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Pavan J, Noaro G, Facchinetti A, Salvagnin D, Sparacino G, Del Favero S. A strategy based on integer programming for optimal dosing and timing of preventive hypoglycemic treatments in type 1 diabetes management. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 250:108179. [PMID: 38642427 DOI: 10.1016/j.cmpb.2024.108179] [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: 01/04/2024] [Revised: 03/29/2024] [Accepted: 04/13/2024] [Indexed: 04/22/2024]
Abstract
BACKGROUND AND OBJECTIVES One of the major problems related to type 1 diabetes (T1D) management is hypoglycemia, a condition characterized by low blood glucose levels and responsible for reduced quality of life and increased mortality. Fast-acting carbohydrates, also known as hypoglycemic treatments (HT), can counteract this event. In the literature, dosage and timing of HT are usually based on heuristic rules. In the present work, we propose an algorithm for mitigating hypoglycemia by suggesting preventive HT consumption, with dosages and timing determined by solving an optimization problem. METHODS By leveraging integer programming and linear inequality constraints, the algorithm can bind the amount of suggested carbohydrates to standardized quantities (i.e., those available in "off-the-shelf" HT) and the minimal distance between consecutive suggestions (to reduce the nuisance for patients). RESULTS The proposed method was tested in silico and compared with competitor algorithms using the UVa/Padova T1D simulator. At the cost of a slight increase of HT consumed per day, the proposed algorithm produces the lowest median and interquartile range of the time spent in hypoglycemia, with a statistically significant improvement over most competitor algorithms. Also, the average number of hypoglycemic events per day is reduced to 0 in median. CONCLUSIONS Thanks to its positive performances and reduced computational burden, the proposed algorithm could be a candidate tool for integration in a DSS aimed at improving T1D management.
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Affiliation(s)
- J Pavan
- Department of Information Engineering, University of Padova, Via Gradenigo 6/A, Padova, 35131, Italy.
| | - G Noaro
- Department of Information Engineering, University of Padova, Via Gradenigo 6/A, Padova, 35131, Italy.
| | - A Facchinetti
- Department of Information Engineering, University of Padova, Via Gradenigo 6/A, Padova, 35131, Italy.
| | - D Salvagnin
- Department of Information Engineering, University of Padova, Via Gradenigo 6/A, Padova, 35131, Italy.
| | - G Sparacino
- Department of Information Engineering, University of Padova, Via Gradenigo 6/A, Padova, 35131, Italy.
| | - S Del Favero
- Department of Information Engineering, University of Padova, Via Gradenigo 6/A, Padova, 35131, Italy.
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Tyler NS, Jacobs PG. Artificial Intelligence in Decision Support Systems for Type 1 Diabetes. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3214. [PMID: 32517068 PMCID: PMC7308977 DOI: 10.3390/s20113214] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 05/29/2020] [Accepted: 06/02/2020] [Indexed: 12/16/2022]
Abstract
Type 1 diabetes (T1D) is a chronic health condition resulting from pancreatic beta cell dysfunction and insulin depletion. While automated insulin delivery systems are now available, many people choose to manage insulin delivery manually through insulin pumps or through multiple daily injections. Frequent insulin titrations are needed to adequately manage glucose, however, provider adjustments are typically made every several months. Recent automated decision support systems incorporate artificial intelligence algorithms to deliver personalized recommendations regarding insulin doses and daily behaviors. This paper presents a comprehensive review of computational and artificial intelligence-based decision support systems to manage T1D. Articles were obtained from PubMed, IEEE Xplore, and ScienceDirect databases. No time period restrictions were imposed on the search. After removing off-topic articles and duplicates, 562 articles were left to review. Of those articles, we identified 61 articles for comprehensive review based on algorithm evaluation using real-world human data, in silico trials, or clinical studies. We grouped decision support systems into general categories of (1) those which recommend adjustments to insulin and (2) those which predict and help avoid hypoglycemia. We review the artificial intelligence methods used for each type of decision support system, and discuss the performance and potential applications of these systems.
