estimated by a Customizable Algorithm. Can J Diabetes 2016; 41:184-189. [PMID: 27939876 DOI: 10.1016/j.jcjd.2016.09.007] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2016] [Revised: 09/05/2016] [Accepted: 09/21/2016] [Indexed: 12/28/2022]
Abstract
OBJECTIVES The preferred countermeasure to avoid exercise-related hypoglycemia was investigated in a group of patients with type 1 diabetes participating in a stressful event, a 24×1-hour relay marathon. The carbohydrates actually consumed were compared to those estimated for each patient by applying a customizable algorithm, Exercise Carbohydrates Requirement Estimating Software (ECRES), based on patient's usual therapy and diet and on the exercise characteristics. METHODS Glycemia was tested at the start, middle and end of the races. Usual therapies and diets and the adopted countermeasures were recorded in detail. RESULTS We studied 19 patients who walked/ran 10.4±2.8 km with a heart rate of 167±11 beats per minute. Of the 19 patients, 7 patients reduced the administered insulin (premeal bolus or basal infusion rate). Glycemia fell by the end of the races (p=0.006; median -1.8 mmol⋅L-1; interquartile range -0.4 mmol⋅L-1 to -5.3 mmol⋅L-1), despite 9 patients being hyperglycemic at the start. Of the patients, 14 concluded the race with glycemia on target, and 4 patients were hyperglycemic. Amounts of carbohydrates actually consumed (median 30 g; interquartile range 0 g to 71 g) were not significantly different from those estimated by ECRES (median 38 g; interquartile range 24 g to 68 g), the 2 quantities being significantly related (R=0.64; p=0.003). ECRES estimated lower carbohydrate levels (-13 g) than the amounts actually consumed by the 4 patients who concluded their exercises with hyperglycemia. CONCLUSIONS Patients preferred to consume extra carbohydrates to avoid the possible exercise-induced hypoglycemia. ECRES would provide satisfactory estimates of the carbohydrate requirements, even for a stressful condition, and almost equal to the quantities consumed following medical advice.
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Daskalaki E, Diem P, Mougiakakou SG. Model-Free Machine Learning in Biomedicine: Feasibility Study in Type 1 Diabetes. PLoS One 2016; 11:e0158722. [PMID: 27441367 PMCID: PMC4956312 DOI: 10.1371/journal.pone.0158722] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2015] [Accepted: 06/21/2016] [Indexed: 11/23/2022] Open
Abstract
Although reinforcement learning (RL) is suitable for highly uncertain systems, the applicability of this class of algorithms to medical treatment may be limited by the patient variability which dictates individualised tuning for their usually multiple algorithmic parameters. This study explores the feasibility of RL in the framework of artificial pancreas development for type 1 diabetes (T1D). In this approach, an Actor-Critic (AC) learning algorithm is designed and developed for the optimisation of insulin infusion for personalised glucose regulation. AC optimises the daily basal insulin rate and insulin:carbohydrate ratio for each patient, on the basis of his/her measured glucose profile. Automatic, personalised tuning of AC is based on the estimation of information transfer (IT) from insulin to glucose signals. Insulin-to-glucose IT is linked to patient-specific characteristics related to total daily insulin needs and insulin sensitivity (SI). The AC algorithm is evaluated using an FDA-accepted T1D simulator on a large patient database under a complex meal protocol, meal uncertainty and diurnal SI variation. The results showed that 95.66% of time was spent in normoglycaemia in the presence of meal uncertainty and 93.02% when meal uncertainty and SI variation were simultaneously considered. The time spent in hypoglycaemia was 0.27% in both cases. The novel tuning method reduced the risk of severe hypoglycaemia, especially in patients with low SI.
