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Wang YY, Ying HM, Tian F, Qian XL, Zhou ZF, Zhou CC. Automated insulin delivery in children with type 1 diabetes during physical activity: a meta-analysis. J Pediatr Endocrinol Metab 2024; 37:505-515. [PMID: 38700489 DOI: 10.1515/jpem-2024-0098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2024] [Accepted: 04/18/2024] [Indexed: 05/05/2024]
Abstract
OBJECTIVES The aim of this study was to evaluate the performance of the automated insulin delivery (AID) in adolescents, and children with type 1 diabetes (T1D) during physical activity. METHODS Relevant studies were searched electronically in the Cochrane Library, PubMed, and Embase utilizing the key words "Child", "Insulin Infusion Systems", and "Diabetes Mellitus" from inception to 17th March 2024 to evaluate the performance of the AID in adolescents, and children with T1D during physical activity. RESULTS Twelve studies involving 514 patients were identified. AID did not show a beneficial effect on duration of hypoglycemia<70 mg/dL during study period (p>0.05; I2=96 %) and during the physical activity (p>0.99). Percentage of sensor glucose values in TIR was higher in AID than the non-AID pumps during study period (p<0.001; I2=94 %). The duration of hyperglycemic time was significantly decreased in AID group compared to the non-AID pumps group during study period (p<0.05; I2>50 %). CONCLUSIONS AID improved TIR and decreased the duration of hyperglycemic time, but did not appear to have a significant beneficial effect on the already low post-exercise duration of hypoglycemia achievable by open loop or sensor-augmented pumps in adolescents and children with T1D during physical activity; further research is needed to confirm the beneficial effect of AID on duration of hypoglycemia.
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Affiliation(s)
- Yuan-Yuan Wang
- Department of Endocrinology, 631689 Xixi Hospital of Hangzhou , Hangzhou, Zhejiang Province, P.R. China
| | - Hui-Min Ying
- Department of Endocrinology, 631689 Xixi Hospital of Hangzhou , Hangzhou, Zhejiang Province, P.R. China
| | - Fang Tian
- Department of Endocrinology, 631689 Xixi Hospital of Hangzhou , Hangzhou, Zhejiang Province, P.R. China
| | - Xiao-Lu Qian
- Department of Endocrinology, 631689 Xixi Hospital of Hangzhou , Hangzhou, Zhejiang Province, P.R. China
| | - Zhen-Feng Zhou
- Department of Anesthesiology, Hangzhou Women's Hospital (Hangzhou Maternity and Child Health Care Hospital, Hangzhou First People's Hospital Qianjiang New City Campus, Zhejiang Chinese Medical University), Hangzhou, P.R. China
| | - Chun-Cong Zhou
- Department of Urolithiasis and Anorectal Surgery, Ningbo No. 2 Hospital, Ningbo, Zhejiang Province, P.R. China
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Kovatchev B, Castillo A, Pryor E, Kollar LL, Barnett CL, DeBoer MD, Brown SA. Neural-Net Artificial Pancreas: A Randomized Crossover Trial of a First-in-Class Automated Insulin Delivery Algorithm. Diabetes Technol Ther 2024; 26:375-382. [PMID: 38277161 DOI: 10.1089/dia.2023.0469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/27/2024]
Abstract
Background: Automated insulin delivery (AID) is now integral to the clinical practice of type 1 diabetes (T1D). The objective of this pilot-feasibility study was to introduce a new regulatory and clinical paradigm-a Neural-Net Artificial Pancreas (NAP)-an encoding of an AID algorithm into a neural network that approximates its action and assess NAP versus the original AID algorithm. Methods: The University of Virginia Model-Predictive Control (UMPC) algorithm was encoded into a neural network, creating its NAP approximation. Seventeen AID users with T1D were recruited and 15 participated in two consecutive 20-h hotel sessions, receiving in random order either NAP or UMPC. Their demographic characteristics were ages 22-68 years old, duration of diabetes 7-58 years, gender 10/5 female/male, White Non-Hispanic/Black 13/2, and baseline glycated hemoglobin 5.4%-8.1%. Results: The time-in-range (TIR) difference between NAP and UMPC, adjusted for entry glucose level, was 1 percentage point, with absolute TIR values of 86% (NAP) and 87% (UMPC). The two algorithms achieved similar times <70 mg/dL of 2.0% versus 1.8% and coefficients of variation of 29.3% (NAP) versus 29.1 (UMPC)%. Under identical inputs, the average absolute insulin-recommendation difference was 0.031 U/h. There were no serious adverse events on either controller. NAP had sixfold lower computational demands than UMPC. Conclusion: In a randomized crossover study, a neural-network encoding of a complex model-predictive control algorithm demonstrated similar performance, at a fraction of the computational demands. Regulatory and clinical doors are therefore open for contemporary machine-learning methods to enter the AID field. Clinical Trial Registration number: NCT05876273.
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Affiliation(s)
- Boris Kovatchev
- Center for Diabetes Technology, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Alberto Castillo
- Center for Diabetes Technology, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Elliott Pryor
- Center for Diabetes Technology, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Laura L Kollar
- Center for Diabetes Technology, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Charlotte L Barnett
- Center for Diabetes Technology, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Mark D DeBoer
- Center for Diabetes Technology, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Sue A Brown
- Center for Diabetes Technology, University of Virginia School of Medicine, Charlottesville, Virginia, USA
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ElSayed NA, Aleppo G, Bannuru RR, Bruemmer D, Collins BS, Ekhlaspour L, Hilliard ME, Johnson EL, Khunti K, Lingvay I, Matfin G, McCoy RG, Perry ML, Pilla SJ, Polsky S, Prahalad P, Pratley RE, Segal AR, Seley JJ, Stanton RC, Gabbay RA. 14. Children and Adolescents: Standards of Care in Diabetes-2024. Diabetes Care 2024; 47:S258-S281. [PMID: 38078582 PMCID: PMC10725814 DOI: 10.2337/dc24-s014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, an interprofessional expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.
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Cobry EC, Pyle L, Karami AJ, Sakamoto C, Meltzer LJ, Jost E, Towers L, Paul Wadwa R. Impact of 6-months of an advanced hybrid closed-loop system on sleep and psychosocial outcomes in youth with type 1 diabetes and their parents. Diabetes Res Clin Pract 2024; 207:111087. [PMID: 38181984 PMCID: PMC10942664 DOI: 10.1016/j.diabres.2023.111087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 12/22/2023] [Accepted: 12/27/2023] [Indexed: 01/07/2024]
Abstract
INTRODUCTION Youth with type 1 diabetes (T1D) and parents experience reduced quality of life and sleep quality due to nocturnal monitoring, hypoglycemia fear, and diabetes-related disruptions. This study examined the sleep and quality of life impact of advanced technology. METHODS Thirty-nine youth with T1D, aged 2-17 years, starting an advanced hybrid closed-loop (HCL) system and a parent participated in an observational study. Surveys, actigraphy, sleep diaries, and glycemic data (youth) were captured prior to HCL, at one week, 3 months, and 6 months. Outcomes were modeled using linear mixed effects models with random intercepts to account for within-subject correlation, with least-squares means at each timepoint compared to baseline. RESULTS Parents and youth reported improvements in health-related quality of life and fear of hypoglycemia after HCL initiation. Concurrently, nocturnal glycemia improved. Actigraphy-derived sleep outcomes showed improved 6 month adolescent efficiency and 3 and 6 month parent wake after sleep onset. Additionally, parents reported improved subjective sleep quality and child sleep-related impairment at 3 months. CONCLUSIONS With nocturnal glycemic improvements in youth using HCL technology, some aspects of parent and youth sleep and quality of life improved. This may reflect decreased parental monitoring and worry and highlights benefits for youth beyond glycemia.
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Affiliation(s)
- Erin C Cobry
- University of Colorado Anschutz Medical Campus, Barbara Davis Center for Diabetes, Aurora, CO, USA.
| | - Laura Pyle
- University of Colorado Anschutz Medical Campus, Barbara Davis Center for Diabetes, Aurora, CO, USA; Colorado School of Public Health, Department of Biostatistics and Informatics, Aurora, CO, USA
| | - Angela J Karami
- University of Colorado Anschutz Medical Campus, Barbara Davis Center for Diabetes, Aurora, CO, USA
| | - Casey Sakamoto
- University of Colorado Anschutz Medical Campus, Barbara Davis Center for Diabetes, Aurora, CO, USA; Colorado School of Public Health, Department of Biostatistics and Informatics, Aurora, CO, USA
| | - Lisa J Meltzer
- National Jewish Health, Denver, CO, USA; Nyxeos Consulting, Denver, CO, USA
| | - Emily Jost
- University of Colorado Anschutz Medical Campus, Barbara Davis Center for Diabetes, Aurora, CO, USA
| | - Lindsey Towers
- University of Colorado Anschutz Medical Campus, Barbara Davis Center for Diabetes, Aurora, CO, USA
| | - R Paul Wadwa
- University of Colorado Anschutz Medical Campus, Barbara Davis Center for Diabetes, Aurora, CO, USA
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Pellizzari E, Prendin F, Cappon G, Sparacino G, Facchinetti A. drCORRECT: An Algorithm for the Preventive Administration of Postprandial Corrective Insulin Boluses in Type 1 Diabetes Management. J Diabetes Sci Technol 2023:19322968231221768. [PMID: 38158565 DOI: 10.1177/19322968231221768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
BACKGROUND In type 1 diabetes therapy, precise tuning of postprandial corrective insulin boluses (CIBs) is crucial to mitigate hyperglycemia without inducing dangerous hypoglycemic events. Several heuristic formulas accounting for continuous glucose monitoring (CGM) trend have been proposed in the literature. However, these formulas suggest a lot of quantized CIB adjustments, and they lack personalization. METHOD drCORRECT algorithm proposed in this work employs a patient-specific time parameter and the "dynamic risk" (DR) measure to determine postprandial CIB suggestion. The expected benefits include the reduction of time in hyperglycemia, thanks to the preventive action exploited through DR. drCORRECT has been assessed retrospectively vs the literature methods proposed by Aleppo et al (AL), Bruttomesso et al (BR), and Ziegler et al (ZI) using a data set of 49 CGM daily traces recorded in free-living conditions. Retrospective evaluation of the algorithms is made possible by the use of ReplayBG, a digital twin-based tool that allows assessing alternative insulin therapies on already collected glucose data. Efficacy in terms of glucose control was measured by temporal, risk indicators, and dedicated hyperglycemic/hypoglycemic events metrics. RESULTS drCORRECT significantly reduces time spent in hyperglycemia when compared with AL and BR (33.52 [24.16, 39.89]% vs 39.76 [22.54, 48.15]% and 36.32 [26.91, 45.93]%, respectively); significantly reduces daily injected insulin (5.97 [3.80, 8.06] U vs 7.5 [5.21, 10.34] U), glycemia risk index (38.78 [26.58, 55.39] vs 40.78 [27.95, 70.30]), and time spent in hypoglycemia (0.00 [0.00, 1.74]% vs 0.00 [0.00, 10.23]%) when compared with ZI, resulting overall in a safer strategy. CONCLUSIONS The proposed drCORRECT algorithm allows preventive actions thanks to the personalized timing configuration and the introduction of the innovative DR-based CIB threshold, proving to be a valid alternative to the available heuristic literature methods.
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Affiliation(s)
- Elisa Pellizzari
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Francesco Prendin
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Giacomo Cappon
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Giovanni Sparacino
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Andrea Facchinetti
- Department of Information Engineering, University of Padova, Padova, Italy
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Cobelli C, Kovatchev B. Developing the UVA/Padova Type 1 Diabetes Simulator: Modeling, Validation, Refinements, and Utility. J Diabetes Sci Technol 2023; 17:1493-1505. [PMID: 37743740 PMCID: PMC10658679 DOI: 10.1177/19322968231195081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Arguably, diabetes mellitus is one of the best quantified human conditions. In the past 50 years, the metabolic monitoring technologies progressed from occasional assessment of average glycemia via HbA1c, through episodic blood glucose readings, to continuous glucose monitoring (CGM) producing data points every few minutes. The high-temporal resolution of CGM data enabled increasingly intensive treatments, from decision support assisting insulin injection or oral medication, to automated closed-loop control, known as the "artificial pancreas." Throughout this progress, mathematical models and computer simulation of the human metabolic system became indispensable for the technological progress of diabetes treatment, enabling every step, from assessment of insulin sensitivity via the now classic Minimal Model of Glucose Kinetics, to in silico trials replacing animal experiments, to automated insulin delivery algorithms. In this review, we follow these developments, beginning with the Minimal Model, which evolved through the years to become large and comprehensive and trigger a paradigm change in the design of diabetes optimization strategies: in 2007, we introduced a sophisticated model of glucose-insulin dynamics and a computer simulator equipped with a "population" of N = 300 in silico "subjects" with type 1 diabetes. In January 2008, in an unprecedented decision, the Food and Drug Administration (FDA) accepted this simulator as a substitute to animal trials for the pre-clinical testing of insulin treatment strategies. This opened the field for rapid and cost-effective development and pre-clinical testing of new treatment approaches, which continues today. Meanwhile, animal experiments for the purpose of designing new insulin treatment algorithms have been abandoned.