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Affiliation(s)
| | - Peter G. Jacobs
- Artificial Intelligence for Medical Systems Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA;
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Lopez PE, Smart CE, McElduff P, Foskett DC, Price DA, Paterson MA, King BR. Optimizing the combination insulin bolus split for a high-fat, high-protein meal in children and adolescents using insulin pump therapy. Diabet Med 2017; 34:1380-1384. [PMID: 28574182 DOI: 10.1111/dme.13392] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/30/2017] [Indexed: 12/26/2022]
Abstract
AIMS To determine the optimum combination bolus split to maintain postprandial glycaemia with a high-fat and high-protein meal in young people with Type 1 diabetes. METHODS A total of 19 young people (mean age 12.9 ± 6.7 years) participated in a randomized, repeated-measures trial comparing postprandial glycaemic control across six study conditions after a high-fat and high-protein meal. A standard bolus and five different combination boluses were delivered over 2 h in the following splits: 70/30 = 70% standard /30% extended bolus; 60/40=60% standard/40% extended bolus; 50/50=50% standard/50% extended bolus; 40/60=40% standard/60% extended bolus; and 30/70=30% standard/70% extended bolus. Insulin dose was determined using the participant's optimized insulin:carbohydrate ratio. Continuous glucose monitoring was used to assess glucose excursions for 6 h after the test meal. RESULTS Standard bolus and combination boluses 70/30 and 60/40 controlled the glucose excursion up to 120 min. From 240 to 300 min after the meal, the glucose area under the curve was significantly lower for combination bolus 30/70 compared with standard bolus (P=0.004). CONCLUSIONS High-fat and high-protein meals require a ≥60% insulin:carbohydrate ratio as a standard bolus to control the initial postprandial rise. Additional insulin at an insulin:carbohydrate ratio of up to 70% is needed in the extended bolus for a high fat and protein meal to prevent delayed hyperglycaemia.
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Affiliation(s)
- P E Lopez
- John Hunter Hospital, Newcastle, NSW, Australia
- University of Newcastle, Newcastle, NSW, Australia
- Hunter Medical Research Institute, Newcastle, NSW, Australia
- Mothers and Babies Research Centre, Newcastle, NSW, Australia
| | - C E Smart
- John Hunter Hospital, Newcastle, NSW, Australia
- University of Newcastle, Newcastle, NSW, Australia
- Mothers and Babies Research Centre, Newcastle, NSW, Australia
| | - P McElduff
- University of Newcastle, Newcastle, NSW, Australia
- Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - D C Foskett
- Insulin Pump Angels, Gold Coast, Queensland, Australia
| | - D A Price
- Pacific Private Clinic, Gold Coast, Queensland, Australia
- Bond University, Gold Coast, Queensland, Australia
- Griffith University, Gold Coast, Queensland, Australia
| | - M A Paterson
- John Hunter Hospital, Newcastle, NSW, Australia
- University of Newcastle, Newcastle, NSW, Australia
- Hunter Medical Research Institute, Newcastle, NSW, Australia
- Mothers and Babies Research Centre, Newcastle, NSW, Australia
| | - B R King
- John Hunter Hospital, Newcastle, NSW, Australia
- University of Newcastle, Newcastle, NSW, Australia
- Hunter Medical Research Institute, Newcastle, NSW, Australia
- Mothers and Babies Research Centre, Newcastle, NSW, Australia
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Bode BW, Kaufman FR, Vint N. An Expert Opinion on Advanced Insulin Pump Use in Youth with Type 1 Diabetes. Diabetes Technol Ther 2017; 19:145-154. [PMID: 28135116 DOI: 10.1089/dia.2016.0354] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Among children and adolescents with type 1 diabetes mellitus, the use of insulin pump therapy has increased since its introduction in the early 1980s. Optimal management of type 1 diabetes mellitus depends on sufficient understanding by patients, their families, and healthcare providers on how to use pump technology. The goal for the use of insulin pump therapy should be to advance proficiency over time from the basics taught at the initiation of pump therapy to utilizing advanced settings to obtain optimal glycemic control. However, this goal is often not met, and appropriate understanding of the full features of pump technology can be lacking. The objective of this review is to provide an expert perspective on the advanced features and use of insulin pump therapy, including practical guidelines for the successful use of insulin pump technology, and other considerations specific to patients and healthcare providers.