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Affiliation(s)
- Elena Daskalaki
- Diabetes Technology Research Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, 3008 Bern, Switzerland
| | - Peter Diem
- Division of Endocrinology, Diabetes and Clinical Nutrition, Bern University Hospital “Inselspital”, 3010 Bern, Switzerland
| | - Stavroula G. Mougiakakou
- Diabetes Technology Research Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, 3008 Bern, Switzerland
- Division of Endocrinology, Diabetes and Clinical Nutrition, Bern University Hospital “Inselspital”, 3010 Bern, Switzerland
- * E-mail:
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Renard E, Farret A, Kropff J, Bruttomesso D, Messori M, Place J, Visentin R, Calore R, Toffanin C, Di Palma F, Lanzola G, Magni P, Boscari F, Galasso S, Avogaro A, Keith-Hynes P, Kovatchev B, Del Favero S, Cobelli C, Magni L, DeVries JH. Day-and-Night Closed-Loop Glucose Control in Patients With Type 1 Diabetes Under Free-Living Conditions: Results of a Single-Arm 1-Month Experience Compared With a Previously Reported Feasibility Study of Evening and Night at Home. Diabetes Care 2016; 39:1151-60. [PMID: 27208331 DOI: 10.2337/dc16-0008] [Citation(s) in RCA: 87] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2016] [Accepted: 04/17/2016] [Indexed: 02/03/2023]
Abstract
OBJECTIVE After testing of a wearable artificial pancreas (AP) during evening and night (E/N-AP) under free-living conditions in patients with type 1 diabetes (T1D), we investigated AP during day and night (D/N-AP) for 1 month. RESEARCH DESIGN AND METHODS Twenty adult patients with T1D who completed a previous randomized crossover study comparing 2-month E/N-AP versus 2-month sensor augmented pump (SAP) volunteered for 1-month D/N-AP nonrandomized extension. AP was executed by a model predictive control algorithm run by a modified smartphone wirelessly connected to a continuous glucose monitor (CGM) and insulin pump. CGM data were analyzed by intention-to-treat with percentage time-in-target (3.9-10 mmol/L) over 24 h as the primary end point. RESULTS Time-in-target (mean ± SD, %) was similar over 24 h with D/N-AP versus E/N-AP: 64.7 ± 7.6 vs. 63.6 ± 9.9 (P = 0.79), and both were higher than with SAP: 59.7 ± 9.6 (P = 0.01 and P = 0.06, respectively). Time below 3.9 mmol/L was similarly and significantly reduced by D/N-AP and E/N-AP versus SAP (both P < 0.001). SD of blood glucose concentration (mmol/L) was lower with D/N-AP versus E/N-AP during whole daytime: 3.2 ± 0.6 vs. 3.4 ± 0.7 (P = 0.003), morning: 2.7 ± 0.5 vs. 3.1 ± 0.5 (P = 0.02), and afternoon: 3.3 ± 0.6 vs. 3.5 ± 0.8 (P = 0.07), and was lower with D/N-AP versus SAP over 24 h: 3.1 ± 0.5 vs. 3.3 ± 0.6 (P = 0.049). Insulin delivery (IU) over 24 h was higher with D/N-AP and SAP than with E/N-AP: 40.6 ± 15.5 and 42.3 ± 15.5 vs. 36.6 ± 11.6 (P = 0.03 and P = 0.0004, respectively). CONCLUSIONS D/N-AP and E/N-AP both achieved better glucose control than SAP under free-living conditions. Although time in the different glycemic ranges was similar between D/N-AP and E/N-AP, D/N-AP further reduces glucose variability.
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Affiliation(s)
- Eric Renard
- Department of Endocrinology, Diabetes, Nutrition, Montpellier University Hospital; INSERM Clinical Investigation Centre 1411; Institute of Functional Genomics, CNRS UMR 5203, INSERM U1191, University of Montpellier, Montpellier, France
| | - Anne Farret
- Department of Endocrinology, Diabetes, Nutrition, Montpellier University Hospital; INSERM Clinical Investigation Centre 1411; Institute of Functional Genomics, CNRS UMR 5203, INSERM U1191, University of Montpellier, Montpellier, France
| | - Jort Kropff
- Department of Endocrinology, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Daniela Bruttomesso
- Unit of Metabolic Diseases, Department of Internal Medicine, University of Padova, Padova, Italy
| | - Mirko Messori
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
| | - Jerome Place
- Department of Endocrinology, Diabetes, Nutrition, Montpellier University Hospital; INSERM Clinical Investigation Centre 1411; Institute of Functional Genomics, CNRS UMR 5203, INSERM U1191, University of Montpellier, Montpellier, France
| | - Roberto Visentin
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Roberta Calore
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Chiara Toffanin
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
| | - Federico Di Palma
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
| | - Giordano Lanzola
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Paolo Magni
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Federico Boscari
- Unit of Metabolic Diseases, Department of Internal Medicine, University of Padova, Padova, Italy
| | - Silvia Galasso
- Unit of Metabolic Diseases, Department of Internal Medicine, University of Padova, Padova, Italy
| | - Angelo Avogaro
- Unit of Metabolic Diseases, Department of Internal Medicine, University of Padova, Padova, Italy
| | | | - Boris Kovatchev
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | - Simone Del Favero
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Claudio Cobelli
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Lalo Magni
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
| | - J Hans DeVries
- Department of Endocrinology, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
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Kovatchev B, Tamborlane WV, Cefalu WT, Cobelli C. The Artificial Pancreas in 2016: A Digital Treatment Ecosystem for Diabetes. Diabetes Care 2016; 39:1123-6. [PMID: 27330124 PMCID: PMC4915552 DOI: 10.2337/dc16-0824] [Citation(s) in RCA: 68] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Affiliation(s)
- Boris Kovatchev
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | - William V Tamborlane
- Division of Pediatric Endocrinology, Department of Pediatrics, Yale School of Medicine, New Haven, CT
| | - William T Cefalu
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LA
| | - Claudio Cobelli
- Department of Information Engineering, University of Padova, Padova, Italy
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Blauw H, Keith-Hynes P, Koops R, DeVries JH. A Review of Safety and Design Requirements of the Artificial Pancreas. Ann Biomed Eng 2016; 44:3158-3172. [PMID: 27352278 PMCID: PMC5093196 DOI: 10.1007/s10439-016-1679-2] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2016] [Accepted: 06/13/2016] [Indexed: 01/03/2023]
Abstract
As clinical studies with artificial pancreas systems for automated blood glucose control in patients with type 1 diabetes move to unsupervised real-life settings, product development will be a focus of companies over the coming years. Directions or requirements regarding safety in the design of an artificial pancreas are, however, lacking. This review aims to provide an overview and discussion of safety and design requirements of the artificial pancreas. We performed a structured literature search based on three search components—type 1 diabetes, artificial pancreas, and safety or design—and extended the discussion with our own experiences in developing artificial pancreas systems. The main hazards of the artificial pancreas are over- and under-dosing of insulin and, in case of a bi-hormonal system, of glucagon or other hormones. For each component of an artificial pancreas and for the complete system we identified safety issues related to these hazards and proposed control measures. Prerequisites that enable the control algorithms to provide safe closed-loop control are accurate and reliable input of glucose values, assured hormone delivery and an efficient user interface. In addition, the system configuration has important implications for safety, as close cooperation and data exchange between the different components is essential.