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Affiliation(s)
| | - Boris Kovatchev
- Center for Diabetes Technology,
University of Virginia, Charlottesville, VA, USA
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7
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Cappon G, Vettoretti M, Sparacino G, Favero SD, Facchinetti A. ReplayBG: A Digital Twin-Based Methodology to Identify a Personalized Model From Type 1 Diabetes Data and Simulate Glucose Concentrations to Assess Alternative Therapies. IEEE Trans Biomed Eng 2023; 70:3227-3238. [PMID: 37368794 DOI: 10.1109/tbme.2023.3286856] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2023]
Abstract
OBJECTIVE Design and assessment of new therapies for type 1 diabetes (T1D) management can be greatly facilitated by in silico simulations. The ReplayBG simulation methodology here proposed allows "replaying" the scenario behind data already collected by simulating the glucose concentration obtained in response to alternative insulin/carbohydrate therapies and evaluate their efficacy leveraging the concept of digital twin. METHODS ReplayBG is based on two steps. First, a personalized model of glucose-insulin dynamics is identified using insulin, carbohydrate, and continuous glucose monitoring (CGM) data. Then, this model is used to simulate the glucose concentration that would have been obtained by "replaying" the same portion of data using a different therapy. The validity of the methodology was evaluated on 100 virtual subjects using the UVa/Padova T1D Simulator (T1DS). In particular, the glucose concentration traces simulated by ReplayBG are compared with those provided by T1DS in five different scenarios of insulin and carbohydrate treatment modifications. Furthermore, we compared ReplayBG with a state-of-the-art methodology for the scope. Finally, two case studies using real data are also presented. RESULTS ReplayBG simulates with high accuracy the effect of the considered insulin and carbohydrate treatment alterations, performing significantly better than state-of-art method in almost all considered situations. CONCLUSION ReplayBG proved to be a reliable and robust tool to retrospectively explore the effect of new treatments for T1D on the glucose dynamics. It is freely available as open source software at https://github.com/gcappon/replay-bg. SIGNIFICANCE ReplayBG offers a new approach to preliminary evaluate new therapies for T1D management before clinical trials.
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8
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Faccioli S, Prendin F, Facchinetti A, Sparacino G, Del Favero S. Combined Use of Glucose-Specific Model Identification and Alarm Strategy Based on Prediction-Funnel to Improve Online Forecasting of Hypoglycemic Events. J Diabetes Sci Technol 2023; 17:1295-1303. [PMID: 35611461 PMCID: PMC10563526 DOI: 10.1177/19322968221093665] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Advanced decision support systems for type 1 diabetes (T1D) management often embed prediction modules, which allow T1D people to take preventive actions to avoid critical episodes like hypoglycemia. Real-time prediction of blood glucose (BG) concentration relies on a subject-specific model of glucose-insulin dynamics. Model parameter identification is usually based on the mean square error (MSE) cost function, and the model is usually used to predict BG at a single prediction horizon (PH). Finally, a hypo-alarm is raised if the predicted BG crosses a threshold. This work aims to show that real-time hypoglycemia forecasting can be improved by leveraging: a glucose-specific mean square error (gMSE) cost function in model's parameters identification, and a "prediction-funnel," that is, confidence intervals (CIs) for multiple PHs, within the hypo-alarm-raising strategy. METHODS Autoregressive integrated moving average with exogenous input (ARIMAX) models are selected to illustrate the proposed solution (use of gMSE and prediction-funnel) and its assessment against the conventional approach (MSE and single PH). The gMSE penalizes the model misfit in unsafe BG ranges (e.g., hypoglycemia), and the prediction-funnel allows raising an alarm by monitoring if the CIs cross a suitable threshold. The algorithms were evaluated by measuring precision (P), recall (R), F1-score (F1), false positive per day (FP/day), and time gain (TG) on a real dataset collected in 11 T1D individuals. RESULTS The best performance is achieved exploiting both the gMSE and the prediction-funnel: P = 65%, R = 88%, F1 = 75%, FP/day = 0.29, and mean TG = 15 minutes. CONCLUSIONS The combined use of a glucose-specific metric and an alarm-raising strategy based on the prediction-funnel allows achieving a more effective and reliable hypoglycemia prediction algorithm.
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Affiliation(s)
- Simone Faccioli
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Francesco Prendin
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Andrea Facchinetti
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Giovanni Sparacino
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Simone Del Favero
- Department of Information Engineering, University of Padova, Padova, Italy
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9
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Burnside MJ, Lewis DM, Crocket HR, Meier RA, Williman JA, Sanders OJ, Jefferies CA, Faherty AM, Paul RG, Lever CS, Price SKJ, Frewen CM, Jones SD, Gunn TC, Lampey C, Wheeler BJ, de Bock MI. Extended Use of an Open-Source Automated Insulin Delivery System in Children and Adults with Type 1 Diabetes: The 24-Week Continuation Phase Following the CREATE Randomized Controlled Trial. Diabetes Technol Ther 2023; 25:250-259. [PMID: 36763345 DOI: 10.1089/dia.2022.0484] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
Aim: To assess long-term efficacy and safety of open-source automated insulin delivery (AID) in children and adults (7-70 years) with type 1 diabetes. Methods: Both arms of a 24-week randomized controlled trial comparing open-source AID (OpenAPS algorithm within a modified version of AndroidAPS, preproduction DANA-i™ insulin pump, Dexcom G6 continuous glucose monitor) with sensor-augmented pump therapy (SAPT), entered a 24-week continuation phase where the SAPT arm (termed SAPT-AID) crossed over to join the open-source AID arm (termed AID-AID). Most participants (69/94) used a preproduction YpsoPump® insulin pump during the continuation phase. Analyses incorporated all 52 weeks of data, and combined between-group and within-subject differences to calculate an overall "treatment effect" of AID versus SAPT. Results: Mean time in range (TIR; 3.9-10 mmol/L [70-180 mg/dL]) was 12.2% higher with AID than SAPT (95% confidence interval [CI] 10.4 to 14.1; P < 0.001). TIR was 56.9% (95% CI 54.2 to 59.6) with SAPT and 69.1% (95% CI 67.1 to 71.1) with AID. The treatment effect did not differ by age (P = 0.39) or insulin pump type (P = 0.37). HbA1c was 5.1 mmol/mol lower [0.5%] with AID (95% CI -6.6 to -3.6; P < 0.001). There were no episodes of diabetic ketoacidosis or severe hypoglycemia with either treatment over the 48 weeks. Six participants (all in SAPT-AID) withdrew: three with hardware issues, two preferred SAPT, and one with infusion-site skin irritation. Conclusion: Further evaluation of the community derived automated insulin delivery (CREATE) trial to 48 weeks confirms that open-source AID is efficacious and safe with different insulin pumps, and demonstrates sustained glycemic improvements without additional safety concerns.
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Affiliation(s)
- Mercedes J Burnside
- Department of Pediatrics, University of Otago, Christchurch, Christchurch, New Zealand
- Pediatric Department, Te Whatu Ora Health New Zealand Waitaha Canterbury, Christchurch, New Zealand
| | | | - Hamish R Crocket
- Te Huataki Waiora School of Health, Sport & Human Performance, University of Waikato, Hamilton, New Zealand
| | - Renee A Meier
- Department of Pediatrics, University of Otago, Christchurch, Christchurch, New Zealand
| | - Jonathan A Williman
- Department of Population Health, University of Otago, Christchurch, Christchurch, New Zealand
| | - Olivia J Sanders
- Department of Pediatrics, University of Otago, Christchurch, Christchurch, New Zealand
- Pediatric Department, Te Whatu Ora Health New Zealand Waitaha Canterbury, Christchurch, New Zealand
| | - Craig A Jefferies
- Department of Pediatric Endocrinology, Starship Children's Health, Te Whatu Ora Te Toka Tumai, Auckland, New Zealand
- Liggins Institute and Department of Pediatrics, University of Auckland, Auckland, New Zealand
| | - Ann M Faherty
- Department of Pediatric Endocrinology, Starship Children's Health, Te Whatu Ora Te Toka Tumai, Auckland, New Zealand
| | - Ryan G Paul
- Te Huataki Waiora School of Health, Sport & Human Performance, University of Waikato, Hamilton, New Zealand
- Waikato Regional Diabetes Service, Te Whatu Ora Health New Zealand Waikato, Hamilton, New Zealand
| | - Claire S Lever
- Waikato Regional Diabetes Service, Te Whatu Ora Health New Zealand Waikato, Hamilton, New Zealand
| | - Sarah K J Price
- Waikato Regional Diabetes Service, Te Whatu Ora Health New Zealand Waikato, Hamilton, New Zealand
| | - Carla M Frewen
- Department of Women's and Children's Health, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand
| | - Shirley D Jones
- Department of Women's and Children's Health, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand
| | - Tim C Gunn
- Nightscout New Zealand, Hamilton, New Zealand
| | - Christina Lampey
- Department of Pediatric Endocrinology, Starship Children's Health, Te Whatu Ora Te Toka Tumai, Auckland, New Zealand
| | - Benjamin J Wheeler
- Department of Women's and Children's Health, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand
- Pediatric Department, Te Whatu Ora Southern, Dunedin, New Zealand
| | - Martin I de Bock
- Department of Pediatrics, University of Otago, Christchurch, Christchurch, New Zealand
- Pediatric Department, Te Whatu Ora Health New Zealand Waitaha Canterbury, Christchurch, New Zealand
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10
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Schiavon M, Galderisi A, Basu A, Kudva YC, Cengiz E, Dalla Man C. A New Index of Insulin Sensitivity from Glucose Sensor and Insulin Pump Data: In Silico and In Vivo Validation in Youths with Type 1 Diabetes. Diabetes Technol Ther 2023; 25:270-278. [PMID: 36648253 PMCID: PMC10066780 DOI: 10.1089/dia.2022.0397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Background: Estimation of insulin sensitivity (SI) and its daily variation are key for optimizing insulin therapy in patients with type 1 diabetes (T1D). We recently developed a method for SI estimation from continuous glucose monitoring (CGM) and continuous subcutaneous insulin infusion (CSII) data in adults with T1D (SISP) and validated it under restrained experimental conditions. Herein, we validate in vivo a new version of SISP performing well in daily life unrestrained conditions. Methods: The new SISP was tested in both simulated and real data. The simulated dataset consists of 100 virtual adults of the UVa/Padova T1D Simulator monitored during an open-loop experiment, whereas the real dataset consists of 10 youths with T1D monitored during a hybrid closed-loop meal study. In both datasets, participants underwent two consecutive meals (breakfast and lunch, at 7 and 11 am) with the same carbohydrate content (70 g). Plasma glucose and insulin were measured during each meal to estimate the oral glucose minimal model SI (SIMM). CGM and CSII data were used for SISP calculation, which was then validated against the gold standard SIMM. Results: SISP was estimated with good precision (median coefficient of variation <20%) in 100% of the real and 91% of the simulated meals. SISP and SIMM were highly correlated, both in the simulated and real datasets (R = 0.82 and R = 0.83, P < 0.001), and exhibited a similar intraday pattern. Conclusions: SISP is suitable for estimating SI in both closed- and open-loop settings, provided that the subject wears a CGM sensor and a subcutaneous insulin pump.
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Affiliation(s)
- Michele Schiavon
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Alfonso Galderisi
- Department of Woman and Child's Health, University of Padova, Padova, Italy
- Department of Pediatrics, Yale University, New Haven, Connecticut, USA
| | - Ananda Basu
- Division of Endocrinology, University of Virginia, Charlottesville, Virginia, USA
| | - Yogish C. Kudva
- Division of Endocrinology, Diabetes, Metabolism, and Nutrition, Department of Internal Medicine, Mayo Clinic and Foundation, Rochester, Minnesota, USA
| | - Eda Cengiz
- Pediatric Diabetes Program, University of California San Francisco (UCSF) School of Medicine, San Francisco, California, USA
| | - Chiara Dalla Man
- Department of Information Engineering, University of Padova, Padova, Italy
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11
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Sanz R, García P, Romero-Vivó S, Díez JL, Bondia J. Near-optimal feedback control for postprandial glucose regulation in type 1 diabetes. ISA TRANSACTIONS 2023; 133:345-352. [PMID: 36116963 DOI: 10.1016/j.isatra.2022.06.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 04/19/2022] [Accepted: 06/26/2022] [Indexed: 06/15/2023]
Abstract
This paper is focused on feedback control of postprandial glucose levels for patients with type 1 Diabetes Mellitus. There are two important limitations that make this a challenging problem. First, the slow subcutaneous insulin pharmacokinetics that introduces a significant lag into the control loop. Second, the positivity constraint on the control action, meaning that it is not possible to remove insulin from the body. In this paper, both issues are explicitly considered in the design process using the internal model control framework, to derive a near-optimal feedback controller. Optimality is understood here as minimizing the blood glucose peak after a meal intake and, at the same time, preventing glucose values below a prescribed threshold. It is shown how the proposed controller approaches the optimal closed-loop performance as a limit case. The theoretical results are supported by a numerical example and the feasibility of the overall strategy under uncertainties is illustrated using an extended version UVa/Padova metabolic simulator.
