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Cuerda Del Pino A, Martín-San Agustín R, José Laguna Sanz A, Díez JL, Palanca A, Rossetti P, Gumbau-Gimenez M, Ampudia-Blasco FJ, Bondia J. Accuracy of Two Continuous Glucose Monitoring Devices During Aerobic and High-Intensity Interval Training in Individuals with Type 1 Diabetes. Diabetes Technol Ther 2024; 26:411-419. [PMID: 38215205 DOI: 10.1089/dia.2023.0535] [Citation(s) in RCA: 1] [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: 01/14/2024]
Abstract
Background: This study aimed to evaluate the accuracy of Dexcom G6 (DG6) and FreeStyle Libre-2 (FSL2) during aerobic training and high-intensity interval training (HIIT) in individuals with type 1 diabetes. Methods: Twenty-six males (mean age 29.3 ± 6.3 years and mean duration of diabetes 14.9 ± 6.1 years) participated in this study. Interstitial glucose levels were measured using DG6 and FSL2, while plasma glucose levels were measured every 10 min using YSI 2500 as the reference for glucose measurements in this study. The measurements began 20 min before the start of exercise and continued for 20 min after exercise. Seven measurements were taken for each subject and exercise. Results: Both DG6 and FSL2 devices showed significant differences compared to YSI glucose data for both aerobic and HIIT exercises. Continuous glucose monitoring (CGM) devices exhibited superior performance during HIIT than aerobic training, with DG6 showing a mean absolute relative difference of 14.03% versus 31.98%, respectively. In the comparison between the two devices, FSL2 demonstrated significantly higher effectiveness in aerobic training, yet its performance was inferior to DG6 during HIIT. According to the 40/40 criteria, both sensors performed similarly, with marks over 93% for all ranges and both exercises, and above 99% for HIIT and in the >180 mg/dL range, which is in accordance with FDA guidelines. Conclusions: The findings suggest that the accuracy of DG6 and FSL2 deteriorates during and immediately after exercise but remains acceptable for both devices during HIIT. However, accuracy is compromised with DG6 during aerobic exercise. This study is the first to compare the accuracy of two CGMs, DG6, and FSL2, during two exercise modalities, using plasma glucose YSI measurements as the gold standard for comparisons. It was registered at clinicaltrials.gov (NCT06080542).
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Affiliation(s)
- Alba Cuerda Del Pino
- Clinimetry and Technological Development in Therapeutic Exercise Research Group (CLIDET), Department of Physiotherapy, University of Valencia, Valencia, Spain
| | - Rodrigo Martín-San Agustín
- Clinimetry and Technological Development in Therapeutic Exercise Research Group (CLIDET), Department of Physiotherapy, University of Valencia, Valencia, Spain
| | - Alejandro José Laguna Sanz
- Clinimetry and Technological Development in Therapeutic Exercise Research Group (CLIDET), Department of Physiotherapy, University of Valencia, Valencia, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
- Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, València, Spain
| | - José-Luis Díez
- Clinimetry and Technological Development in Therapeutic Exercise Research Group (CLIDET), Department of Physiotherapy, University of Valencia, Valencia, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
- Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, València, Spain
| | - Ana Palanca
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
- Cardiometabolic Risk and Diabetes Research Group, INCLIVA Biomedical Research Institute, Valencia, Spain
| | - Paolo Rossetti
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Endocrinology and Nutrition, University and Polytechnic La Fe Hospital of Valencia, Valencia, Spain
| | - Maria Gumbau-Gimenez
- Clinimetry and Technological Development in Therapeutic Exercise Research Group (CLIDET), Department of Physiotherapy, University of Valencia, Valencia, Spain
| | - F Javier Ampudia-Blasco
- Clinimetry and Technological Development in Therapeutic Exercise Research Group (CLIDET), Department of Physiotherapy, University of Valencia, Valencia, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
- Cardiometabolic Risk and Diabetes Research Group, INCLIVA Biomedical Research Institute, Valencia, Spain
- Department of Endocrinology and Nutrition, Clinic University Hospital of Valencia, Valencia, Spain
- Department of Medicine, University of Valencia, Valencia, Spain
| | - Jorge Bondia
- Clinimetry and Technological Development in Therapeutic Exercise Research Group (CLIDET), Department of Physiotherapy, University of Valencia, Valencia, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
- Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, València, Spain
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Hammour G, Mandic DP. An In-Ear PPG-Based Blood Glucose Monitor: A Proof-of-Concept Study. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23063319. [PMID: 36992029 PMCID: PMC10057625 DOI: 10.3390/s23063319] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 03/12/2023] [Accepted: 03/17/2023] [Indexed: 06/12/2023]
Abstract
Monitoring diabetes saves lives. To this end, we introduce a novel, unobtrusive, and readily deployable in-ear device for the continuous and non-invasive measurement of blood glucose levels (BGLs). The device is equipped with a low-cost commercially available pulse oximeter whose infrared wavelength (880 nm) is used for the acquisition of photoplethysmography (PPG). For rigor, we considered a full range of diabetic conditions (non-diabetic, pre-diabetic, type I diabetic, and type II diabetic). Recordings spanned nine different days, starting in the morning while fasting, up to a minimum of a two-hour period after eating a carbohydrate-rich breakfast. The BGLs from PPG were estimated using a suite of regression-based machine learning models, which were trained on characteristic features of PPG cycles pertaining to high and low BGLs. The analysis shows that, as desired, an average of 82% of the BGLs estimated from PPG lie in region A of the Clarke error grid (CEG) plot, with 100% of the estimated BGLs in the clinically acceptable CEG regions A and B. These results demonstrate the potential of the ear canal as a site for non-invasive blood glucose monitoring.