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Affiliation(s)
- Helga Blauw
- Department of Endocrinology, Academic Medical Center, University of Amsterdam, P.O Box 22660, 1100 DD, Amsterdam, The Netherlands. .,Inreda Diabetic BV, Goor, The Netherlands.
| | - Patrick Keith-Hynes
- TypeZero Technologies, LLC, Charlottesville, VA, USA.,Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
| | | | - J Hans DeVries
- Department of Endocrinology, Academic Medical Center, University of Amsterdam, P.O Box 22660, 1100 DD, Amsterdam, The Netherlands
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Hinshaw L, Schiavon M, Dadlani V, Mallad A, Dalla Man C, Bharucha A, Basu R, Geske JR, Carter RE, Cobelli C, Basu A, Kudva YC. Effect of Pramlintide on Postprandial Glucose Fluxes in Type 1 Diabetes. J Clin Endocrinol Metab 2016; 101:1954-62. [PMID: 26930181 PMCID: PMC4870844 DOI: 10.1210/jc.2015-3952] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
CONTEXT Early postprandial hyperglycemia and delayed hypoglycemia remain major problems in current management of type 1 diabetes (T1D). OBJECTIVE Our objective was to investigate the effects of pramlintide, known to suppress glucagon and delay gastric emptying, on postprandial glucose fluxes in T1D. DESIGN This was a single-center, inpatient, randomized, crossover study. PATIENTS Twelve patients with T1D who completed the study were analyzed. INTERVENTIONS Subjects were studied on two occasions with or without pramlintide. Triple tracer mixed-meal method and oral minimal model were used to estimate postprandial glucose turnover and insulin sensitivity (SI). Integrated liver insulin sensitivity was calculated based on glucose turnover. Plasma glucagon and insulin were measured. MAIN OUTCOME MEASURE Glucose turnover and SI were the main outcome measures. RESULTS With pramlintide, 2-hour postprandial glucose, insulin, glucagon, glucose turnover, and SI indices showed: plasma glucose excursions were reduced (difference in incremental area under the curve [iAUC], 444.0 mMmin, P = .0003); plasma insulin concentrations were lower (difference in iAUC, 7642.0 pMmin; P = .0099); plasma glucagon excursions were lower (difference in iAUC, 1730.6 pg/mlmin; P = .0147); meal rate of glucose appearance was lower (difference in iAUC: 1196.2 μM/kg fat free mass [FFM]; P = .0316), endogenous glucose production was not different (difference in iAUC: -105.5 μM/kg FFM; P = .5842), rate of glucose disappearance was lower (difference in iAUC: 1494.2 μM/kg FFM; P = .0083). SI and liver insulin sensitivity were not different between study visits (P > .05). CONCLUSIONS Inhibition of glucagon and gastric emptying delaying reduced 2-hour prandial glucose excursions in T1D by delaying meal rate of glucose appearance.