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Affiliation(s)
- R Sanz
- Instituto de Automática e Informática Industrial, Universitat Politècnica de València, 46022 València, Spain.
| | - P García
- Instituto de Automática e Informática Industrial, Universitat Politècnica de València, 46022 València, Spain.
| | - S Romero-Vivó
- Instituto de Matemática Multidisciplinar, Universitat Politècnica de València, 46022 València, Spain; Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, 28029 Madrid, Spain.
| | - J L Díez
- Instituto de Automática e Informática Industrial, Universitat Politècnica de València, 46022 València, Spain; Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, 28029 Madrid, Spain.
| | - J Bondia
- Instituto de Automática e Informática Industrial, Universitat Politècnica de València, 46022 València, Spain; Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, 28029 Madrid, Spain.
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12
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Renard E. Automated insulin delivery systems: from early research to routine care of type 1 diabetes. Acta Diabetol 2023; 60:151-161. [PMID: 35994106 DOI: 10.1007/s00592-022-01929-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 06/22/2022] [Indexed: 01/24/2023]
Abstract
Automated insulin delivery (AID) systems, so-called closed-loop systems or artificial pancreas, are based upon the concept of insulin supply driven by blood glucose levels and their variations according to body glucose needs, glucose intakes and insulin action. They include a continuous glucose monitoring device which provides a signal to a control algorithm tuning insulin delivery from an infusion pump. The control algorithm is the key of the system since it commands insulin administration in order to maintain blood glucose in a predefined target range and close to a near-normal glucose level. The last two decades have shown dramatic advances toward the use in free life of AID systems for routine care of type 1 diabetes through step-by-step demonstrations of feasibility, safety and efficacy in successive hospital, transitional and outpatient trials. Because of the constraints of pharmacokinetics and dynamics of subcutaneous insulin delivery, the currently available AID systems are all 'hybrid' or 'semi-automated' insulin delivery systems with a need of meal and exercise announcements in order to anticipate rapid glucose variations through pre-meal bolus or pre-exercise reduction of infusion rate. Nevertheless, these AID systems significantly improve time spent in a near-normal range with a reduction of the risk of hypoglycemia and the mental load of managing diabetes in everyday life, representing a milestone in insulin therapy. Expected progression toward fully automated, further miniaturized and integrated, possibly implantable on long-term and more physiological closed-loop systems paves the way for a functional cure of type 1 diabetes.
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Affiliation(s)
- Eric Renard
- Department of Endocrinology, Diabetes, Nutrition, Montpellier University Hospital, Montpellier, France.
- INSERM Clinical Investigation Centre CIC 1411, Montpellier, France.
- Department of Physiology, Institute of Functional Genomics, CNRS, INSERM, University of Montpellier, Montpellier, France.
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13
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Affan A, Zurada JM, Inanc T. Control-Relevant Adaptive Personalized Modeling From Limited Clinical Data for Precise Warfarin Management. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2023; 3:242-251. [PMID: 36846361 PMCID: PMC9955254 DOI: 10.1109/ojemb.2023.3240072] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 10/28/2022] [Accepted: 01/06/2023] [Indexed: 12/26/2023] Open
Abstract
Warfarin is a challenging drug to administer due to the narrow therapeutic index of the International Normalized Ratio (INR), the inter- and intra-variability of patients, limited clinical data, genetics, and the effects of other medications. Goal: To predict the optimal warfarin dosage in the presence of the aforementioned challenges, we present an adaptive individualized modeling framework based on model (In)validation and semi-blind robust system identification. The model (In)validation technique adapts the identified individualized patient model according to the change in the patient's status to ensure the model's suitability for prediction and controller design. Results: To implement the proposed adaptive modeling framework, the clinical data of warfarin-INR of forty-four patients has been collected at the Robley Rex Veterans Administration Medical Center, Louisville. The proposed algorithm is compared with recursive ARX and ARMAX model identification methods. The results of identified models using one-step-ahead prediction and minimum mean squared analysis (MMSE) show that the proposed framework effectively predicts the warfarin dosage to keep the INR values within the desired range and adapt the individualized patient model to exhibit the true status of the patient throughout treatment. Conclusion: This paper proposes an adaptive personalized patient modeling framework from limited patientspecific clinical data. It is shown by rigorous simulations that the proposed framework can accurately predict a patient's doseresponse characteristics and it can alert the clinician whenever identified models are no longer suitable for prediction and adapt the model to the current status of the patient to reduce the prediction error.
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Affiliation(s)
- Affan Affan
- Electrical and Computer Engineering DepartmentUniversity of LouisvilleLouisvilleKY40292USA
| | - Jacek M. Zurada
- Electrical and Computer Engineering DepartmentUniversity of LouisvilleLouisvilleKY40292USA
- Information Technology InstituteAcademy of Social Sciences90-193LodzPoland
| | - Tamer Inanc
- Electrical and Computer Engineering DepartmentUniversity of LouisvilleLouisvilleKY40292USA
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14
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ElSayed NA, Aleppo G, Aroda VR, Bannuru RR, Brown FM, Bruemmer D, Collins BS, Hilliard ME, Isaacs D, Johnson EL, Kahan S, Khunti K, Leon J, Lyons SK, Perry ML, Prahalad P, Pratley RE, Seley JJ, Stanton RC, Gabbay RA, on behalf of the American Diabetes Association. 14. Children and Adolescents: Standards of Care in Diabetes-2023. Diabetes Care 2023; 46:S230-S253. [PMID: 36507640 PMCID: PMC9810473 DOI: 10.2337/dc23-s014] [Citation(s) in RCA: 57] [Impact Index Per Article: 57.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.
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15
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Rodríguez-Sarmiento DL, León-Vargas F, García-Jaramillo M. Artificial pancreas systems: experiences from concept to commercialisation. Expert Rev Med Devices 2022; 19:877-894. [DOI: 10.1080/17434440.2022.2150546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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16
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Prendin F, Díez JL, Del Favero S, Sparacino G, Facchinetti A, Bondia J. Assessment of Seasonal Stochastic Local Models for Glucose Prediction without Meal Size Information under Free-Living Conditions. SENSORS (BASEL, SWITZERLAND) 2022; 22:8682. [PMID: 36433278 PMCID: PMC9694694 DOI: 10.3390/s22228682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 11/03/2022] [Accepted: 11/07/2022] [Indexed: 06/16/2023]
Abstract
Accurate blood glucose (BG) forecasting is key in diabetes management, as it allows preventive actions to mitigate harmful hypoglycemic/hyperglycemic episodes. Considering the encouraging results obtained by seasonal stochastic models in proof-of-concept studies, this work assesses the methodology in two datasets (open-loop and closed-loop) recorded in free-living conditions. First, similar postprandial glycemic profiles are grouped together with fuzzy C-means clustering. Then, a seasonal stochastic model is identified for each cluster. Finally, real-time BG forecasting is performed by weighting each model’s prediction. The proposed methodology (named C-SARIMA) is compared to other linear and nonlinear black-box methods: autoregressive integrated moving average (ARIMA), its variant with input (ARIMAX), a feed-forward neural network (NN), and its modified version (NN-X) fed by BG, insulin, and carbohydrates (timing and dosing) information for several prediction horizons (PHs). In the open-loop dataset, C-SARIMA grants a median root-mean-squared error (RMSE) of 20.13 mg/dL (PH = 30) and 27.23 mg/dL (PH = 45), not significantly different from ARIMA and NN. Over a longer PH, C-SARIMA achieves an RMSE = 31.96 mg/dL (PH = 60) and RMSE = 33.91 mg/dL (PH = 75), significantly outperforming the ARIMA and NN, without significant differences from the ARIMAX for PH ≥ 45 and the NN-X for PH ≥ 60. Similar results hold on the closed-loop dataset: for PH = 30 and 45 min, the C-SARIMA achieves an RMSE = 21.63 mg/dL and RMSE = 29.67 mg/dL, not significantly different from the ARIMA and NN. On longer PH, the C-SARIMA outperforms the ARIMA for PH > 45 and the NN for PH > 60 without significant differences from the ARIMAX for PH ≥ 45. Although using less input information, the C-SARIMA achieves similar performance to other prediction methods such as the ARIMAX and NN-X and outperforming the CGM-only approaches on PH > 45min.
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Affiliation(s)
- Francesco Prendin
- Department of Information Engineering (DEI), University of Padova, Via G. Gradenigo 6/B, 35131 Padova, Italy
| | - José-Luis Díez
- Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, Camino de Vera, s/n, 46022 València, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Simone Del Favero
- Department of Information Engineering (DEI), University of Padova, Via G. Gradenigo 6/B, 35131 Padova, Italy
| | - Giovanni Sparacino
- Department of Information Engineering (DEI), University of Padova, Via G. Gradenigo 6/B, 35131 Padova, Italy
| | - Andrea Facchinetti
- Department of Information Engineering (DEI), University of Padova, Via G. Gradenigo 6/B, 35131 Padova, Italy
| | - Jorge Bondia
- Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, Camino de Vera, s/n, 46022 València, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, 28029 Madrid, Spain
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17
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Camerlingo N, Vettoretti M, Del Favero S, Facchinetti A, Choudhary P, Sparacino G. Generation of post-meal insulin correction boluses in type 1 diabetes simulation models for in-silico clinical trials: More realistic scenarios obtained using a decision tree approach. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 221:106862. [PMID: 35597208 DOI: 10.1016/j.cmpb.2022.106862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 04/19/2022] [Accepted: 05/07/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE In type 1 diabetes (T1D) research, in-silico clinical trials (ISCTs) notably facilitate the design/testing of new therapies. Published simulation tools embed mathematical models of blood glucose (BG) and insulin dynamics, continuous glucose monitoring (CGM) sensors, and insulin treatments, but lack a realistic description of some aspects of patient lifestyle impacting on glucose control. Specifically, to effectively simulate insulin correction boluses, required to treat post-meal hyperglycemia (BG > 180 mg/dL), the timing of the bolus may be influenced by subjects' behavioral attitudes. In this work, we develop an easily interpretable model of the variability of correction bolus timing observed in real data, and embed it into a popular simulation tool for ISCTs. METHODS Using data collected in 196 adults with T1D monitored in free-living conditions, we trained a decision tree (DT) model to classify whether a correction bolus is injected in a future time window, based on predictors collected back in time, related to CGM data, previous insulin boluses and subject's characteristics. The performance was compared to that of a logistic regression classifier with LASSO regularization (LC), trained on the same dataset. After validation, the DT was embedded within a popular T1D simulation tool and an ISCT was performed to compare the simulated correction boluses against those observed in a subset of data not used for model training. RESULTS The DT provided better classification performance (accuracy: 0.792, sensitivity: 0.430, specificity: 0.878, precision: 0.455) than the LC and presented good interpretability. The most predictive features were related to CGM (and its temporal variations), time since the last insulin bolus, and time of the day. The correction boluses simulated by the DT, after implementation in the simulation tool, showed a good agreement with real-world data. CONCLUSIONS The DT developed in this work represents a simple set of rules to mimic the same timing of correction boluses observed on real data. The inclusion of the model in simulation tools allows investigators to perform ISCTs that more realistically represent the patient behavior in taking correction boluses and the post-prandial BG response. In the future, more complex models can be investigated.
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Affiliation(s)
- N Camerlingo
- Department of Information Engineering, University of Padova, Via G. Gradenigo 6B, Padova 35131, Italy
| | - M Vettoretti
- Department of Information Engineering, University of Padova, Via G. Gradenigo 6B, Padova 35131, Italy
| | - S Del Favero
- Department of Information Engineering, University of Padova, Via G. Gradenigo 6B, Padova 35131, Italy
| | - A Facchinetti
- Department of Information Engineering, University of Padova, Via G. Gradenigo 6B, Padova 35131, Italy
| | - P Choudhary
- Department of Diabetes, Leicester Diabetes Centre, University of Leicester, Gwendolen Rd, Leicester LE5 4PW, United Kingdom
| | - G Sparacino
- Department of Information Engineering, University of Padova, Via G. Gradenigo 6B, Padova 35131, Italy.
