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Brummer J, Glasbrenner C, Hechenbichler Figueroa S, Koehler K, Höchsmann C. Continuous glucose monitoring for automatic real-time assessment of eating events and nutrition: a scoping review. Front Nutr 2024; 10:1308348. [PMID: 38264192 PMCID: PMC10804456 DOI: 10.3389/fnut.2023.1308348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 12/13/2023] [Indexed: 01/25/2024] Open
Abstract
Background Accurate dietary assessment remains a challenge, particularly in free-living settings. Continuous glucose monitoring (CGM) shows promise in optimizing the assessment and monitoring of ingestive activity (IA, i.e., consumption of calorie-containing foods/beverages), and it might enable administering dietary Just-In-Time Adaptive Interventions (JITAIs). Objective In a scoping review, we aimed to answer the following questions: (1) Which CGM approaches to automatically detect IA in (near-)real-time have been investigated? (2) How accurate are these approaches? (3) Can they be used in the context of JITAIs? Methods We systematically searched four databases until October 2023 and included publications in English or German that used CGM-based approaches for human (all ages) IA detection. Eligible publications included a ground-truth method as a comparator. We synthesized the evidence qualitatively and critically appraised publication quality. Results Of 1,561 potentially relevant publications identified, 19 publications (17 studies, total N = 311; for 2 studies, 2 publications each were relevant) were included. Most publications included individuals with diabetes, often using meal announcements and/or insulin boluses accompanying meals. Inpatient and free-living settings were used. CGM-only approaches and CGM combined with additional inputs were deployed. A broad range of algorithms was tested. Performance varied among the reviewed methods, ranging from unsatisfactory to excellent (e.g., 21% vs. 100% sensitivity). Detection times ranged from 9.0 to 45.0 min. Conclusion Several CGM-based approaches are promising for automatically detecting IA. However, response times need to be faster to enable JITAIs aimed at impacting acute IA. Methodological issues and overall heterogeneity among articles prevent recommending one single approach; specific cases will dictate the most suitable approach.
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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: 37] [Impact Index Per Article: 9.3] [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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Dynamic Rule-Based Algorithm to Tune Insulin-on-Board Constraints for a Hybrid Artificial Pancreas System. JOURNAL OF HEALTHCARE ENGINEERING 2020; 2020:1414597. [PMID: 32399164 PMCID: PMC7201789 DOI: 10.1155/2020/1414597] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 04/16/2019] [Accepted: 05/14/2019] [Indexed: 11/18/2022]
Abstract
The artificial pancreas (AP) is a system intended to control blood glucose levels through automated insulin infusion, reducing the burden of subjects with type 1 diabetes to manage their condition. To increase patients' safety, some systems limit the allowed amount of insulin active in the body, known as insulin-on-board (IOB). The safety auxiliary feedback element (SAFE) layer has been designed previously to avoid overreaction of the controller and thus avoiding hypoglycemia. In this work, a new method, so-called “dynamic rule-based algorithm,” is presented in order to adjust the limits of IOB in real time. The algorithm is an extension of a previously designed method which aimed to adjust the limits of IOB for a meal with 60 grams of carbohydrates (CHO). The proposed method is intended to be applied on hybrid AP systems during 24 h operation. It has been designed by combining two different strategies to set IOB limits for different situations: (1) fasting periods and (2) postprandial periods, regardless of the size of the meal. The UVa/Padova simulator is considered to assess the performance of the method, considering challenging scenarios. In silico results showed that the method is able to reduce the time spent in hypoglycemic range, improving patients' safety, which reveals the feasibility of the approach to be included in different control algorithms.