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Affiliation(s)
- Ling Hinshaw
- Division of Endocrinology and Metabolism (L.H., V.D., A.M., R.B., A.B., Y.C.K.), Mayo Clinic, Rochester, Minnesota; Department of Information Engineering (M.S., C.D.M., C.C.), University of Padova, Padova, Italy; Division of Gastroenterology (A.B.), Mayo Clinic, Rochester, Minnesota; Department of Health Sciences Research (J.R.G., R.E.C.), Mayo Clinic, Rochester, Minnesota 55905
| | - Michele Schiavon
- Division of Endocrinology and Metabolism (L.H., V.D., A.M., R.B., A.B., Y.C.K.), Mayo Clinic, Rochester, Minnesota; Department of Information Engineering (M.S., C.D.M., C.C.), University of Padova, Padova, Italy; Division of Gastroenterology (A.B.), Mayo Clinic, Rochester, Minnesota; Department of Health Sciences Research (J.R.G., R.E.C.), Mayo Clinic, Rochester, Minnesota 55905
| | - Vikash Dadlani
- Division of Endocrinology and Metabolism (L.H., V.D., A.M., R.B., A.B., Y.C.K.), Mayo Clinic, Rochester, Minnesota; Department of Information Engineering (M.S., C.D.M., C.C.), University of Padova, Padova, Italy; Division of Gastroenterology (A.B.), Mayo Clinic, Rochester, Minnesota; Department of Health Sciences Research (J.R.G., R.E.C.), Mayo Clinic, Rochester, Minnesota 55905
| | - Ashwini Mallad
- Division of Endocrinology and Metabolism (L.H., V.D., A.M., R.B., A.B., Y.C.K.), Mayo Clinic, Rochester, Minnesota; Department of Information Engineering (M.S., C.D.M., C.C.), University of Padova, Padova, Italy; Division of Gastroenterology (A.B.), Mayo Clinic, Rochester, Minnesota; Department of Health Sciences Research (J.R.G., R.E.C.), Mayo Clinic, Rochester, Minnesota 55905
| | - Chiara Dalla Man
- Division of Endocrinology and Metabolism (L.H., V.D., A.M., R.B., A.B., Y.C.K.), Mayo Clinic, Rochester, Minnesota; Department of Information Engineering (M.S., C.D.M., C.C.), University of Padova, Padova, Italy; Division of Gastroenterology (A.B.), Mayo Clinic, Rochester, Minnesota; Department of Health Sciences Research (J.R.G., R.E.C.), Mayo Clinic, Rochester, Minnesota 55905
| | - Adil Bharucha
- Division of Endocrinology and Metabolism (L.H., V.D., A.M., R.B., A.B., Y.C.K.), Mayo Clinic, Rochester, Minnesota; Department of Information Engineering (M.S., C.D.M., C.C.), University of Padova, Padova, Italy; Division of Gastroenterology (A.B.), Mayo Clinic, Rochester, Minnesota; Department of Health Sciences Research (J.R.G., R.E.C.), Mayo Clinic, Rochester, Minnesota 55905
| | - Rita Basu
- Division of Endocrinology and Metabolism (L.H., V.D., A.M., R.B., A.B., Y.C.K.), Mayo Clinic, Rochester, Minnesota; Department of Information Engineering (M.S., C.D.M., C.C.), University of Padova, Padova, Italy; Division of Gastroenterology (A.B.), Mayo Clinic, Rochester, Minnesota; Department of Health Sciences Research (J.R.G., R.E.C.), Mayo Clinic, Rochester, Minnesota 55905
| | - Jennifer R Geske
- Division of Endocrinology and Metabolism (L.H., V.D., A.M., R.B., A.B., Y.C.K.), Mayo Clinic, Rochester, Minnesota; Department of Information Engineering (M.S., C.D.M., C.C.), University of Padova, Padova, Italy; Division of Gastroenterology (A.B.), Mayo Clinic, Rochester, Minnesota; Department of Health Sciences Research (J.R.G., R.E.C.), Mayo Clinic, Rochester, Minnesota 55905
| | - Rickey E Carter
- Division of Endocrinology and Metabolism (L.H., V.D., A.M., R.B., A.B., Y.C.K.), Mayo Clinic, Rochester, Minnesota; Department of Information Engineering (M.S., C.D.M., C.C.), University of Padova, Padova, Italy; Division of Gastroenterology (A.B.), Mayo Clinic, Rochester, Minnesota; Department of Health Sciences Research (J.R.G., R.E.C.), Mayo Clinic, Rochester, Minnesota 55905
| | - Claudio Cobelli
- Division of Endocrinology and Metabolism (L.H., V.D., A.M., R.B., A.B., Y.C.K.), Mayo Clinic, Rochester, Minnesota; Department of Information Engineering (M.S., C.D.M., C.C.), University of Padova, Padova, Italy; Division of Gastroenterology (A.B.), Mayo Clinic, Rochester, Minnesota; Department of Health Sciences Research (J.R.G., R.E.C.), Mayo Clinic, Rochester, Minnesota 55905
| | - Ananda Basu
- Division of Endocrinology and Metabolism (L.H., V.D., A.M., R.B., A.B., Y.C.K.), Mayo Clinic, Rochester, Minnesota; Department of Information Engineering (M.S., C.D.M., C.C.), University of Padova, Padova, Italy; Division of Gastroenterology (A.B.), Mayo Clinic, Rochester, Minnesota; Department of Health Sciences Research (J.R.G., R.E.C.), Mayo Clinic, Rochester, Minnesota 55905
| | - Yogish C Kudva
- Division of Endocrinology and Metabolism (L.H., V.D., A.M., R.B., A.B., Y.C.K.), Mayo Clinic, Rochester, Minnesota; Department of Information Engineering (M.S., C.D.M., C.C.), University of Padova, Padova, Italy; Division of Gastroenterology (A.B.), Mayo Clinic, Rochester, Minnesota; Department of Health Sciences Research (J.R.G., R.E.C.), Mayo Clinic, Rochester, Minnesota 55905
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Gonder-Frederick LA, Grabman JH, Kovatchev B, Brown SA, Patek S, Basu A, Pinsker JE, Kudva YC, Wakeman CA, Dassau E, Cobelli C, Zisser HC, Doyle FJ. Is Psychological Stress a Factor for Incorporation Into Future Closed-Loop Systems? J Diabetes Sci Technol 2016; 10:640-6. [PMID: 26969142 PMCID: PMC5038545 DOI: 10.1177/1932296816635199] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