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18
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León-Vargas F, Arango Oviedo JA, Luna Wandurraga HJ. Two Decades of Research in Artificial Pancreas: Insights from a Bibliometric Analysis. J Diabetes Sci Technol 2022; 16:434-445. [PMID: 33853377 PMCID: PMC8861788 DOI: 10.1177/19322968211005500] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [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 Artificial pancreas is a well-known research topic devoted to achieving better glycemic outcomes that has been attracting increasing attention over the years. However, there is a lack of systematic, chronological, and synthesizing studies that show the background of the knowledge generation in this field. This study implements a bibliometric analysis to recognize the main documents, type of publications, research categories, countries, keywords, organizations, and authors related to this topic. METHODS Web of Science core collection database was accessed from 2000 to 2020 in order to select high-quality scientific documents based on a specific search query. Bibexcel, MS Excel, Power BI, R-Studio, VOSviewer, and CorText software were used for a descriptive and network analysis based on the local database obtained. Bibliometric parameters as the h-index, frequencies, co-authorship and co-ocurrences were computed. RESULTS A total of 756 documents were included that show a growing scientific production on this topic with an increasing contribution from engineering. Outstanding authors, organizations, and countries were identified. An analysis of trends in research was conducted according to the scientific categories of the Web of Science database to identify the main research interests of the last 2 decades and the emerging areas with greater prominence in the coming years. A keyword network analysis allowed to identify the main stages in the development of the AP research over time. CONCLUSIONS Results reveal a comprehensive background of the knowledge generation for the AP topic during the last 2 decades, which has been strengthened with international collaborations and a remarkable interdisciplinarity between endocrinology and engineering, giving rise to a growing number of research areas over time, where computer science and medical informatics stand out as the main emerging research areas.
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Affiliation(s)
- Fabian León-Vargas
- Universidad Antonio Nariño, Bogotá,
Colombia
- Fabian León-Vargas, PhD, Universidad
Antonio Nariño, Cll 22 Sur # 12D – 81, Bogotá, 111511, Colombia.
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19
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Carlson AL, Sherr JL, Shulman DI, Garg SK, Pop-Busui R, Bode BW, Lilenquist DR, Brazg RL, Kaiserman KB, Kipnes MS, Thrasher JR, Reed JHC, Slover RH, Philis-Tsimikas A, Christiansen M, Grosman B, Roy A, Vella M, Jonkers RA, Chen X, Shin J, Cordero TL, Lee SW, Rhinehart AS, Vigersky RA. Safety and Glycemic Outcomes During the MiniMed™ Advanced Hybrid Closed-Loop System Pivotal Trial in Adolescents and Adults with Type 1 Diabetes. Diabetes Technol Ther 2022; 24:178-189. [PMID: 34694909 PMCID: PMC8971997 DOI: 10.1089/dia.2021.0319] [Citation(s) in RCA: 93] [Impact Index Per Article: 46.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Introduction: This trial assessed safety and effectiveness of an advanced hybrid closed-loop (AHCL) system with automated basal (Auto Basal) and automated bolus correction (Auto Correction) in adolescents and adults with type 1 diabetes (T1D). Materials and Methods: This multicenter single-arm study involved an intent-to-treat population of 157 individuals (39 adolescents aged 14-21 years and 118 adults aged ≥22-75 years) with T1D. Study participants used the MiniMed™ AHCL system during a baseline run-in period in which sensor-augmented pump +/- predictive low glucose management or Auto Basal was enabled for ∼14 days. Thereafter, Auto Basal and Auto Correction were enabled for a study phase (∼90 days), with glucose target set to 100 or 120 mg/dL for ∼45 days, followed by the other target for ∼45 days. Study endpoints included safety events and change in mean A1C, time in range (TIR, 70-180 mg/dL) and time below range (TBR, <70 mg/dL). Run-in and study phase values were compared using Wilcoxon signed-rank test or paired t-test. Results: Overall group time spent in closed loop averaged 94.9% ± 5.4% and involved only 1.2 ± 0.8 exits per week. Compared with run-in, AHCL reduced A1C from 7.5% ± 0.8% to 7.0% ± 0.5% (<0.001, Wilcoxon signed-rank test, n = 155), TIR increased from 68.8% ± 10.5% to 74.5% ± 6.9% (<0.001, Wilcoxon signed-rank test), and TBR reduced from 3.3% ± 2.9% to 2.3% ± 1.7% (<0.001, Wilcoxon signed-rank test). Similar benefits to glycemia were observed for each age group and were more pronounced for the nighttime (12 AM-6 AM). The 100 mg/dL target increased TIR to 75.4% (n = 155), which was further optimized at a lower active insulin time (AIT) setting (i.e., 2 h), without increasing TBR. There were no severe hypoglycemic or diabetic ketoacidosis events during the study phase. Conclusions: These findings show that the MiniMed AHCL system is safe and allows for achievement of recommended glycemic targets in adolescents and adults with T1D. Adjustments in target and AIT settings may further optimize glycemia and improve user experience. Clinical Trial Registration number: NCT03959423.
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Affiliation(s)
- Anders L. Carlson
- International Diabetes Center, HealthPartners Institute, Minneapolis, Minnesota, USA
| | - Jennifer L. Sherr
- Yale University School of Medicine Pediatric Endocrinology, New Haven, Connecticut, USA
| | - Dorothy I. Shulman
- University of South Florida Diabetes and Endocrinology, Tampa, Florida, USA
| | - Satish K. Garg
- Barbara Davis Center of Childhood Diabetes, Aurora, Colorado, USA
| | - Rodica Pop-Busui
- Division of Metabolism, Endocrinology and Diabetes, University of Michigan, Ann Arbor, Michigan, USA
| | | | | | - Ron L. Brazg
- Rainier Clinical Research Center, Renton, Washington, USA
| | | | - Mark S. Kipnes
- Diabetes and Glandular Disease Clinic, San Antonio, Texas, USA
| | - James R. Thrasher
- Arkansas Diabetes and Endocrinology Center, Little Rock, Arkansas, USA
| | | | - Robert H. Slover
- Barbara Davis Center of Childhood Diabetes, Aurora, Colorado, USA
| | | | | | | | | | | | | | | | - John Shin
- Medtronic, Northridge, California, USA
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20
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Templer S. Closed-Loop Insulin Delivery Systems: Past, Present, and Future Directions. Front Endocrinol (Lausanne) 2022; 13:919942. [PMID: 35733769 PMCID: PMC9207329 DOI: 10.3389/fendo.2022.919942] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 05/06/2022] [Indexed: 12/16/2022] Open
Abstract
Closed-loop (artificial pancreas) systems for automated insulin delivery have been likened to the holy grail of diabetes management. The first iterations of glucose-responsive insulin delivery were pioneered in the 1960s and 1970s, with the development of systems that used venous glucose measurements to dictate intravenous infusions of insulin and dextrose in order to maintain normoglycemia. Only recently have these bulky, bedside technologies progressed to miniaturized, wearable devices. These modern closed-loop systems use interstitial glucose sensing, subcutaneous insulin pumps, and increasingly sophisticated algorithms. As the number of commercially available hybrid closed-loop systems has grown, so too has the evidence supporting their efficacy. Future challenges in closed-loop technology include the development of fully closed-loop systems that do not require user input for meal announcements or carbohydrate counting. Another evolving avenue in research is the addition of glucagon to mitigate the risk of hypoglycemia and allow more aggressive insulin dosing.
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21
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Abstract
The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc22-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc22-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.
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22
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Thabit H, Lal R, Leelarathna L. Automated insulin dosing systems: Advances after a century of insulin. Diabet Med 2021; 38:e14695. [PMID: 34547133 PMCID: PMC8763058 DOI: 10.1111/dme.14695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 09/05/2021] [Accepted: 09/16/2021] [Indexed: 11/29/2022]
Abstract
The daily complexities of insulin therapy and glucose variability in type 1 diabetes still pose significant challenges, despite advancements in modern insulin analogues. Minimising hypoglycaemia and optimising time spent within target glucose range are recommended to reduce the risk of diabetes-related complications and distress. Access to structured education and adjuvant diabetes technologies, such as insulin pumps and glucose sensors, are recommended by National Institute for Health and Care Excellence (NICE) to enable people with type 1 diabetes achieve their glycaemic goals. One hundred years after the discovery of insulin, automated insulin dosing (AID, a.k.a. closed loop or artificial pancreas) systems are a reality with a number of systems available and being used in usual clinical practice. Evidence from randomised clinical trials and real-world prospective studies support efficacy, effectiveness and safety of AID systems. Qualitative evaluations reveal treatment satisfaction and positive effects on quality of life. Current insulin-only AID systems still require carbohydrate and activity announcement (hybrid closed loop) due to the inherent pharmacokinetic limitations of rapid-acting insulin analogies. Ultra-rapid acting insulin and adjunctive use of other therapies (e.g. glucagon, pramlitide) are being evaluated to achieve full closed loop. Open-source AID (OS-AID) systems have been developed by the diabetes community, driven by a desire for safety and to accelerate technological advancement. In addition to effectiveness and safety, real-world prospective studies suggest that OS-AID systems fulfil unmet needs of commercially approved systems. The development, ongoing challenges and expectations of AID are outlined in this review.
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Affiliation(s)
- Hood Thabit
- Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
- Division of Diabetes, Endocrinology and Gastroenterology, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Rayhan Lal
- Division of Endocrinology, Department of Medicine & Paediatrics, Stanford University, Stanford, California, USA
- Stanford Diabetes Research Center, Stanford University, Stanford, California, USA
| | - Lalantha Leelarathna
- Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
- Division of Diabetes, Endocrinology and Gastroenterology, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
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23
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Moon SJ, Jung I, Park CY. Current Advances of Artificial Pancreas Systems: A Comprehensive Review of the Clinical Evidence. Diabetes Metab J 2021; 45:813-839. [PMID: 34847641 PMCID: PMC8640161 DOI: 10.4093/dmj.2021.0177] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 10/24/2021] [Indexed: 12/19/2022] Open
Abstract
Since Banting and Best isolated insulin in the 1920s, dramatic progress has been made in the treatment of type 1 diabetes mellitus (T1DM). However, dose titration and timely injection to maintain optimal glycemic control are often challenging for T1DM patients and their families because they require frequent blood glucose checks. In recent years, technological advances in insulin pumps and continuous glucose monitoring systems have created paradigm shifts in T1DM care that are being extended to develop artificial pancreas systems (APSs). Numerous studies that demonstrate the superiority of glycemic control offered by APSs over those offered by conventional treatment are still being published, and rapid commercialization and use in actual practice have already begun. Given this rapid development, keeping up with the latest knowledge in an organized way is confusing for both patients and medical staff. Herein, we explore the history, clinical evidence, and current state of APSs, focusing on various development groups and the commercialization status. We also discuss APS development in groups outside the usual T1DM patients and the administration of adjunct agents, such as amylin analogues, in APSs.
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Affiliation(s)
- Sun Joon Moon
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Inha Jung
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Cheol-Young Park
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea
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24
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Faccioli S, Facchinetti A, Sparacino G, Pillonetto G, Del Favero S. Linear Model Identification for Personalized Prediction and Control in Diabetes. IEEE Trans Biomed Eng 2021; 69:558-568. [PMID: 34347589 DOI: 10.1109/tbme.2021.3101589] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Type-1 diabetes (T1D) is a metabolic disease, characterized by impaired blood glucose (BG) regulation, which forces patients to multiple daily therapeutic actions, the most critical of which is exogenous insulin administration. T1D management can considerably benefit of mathematical models enabling accurate BG predictions and effective/safe automated insulin delivery. In building these models, dealing with large inter- and intra-patient variability in glucose-insulin dynamics represents a major challenge. The aim of the present work is to assess linear black-box methods, including a novel non-parametric methodology, for learning individualized models of glucose response to insulin and meal, suitable for model-based prediction and control. METHODS We focus on data-driven techniques for linear model-learning and compare the state-of-art parametric pipeline, exploring all its degrees of freedom (including population vs. individualized parameter identification, model class chosen among ARX/ARMAX/ARIMAX/Box-Jenkins, model order selection criteria, etc.), with a novel non-parametric approach based on Gaussian regression and stable spline kernel. By using data collected in 11 T1D individuals, we evaluate effectiveness of the different models by measuring root mean squared error (RMSE), coefficient of determination (COD), and time gain of the associated BG predictors. RESULTS Among the tested approaches, the non-parametric technique results in the best prediction performance: median RMSE=29.8mg/dL, and median COD=57.4%, for a prediction horizon (PH) of 60 min. With respect to the state-of-the-art parametric techniques, the non-parametric approach grants a COD improvement of about 2%, 7%, 21%, and 41% for PH = 30, 60, 90, and 120 min (paired-sample t-test p 0.001, p=0.003, p=0.03, and p=0.07 respectively). CONCLUSION Non-parametric linear model-learning grants statistically significant improvement with respect to the state-of-art parametric approach. The higher PH, the more pronounced the improvement. SIGNIFICANCE The use of a linear non-parametric model-learning approach for model-based prediction and control could bring to a more prompt, safe and effective T1D management.