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Oviedo S, Contreras I, Bertachi A, Quirós C, Giménez M, Conget I, Vehi J. Minimizing postprandial hypoglycemia in Type 1 diabetes patients using multiple insulin injections and capillary blood glucose self-monitoring with machine learning techniques. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 178:175-180. [PMID: 31416546 DOI: 10.1016/j.cmpb.2019.06.025] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2019] [Revised: 06/11/2019] [Accepted: 06/26/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND Diabetic patients treated with intensive insulin therapies require a tight glycemic control and may benefit from advanced tools to predict blood glucose (BG) concentration levels and hypo/hyperglycemia events. Prediction systems using machine learning techniques have mainly focused on applications for sensor augmented pump (SAP) therapy. In contrast, insulin bolus calculators that rely on BG prediction for multiple daily insulin (MDI) injections for patients under self-monitoring blood glucose (SMBG) are scarce because of insufficient data sources and limited prediction capability of forecasting models. METHODS We trained individualized models that can predict postprandial hypoglycemia via different machine learning algorithms using retrospective data from 10 real patients. In addition, we designed and tested a hypoglycemia reduction strategy for a similar in silico population. The system generates a bolus reduction suggestion as the scaled weighted sum of the predictions. We evaluated the general and postprandial glycemic outcomes of the in silico population to assess the systems capability of avoiding hypoglycemias. RESULTS The median [IQR] sensitivity and specificity for hypoglycemia cases where the BG level was below 70 mg/dL were 0.49 [0.2-0.5] and 0.74 [0.7-0.9], respectively. For hypoglycemia cases where the BG level was below 54 mg/dL, the median [IQR] sensitivity and specificity were 0.51 [0.4-0.6] and 0.74 [0.7-0.8], respectively. CONCLUSIONS The results indicated a decrease of 37% in the median number of postprandial hypoglycemias median decrease of 44% for hypoglycemias of 70 mg/dL and 54 mg/dL, respectively. This dramatic reduction makes this method a good candidate to be integrated into any Decision Support System for diabetes management.
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Affiliation(s)
- Silvia Oviedo
- Institut d'Informatica i Aplicacions. Universitat de Girona, Spain.
| | - Ivan Contreras
- Institut d'Informatica i Aplicacions. Universitat de Girona, Spain.
| | - Arthur Bertachi
- Institut d'Informatica i Aplicacions. Universitat de Girona, Spain; Federal University of Technology Paraná (UTFPR), Guarapuava 85053-525, Brazil.
| | - Carmen Quirós
- Servicio de Endocrinología y Nutrición. Hospital Universitari Mutua de Terrassa, Terrassa, Spain.
| | - Marga Giménez
- Diabetes Unit. Endocrinology and Nutrition Dpt. IDIBAPS (Institut d'investigacions biomédiques August Pi I Sunyer), Barcelona, Spain; Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Spain.
| | - Ignacio Conget
- Diabetes Unit. Endocrinology and Nutrition Dpt. IDIBAPS (Institut d'investigacions biomédiques August Pi I Sunyer), Barcelona, Spain; Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Spain.
| | - Josep Vehi
- Institut d'Informatica i Aplicacions. Universitat de Girona, Spain; Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Spain.
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Rosales N, De Battista H, Vehí J, Garelli F. Open-loop glucose control: Automatic IOB-based super-bolus feature for commercial insulin pumps. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 159:145-158. [PMID: 29650309 DOI: 10.1016/j.cmpb.2018.03.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Revised: 02/06/2018] [Accepted: 03/09/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE Although there has been significant progress towards closed-loop type 1 diabetes mellitus (T1DM) treatments, most diabetic patients still treat this metabolic disorder in an open-loop manner, based on insulin pump therapy (basal and bolus insulin infusion). This paper presents a method for automatic insulin bolus shaping based on insulin-on-board (IOB) as an alternative to conventional bolus dosing. METHODS The methodology presented allows the pump to generate the so-called super-bolus (SB) employing a two-compartment IOB dynamic model. The extra amount of insulin to boost the bolus and the basal cutoff time are computed using the duration of insulin action (DIA). In this way, the pump automatically re-establishes basal insulin when IOB reaches its basal level. Thus, detrimental transients caused by manual or a-priori computations are avoided. RESULTS The potential of this method is illustrated via in-silico trials over a 30 patients cohort in single meal and single day scenarios. In the first ones, improvements were found (standard treatment vs. automatic SB) both in percentage time in euglycemia (75g meal: 81.9 ± 15.59 vs. 89.51 ± 11.95, ρ ≃ 0; 100g meal: 75.12 ± 18.23 vs. 85.46 ± 14.96, ρ ≃ 0) and time in hypoglecymia (75g meal: 5.92 ± 14.48 vs. 0.97 ± 4.15, ρ=0.008; 100g meal: 9.5 ± 17.02 vs. 1.85 ± 7.05, ρ=0.014). In a single day scenario, considering intra-patient variability, the time in hypoglycemia was reduced (9.57 ± 14.48 vs. 4.21 ± 6.18, ρ=0.028) and improved the time in euglycemia (79.46 ± 17.46 vs. 86.29 ± 11.73, ρ=0.007). CONCLUSIONS The automatic IOB-based SB has the potential of a better performance in comparison with the standard treatment, particularly for high glycemic index meals with high carbohydrate content. Both glucose excursion and time spent in hypoglycemia were reduced.