BACKGROUND The relationship between daily psychological stress and BG fluctuations in type 1 diabetes (T1DM) is unclear. More research is needed to determine if stress-related BG changes should be considered in glucose control algorithms. This study in the usual free-living environment examined relationships among routine daily stressors and BG profile measures generated from CGM readings. METHODS A total of 33 participants with T1DM on insulin pumps wore a CGM device for 1 week and recorded daily ratings of psychological stress, carbohydrates, and insulin boluses. RESULTS Within-subjects ANCOVAs found a significant relationship between daily stress and indices of BG variability/instability (r = .172 to .185, P = .011 to .018, r(2) = 2.97% to 3.43%), increased % time in hypoglycemia (r = .153, P = .036, r(2) = 2.33%) and decreased carbohydrate consumption (r = -.157, P = .031, r(2) = 2.47%). Models accounted for more variance for individuals reporting the highest daily stress. There was no relationship between stress and mean daily glucose or low/high glucose risk indices. CONCLUSIONS These preliminary findings suggest that naturally occurring daily stressors can be associated with increased glucose instability and hypoglycemia, as well as decreased food consumption. In addition, findings support the hypothesis that some individuals are more metabolically reactive to stress. More rigorous studies using CGM technology are needed to understand whether the impact of daily stress on BG is clinically meaningful and if it is a behavioral factor that should be considered in glucose control systems for some individuals.
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Affiliation(s)
- Linda A Gonder-Frederick
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA Behavioral Medicine Center, University of Virginia, Charlottesville, VA, USA
| | - Jesse H Grabman
- Behavioral Medicine Center, University of Virginia, Charlottesville, VA, USA
| | - Boris Kovatchev
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA Behavioral Medicine Center, University of Virginia, Charlottesville, VA, USA
| | - Sue A Brown
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
| | - Stephen Patek
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
| | - Ananda Basu
- Endocrine Research Unit, Mayo Clinic, Rochester, MN, USA
| | | | - Yogish C Kudva
- Endocrine Research Unit, Mayo Clinic, Rochester, MN, USA
| | - Christian A Wakeman
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
| | - Eyal Dassau
- William Sansum Diabetes Center, Santa Barbara, CA, USA Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA, USA John A. Paulson School of Engineering and Applied Science, Harvard University, Cambridge, MA, USA
| | - Claudio Cobelli
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Howard C Zisser
- Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA, USA Insulet Corporation, Santa Barbara, CA, USA
| | - Francis J Doyle
- William Sansum Diabetes Center, Santa Barbara, CA, USA Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA, USA John A. Paulson School of Engineering and Applied Science, Harvard University, Cambridge, MA, USA
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Mamun KAA, McFarlane N. Integrated real time bowel sound detector for artificial pancreas systems. SENSING AND BIO-SENSING RESEARCH 2016. [DOI: 10.1016/j.sbsr.2016.01.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/22/2022] Open
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42
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Wang Q, Xie J, Molenaar P, Ulbrecht JS. Model Predictive Control for Type 1 Diabetes Based on Personalized Linear Time-Varying Subject Model Consisting of Both Insulin and Meal Inputs: An in Silico Evaluation. J Diabetes Sci Technol 2015; 9:941-2. [PMID: 25972282 PMCID: PMC4525652 DOI: 10.1177/1932296815586426] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Qian Wang
- Department of Mechanical and Nuclear Engineering, Pennsylvania State University, University Park, PA, USA
| | - JinYu Xie
- Department of Mechanical and Nuclear Engineering, Pennsylvania State University, University Park, PA, USA
| | - Peter Molenaar
- Department of Human Development and Family Studies, Pennsylvania State University, University Park, PA, USA
| | - Jan S Ulbrecht
- Departments of Biobehavioral Health and Medicine, Pennsylvania State University, University Park, PA, USA
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43
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Cahill EM, O’Cearbhaill ED. Toward Biofunctional Microneedles for Stimulus Responsive Drug Delivery. Bioconjug Chem 2015; 26:1289-96. [DOI: 10.1021/acs.bioconjchem.5b00211] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Ellen M. Cahill
- School of Mechanical
and Materials Engineering, §UCD Centre for Biomedical Engineering, and ‡UCD Conway Institute of Biomolecular
and Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland
| | - Eoin D. O’Cearbhaill
- School of Mechanical
and Materials Engineering, §UCD Centre for Biomedical Engineering, and ‡UCD Conway Institute of Biomolecular
and Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland
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44