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Colmegna P, Cengiz E, Garcia-Tirado J, Kraemer K, Breton MD. Impact of Accelerating Insulin on an Artificial Pancreas System Without Meal Announcement: An In Silico Examination. J Diabetes Sci Technol 2021; 15:833-841. [PMID: 32546001 PMCID: PMC8258534 DOI: 10.1177/1932296820928067] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Controlling postprandial blood glucose without the benefit of an appropriately sized premeal insulin bolus has been challenging given the delays in absorption and action of subcutaneously injected insulin during conventional and artificial pancreas (AP) system diabetes treatment. We aim to understand the impact of accelerating insulin and increasing aggressiveness of the AP controller as potential solutions to address the postprandial hyperglycemia challenge posed by unannounced meals through a simulation study. METHODS Accelerated rapid-acting insulin analogue is modeled within the UVA/Padova simulation platform by uniformly reducing its pharmacokinetic time constants (α multiplier) and used with a model predictive control, where the controller's aggressiveness depends on α. Two sets of single-meal simulations were performed: (1) where we only tune the controller's aggressiveness and (2) where we also accelerate insulin absorption and action to assess postprandial glycemic control during each intervention. RESULTS Mean percent of time spent within the 70 to 180 mg/dL postprandial glycemic range is significantly higher in set (2) than in set (1): 79.9, 95% confidence interval [77.0, 82.7] vs 88.8 [86.8, 90.9] ([Note to typesetter: Set all unnecessary math in text format and insert appropriate spaces between operators.] P < .05) for α = 2, and 81.4 [78.6, 84.3] vs 94.1 [92.6, 95.6] (P < .05) for α = 3. A decrease in percent of time below 70 mg/dL is also detected: 0.9 [0.4, 2.2] vs 0.6 [0.2, 1.4] (P = .23) for α = 2 and 1.4 [0.7, 2.8] vs 0.4 [0.1, 1.4] (P < .05) for α = 3. CONCLUSION These proof-of-concept simulations suggest that an AP without prandial insulin boluses combined with significantly faster insulin analogues could match the glycemic performance obtained with an optimal hybrid AP.
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Affiliation(s)
- Patricio Colmegna
- Center for Diabetes Technology, University of Virginia, Charlottesville, USA
- National Scientific and Technical Research Council, Buenos Aires, Argentina
- Patricio Colmegna, PhD, Center for Diabetes Technology, University of Virginia, 560 Ray C Hunt Dr, Charlottesville, VA 22903, USA.
| | - Eda Cengiz
- Division of Pediatric Endocrinology and Diabetes, Yale University School of Medicine, New Haven, CT, USA
- Bahcesehir University School of Medicine, Istanbul, Turkey
| | - Jose Garcia-Tirado
- Center for Diabetes Technology, University of Virginia, Charlottesville, USA
| | - Kristen Kraemer
- Division of Pediatric Endocrinology and Diabetes, Yale University School of Medicine, New Haven, CT, USA
| | - Marc D. Breton
- Center for Diabetes Technology, University of Virginia, Charlottesville, USA
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Chen NS, Boughton CK, Hartnell S, Fuchs J, Allen JM, Willinska ME, Thankamony A, de Beaufort C, Campbell FM, Fröhlich-Reiterer E, Hofer SE, Kapellen TM, Rami-Merhar B, Ghatak A, Randell TL, Besser REJ, Elleri D, Trevelyan N, Denvir L, Davis N, Gurnell E, Lindsay R, Morris D, Scott EM, Bally L, Thabit H, Leelarathna L, Evans ML, Murphy HR, Mader JK, Hovorka R. User Engagement With the CamAPS FX Hybrid Closed-Loop App According to Age and User Characteristics. Diabetes Care 2021; 44:e148-e150. [PMID: 34021021 PMCID: PMC8323184 DOI: 10.2337/dc20-2762] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 04/06/2021] [Indexed: 02/03/2023]
Affiliation(s)
- Natalie S Chen
- Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, U.K
| | - Charlotte K Boughton
- Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, U.K. .,Wolfson Diabetes and Endocrine Clinic, Cambridge University Hospitals NHS Foundation Trust, Cambridge, U.K
| | - Sara Hartnell
- Wolfson Diabetes and Endocrine Clinic, Cambridge University Hospitals NHS Foundation Trust, Cambridge, U.K
| | - Julia Fuchs
- Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, U.K.,Department of Paediatrics, University of Cambridge, Cambridge, U.K
| | - Janet M Allen
- Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, U.K
| | - Malgorzata E Willinska
- Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, U.K
| | - Ajay Thankamony
- Department of Paediatrics, University of Cambridge, Cambridge, U.K
| | - Carine de Beaufort
- Diabetes Endocrinology Care Clinique Pédiatrique, Clinique Pédiatrique, Centre Hospitalier de Luxembourg, Luxembourg
| | - Fiona M Campbell
- Department of Paediatric Diabetes, Leeds Children's Hospital, Leeds, U.K
| | - Elke Fröhlich-Reiterer
- Department of Pediatrics and Adolescent Medicine, Medical University of Graz, Graz, Austria
| | - Sabine E Hofer
- Department of Pediatrics I, Medical University of Innsbruck, Innsbruck, Austria
| | - Thomas M Kapellen
- Hospital for Children and Adolescents, University of Leipzig, Leipzig, Germany
| | - Birgit Rami-Merhar
- Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
| | - Atrayee Ghatak
- Alder Hey Children's NHS Foundation Trust, Liverpool, U.K
| | | | - Rachel E J Besser
- Department of Paediatrics, University of Oxford, Oxford, U.K.,NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, U.K
| | | | | | | | - Nikki Davis
- Southampton Children's Hospital, Southampton, U.K
| | - Eleanor Gurnell
- Wolfson Diabetes and Endocrine Clinic, Cambridge University Hospitals NHS Foundation Trust, Cambridge, U.K
| | | | | | - Eleanor M Scott
- Department of Population and Clinical Sciences, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, U.K
| | - Lia Bally
- Department of Diabetes, Endocrinology, Clinical Nutrition and Metabolism, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Hood Thabit
- Diabetes, Endocrinology and Metabolism Centre, Manchester University NHS Foundation Trust, Manchester, U.K
| | - Lalantha Leelarathna
- Diabetes, Endocrinology and Metabolism Centre, Manchester University NHS Foundation Trust, Manchester, U.K
| | - Mark L Evans
- Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, U.K.,Wolfson Diabetes and Endocrine Clinic, Cambridge University Hospitals NHS Foundation Trust, Cambridge, U.K
| | - Helen R Murphy
- Norwich Medical School, University of East Anglia, Norwich, U.K
| | - Julia K Mader
- Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, Graz, Austria
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Abstract
The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc21-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc21-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.
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Quintal A, Messier V, Rabasa-Lhoret R, Racine E. A qualitative study exploring the expectations of people living with type 1 diabetes regarding prospective use of a hybrid closed-loop system. Diabet Med 2020; 37:1832-1840. [PMID: 32298490 PMCID: PMC8232376 DOI: 10.1111/dme.14309] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/09/2020] [Indexed: 01/09/2023]
Abstract
AIM To identify the expectations of a diversified sample of informed adults with type 1 diabetes on their prospective use of a hybrid closed-loop system. METHODS Semi-structured interviews were conducted with 16 adults with type 1 diabetes who shared their expectations on an experimental hybrid closed-loop system after receiving information on its design, functioning and capability. The sample had equal representation of genders and diabetes management methods and was diversified according to age, education and occupation when possible. Qualitative content analysis of the interview transcripts with MaxQDA was used to identify expected benefits, expected inconveniences and concerns, expected improvements to design and functionalities, and interest and trust in the system. RESULTS Participants expected benefits regarding diabetes management, clinical outcomes, psychosocial aspects of their lives, nutrition and meals, and physical activity. Participants expected inconveniences or shared concerns regarding wearability, costs and technical limitations. According to participants, improvements could be made to the system's physical appearance, practical convenience, functionalities, and software integration. Overall, 12 participants would use the system. While participants' trust could be immediate or grow over time, it could ultimately be conditional on the system's performance. CONCLUSION Prospective users' general enthusiasm and trust foster the clinical and commercial success of hybrid closed-loop systems. However, poor user satisfaction caused by unrealistic expectations and plausible inconveniences and concerns may limit this success. Providing prospective users with comprehensive information while validating their understanding could mitigate unrealistic expectations. Improvements to design and coverage policies could favour uptake.
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Affiliation(s)
- A Quintal
- Pragmatic Health Ethics Research Unit, Institut de Recherches Cliniques de Montréal, Montreal, QC, Canada
- Département de Médecine Sociale et Préventive, University of Montréal, Montreal, QC, Canada
| | - V Messier
- Metabolic Diseases Research Unit and Diabetes Clinic, Institut de Recherches Cliniques de Montréal, Montreal, QC, Canada
| | - R Rabasa-Lhoret
- Metabolic Diseases Research Unit and Diabetes Clinic, Institut de Recherches Cliniques de Montréal, Montreal, QC, Canada
- Department of Nutrition, University of Montréal, Montreal, QC, Canada
- Montreal Diabetes Research Centre and Endocrinology Division, Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada
- Department of Medicine, University of Montréal, Montréal, QC, Canada
| | - E Racine
- Pragmatic Health Ethics Research Unit, Institut de Recherches Cliniques de Montréal, Montreal, QC, Canada
- Département de Médecine Sociale et Préventive, University of Montréal, Montreal, QC, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- Experimental Medicine and Biomedical Ethics Unit, McGill University, Montreal, QC, Canada
- Department of Medicine, University of Montréal, Montréal, QC, Canada
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Paoletti N, Liu KS, Chen H, Smolka SA, Lin S. Data-Driven Robust Control for a Closed-Loop Artificial Pancreas. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:1981-1993. [PMID: 31027048 DOI: 10.1109/tcbb.2019.2912609] [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
We present a fully closed-loop design for an artificial pancreas (AP) that regulates the delivery of insulin for the control of Type I diabetes. Our AP controller operates in a fully automated fashion, without requiring any manual interaction with the patient (e.g., in the form of meal announcements). A major obstacle to achieving closed-loop insulin control are the "unknown disturbances" related to various aspects of a patient's daily behavior, especially meals and physical activity. Such disturbances can significantly affect the patient's blood glucose levels. To handle such uncertainties, we present a data-driven, robust, model-predictive control framework in which we capture a wide range of individual meal and exercise patterns using uncertainty sets learned from historical data. These uncertainty sets are then used in the insulin controller to achieve automated, precise, and personalized insulin therapy. We provide an extensive in silico evaluation of our robust AP design, demonstrating the potential of the approach. In particular, without the benefit of explicit meal announcements, our approach can regulate glucose levels for large clusters of meal profiles learned from population-wide survey data and cohorts of virtual patients, even in the presence of high carbohydrate disturbances.
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Cobry EC, Hamburger E, Jaser SS. Impact of the Hybrid Closed-Loop System on Sleep and Quality of Life in Youth with Type 1 Diabetes and Their Parents. Diabetes Technol Ther 2020; 22:794-800. [PMID: 32212971 PMCID: PMC7698988 DOI: 10.1089/dia.2020.0057] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Background: Insufficient sleep is common in youth with type 1 diabetes (T1D) and parents, likely secondary to diabetes-related disturbances, including fear of hypoglycemia, nocturnal glucose monitoring, hypoglycemia, and device alarms. Hybrid closed-loop (HCL) systems improve glycemic variability and potentially reduce nocturnal awakenings. Methods: Adolescents with T1D (N = 37, mean age 13.9 years, 62% female, mean HbA1c 8.3%) and their parents were enrolled in this observational study when starting the Medtronic 670G HCL system. Participants completed study measures (sleep and psychosocial surveys and actigraphy with sleep diaries) before starting auto mode and ∼3 months later. Results: Based on actigraphy data, neither adolescents' nor parents' sleep characteristics changed significantly pre-post device initiation. Adolescents' mean total sleep time decreased from 7 h 16 min (IQR: [6:43-7:47]) to 7 h 9 min (IQR: [6:44-7:52]), while parents' total sleep time decreased from 6 h 47 min (IQR: [6:16-7:10]) to 6 h 38 min (IQR: [5:57-6:57]). Although there were no significant differences in most of the survey measures, there was a moderate effect for improved sleep quality in parents and fear of hypoglycemia in adolescents. In addition, adolescents reported a significant increase in self-reported glucose monitoring satisfaction. Adolescents averaged 44.7% use of auto mode at 3 months. Conclusions: Our data support previous research showing youth with T1D and their parents are not achieving the recommended duration of sleep. Lack of improvement in sleep may be due to steep learning curves involved with new technology. We observed moderate improvements in parental subjective report of sleep quality despite no change in objective measures of sleep duration. Further evaluation of sleep with long-term HCL use and larger sample size is needed.