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Affiliation(s)
- Nicolás Rosales
- Grupo de Control Aplicado (GCA), Instituto LEICI, UNLP-CONICET. Facultad de Ingeniería, Universidad Nacional de La Plata, Argentina.
| | - Hernán De Battista
- Grupo de Control Aplicado (GCA), Instituto LEICI, UNLP-CONICET. Facultad de Ingeniería, Universidad Nacional de La Plata, Argentina
| | - Josep Vehí
- Institut d'Informàtica i Aplicacions, Universitat de Girona, Campus de Montilivi, Girona, Spain; Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Girona, Spain
| | - Fabricio Garelli
- Grupo de Control Aplicado (GCA), Instituto LEICI, UNLP-CONICET. Facultad de Ingeniería, Universidad Nacional de La Plata, Argentina
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Vaz EC, Porfírio GJM, Nunes HRDC, Nunes-Nogueira VDS. Effectiveness and safety of carbohydrate counting in the management of adult patients with type 1 diabetes mellitus: a systematic review and meta-analysis. ARCHIVES OF ENDOCRINOLOGY AND METABOLISM 2018; 62:337-345. [PMID: 29791661 PMCID: PMC10118793 DOI: 10.20945/2359-3997000000045] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Accepted: 02/07/2018] [Indexed: 11/23/2022]
Abstract
OBJECTIVE This study aimed to evaluate the effectiveness and safety of carbohydrate counting (CHOC) in the treatment of adult patients with type 1 diabetes mellitus (DM1). MATERIALS AND METHODS We performed a systematic review of randomized studies that compared CHOC with general dietary advice in adult patients with DM1. The primary outcomes were changes in glycated hemoglobin (HbA1c), quality of life, and episodes of severe hypoglycemia. We searched the following electronic databases: Embase, PubMed, Lilacs, and the Cochrane Central Register of Controlled Trials. The quality of evidence was analyzed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE). RESULTS A total of 3,190 articles were identified, and two reviewers independently screened the titles and abstracts. From the 15 potentially eligible studies, five were included, and 10 were excluded because of the lack of randomization or different control/intervention groups. Meta-analysis showed that the final HbA1c was significantly lower in the CHOC group than in the control group (mean difference, random, 95% CI: -0.49 (-0.85, -0.13), p = 0.006). The meta-analysis of severe hypoglycemia and quality of life did not show any significant differences between the groups. According to the GRADE, the quality of evidence for severe hypoglycemia, quality of life, and change in HbA1c was low, very low, and moderate, respectively. CONCLUSION The meta-analysis showed evidence favoring the use of CHOC in the management of DM1. However, this benefit was limited to final HbA1c, which was significantly lower in the CHOC than in the control group.