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Agianniotis A, Anthimopoulos M, Daskalaki E, Drapela A, Stettler C, Diem P, Mougiakakou S. GoCARB in the Context of an Artificial Pancreas. J Diabetes Sci Technol 2015; 9:549-55. [PMID: 25904142 PMCID: PMC4604547 DOI: 10.1177/1932296815583333] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND In an artificial pancreas (AP), the meals are either manually announced or detected and their size estimated from the blood glucose level. Both methods have limitations, which result in suboptimal postprandial glucose control. The GoCARB system is designed to provide the carbohydrate content of meals and is presented within the AP framework. METHOD The combined use of GoCARB with a control algorithm is assessed in a series of 12 computer simulations. The simulations are defined according to the type of the control (open or closed loop), the use or not-use of GoCARB and the diabetics' skills in carbohydrate estimation. RESULTS For bad estimators without GoCARB, the percentage of the time spent in target range (70-180 mg/dl) during the postprandial period is 22.5% and 66.2% for open and closed loop, respectively. When the GoCARB is used, the corresponding percentages are 99.7% and 99.8%. In case of open loop, the time spent in severe hypoglycemic events (<50 mg/dl) is 33.6% without the GoCARB and is reduced to 0.0% when the GoCARB is used. In case of closed loop, the corresponding percentage is 1.4% without the GoCARB and is reduced to 0.0% with the GoCARB. CONCLUSION The use of GoCARB improves the control of postprandial response and glucose profiles especially in the case of open loop. However, the most efficient regulation is achieved by the combined use of the control algorithm and the GoCARB.
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Affiliation(s)
- Aristotelis Agianniotis
- Diabetes Technology Research Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Switzerland
| | - Marios Anthimopoulos
- Diabetes Technology Research Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Switzerland
| | - Elena Daskalaki
- Diabetes Technology Research Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Switzerland
| | - Aurélie Drapela
- Diabetes Technology Research Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Switzerland
| | - Christoph Stettler
- Department of Endocrinology, Diabetes & Clinical Nutrition, Bern University Hospital "Inselspital," Bern, Switzerland
| | - Peter Diem
- Department of Endocrinology, Diabetes & Clinical Nutrition, Bern University Hospital "Inselspital," Bern, Switzerland
| | - Stavroula Mougiakakou
- Diabetes Technology Research Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Switzerland Department of Endocrinology, Diabetes & Clinical Nutrition, Bern University Hospital "Inselspital," Bern, Switzerland
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Abstract
PURPOSE OF REVIEW This article describes recent progress in the automated control of glycemia in type 1 diabetes with artificial pancreas devices that combine continuous glucose monitoring with automated decision-making and insulin delivery. RECENT FINDINGS After a gestation period of closely supervised feasibility studies in research centers, the last 2 years have seen publication of studies testing these devices in outpatient environments, and many more such studies are ongoing. The most basic form of automation, suspension of insulin delivery for actual or predicted hypoglycemia, has been shown to be effective and well tolerated, and a first-generation device has actually reached the market. Artificial pancreas devices that actively dose insulin fall into two categories, those that dose insulin alone and those that also use glucagon to prevent and treat hypoglycemia (bihormonal artificial pancreas). Initial outpatient clinical trials have shown that both strategies can improve glycemic management in comparison with patient-controlled insulin pump therapy, but only the bihormonal strategy has been tested without restrictions on exercise. SUMMARY Artificial pancreas technology has the potential to reduce acute and chronic complications of diabetes and mitigate the burden of diabetes self-management. Successful outpatient studies bring these technologies one step closer to availability for patients.
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Affiliation(s)
- Steven J Russell
- Massachusetts General Hospital Diabetes Research Center, Boston, Massachusetts, USA
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46
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Siegel RA. Stimuli sensitive polymers and self regulated drug delivery systems: a very partial review. J Control Release 2014; 190:337-51. [PMID: 24984012 PMCID: PMC4142101 DOI: 10.1016/j.jconrel.2014.06.035] [Citation(s) in RCA: 73] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2014] [Revised: 06/18/2014] [Accepted: 06/21/2014] [Indexed: 10/25/2022]
Abstract
Since the early days of the Journal of Controlled Release, there has been considerable interest in materials that can release drug on an "on-demand" basis. So called "stimuli-responsive" and "intelligent" systems have been designed to deliver drug at various times or at various sites in the body, according to a stimulus that is either endogenous or externally applied. In the past three decades, research along these lines has taken numerous directions, and each new generation of investigators has discovered new physicochemical principles and chemical schemes by which the release properties of materials can be altered. No single review could possibly do justice to all of these approaches. In this article, some general observations are made, and a partial history of the field is presented. Both open loop and closed loop systems are discussed. Special emphasis is placed on stimuli-responsive hydrogels, and on systems that can respond repeatedly. It is argued that the most success at present and in the foreseeable future is with systems in which biosensing and actuation (i.e. drug delivery) are separated, with a human and/or cybernetic operator linking the two.