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Affiliation(s)
- Erin C. Cobry
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, Colorado, USA
- Address correspondence to: Erin C. Cobry, MD, Barbara Davis Center, University of Colorado School of Medicine, 1775 Aurora Court, MSA140, Aurora, CO 80045
| | - Emily Hamburger
- Department of Psychology Univeristy of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Sarah S. Jaser
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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31
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In-Silico Evaluation of Glucose Regulation Using Policy Gradient Reinforcement Learning for Patients with Type 1 Diabetes Mellitus. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10186350] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
In this paper, we test and evaluate policy gradient reinforcement learning for automated blood glucose control in patients with Type 1 Diabetes Mellitus. Recent research has shown that reinforcement learning is a promising approach to accommodate the need for individualized blood glucose level control algorithms. The motivation for using policy gradient algorithms comes from the fact that adaptively administering insulin is an inherently continuous task. Policy gradient algorithms are known to be superior in continuous high-dimensional control tasks. Previously, most of the approaches for automated blood glucose control using reinforcement learning has used a finite set of actions. We use the Trust-Region Policy Optimization algorithm in this work. It represents the state of the art for deep policy gradient algorithms. The experiments are carried out in-silico using the Hovorka model, and stochastic behavior is modeled through simulated carbohydrate counting errors to illustrate the full potential of the framework. Furthermore, we use a model-free approach where no prior information about the patient is given to the algorithm. Our experiments show that the reinforcement learning agent is able to compete with and sometimes outperform state-of-the-art model predictive control in blood glucose regulation.
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32
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Galderisi A, Cohen N, Calhoun P, Kraemer K, Breton M, Weinzimer S, Cengiz E. Effect of Afrezza on Glucose Dynamics During HCL Treatment. Diabetes Care 2020; 43:2146-2152. [PMID: 32661108 PMCID: PMC7440894 DOI: 10.2337/dc20-0091] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Accepted: 06/15/2020] [Indexed: 02/03/2023]
Abstract
OBJECTIVE A major obstacle in optimizing the performance of closed-loop automated insulin delivery systems has been the delay in insulin absorption and action that results from the subcutaneous (SC) route of insulin delivery leading to exaggerated postmeal hyperglycemic excursions. We aimed to investigate the effect of Afrezza inhaled insulin with ultrafast-in and -out action profile on improving postprandial blood glucose control during hybrid closed-loop (HCL) treatment in young adults with type 1 diabetes. RESEARCH DESIGN AND METHODS We conducted an inpatient, three-way, randomized crossover standardized meal study to assess the efficacy and safety of Afrezza at a low (AL) and a high (AH) dose as compared with a standard SC rapid-acting insulin (aspart) premeal bolus during Diabetes Assistant (DiAs) HCL treatment. Participants received two sequential meals on three study days, and premeal insulin bolus was determined based on home insulin-to-carbohydrate ratio for each meal (rounded up to the closest available Afrezza cartridge dose for AH and down for AL). The primary efficacy outcome was the peak postprandial plasma glucose (PPG) level calculated by pooling data for up to 4 h after the start of each meal. Secondary outcomes included hyperglycemic, hypoglycemic, and euglycemic venous glucose metrics. RESULTS The mean ± SD PPG for the rapid-acting insulin control arm and AH was similar (185 ± 50 mg/dL vs. 195 ± 46 mg/dL, respectively; P = 0.45), while it was higher for meals using AL (208 ± 54 mg/dL, P = 0.04). The AH achieved significantly lower early PPG level than the control arm (30 min; P < 0.001), and improvement in PPG waned at later time points (120 and 180 min; P = 0.02) coinciding with the end of Afrezza glucodynamic action. CONCLUSIONS Afrezza (AH) premeal bolus reduced the early glycemic excursion and improved PPG during HCL compared with aspart premeal bolus. The improvement in PPG was not sustained after the end of Afrezza glucodynamic action at 120 min.
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Affiliation(s)
- Alfonso Galderisi
- Department of Pediatrics, Yale School of Medicine, New Haven, CT .,Department of Women's and Children's Health, University of Padova, Padova, Italy
| | | | | | - Kristen Kraemer
- Department of Pediatrics, Yale School of Medicine, New Haven, CT
| | - Marc Breton
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | - Stuart Weinzimer
- Department of Pediatrics, Yale School of Medicine, New Haven, CT
| | - Eda Cengiz
- Department of Pediatrics, Yale School of Medicine, New Haven, CT.,Bahcesehir University School of Medicine, Istanbul, Turkey
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Senior P, Lam A, Farnsworth K, Perkins B, Rabasa-Lhoret R. Assessment of Risks and Benefits of Beta Cell Replacement Versus Automated Insulin Delivery Systems for Type 1 Diabetes. Curr Diab Rep 2020; 20:52. [PMID: 32865637 DOI: 10.1007/s11892-020-01339-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
PURPOSE OF REVIEW Current approaches to insulin replacement in type 1 diabetes are unable to achieve optimal levels of glycemic control without substantial risk of hypoglycemia and substantial burden of self-management. Advances in biology and technology present beta cell replacement and automated insulin delivery as two alternative approaches. Here we discuss current and future prospects for the relative risks and benefits for biological and psychosocial outcomes from the perspective of researchers, clinicians, and persons living with diabetes. RECENT FINDINGS Beta cell replacement using pancreas or islet transplant can achieve insulin independence but requires immunosuppression. Although insulin independence may not be sustained, time in range of 80-90%, minimal glycemic variability and abolition of hypoglycemia is routine after islet transplantation. Clinical trials of potentially unlimited supply of stem cell-derived beta cells are showing promise. Automated insulin delivery (AID) systems can achieve 70-75% time in range, with reduced glycemic variability. Impatient with the pace of commercially available AID, users have developed their own algorithms which appear to be at least equivalent to systems developed within conventional regulatory frameworks. The importance of psychosocial factors and the preferences and values of persons living with diabetes are emerging as key elements on which therapies should be evaluated beyond their impact of biological outcomes. Biology or technology to deliver glucose dependent insulin secretion is associated with substantial improvements in glycemia and prevention of hypoglycemia while relieving much of the substantial burden of diabetes. Automated insulin delivery, currently, represents a more accessible bridge to a biologic cure that we expect future cellular therapies to deliver.
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Affiliation(s)
- Peter Senior
- Division of Endocrinology and Metabolism, University of Alberta, 9.114 CSB, Edmonton, AB, Canada.
- Innovations in Type 1 Diabetes, Diabetes Action Canada, Toronto, Canada.
| | - Anna Lam
- Division of Endocrinology and Metabolism, University of Alberta, 9.114 CSB, Edmonton, AB, Canada
| | - Kate Farnsworth
- Innovations in Type 1 Diabetes, Diabetes Action Canada, Toronto, Canada
| | - Bruce Perkins
- Innovations in Type 1 Diabetes, Diabetes Action Canada, Toronto, Canada
- Leadership Sinai Centre for Diabetes, University of Toronto, Toronto, ON, Canada
| | - Remi Rabasa-Lhoret
- Innovations in Type 1 Diabetes, Diabetes Action Canada, Toronto, Canada
- Institutes de Recherche Cliniques de Montreal, Montreal, QC, Canada
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Fuchs J, Hovorka R. Closed-loop control in insulin pumps for type-1 diabetes mellitus: safety and efficacy. Expert Rev Med Devices 2020; 17:707-720. [PMID: 32569476 PMCID: PMC7441745 DOI: 10.1080/17434440.2020.1784724] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 06/16/2020] [Indexed: 12/11/2022]
Abstract
INTRODUCTION Type 1 diabetes is a lifelong disease with high management burden. The majority of people with type 1 diabetes fail to achieve glycemic targets. Algorithm-driven automated insulin delivery (closed-loop) systems aim to address these challenges. This review provides an overview of commercial and emerging closed-loop systems. AREAS COVERED We review safety and efficacy of commercial and emerging hybrid closed-loop systems. A literature search was conducted and clinical trials using day-and-night closed-loop systems during free-living conditions were used to report on safety data. We comment on efficacy where robust randomized controlled trial data for a particular system are available. We highlight similarities and differences between commercial systems. EXPERT OPINION Study data shows that hybrid closed-loop systems are safe and effective, consistently improving glycemic control when compared to standard therapy. While a fully closed-loop system with minimal burden remains the end-goal, these hybrid closed-loop systems have transformative potential in diabetes care.
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Affiliation(s)
- Julia Fuchs
- Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Roman Hovorka
- Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
- Department of Paediatrics, University of Cambridge, Cambridge, United Kingdom
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35
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Toroghi MK, Cluett WR, Mahadevan R. A Personalized Multiscale Modeling Framework for Dose Selection in Precision Medicine. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c01070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Masood Khaksar Toroghi
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada, M5S 3E5
| | - William R. Cluett
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada, M5S 3E5
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada, M5S 3E5
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada, M5S 3E5
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36
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Fabris C, Kovatchev B. The closed‐loop artificial pancreas in 2020. Artif Organs 2020; 44:671-679. [DOI: 10.1111/aor.13704] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 04/06/2020] [Indexed: 12/13/2022]
Affiliation(s)
- Chiara Fabris
- Center for Diabetes Technology University of Virginia Charlottesville VA USA
| | - Boris Kovatchev
- Center for Diabetes Technology University of Virginia Charlottesville VA USA
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37
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Renard E. Certified Interoperability Allows a More Secure Move to the Artificial Pancreas Through a New Concept: "Make-It-Yourself". J Diabetes Sci Technol 2020; 14:195-197. [PMID: 31958988 PMCID: PMC7196868 DOI: 10.1177/1932296820901612] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Eric Renard
- Department of Endocrinology, Diabetes,
Nutrition, Montpellier University Hospital, France
- Institute of Functional Genomics,
University of Montpellier, France
- INSERM Clinical Investigation Centre,
Montpellier, France
- Eric Renard, MD, PhD, Department of
Endocrinology, Diabetes, Nutrition, Montpellier University Hospital, Lapeyronie
Hospital, Avenue Doyen Gaston Giraud, Montpellier cedex 5 34295, France.
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Kovatchev B, Anderson SM, Raghinaru D, Kudva YC, Laffel LM, Levy C, Pinsker JE, Wadwa RP, Buckingham B, Doyle FJ, Brown SA, Church MM, Dadlani V, Dassau E, Ekhlaspour L, Forlenza GP, Isganaitis E, Lam DW, Lum J, Beck RW. Randomized Controlled Trial of Mobile Closed-Loop Control. Diabetes Care 2020; 43:607-615. [PMID: 31937608 PMCID: PMC7035585 DOI: 10.2337/dc19-1310] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Accepted: 12/19/2019] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Assess the efficacy of inControl AP, a mobile closed-loop control (CLC) system. RESEARCH DESIGN AND METHODS This protocol, NCT02985866, is a 3-month parallel-group, multicenter, randomized unblinded trial designed to compare mobile CLC with sensor-augmented pump (SAP) therapy. Eligibility criteria were type 1 diabetes for at least 1 year, use of insulin pumps for at least 6 months, age ≥14 years, and baseline HbA1c <10.5% (91 mmol/mol). The study was designed to assess two coprimary outcomes: superiority of CLC over SAP in continuous glucose monitor (CGM)-measured time below 3.9 mmol/L and noninferiority in CGM-measured time above 10 mmol/L. RESULTS Between November 2017 and May 2018, 127 participants were randomly assigned 1:1 to CLC (n = 65) versus SAP (n = 62); 125 participants completed the study. CGM time below 3.9 mmol/L was 5.0% at baseline and 2.4% during follow-up in the CLC group vs. 4.7% and 4.0%, respectively, in the SAP group (mean difference -1.7% [95% CI -2.4, -1.0]; P < 0.0001 for superiority). CGM time above 10 mmol/L was 40% at baseline and 34% during follow-up in the CLC group vs. 43% and 39%, respectively, in the SAP group (mean difference -3.0% [95% CI -6.1, 0.1]; P < 0.0001 for noninferiority). One severe hypoglycemic event occurred in the CLC group, which was unrelated to the study device. CONCLUSIONS In meeting its coprimary end points, superiority of CLC over SAP in CGM-measured time below 3.9 mmol/L and noninferiority in CGM-measured time above 10 mmol/L, the study has demonstrated that mobile CLC is feasible and could offer certain usability advantages over embedded systems, provided the connectivity between system components is stable.