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Affiliation(s)
- Eliege Carolina Vaz
- Departamento de Clínica Médica, Faculdade de Medicina de Botucatu, Universidade Estadual de São Paulo (Unesp), Botucatu, SP, Brasil
| | - Gustavo José Martiniano Porfírio
- Centro Cochrane do Brasil, Disciplina de Medicina de Urgência e Medicina Baseada em Evidências, Universidade Federal de São Paulo (Unifesp), São Paulo, SP, Brasil
| | - Hélio Rubens de Carvalho Nunes
- Departamento de Saúde Pública, Faculdade de Medicina de Botucatu, Universidade Estadual de São Paulo (Unesp), Botucatu, SP, Brasil
| | - Vania Dos Santos Nunes-Nogueira
- Departamento de Clínica Médica, Faculdade de Medicina de Botucatu, Universidade Estadual de São Paulo (Unesp), Botucatu, SP, Brasil
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Rossetti P, Quirós C, Moscardó V, Comas A, Giménez M, Ampudia-Blasco FJ, León F, Montaser E, Conget I, Bondia J, Vehí J. Closed-Loop Control of Postprandial Glycemia Using an Insulin-on-Board Limitation Through Continuous Action on Glucose Target. Diabetes Technol Ther 2017; 19:355-362. [PMID: 28459603 DOI: 10.1089/dia.2016.0443] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
BACKGROUND Postprandial (PP) control remains a challenge for closed-loop (CL) systems. Few studies with inconsistent results have systematically investigated the PP period. OBJECTIVE To compare a new CL algorithm with current pump therapy (open loop [OL]) in the PP glucose control in type 1 diabetes (T1D) subjects. METHODS A crossover randomized study was performed in two centers. Twenty T1D subjects (F/M 13/7, age 40.7 ± 10.4 years, disease duration 22.6 ± 9.9 years, and A1c 7.8% ± 0.7%) underwent an 8-h mixed meal test on four occasions. In two (CL1/CL2), after meal announcement, a bolus was given followed by an algorithm-driven basal infusion based on continuous glucose monitoring (CGM). Alternatively, in OL1/OL2 conventional pump therapy was used. Main outcome measures were as follows: glucose variability, estimated with the coefficient of variation (CV) of the area under the curve (AUC) of plasma glucose (PG) and CGM values, and from the analysis of the glucose time series; mean, maximum (Cmax), and time to Cmax glucose concentrations and time in range (<70, 70-180, >180 mg/dL). RESULTS CVs of the glucose AUCs were low and similar in all studies (around 10%). However, CL achieved greater reproducibility and better PG control in the PP period: CL1 = CL2<OL1<OL2 (PGmean 123 ± 47 and 125 ± 44 vs. 152 ± 53 and 159 ± 54 mg/dL) and Cmax OL 217.1 ± 67.0 mg/dL versus CL 183.3 ± 63.9 mg/dL, P < 0.0001. Time-in-range was higher with CL versus OL (80% vs. 64%; P < 0.001). Neither the time below 70 mg/dL (CL 6.1% vs. OL 3.2%; P > 0.05) nor the need for oral glucose was significantly different (CL 40.0% vs. OL 22.5% of meals; P = 0.054). CONCLUSIONS This novel CL algorithm effectively and consistently controls PP glucose excursions without increasing hypoglycemia. Study registered at ClinicalTrials.gov : study number NCT02100488.
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Affiliation(s)
- Paolo Rossetti
- 1 Internal Medicine Department, Hospital Francesc de Borja , Gandía, Spain
| | - Carmen Quirós
- 2 Diabetes Unit, Endocrinology Department, Hospital Clínic i Universitari , Barcelona, Spain
| | - Vanessa Moscardó
- 3 Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València , Valencia, Spain
| | - Anna Comas
- 4 Institute of Informatics and Applications, University of Girona , Girona, Spain
| | - Marga Giménez
- 2 Diabetes Unit, Endocrinology Department, Hospital Clínic i Universitari , Barcelona, Spain
| | - F Javier Ampudia-Blasco
- 5 Diabetes Reference Unit, Endocrinology and Nutrition Department, Hospital Clínico Universitario de Valencia , Valencia, Spain
| | - Fabián León
- 4 Institute of Informatics and Applications, University of Girona , Girona, Spain
| | - Eslam Montaser
- 3 Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València , Valencia, Spain
| | - Ignacio Conget
- 2 Diabetes Unit, Endocrinology Department, Hospital Clínic i Universitari , Barcelona, Spain
| | - Jorge Bondia
- 3 Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València , Valencia, Spain
| | - Josep Vehí
- 4 Institute of Informatics and Applications, University of Girona , Girona, Spain
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de Pereda D, Romero-Vivo S, Ricarte B, Rossetti P, Ampudia-Blasco FJ, Bondia J. Real-time estimation of plasma insulin concentration from continuous glucose monitor measurements. Comput Methods Biomech Biomed Engin 2015; 19:934-42. [DOI: 10.1080/10255842.2015.1077234] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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Boronat M, Sánchez-Hernández RM, Rodríguez-Cordero J, Jiménez-Ortega A, Nóvoa FJ. Suspension of basal insulin to avoid hypoglycemia in type 1 diabetes treated with insulin pump. Endocrinol Diabetes Metab Case Rep 2015; 2015:140081. [PMID: 25614824 PMCID: PMC4285755 DOI: 10.1530/edm-14-0081] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2014] [Accepted: 12/11/2014] [Indexed: 01/13/2023] Open