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Affiliation(s)
- Ronald A Siegel
- Department of Pharmaceutics, University of Minnesota, Minneapolis, MN 55455 USA; Department Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455 USA.
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47
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Russell SJ, El-Khatib FH, Sinha M, Magyar KL, McKeon K, Goergen LG, Balliro C, Hillard MA, Nathan DM, Damiano ER. Outpatient glycemic control with a bionic pancreas in type 1 diabetes. N Engl J Med 2014; 371:313-325. [PMID: 24931572 PMCID: PMC4183762 DOI: 10.1056/nejmoa1314474] [Citation(s) in RCA: 381] [Impact Index Per Article: 38.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
BACKGROUND The safety and effectiveness of automated glycemic management have not been tested in multiday studies under unrestricted outpatient conditions. METHODS In two random-order, crossover studies with similar but distinct designs, we compared glycemic control with a wearable, bihormonal, automated, "bionic" pancreas (bionic-pancreas period) with glycemic control with an insulin pump (control period) for 5 days in 20 adults and 32 adolescents with type 1 diabetes mellitus. The automatically adaptive algorithm of the bionic pancreas received data from a continuous glucose monitor to control subcutaneous delivery of insulin and glucagon. RESULTS Among the adults, the mean plasma glucose level over the 5-day bionic-pancreas period was 138 mg per deciliter (7.7 mmol per liter), and the mean percentage of time with a low glucose level (<70 mg per deciliter [3.9 mmol per liter]) was 4.8%. After 1 day of automatic adaptation by the bionic pancreas, the mean (±SD) glucose level on continuous monitoring was lower than the mean level during the control period (133±13 vs. 159±30 mg per deciliter [7.4±0.7 vs. 8.8±1.7 mmol per liter], P<0.001) and the percentage of time with a low glucose reading was lower (4.1% vs. 7.3%, P=0.01). Among the adolescents, the mean plasma glucose level was also lower during the bionic-pancreas period than during the control period (138±18 vs. 157±27 mg per deciliter [7.7±1.0 vs. 8.7±1.5 mmol per liter], P=0.004), but the percentage of time with a low plasma glucose reading was similar during the two periods (6.1% and 7.6%, respectively; P=0.23). The mean frequency of interventions for hypoglycemia among the adolescents was lower during the bionic-pancreas period than during the control period (one per 1.6 days vs. one per 0.8 days, P<0.001). CONCLUSIONS As compared with an insulin pump, a wearable, automated, bihormonal, bionic pancreas improved mean glycemic levels, with less frequent hypoglycemic episodes, among both adults and adolescents with type 1 diabetes mellitus. (Funded by the National Institute of Diabetes and Digestive and Kidney Diseases and others; ClinicalTrials.gov numbers, NCT01762059 and NCT01833988.).
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Affiliation(s)
- Steven J Russell
- Diabetes Unit and Department of Medicine, Massachusetts General Hospital and Harvard Medical School (S.J.R., M.S., K.L.M, L.G.G., C.B., M.A.H., D.M.N.), and the Department of Biomedical Engineering, Boston University (F.H.E.-K., K.M., E.R.D.) - both in Boston
| | - Firas H El-Khatib
- Diabetes Unit and Department of Medicine, Massachusetts General Hospital and Harvard Medical School (S.J.R., M.S., K.L.M, L.G.G., C.B., M.A.H., D.M.N.), and the Department of Biomedical Engineering, Boston University (F.H.E.-K., K.M., E.R.D.) - both in Boston
| | - Manasi Sinha
- Diabetes Unit and Department of Medicine, Massachusetts General Hospital and Harvard Medical School (S.J.R., M.S., K.L.M, L.G.G., C.B., M.A.H., D.M.N.), and the Department of Biomedical Engineering, Boston University (F.H.E.-K., K.M., E.R.D.) - both in Boston
| | - Kendra L Magyar
- Diabetes Unit and Department of Medicine, Massachusetts General Hospital and Harvard Medical School (S.J.R., M.S., K.L.M, L.G.G., C.B., M.A.H., D.M.N.), and the Department of Biomedical Engineering, Boston University (F.H.E.-K., K.M., E.R.D.) - both in Boston
| | - Katherine McKeon
- Diabetes Unit and Department of Medicine, Massachusetts General Hospital and Harvard Medical School (S.J.R., M.S., K.L.M, L.G.G., C.B., M.A.H., D.M.N.), and the Department of Biomedical Engineering, Boston University (F.H.E.-K., K.M., E.R.D.) - both in Boston
| | - Laura G Goergen
- Diabetes Unit and Department of Medicine, Massachusetts General Hospital and Harvard Medical School (S.J.R., M.S., K.L.M, L.G.G., C.B., M.A.H., D.M.N.), and the Department of Biomedical Engineering, Boston University (F.H.E.-K., K.M., E.R.D.) - both in Boston