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Affiliation(s)
- Bruce W Bode
- 1 Atlanta Diabetes Associates , Atlanta, Georgia
| | - Francine R Kaufman
- 2 The Center for Endocrinology, Diabetes and Metabolism, Children's Hospital Los Angeles , Los Angeles, California
- 3 Medtronic , Northridge, California
| | - Nan Vint
- 4 Lilly USA, LLC, Lilly Corporate Center , US Medical Affairs, Indianapolis, Indiana
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Identification of intra-patient variability in the postprandial response of patients with type 1 diabetes. Biomed Signal Process Control 2014. [DOI: 10.1016/j.bspc.2013.07.003] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Glucose predictability, blood capillary permeability, and glucose utilization rate in subcutaneous, skeletal muscle, and visceral fat tissues. Comput Biol Med 2013; 43:1680-6. [DOI: 10.1016/j.compbiomed.2013.08.008] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2012] [Revised: 08/12/2013] [Accepted: 08/15/2013] [Indexed: 11/23/2022]
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Schwartz FL, Marling CR. Use of Automated Bolus Calculators for Diabetes Management. EUROPEAN ENDOCRINOLOGY 2013; 9:92-95. [PMID: 29922360 DOI: 10.17925/ee.2013.09.02.92] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2013] [Accepted: 07/25/2013] [Indexed: 11/24/2022]
Abstract
Fewer than 30 % of patients with diabetes who are on insulin therapy achieve target glycated haemoglobin (HbA1C) levels. Automated bolus calculators (ABCs) are now almost universally used for patients on insulin pump therapy to calculate pre-meal insulin doses. Use of ABCs in glucose monitors and smart phone applications have the potential to improve glucose control in a larger population of individuals with diabetes on insulin therapy by overcoming the fear of hypoglycaemia and assisting those with low numeracy skills.
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Affiliation(s)
- Frank L Schwartz
- Professor of Endocrinology, The Diabetes Institute, Ohio University Heritage College of Osteopathic Medicine, Athens, Ohio, US
| | - Cynthia R Marling
- Associate Professor, School of Electrical Engineering and Computer Science, Russ College of Engineering and Technology, Ohio University, Athens, Ohio, US
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de Pereda D, Romero-Vivo S, Ricarte B, Bondia J. On the prediction of glucose concentration under intra-patient variability in type 1 diabetes: a monotone systems approach. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2012; 108:993-1001. [PMID: 22742877 DOI: 10.1016/j.cmpb.2012.05.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2012] [Revised: 05/18/2012] [Accepted: 05/22/2012] [Indexed: 06/01/2023]
Abstract
Insulin therapy in type 1 diabetes aims to mimic the pattern of endogenous insulin secretion found in healthy subjects. Glucose-insulin models are widely used in the development of new predictive control strategies in order to maintain the plasma glucose concentration within a narrow range, avoiding the risks of high or low levels of glucose in the blood. However, due to the high variability of this biological process, the exact values of the model parameters are unknown, but they can be bounded by intervals. In this work, the computation of tight glucose concentration bounds under parametric uncertainty for the development of robust prediction tools is addressed. A monotonicity analysis of the model states and parameters is performed. An analysis of critical points, state transformations and application of differential inequalities are proposed to deal with non-monotone parameters. In contrast to current methods, the guaranteed simulations for the glucose-insulin model are carried out by considering uncertainty in all the parameters and initial conditions. Furthermore, no time-discretisation is required, which helps to reduce the computational time significantly. As a result, we are able to compute a tight glucose envelope that bounds all the possible patient's glycemic responses with low computational effort.
