المؤلفون: Virginia Gabo, Ravi Reddy, Peter G. Jacobs, Deborah Branigan, Brian Senf, Navid Resalat, Florian H. Guillot, Katrina Ramsey, Nichole S. Tyler, Jessica R. Castle, Joseph El Youssef, Joseph Leitschuh, Isabelle Isa Kristin Steineck, Leah M. Wilson

المصدر: Diabetes Care. 43:2721-2729

مصطلحات موضوعية: Adult, Blood Glucose, Male, Pancreas, Artificial, medicine.medical_specialty, Endocrinology, Diabetes and Metabolism, medicine.medical_treatment, Urology, 030209 endocrinology & metabolism, Hypoglycemia, Artificial pancreas, Glucagon, Oregon, Young Adult, 03 medical and health sciences, Insulin Infusion Systems, 0302 clinical medicine, Diabetes mellitus, Outpatients, Internal Medicine, medicine, Humans, Hypoglycemic Agents, Insulin, 030212 general & internal medicine, Exercise physiology, Exercise, Advanced and Specialized Nursing, Type 1 diabetes, Cross-Over Studies, business.industry, Middle Aged, medicine.disease, Crossover study, Diabetes Mellitus, Type 1, Hyperglycemia, Feasibility Studies, Female, business

الوصف: OBJECTIVE To assess the efficacy and feasibility of a dual-hormone (DH) closed-loop system with insulin and a novel liquid stable glucagon formulation compared with an insulin-only closed-loop system and a predictive low glucose suspend (PLGS) system. RESEARCH DESIGN AND METHODS In a 76-h, randomized, crossover, outpatient study, 23 participants with type 1 diabetes used three modes of the Oregon Artificial Pancreas system: 1) dual-hormone (DH) closed-loop control, 2) insulin-only single-hormone (SH) closed-loop control, and 3) PLGS system. The primary end point was percentage time in hypoglycemia ( RESULTS DH reduced hypoglycemia compared with SH during and after exercise (DH 0.0% [interquartile range 0.0–4.2], SH 8.3% [0.0–12.5], P = 0.025). There was an increased time in hyperglycemia (>180 mg/dL) during and after exercise for DH versus SH (20.8% DH vs. 6.3% SH, P = 0.038). Mean glucose during the entire study duration was DH, 159.2; SH, 151.6; and PLGS, 163.6 mg/dL. Across the entire study duration, DH resulted in 7.5% more time in target range (70–180 mg/dL) compared with the PLGS system (71.0% vs. 63.4%, P = 0.044). For the entire study duration, DH had 28.2% time in hyperglycemia vs. 25.1% for SH (P = 0.044) and 34.7% for PLGS (P = 0.140). Four participants experienced nausea related to glucagon, leading three to withdraw from the study. CONCLUSIONS The glucagon formulation demonstrated feasibility in a closed-loop system. The DH system reduced hypoglycemia during and after exercise, with some increase in hyperglycemia.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::239d793cfe1b212548d7861dba424a0dTest
https://doi.org/10.2337/dc19-2267Test

المؤلفون: Ravi Reddy, Peter G. Jacobs, Nichole S. Tyler, Jessica R. Castle, Virginia Gabo, Brian Senf, Leah M. Wilson

المصدر: J Diabetes Sci Technol

مصطلحات موضوعية: Adult, Blood Glucose, Male, Decision support system, Time Factors, Computer science, Endocrinology, Diabetes and Metabolism, Biomedical Engineering, Monitoring, Ambulatory, 030209 endocrinology & metabolism, Bioengineering, Smartphone application, Decision Support Techniques, 03 medical and health sciences, 0302 clinical medicine, Human–computer interaction, Internal Medicine, medicine, Humans, Hypoglycemic Agents, Insulin, 030212 general & internal medicine, Glycemic, Type 1 diabetes, Attitude to Computers, Blood Glucose Self-Monitoring, Original Articles, Middle Aged, Patient Acceptance of Health Care, medicine.disease, Mobile Applications, Self Care, Diabetes Mellitus, Type 1, Treatment Outcome, Smartphone app, Female, Patient input, Smartphone, Diffusion of Innovation, Patient Participation, Glucose monitors, Biomarkers