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Affiliation(s)
- Boris Kovatchev
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | - Stacey M Anderson
- Division of Endocrinology and Metabolism, Department of Medicine, University of Virginia, Charlottesville, VA
| | | | - Yogish C Kudva
- Division of Endocrinology, Diabetes, Metabolism and Nutrition, Department of Internal Medicine, Mayo Clinic, Rochester, MN
| | - Lori M Laffel
- Joslin Diabetes Center, Harvard Medical School, Boston, MA
| | - Carol Levy
- Division of Endocrinology, Icahn School of Medicine at Mount Sinai, New York, NY
| | | | - R Paul Wadwa
- Barbara Davis Center for Diabetes, University of Colorado, Anschutz Medical Campus, Aurora, CO
| | - Bruce Buckingham
- Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA
| | - Francis J Doyle
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
| | - Sue A Brown
- Division of Endocrinology and Metabolism, Department of Medicine, University of Virginia, Charlottesville, VA
| | | | - Vikash Dadlani
- Division of Endocrinology, Diabetes, Metabolism and Nutrition, Department of Internal Medicine, Mayo Clinic, Rochester, MN
| | - Eyal Dassau
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
| | - Laya Ekhlaspour
- Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA
| | - Gregory P Forlenza
- Barbara Davis Center for Diabetes, University of Colorado, Anschutz Medical Campus, Aurora, CO
| | | | - David W Lam
- Division of Endocrinology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - John Lum
- Jaeb Center for Health Research, Tampa, FL
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Berget C, Thomas SE, Messer LH, Thivener K, Slover RH, Wadwa RP, Alonso GT. A Clinical Training Program for Hybrid Closed Loop Therapy in a Pediatric Diabetes Clinic. J Diabetes Sci Technol 2020; 14:290-296. [PMID: 30862242 PMCID: PMC7196862 DOI: 10.1177/1932296819835183] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
BACKGROUND Hybrid closed loop (HCL) therapy is now available in clinical practice for treatment of type 1 diabetes; however, there is limited research on how to educate patients on this new therapy. The purpose of this quality improvement project was to optimize a HCL education program for pediatric patients with type 1 diabetes (T1D). METHODS Our multidisciplinary team developed a novel HCL clinical training program for current insulin pump users, using a quality improvement process called the Plan-Do-Study-Act model. Seventy-two patients participated in the HCL training program, which included (1) an in-person group class to reinforce conventional insulin pump and CGM use on the new system, (2) a live video conference class to teach HCL use, and (3) three follow-up phone calls in the first 4 weeks after HCL training to assess system use, make insulin adjustments, and provide targeted reeducation. Diabetes educators collected data during follow-up calls, and patients completed a training satisfaction survey. RESULTS The quality improvement process resulted in a training program that emphasized education on HCL exits, CGM use, and optimizing insulin to carbohydrate ratio settings. Patients successfully sustained time in HCL in the initial weeks of use and rated the trainings and follow-up calls highly. CONCLUSIONS Ongoing educational support is vital in the early weeks of HCL use. This quality improvement project is the first to examine strategies for implementation of HCL therapy into a large pediatric diabetes center, and may inform best practices for implementation of new diabetes technologies into other diabetes clinics.
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Affiliation(s)
- Cari Berget
- University of Colorado, Denver, Barbara
Davis Center for Childhood Diabetes, Aurora, CO, USA
- Cari Berget, RN, MPH, CDE University of
Colorado, Denver, Barbara Davis Center for Childhood Diabetes, 1775 Aurora Ct.,
Aurora, CO 80045, USA.
| | - Sarah E. Thomas
- University of Colorado, Denver, Barbara
Davis Center for Childhood Diabetes, Aurora, CO, USA
| | - Laurel H. Messer
- University of Colorado, Denver, Barbara
Davis Center for Childhood Diabetes, Aurora, CO, USA
| | - Katelin Thivener
- University of Colorado, Denver, Barbara
Davis Center for Childhood Diabetes, Aurora, CO, USA
| | - Robert H. Slover
- University of Colorado, Denver, Barbara
Davis Center for Childhood Diabetes, Aurora, CO, USA
| | - R. Paul Wadwa
- University of Colorado, Denver, Barbara
Davis Center for Childhood Diabetes, Aurora, CO, USA
| | - G. Todd Alonso
- University of Colorado, Denver, Barbara
Davis Center for Childhood Diabetes, Aurora, CO, USA
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40
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Kovatchev BP, Kollar L, Anderson SM, Barnett C, Breton MD, Carr K, Gildersleeve R, Oliveri MC, Wakeman CA, Brown SA. Evening and overnight closed-loop control versus 24/7 continuous closed-loop control for type 1 diabetes: a randomised crossover trial. Lancet Digit Health 2020; 2:e64-e73. [PMID: 32864597 PMCID: PMC7453908 DOI: 10.1016/s2589-7500(19)30218-3] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Background Automated closed-loop control (CLC), known as the "artificial pancreas" is emerging as a treatment option for Type 1 Diabetes (T1D), generally superior to sensor-augmented insulin pump (SAP) treatment. It is postulated that evening-night (E-N) CLC may account for most of the benefits of 24-7 CLC; however, a direct comparison has not been done. Methods In this trial (NCT02679287), adults with T1D were randomised 1:1 to two groups, which followed different sequences of four 8-week sessions, resulting in two crossover designs comparing SAP vs E-N CLC and E-N CLC vs 24-7 CLC, respectively. Eligibility: T1D for at least 1 year, using an insulin pump for at least six months, ages 18 years or older. Primary hypothesis: E-N CLC compared to SAP will decrease percent time <70mg/dL (3.9mmol/L) measured by continuous glucose monitoring (CGM) without deterioration in HbA1c. Secondary Hypotheses: 24-7 CLC compared to SAP will increase CGM-measured time in target range (TIR, 70-180mg/dL; 3.9-10mmol/L) and will reduce glucose variability during the day. Findings Ninety-three participants were randomised and 80 were included in the analysis, ages 18-69 years; HbA1c levels 5.4-10.6%; 66% female. Compared to SAP, E-N CLC reduced overall time <70mg/dL from 4.0% to 2.2% () resulting in an absolute difference of 1.8% (95%CI: 1.2-2.4%), p<0.0001. This was accompanied by overall reduction in HbA1c from 7.4% at baseline to 7.1% at the end of study, resulting in an absolute difference of 0.3% (95% CI: 0.1-0.4%), p<0.0001. There were 5 severe hypoglycaemia adverse events attributed to user-directed boluses without malfunction of the investigational device, and no diabetic ketoacidosis events. Interpretation In type 1 diabetes, evening-night closed-loop control was superior to sensor-augmented pump therapy, achieving most of the glycaemic benefits of 24-7 closed-loop.
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Affiliation(s)
| | - Laura Kollar
- University of Virginia Center for Diabetes Technology, Charlottesville, VA USA
| | - Stacey M. Anderson
- University of Virginia Center for Diabetes Technology, Charlottesville, VA USA
| | - Charlotte Barnett
- University of Virginia Center for Diabetes Technology, Charlottesville, VA USA
| | - Marc D. Breton
- University of Virginia Center for Diabetes Technology, Charlottesville, VA USA
| | - Kelly Carr
- University of Virginia Center for Diabetes Technology, Charlottesville, VA USA
| | - Rachel Gildersleeve
- University of Virginia Center for Diabetes Technology, Charlottesville, VA USA
| | - Mary C. Oliveri
- University of Virginia Center for Diabetes Technology, Charlottesville, VA USA
| | | | - Sue A Brown
- Address for Correspondence: Sue A. Brown, M.D., University of Virginia, Center for Diabetes Technology, 560 Ray C. Hunt Drive, Second Floor, Charlottesville, VA, Tel: +1-434-982-0602,
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Garg SK. Reflections on Diabetes Care at the End of the Second Decade of the 21st Century. Diabetes Technol Ther 2020; 22:63-65. [PMID: 31916843 DOI: 10.1089/dia.2020.0014] [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)
- Satish K Garg
- Barbara Davis Center for Childhood Diabetes, University of Colorado Denver, Aurora, Colorado
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Abstract
The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc20-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc20-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.
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Lal RA, Ekhlaspour L, Hood K, Buckingham B. Realizing a Closed-Loop (Artificial Pancreas) System for the Treatment of Type 1 Diabetes. Endocr Rev 2019; 40:1521-1546. [PMID: 31276160 PMCID: PMC6821212 DOI: 10.1210/er.2018-00174] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2018] [Accepted: 02/28/2019] [Indexed: 01/20/2023]
Abstract
Recent, rapid changes in the treatment of type 1 diabetes have allowed for commercialization of an "artificial pancreas" that is better described as a closed-loop controller of insulin delivery. This review presents the current state of closed-loop control systems and expected future developments with a discussion of the human factor issues in allowing automation of glucose control. The goal of these systems is to minimize or prevent both short-term and long-term complications from diabetes and to decrease the daily burden of managing diabetes. The closed-loop systems are generally very effective and safe at night, have allowed for improved sleep, and have decreased the burden of diabetes management overnight. However, there are still significant barriers to achieving excellent daytime glucose control while simultaneously decreasing the burden of daytime diabetes management. These systems use a subcutaneous continuous glucose sensor, an algorithm that accounts for the current glucose and rate of change of the glucose, and the amount of insulin that has already been delivered to safely deliver insulin to control hyperglycemia, while minimizing the risk of hypoglycemia. The future challenge will be to allow for full closed-loop control with minimal burden on the patient during the day, alleviating meal announcements, carbohydrate counting, alerts, and maintenance. The human factors involved with interfacing with a closed-loop system and allowing the system to take control of diabetes management are significant. It is important to find a balance between enthusiasm and realistic expectations and experiences with the closed-loop system.
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Affiliation(s)
- Rayhan A Lal
- Division of Endocrinology, Department of Pediatrics, Stanford University School of Medicine, Stanford, California.,Division of Endocrinology, Department of Medicine, Stanford University School of Medicine, Stanford, California
| | - Laya Ekhlaspour
- Division of Endocrinology, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Korey Hood
- Division of Endocrinology, Department of Pediatrics, Stanford University School of Medicine, Stanford, California.,Department of Psychiatry, Stanford University School of Medicine, Stanford, California
| | - Bruce Buckingham
- Division of Endocrinology, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
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Garcia-Tirado J, Colmegna P, Corbett JP, Ozaslan B, Breton MD. In Silico Analysis of an Exercise-Safe Artificial Pancreas With Multistage Model Predictive Control and Insulin Safety System. J Diabetes Sci Technol 2019; 13:1054-1064. [PMID: 31679400 PMCID: PMC6835197 DOI: 10.1177/1932296819879084] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND Maintaining glycemic equilibrium can be challenging for people living with type 1 diabetes (T1D) as many factors (eg, length, type, duration, insulin on board, stress, and training) will impact the metabolic changes triggered by physical activity potentially leading to both hypoglycemia and hyperglycemia. Therefore, and despite the noted health benefits, many individuals with T1D do not exercise as much as their healthy peers. While technology advances have improved glucose control during and immediately after exercise, it remains one of the key limitations of artificial pancreas (AP) systems, largely because stopping insulin at the onset of exercise may not be enough to prevent impending, exercise-induced hypoglycemia. METHODS A hybrid AP algorithm with subject-specific exercise behavior recognition and anticipatory action is designed to prevent hypoglycemic events during and after moderate-intensity exercise. Our approach relies on a number of key innovations, namely, an activity informed premeal bolus calculator, personalized exercise pattern recognition, and a multistage model predictive control (MS-MPC) strategy that can transition between reactive and anticipatory modes. This AP design was evaluated on 100 in silico subjects from the most up-to-date FDA-accepted UVA/Padova metabolic simulator, emulating an outpatient clinical trial setting. Results with a baseline controller, a regular MPC (rMPC), are also included for comparison purposes. RESULTS In silico experiments reveal that the proposed MS-MPC strategy markedly reduces the number of exercise-related hypoglycemic events (8 vs 68). CONCLUSION An anticipatory mode for insulin administration of a monohormonal AP controller reduces the occurrence of hypoglycemia during moderate-intensity exercise.