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Affiliation(s)
- Diego de Pereda
- Institut Universitari d'Automàtica i Informàtica Industrial, Universitat Politècnica de València, Spain
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Rossetti P, Ampudia-Blasco FJ, Laguna A, Revert A, Vehì J, Ascaso JF, Bondia J. Evaluation of a novel continuous glucose monitoring-based method for mealtime insulin dosing--the iBolus--in subjects with type 1 diabetes using continuous subcutaneous insulin infusion therapy: a randomized controlled trial. Diabetes Technol Ther 2012; 14:1043-52. [PMID: 23003329 PMCID: PMC3482847 DOI: 10.1089/dia.2012.0145] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Prandial insulin dosing is an empirical practice associated frequently with poor reproducibility in postprandial glucose response. Based on continuous glucose monitoring (CGM), a method for prandial insulin administration (iBolus) is presented and evaluated for people with type 1 diabetes using CSII therapy. SUBJECTS AND METHODS An individual patient's model for a 5-h postprandial period was obtained from 6-day ambulatory CGM and used for iBolus calculation in 12 patients with type 1 diabetes. In a double-blind, crossover study each patient underwent four meal tests with 40 g or 100 g of carbohydrates (CHOs), both on two occasions. For each meal, the iBolus or the traditional bolus (tBolus) was given before mealtime (t(0)) in a randomized order. We measured the postprandial glycemic response as the area under the curve of plasma glucose (AUC-PG(0-5h)) and variability as the individual coefficient of variation (CV) of AUC-PG(0-5h). The contribution of the insulin-to-CHO ratio, CHO, plasma glucose at t(0) (PG(t0)), and insulin dose to AUC-PG(0-5h) and its CV was also investigated. RESULTS AUC-PG(0-5h) was similar with either bolus for 40-g (iBolus vs. tBolus, 585.5±127.5 vs. 689.2±180.7 mg/dL·h) or 100-g (752.1±237.7 vs. 760.0±263.2 mg/dL·h) CHO meals. A multiple regression analysis revealed a significant model only for the tBolus, with PG(t0) being the best predictor of AUC-PG(0-5h) explaining approximately 50% of the glycemic response. Observed variability was greater with the iBolus (CV, 16.7±15.3% vs. 10.1±12.5%) but independent of the factors studied. CONCLUSIONS A CGM-based algorithm for calculation of prandial insulin is feasible, although it does not reduce unpredictability of individual glycemic responses. Causes of variability need to be identified and analyzed for further optimization of postprandial glycemic control.
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Affiliation(s)
- Paolo Rossetti
- University Institute of Control Systems and Industrial Computing, Polytechnic University of Valencia, Valencia, Spain.
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León-Vargas F, Calm R, Bondia J, Vehí J. Improving the computational effort of set-inversion-based prandial insulin delivery for its integration in insulin pumps. J Diabetes Sci Technol 2012; 6:1420-8. [PMID: 23294789 PMCID: PMC3570884 DOI: 10.1177/193229681200600623] [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] [Indexed: 11/16/2022]
Abstract
OBJECTIVE Set-inversion-based prandial insulin delivery is a new model-based bolus advisor for postprandial glucose control in type 1 diabetes mellitus (T1DM). It automatically coordinates the values of basal-bolus insulin to be infused during the postprandial period so as to achieve some predefined control objectives. However, the method requires an excessive computation time to compute the solution set of feasible insulin profiles, which impedes its integration into an insulin pump. In this work, a new algorithm is presented, which reduces computation time significantly and enables the integration of this new bolus advisor into current processing features of smart insulin pumps. METHODS A new strategy was implemented that focused on finding the combined basal-bolus solution of interest rather than an extensive search of the feasible set of solutions. Analysis of interval simulations, inclusion of physiological assumptions, and search domain contractions were used. Data from six real patients with T1DM were used to compare the performance between the optimized and the conventional computations. RESULTS In all cases, the optimized version yielded the basal-bolus combination recommended by the conventional method and in only 0.032% of the computation time. Simulations show that the mean number of iterations for the optimized computation requires approximately 3.59 s at 20 MHz processing power, in line with current features of smart pumps. CONCLUSIONS A computationally efficient method for basal-bolus coordination in postprandial glucose control has been presented and tested. The results indicate that an embedded algorithm within smart insulin pumps is now feasible. Nonetheless, we acknowledge that a clinical trial will be needed in order to justify this claim.
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Affiliation(s)
- Fabian León-Vargas
- Institute of Informatics and Applications, University of Girona, Girona, Spain.
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