الوصف: Background: Decision support smartphone applications integrated with continuous glucose monitors may improve glycemic control in type 1 diabetes (T1D). We conducted a survey to understand trends and needs of potential users to inform the design of decision support technology. Methods: A 70-question survey was distributed October 2017 through May 2018 to adults aged 18-80 with T1D from a specialty clinic and T1D Exchange online health community ( myglu.org ). The survey responses were used to evaluate potential features of a diabetes decision support tool by Likert scale and open responses. Results: There were 1542 responses (mean age 46.1 years [SD 15.2], mean duration of diabetes 26.5 years [SD 15.8]). The majority (84.2%) have never used an app to manage diabetes; however, a large majority (77.8%) expressed interest in using a decision support app. The ability to predict and avoid hypoglycemia was the most important feature identified by a majority of the respondents, with 91% of respondents indicating the highest level of interest in these features. The task that respondents find most difficult was management of glucose during exercise (only 47% of participants were confident in glucose management during exercise). The respondents also highly desired features that help manage glucose during exercise (85% of respondents were interested). The responses identified integration and interoperability with peripheral devices/apps and customization of alerts as important. Responses from participants were generally consistent across stratified categories. Conclusions: These results provide valuable insight into patient needs in decision support applications for management of T1D.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2bd8d2b6006a320a1f9e6cc339336377Test
https://doi.org/10.1177/1932296819870231Test

المؤلفون: Joseph El Youssef, Peter G. Jacobs, Jessica R. Castle, Navid Resalat, Nichole S. Tyler

المصدر: PLoS ONE, Vol 14, Iss 7, p e0217301 (2019)
PLoS ONE

مصطلحات موضوعية: Blood Glucose, Male, Peptide Hormones, medicine.medical_treatment, 02 engineering and technology, Biochemistry, Endocrinology, 0302 clinical medicine, Virtual patient, Medicine and Health Sciences, Insulin, education.field_of_study, Multidisciplinary, Organic Compounds, Applied Mathematics, Simulation and Modeling, Monosaccharides, Middle Aged, Chemistry, Physical Sciences, Medicine, Female, Algorithms, Research Article, Adult, medicine.medical_specialty, Endocrine Disorders, Science, 0206 medical engineering, Population, Carbohydrates, Urology, 030209 endocrinology & metabolism, Hypoglycemia, Research and Analysis Methods, Models, Biological, Glucagon, Artificial pancreas, 03 medical and health sciences, Diabetes mellitus, Diabetes Mellitus, medicine, Humans, education, Aged, Diabetic Endocrinology, Type 1 diabetes, business.industry, Organic Chemistry, Chemical Compounds, Biology and Life Sciences, medicine.disease, 020601 biomedical engineering, Hormones, Kinetics, Glucose, Diabetes Mellitus, Type 1, Metabolic Disorders, Hyperglycemia, business, Mathematics

الوصف: PurposeWe introduce two validated single (SH) and dual hormone (DH) mathematical models that represent an in-silico virtual patient population (VPP) for type 1 diabetes (T1D). The VPP can be used to evaluate automated insulin and glucagon delivery algorithms, so-called artificial pancreas (AP) algorithms that are currently being used to help people with T1D better manage their glucose levels. We present validation results comparing these virtual patients with true clinical patients undergoing AP control and demonstrate that the virtual patients behave similarly to people with T1D.MethodsA single hormone virtual patient population (SH-VPP) was created that is comprised of eight differential equations that describe insulin kinetics, insulin dynamics and carbohydrate absorption. The parameters in this model that represent insulin sensitivity were statistically sampled from a normal distribution to create a population of virtual patients with different levels of insulin sensitivity. A dual hormone virtual patient population (DH-VPP) extended this SH-VPP by incorporating additional equations to represent glucagon kinetics and glucagon dynamics. The DH-VPP is comprised of thirteen differential equations and a parameter representing glucagon sensitivity, which was statistically sampled from a normal distribution to create virtual patients with different levels of glucagon sensitivity. We evaluated the SH-VPP and DH-VPP on a clinical data set of 20 people with T1D who participated in a 3.5-day outpatient AP study. Twenty virtual patients were matched with the 20 clinical patients by total daily insulin requirements and body weight. The identical meals given during the AP study were given to the virtual patients and the identical AP control algorithm that was used to control the glucose of the virtual patients was used on the clinical patients. We compared percent time in target range (70-180 mg/dL), time in hypoglycemia (180 mg/dL) for both the virtual patients and the actual patients.ResultsThe subjects in the SH-VPP performed similarly vs. the actual patients (time in range: 78.1 ± 5.1% vs. 74.3 ± 8.1%, p = 0.11; time in hypoglycemia: 3.4 ± 1.3% vs. 2.8 ± 1.7%, p = 0.23). The subjects in the DH-VPP also performed similarly vs. the actual patients (time in range: 75.6 ± 5.5% vs. 71.9 ± 10.9%, p = 0.13; time in hypoglycemia: 0.9 ± 0.8% vs. 1.3 ± 1%, p = 0.19). While the VPPs tended to over-estimate the time in range relative to actual patients, the difference was not statistically significant.ConclusionsWe have verified that a SH-VPP and a DH-VPP performed comparably with actual patients undergoing AP control using an identical control algorithm. The SH-VPP and DH-VPP may be used as a simulator for pre-evaluation of T1D control algorithms.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::75ce61fb347c5c5b7d07b66c646f1c84Test
https://doi.org/10.1371/journal.pone.0217301Test