Abstract
Treatment with continuous s.c. insulin infusion (CSII) provides better glycemic control and lower risk of hypoglycemia than conventional therapy with multiple daily insulin injections. These benefits have been related to a more reliable absorption and an improved pharmacokinetic profile of insulin delivered through CSII therapy. However, even for patients treated with CSII, exaggerated postmeal hyperglycemic excursions and late postabsorptive hypoglycemia can still constitute a therapeutic challenge. Two female patients with type 1 diabetes who began treatment with CSII required to increase their previous breakfast insulin-to-carbohydrate ratio in order to achieve postprandial glycemic goals. However, they simultaneously presented recurrent episodes of late hypoglycemia several hours after breakfast bolus. Advancing the timing of the bolus was ineffective and bothersome for patients. In both cases, the best therapeutic option was to set a basal insulin rate of zero units per hour during 6 h after breakfast. Even so, they need to routinely take a midmorning snack with 10–20 g of carbohydrates to avoid late postabsorptive hypoglycemia. They have been using this insulin schedule for about 3 years without complications. The action of prandial insulin delivered through insulin pumps can be inappropriately delayed for the requirements of some patients. Although suspension of basal rate can be an acceptable therapeutic alternative for them, these cases demonstrate that new strategies to improve the bioavailability of prandial insulin infused through CSII are still needed.
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Affiliation(s)
- Mauro Boronat
- Section of Endocrinology and Nutrition, Hospital Universitario Insular , Las Palmas de Gran Canaria, 35016 , Spain ; Department of Medical and Surgical Sciences, University of Las Palmas de Gran Canaria , Las Palmas de Gran Canaria , Spain
| | - Rosa M Sánchez-Hernández
- Section of Endocrinology and Nutrition, Hospital Universitario Insular , Las Palmas de Gran Canaria, 35016 , Spain
| | - Julia Rodríguez-Cordero
- Section of Endocrinology and Nutrition, Hospital Universitario Insular , Las Palmas de Gran Canaria, 35016 , Spain
| | - Angelines Jiménez-Ortega
- Section of Endocrinology and Nutrition, Hospital Universitario Insular , Las Palmas de Gran Canaria, 35016 , Spain
| | - Francisco J Nóvoa
- Section of Endocrinology and Nutrition, Hospital Universitario Insular , Las Palmas de Gran Canaria, 35016 , Spain ; Department of Medical and Surgical Sciences, University of Las Palmas de Gran Canaria , Las Palmas de Gran Canaria , Spain
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Quemerais MA, Doron M, Dutrech F, Melki V, Franc S, Antonakios M, Charpentier G, Hanaire H, Benhamou PY. Preliminary evaluation of a new semi-closed-loop insulin therapy system over the prandial period in adult patients with type 1 diabetes: the WP6.0 Diabeloop study. J Diabetes Sci Technol 2014; 8:1177-84. [PMID: 25097057 PMCID: PMC4455472 DOI: 10.1177/1932296814545668] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
There is room for improvement in the algorithms used in closed-loop insulin therapy during the prandial period. This pilot study evaluated the efficacy and safety of the Diabeloop algorithm (model predictive control type) during the postprandial period. This 2-center clinical trial compared interstitial glucose levels over two 5-hour periods (with/without the algorithm) following a calibrated lunch. On the control day, the amount of insulin delivered by the pump was determined according to the patient's usual parameters. On the test day, 50% or 75% of the theoretical bolus required was delivered, while the algorithm, informed of carbohydrate intake, proposed changes to insulin delivery every 15 minutes using modeling to forecast glucose levels. The primary endpoint was percentage of time spent at near normoglycemia (70-180 mg/dl). Twelve patients with type 1 diabetes (9 men, age 35.6 ± 12.7 years, HbA1c 7.3 ± 0.8%) were included. The percentage of time spent in the target range was 84.5 ± 20.8 (test day) versus 69.2 ± 33.9% (control day, P = .11). The percentage of time spent in hypoglycemia < 70 mg/dl was 0.2 ± 0.8 (test) versus 4.4 ± 8.2% (control, P = .18). Interstitial glucose at the end of the test (5 hours) was 127.5 ± 40.1 (test) versus 146 ± 53.5 mg/dl (control, P = .25). The insulin doses did not differ, and no differences were observed between the 50% and 75% boluses. In a semi-closed-loop configuration with manual priming boluses (25% or 50% reduction), the Diabeloop v1 algorithm was as successful as the manual method in determining the prandial bolus, without any exposure to excessive hypoglycemic risk.