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Affiliation(s)
- Jose Garcia-Tirado
- University of Virginia, Center for Diabetes Technology, Charlottesville, VA, USA
- Jose Garcia-Tirado, PhD, University of Virginia, Center for Diabetes Technology, 560 Ray C Hunt Dr, Charlottesville, VA 22903, USA.
| | - Patricio Colmegna
- University of Virginia, Center for Diabetes Technology, Charlottesville, VA, USA
- National Scientific and Technical Research Council, Buenos Aires, Argentina
| | - John P. Corbett
- University of Virginia, Center for Diabetes Technology, Charlottesville, VA, USA
- Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA, USA
| | - Basak Ozaslan
- University of Virginia, Center for Diabetes Technology, Charlottesville, VA, USA
- Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA, USA
| | - Marc D. Breton
- University of Virginia, Center for Diabetes Technology, Charlottesville, VA, USA
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Bowers DT, Song W, Wang LH, Ma M. Engineering the vasculature for islet transplantation. Acta Biomater 2019; 95:131-151. [PMID: 31128322 PMCID: PMC6824722 DOI: 10.1016/j.actbio.2019.05.051] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 04/13/2019] [Accepted: 05/20/2019] [Indexed: 12/17/2022]
Abstract
The microvasculature in the pancreatic islet is highly specialized for glucose sensing and insulin secretion. Although pancreatic islet transplantation is a potentially life-changing treatment for patients with insulin-dependent diabetes, a lack of blood perfusion reduces viability and function of newly transplanted tissues. Functional vasculature around an implant is not only necessary for the supply of oxygen and nutrients but also required for rapid insulin release kinetics and removal of metabolic waste. Inadequate vascularization is particularly a challenge in islet encapsulation. Selectively permeable membranes increase the barrier to diffusion and often elicit a foreign body reaction including a fibrotic capsule that is not well vascularized. Therefore, approaches that aid in the rapid formation of a mature and robust vasculature in close proximity to the transplanted cells are crucial for successful islet transplantation or other cellular therapies. In this paper, we review various strategies to engineer vasculature for islet transplantation. We consider properties of materials (both synthetic and naturally derived), prevascularization, local release of proangiogenic factors, and co-transplantation of vascular cells that have all been harnessed to increase vasculature. We then discuss the various other challenges in engineering mature, long-term functional and clinically viable vasculature as well as some emerging technologies developed to address them. The benefits of physiological glucose control for patients and the healthcare system demand vigorous pursuit of solutions to cell transplant challenges. STATEMENT OF SIGNIFICANCE: Insulin-dependent diabetes affects more than 1.25 million people in the United States alone. Pancreatic islets secrete insulin and other endocrine hormones that control glucose to normal levels. During preparation for transplantation, the specialized islet blood vessel supply is lost. Furthermore, in the case of cell encapsulation, cells are protected within a device, further limiting delivery of nutrients and absorption of hormones. To overcome these issues, this review considers methods to rapidly vascularize sites and implants through material properties, pre-vascularization, delivery of growth factors, or co-transplantation of vessel supporting cells. Other challenges and emerging technologies are also discussed. Proper vascular growth is a significant component of successful islet transplantation, a treatment that can provide life-changing benefits to patients.
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Affiliation(s)
- Daniel T Bowers
- Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Wei Song
- Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Long-Hai Wang
- Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Minglin Ma
- Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA.
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Aleppo G, Webb K. Continuous Glucose Monitoring Integration in Clinical Practice: A Stepped Guide to Data Review and Interpretation. J Diabetes Sci Technol 2019; 13:664-673. [PMID: 30453772 PMCID: PMC6610596 DOI: 10.1177/1932296818813581] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND The advent of continuous glucose monitoring (CGM) technology has transformed the approach to diabetes care. Multiple CGM systems are commercially available and increased accuracy has allowed development of hybrid and automated insulin delivery systems. Evidence of CGM clinical benefits has also increased exponentially in the last decade. METHODS Literature search, review of professional guidelines, and consensus statements were used to guide the preparation of this article. The clinical benefits of both professional and personal CGM in clinical practice as well as barriers to wider adotpion were explored. A stepped approach to review and interpretation of CGM data is suggested for use in the clinician's office regardless of the software used. RESULTS Although increasing, the use of CGM in patients with diabetes is still not widespread; multiple barriers are still in place, despite the approval of CGM systems for patients above the age of 2 years old, the extension of coverage for Medicare beneficiaries and the integration of CGM with multiple insulin pump systems. Integration of CGM technology in clinical practice presents various challenges, from concerns relative to time constraints during office visits to lack of systematic approach to interpretation of the data. CONCLUSIONS Understanding the usefulness of personal and professional CGM, appropriate patient selection as well as patient and provider training are crucial for the expansion of CGM therapy use in clinical practice. Utilizing the proposed stepped approach to CGM review and interpretation may allow wider adoption of CGM with more effective and efficient office visits.
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Affiliation(s)
- Grazia Aleppo
- Division of Endocrinology, Metabolism
and Molecular Medicine, Feinberg School of Medicine, Northwestern University,
Chicago, IL, USA
- Northwestern Medicine Diabetes Training
and Education Program, Division of Endocrinology, Metabolism and Molecular Medicine,
Northwestern Medical Group, Chicago, IL, USA
- Grazia Aleppo, MD, FACE, FACP, Division of
Endocrinology, Northwestern University, 645 N Michigan Ave, Ste 530, Chicago, IL
60611, USA.
| | - Kimberly Webb
- Northwestern Medicine Diabetes Training
and Education Program, Division of Endocrinology, Metabolism and Molecular Medicine,
Northwestern Medical Group, Chicago, IL, USA
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Kovatchev B. A Century of Diabetes Technology: Signals, Models, and Artificial Pancreas Control. Trends Endocrinol Metab 2019; 30:432-444. [PMID: 31151733 DOI: 10.1016/j.tem.2019.04.008] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2019] [Revised: 04/14/2019] [Accepted: 04/25/2019] [Indexed: 12/24/2022]
Abstract
Arguably, diabetes mellitus is one of the best-quantified human conditions: elaborate in silico models describe the action of the human metabolic system; real-time signals such as continuous glucose monitoring are readily available; insulin delivery is being automated; and control algorithms are capable of optimizing blood glucose fluctuation in patients' natural environments. The transition of the artificial pancreas (AP) to everyday clinical use is happening now, and is contingent upon seamless concerted work of devices encompassing the patient in a digital treatment ecosystem. This review recounts briefly the story of diabetes technology, which began a century ago with the discovery of insulin, progressed through glucose monitoring and subcutaneous insulin delivery, and is now rapidly advancing towards fully automated clinically viable AP systems.
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Affiliation(s)
- Boris Kovatchev
- University of Virginia School of Medicine, UVA Center for Diabetes Technology, Ivy Translational Research Building, 560 Ray C. Hunt Drive, Charlottesville, VA 22903-2981, USA.
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48
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Kovatchev B. Diabetes Technology: Monitoring, Analytics, and Optimal Control. Cold Spring Harb Perspect Med 2019; 9:cshperspect.a034389. [PMID: 30126835 DOI: 10.1101/cshperspect.a034389] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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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Anderson SM, Buckingham BA, Breton MD, Robic JL, Barnett CL, Wakeman CA, Oliveri MC, Brown SA, Ly TT, Clinton PK, Hsu LJ, Kingman RS, Norlander LM, Loebner SE, Reuschel-DiVirglio S, Kovatchev BP. Hybrid Closed-Loop Control Is Safe and Effective for People with Type 1 Diabetes Who Are at Moderate to High Risk for Hypoglycemia. Diabetes Technol Ther 2019; 21:356-363. [PMID: 31095423 PMCID: PMC6551970 DOI: 10.1089/dia.2019.0018] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Background: Typically, closed-loop control (CLC) studies excluded patients with significant hypoglycemia. We evaluated the effectiveness of hybrid CLC (HCLC) versus sensor-augmented pump (SAP) in reducing hypoglycemia in this high-risk population. Methods: Forty-four subjects with type 1 diabetes, 25 women, 37 ± 2 years old, HbA1c 7.4% ± 0.2% (57 ± 1.5 mmol/mol), diabetes duration 19 ± 2 years, on insulin pump, were enrolled at the University of Virginia (N = 33) and Stanford University (N = 11). Eligibility: increased risk of hypoglycemia confirmed by 1 week of blinded continuous glucose monitor (CGM); randomized to 4 weeks of home use of either HCLC or SAP. Primary/secondary outcomes: risk for hypoglycemia measured by the low blood glucose index (LBGI)/CGM-based time in ranges. Results: Values reported: mean ± standard deviation. From baseline to the final week of study: LBGI decreased more on HCLC (2.51 ± 1.17 to 1.28 ± 0.5) than on SAP (2.1 ± 1.05 to 1.79 ± 0.98), P < 0.001; percent time below 70 mg/dL (3.9 mmol/L) decreased on HCLC (7.2% ± 5.3% to 2.0% ± 1.4%) but not on SAP (5.8% ± 4.7% to 4.8% ± 4.5%), P = 0.001; percent time within the target range 70-180 mg/dL (3.9-10 mmol/L) increased on HCLC (67.8% ± 13.5% to 78.2% ± 10%) but decreased on SAP (65.6% ± 12.9% to 59.6% ± 16.5%), P < 0.001; percent time above 180 mg/dL (10 mmol/L) decreased on HCLC (25.1% ± 15.3% to 19.8% ± 10.1%) but increased on SAP (28.6% ± 14.6% to 35.6% ± 17.6%), P = 0.009. Mean glucose did not change significantly on HCLC (144.9 ± 27.9 to 143.8 ± 14.4 mg/dL [8.1 ± 1.6 to 8.0 ± 0.8 mmol/L]) or SAP (152.5 ± 24.3 to 162.4 ± 28.2 [8.5 ± 1.4 to 9.0 ± 1.6]), P = ns. Conclusions: Compared with SAP therapy, HCLC reduced the risk and frequency of hypoglycemia, while improving time in target range and reducing hyperglycemia in people at moderate to high risk of hypoglycemia.
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Affiliation(s)
- Stacey M. Anderson
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
- Address correspondence to: Stacey M. Anderson, MD, Center for Diabetes Technology, University of Virginia, PO Box 400888, Charlottesville VA 22908-4888
| | - Bruce A. Buckingham
- Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Marc D. Breton
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | - Jessica L. Robic
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | | | | | - Mary C. Oliveri
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | - Sue A. Brown
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | - Trang T. Ly
- Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Paula K. Clinton
- Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Liana J. Hsu
- Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Ryan S. Kingman
- Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Lisa M. Norlander
- Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Sarah E. Loebner
- Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Suzette Reuschel-DiVirglio
- Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Boris P. Kovatchev
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
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Weissberg‐Benchell J, Shapiro JB, Hood K, Laffel LM, Naranjo D, Miller K, Barnard K. Assessing patient-reported outcomes for automated insulin delivery systems: the psychometric properties of the INSPIRE measures. Diabet Med 2019; 36:644-652. [PMID: 30761592 PMCID: PMC6593869 DOI: 10.1111/dme.13930] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/12/2019] [Indexed: 12/17/2022]
Abstract
AIM Participants in clinical trials assessing automated insulin delivery systems report perceived benefits and burdens that reflect their experiences and may predict their likelihood of uptake and continued use of this novel technology. Despite the importance of understanding their perspectives, there are no available validated and reliable measures assessing the psychosocial aspects of automated insulin delivery systems. The present study assesses the initial psychometric properties of the INSPIRE measures, which were developed for youth and adults with Type 1 diabetes, as well as parents and partners. METHODS Data from 292 youth, 159 adults, 150 parents of youth and 149 partners of individuals recruited from the Type 1 Diabetes Exchange Registry were analysed. Participants completed INSPIRE questionnaires and measures of quality of life, fear of hypoglycaemia, diabetes distress, glucose monitoring satisfaction. Exploratory factor analysis assessed factor structures. Associations between INSPIRE scores and other measures, HbA1c , and technology use assessed concurrent and discriminant validity. RESULTS Youth, adult, parent and partner measures assess positive expectancies of automated insulin delivery systems. Measures range from 17 to 22 items and are reliable (α = 0.95-0.97). Youth, adult and parent measures are unidimensional; the partner measure has a two-factor structure (perceptions of impact on partners versus the person with diabetes). Measures showed concurrent and discriminant validity. CONCLUSIONS INSPIRE measures assessing the positive expectancies of automated insulin delivery systems for youth, adults, parents and partners have meaningful factor structures and are internally consistent. The developmentally sensitive INSPIRE measures offer added value as clinical trials test newer systems, systems become commercially available and clinicians initiate using these systems.
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Affiliation(s)
- J. Weissberg‐Benchell
- Department of Psychiatry and Behavioral SciencesAnn and Robert H., Lurie Children's Hospital of ChicagoNorthwestern UniversityFeinberg School of MedicineChicagoIL
| | | | - K. Hood
- Departments of PediatricsPsychiatry& Behavioral Sciences, Stanford University School of MedicineStanfordCA
| | - L. M. Laffel
- Joslin Diabetes CenterHarvard Medical SchoolBostonMA
| | - D. Naranjo
- Departments of PediatricsPsychiatry& Behavioral Sciences, Stanford University School of MedicineStanfordCA
| | - K. Miller
- Jaeb Center for Health ResearchTampaFloridaUSA
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