| | - Courtney Balliro
- Diabetes Unit and Department of Medicine, Massachusetts General Hospital and Harvard Medical School (S.J.R., M.S., K.L.M, L.G.G., C.B., M.A.H., D.M.N.), and the Department of Biomedical Engineering, Boston University (F.H.E.-K., K.M., E.R.D.) - both in Boston
| | - Mallory A Hillard
- Diabetes Unit and Department of Medicine, Massachusetts General Hospital and Harvard Medical School (S.J.R., M.S., K.L.M, L.G.G., C.B., M.A.H., D.M.N.), and the Department of Biomedical Engineering, Boston University (F.H.E.-K., K.M., E.R.D.) - both in Boston
| | - David M Nathan
- Diabetes Unit and Department of Medicine, Massachusetts General Hospital and Harvard Medical School (S.J.R., M.S., K.L.M, L.G.G., C.B., M.A.H., D.M.N.), and the Department of Biomedical Engineering, Boston University (F.H.E.-K., K.M., E.R.D.) - both in Boston
| | - Edward R Damiano
- Diabetes Unit and Department of Medicine, Massachusetts General Hospital and Harvard Medical School (S.J.R., M.S., K.L.M, L.G.G., C.B., M.A.H., D.M.N.), and the Department of Biomedical Engineering, Boston University (F.H.E.-K., K.M., E.R.D.) - both in Boston
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Cefalu WT, Boulton AJ, Tamborlane WV, Moses RG, LeRoith D, Greene EL, Hu FB, Bakris G, Wylie-Rosett J, Rosenstock J, Weinger K, Blonde L, de Groot M, Riddle MC, Henry RR, Golden SH, Rich S, Reynolds L. Status of Diabetes Care: "It just doesn't get any better . . . or does it?". Diabetes Care 2014; 37:1782-5. [PMID: 25093231 PMCID: PMC5131856 DOI: 10.2337/dc14-1073] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Affiliation(s)
- William T. Cefalu
- Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, LA
| | | | | | | | - Derek LeRoith
- Division of Endocrinology, Diabetes and Bone Disease, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Eddie L. Greene
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN
| | - Frank B. Hu
- Departments of Nutrition and Epidemiology, Harvard School of Public Health, Boston, MA
| | - George Bakris
- ASH Comprehensive Hypertension Center, Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, The University of Chicago Medicine, Chicago, IL
| | - Judith Wylie-Rosett
- Department of Epidemiology and Social Medicine, Albert Einstein College of Medicine, Bronx, NY
| | - Julio Rosenstock
- Dallas Diabetes and Endocrine Center at Medical City, Dallas, TX
| | - Katie Weinger
- Joslin Diabetes Center, Harvard Medical School, Boston, MA
| | - Lawrence Blonde
- Ochsner Diabetes Clinical Research Unit, Department of Endocrinology, Diabetes and Metabolism, Ochsner Medical Center, New Orleans, LA
| | - Mary de Groot
- Indiana University School of Medicine, Indianapolis, IN
| | - Matthew C. Riddle
- Division of Endocrinology, Diabetes and Clinical Nutrition, Oregon Health & Science University, Portland, OR
| | | | - Sherita Hill Golden
- Division of Endocrinology and Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Stephen Rich
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA
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Doyle FJ, Huyett LM, Lee JB, Zisser HC, Dassau E. Closed-loop artificial pancreas systems: engineering the algorithms. Diabetes Care 2014; 37:1191-7. [PMID: 24757226 PMCID: PMC3994938 DOI: 10.2337/dc13-2108] [Citation(s) in RCA: 192] [Impact Index Per Article: 19.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
In this two-part Bench to Clinic narrative, recent advances in both the preclinical and clinical aspects of artificial pancreas (AP) development are described. In the preceding Bench narrative, Kudva and colleagues provide an in-depth understanding of the modified glucoregulatory physiology of type 1 diabetes that will help refine future AP algorithms. In the Clinic narrative presented here, we compare and evaluate AP technology to gain further momentum toward outpatient trials and eventual approval for widespread use. We enumerate the design objectives, variables, and challenges involved in AP development, concluding with a discussion of recent clinical advancements. Thanks to the effective integration of engineering and medicine, the dream of automated glucose regulation is nearing reality. Consistent and methodical presentation of results will accelerate this success, allowing head-to-head comparisons that will facilitate adoption of the AP as a standard therapy for type 1 diabetes.
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