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Affiliation(s)
| | - Maeva Doron
- University Grenoble Alpes, Grenoble, France CEA, LETI, DTBS, Laboratoire électronique et systèmes pour la santé, Grenoble, France
| | - Florent Dutrech
- University Grenoble Alpes, Grenoble, France CEA, LETI, DTBS, Laboratoire électronique et systèmes pour la santé, Grenoble, France
| | - Vincent Melki
- Department of Diabetology, Toulouse Rangueil University Hospital, Toulouse, France
| | - Sylvia Franc
- Department of Diabetes, Sud-Francilien Hospital, Corbeil-Essonnes, France CERITD, Corbeil-Essonnes, France
| | - Michel Antonakios
- University Grenoble Alpes, Grenoble, France CEA, LETI, DTBS, Laboratoire électronique et systèmes pour la santé, Grenoble, France
| | - Guillaume Charpentier
- Department of Diabetes, Sud-Francilien Hospital, Corbeil-Essonnes, France CERITD, Corbeil-Essonnes, France
| | - Helene Hanaire
- Department of Diabetology, Toulouse Rangueil University Hospital, Toulouse, France
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Srinivasan A, Lee JB, Dassau E, Doyle FJ. Novel insulin delivery profiles for mixed meals for sensor-augmented pump and closed-loop artificial pancreas therapy for type 1 diabetes mellitus. J Diabetes Sci Technol 2014; 8:957-68. [PMID: 25049364 PMCID: PMC4455363 DOI: 10.1177/1932296814543660] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Maintaining euglycemia for people with type 1 diabetes is highly challenging, and variations in glucose absorption rates with meal composition require meal type specific insulin delivery profiles for optimal blood glucose control. Traditional basal/bolus therapy is not fully optimized for meals of varied fat contents. Thus, regimens for low- and high-fat meals were developed to improve current insulin pump therapy. Simulations of meals with varied fat content demonstrably replicated published data. Subsequently, an insulin profile library with optimized delivery regimens under open and closed loop for various meal compositions was constructed using particle swarm optimization. Calculations showed that the optimal basal bolus insulin profiles for low-fat meals comprise a normal bolus or a short wave. The preferred delivery for high-fat meals is typically biphasic, but can extend to multiple phases depending on meal characteristics. Results also revealed that patients that are highly sensitive to insulin could benefit from biphasic deliveries. Preliminary investigations of the optimal closed-loop regimens also display bi- or multiphasic patterns for high-fat meals. The novel insulin delivery profiles present new waveforms that provide better control of postprandial glucose excursions than existing schemes. Furthermore, the proposed novel regimens are also more or similarly robust to uncertainties in meal parameter estimates, with the closed-loop schemes demonstrating superior performance and robustness.
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Affiliation(s)
- Asavari Srinivasan
- Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Joon Bok Lee
- Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA, USA Sansum Diabetes Research Institute, Santa Barbara, CA, USA
| | - Eyal Dassau
- Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA, USA Sansum Diabetes Research Institute, Santa Barbara, CA, USA
| | - Francis J Doyle
- Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA, USA Sansum Diabetes Research Institute, Santa Barbara, CA, USA
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12
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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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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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