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Abstract
Although insulin therapy was already introduced one-hundred years ago, insulin formulations are still being refined to reduce the risk of hypoglycaemia and of other insulin side effects such as weight gain. This review summarises the available clinical data for some ongoing developments of new insulins and evaluates their potential for future insulin therapy. Once-weekly insulins will most likely be the next addition to the insulin armamentarium. First clinical studies indicate low peak-to-trough fluctuations with these insulins indicating the potential to achieve better glycaemic control or reduce hypoglycaemic events versus available basal insulins. Proof-of-concept has also been established for hepato-preferential and oral insulins; however, adverse effects and low bioavailability still need to be overcome. It will take much longer, before glucose-responsive "smart" insulins will be available. A first clinical study and numerous pre-clinical data show the potential, but also the challenges of designing an insulin that quickly reacts to blood glucose changes and prevents hypoglycaemia and pronounced hyperglycaemia. Nevertheless, it is reassuring that the search for better insulins has never stopped since its first use one-hundred years ago and is still ongoing. New developments have a high potential of further improving the safety and efficacy of insulin therapy in the future.
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Szadkowska A, Michalak A, Łosiewicz A, Kuśmierczyk H, Krawczyk-Rusiecka K, Chrzanowski J, Gawrecki A, Zozulińska-Ziółkiewicz D, Fendler W. Impact of factory-calibrated Freestyle Libre System with new glucose algorithm measurement accuracy and clinical performance in children with type 1 diabetes during summer camp. Pediatr Diabetes 2021; 22:261-270. [PMID: 33034075 DOI: 10.1111/pedi.13135] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 08/08/2020] [Accepted: 09/24/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Factory-calibrated intermittently-scanned Continuous Glucose Monitoring (isCGM) device FreeStyle Libre (FSL) has recently received improvements in its glucose tracking algorithm and calibration procedures, which are claimed to have improved its accuracy. OBJECTIVE To compare the accuracy of two generations of 14-days FSL devices (A in 2016, B in 2019) to self-monitored blood glucose measurements (SMBG) in children with type 1 diabetes in real-life conditions during a summer camp. MATERIALS AND METHODS Two largely independent groups of youth with type 1 diabetes took part in summer camps. In 2016 they used FSL-A, in 2019 FSL-B. On scheduled days, participants performed supervised 8-point glucose profiles with FSL and SMBG. The accuracy vs SMBG was assessed with mean absolute relative difference (MARD) and clinical surveillance error grid (SEG). RESULTS We collected 1655 FSL-SMBG measurement pairs from 78 FSL-A patients (age 13 ± 2.3 years old; HbA1c: 7.6 ± 0.8%) and 1796 from 58 in FSL-B group (age 13.8 ± 2.3 years old, HbA1c: 7.5 ± 1.1%)-in total 3451 measurements. FSL-B displayed lower MARD than FSL-A (11.3 ± 3.1% vs 13.7 ± 4.6%, P = .0003), lower SD of errors (20.2 ± 6.7 mg/dL vs 24.1 ± 9.6 mg/dL, P = .0090) but similar bias (-7.6 ± 11.8 mg/dL vs -6.5 ± 8 mg/dL, P = .5240). Both FSL-A and FSL-B showed significantly higher MARD when glycaemia was decreasing >2 mg/dL/min (FSL-A:22.3 ± 18.5%; FSL-B:17.9 ± 15.8%, P < .0001) compared with stable conditions (FSL-A: 11.4 ± 10.4%, FSL-B:10.1 ± 9.1%) and when the system could not define the glycaemic trend (FSL-A:16.5 ± 16.3%; FSL-B:15.2 ± 14.9%, P < .0001). Both generations demonstrated high percentage of A-class and B-class results in SEG (FSL-A: 96.4%, FSL-B: 97.6%) with a significant shift from B (decrease by 3.7%) to A category (increase by 3.9%) between generations (FSL-A: 16/80.4%; FSL-B:12.3/85.3%, P = .0012). CONCLUSION FSL-B demonstrated higher accuracy when compared to FSL-A However, when glycemia is decreasing or its trend is uncertain, the verification with a glucose meter is still advisable.
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Affiliation(s)
- Agnieszka Szadkowska
- Department of Pediatrics, Diabetology, Endocrinology and Nephrology, Medical University of Lodz, Lodz, Poland
| | - Arkadiusz Michalak
- Department of Pediatrics, Diabetology, Endocrinology and Nephrology, Medical University of Lodz, Lodz, Poland.,Department of Biostatistics and Translational Medicine, Medical University of Lodz, Lodz, Poland
| | - Aleksandra Łosiewicz
- Department of Pediatrics, Diabetology, Endocrinology and Nephrology, Medical University of Lodz, Lodz, Poland
| | - Hanna Kuśmierczyk
- Department of Pediatrics, Diabetology, Endocrinology and Nephrology, Medical University of Lodz, Lodz, Poland
| | - Kinga Krawczyk-Rusiecka
- Department of Endocrinology and Metabolic Diseases, Medical University of Lodz, Lodz, Poland
| | - Jędrzej Chrzanowski
- Department of Biostatistics and Translational Medicine, Medical University of Lodz, Lodz, Poland
| | - Andrzej Gawrecki
- Department of Internal Medicine and Diabetology, Poznan University of Medical Sciences, Poznan, Poland
| | | | - Wojciech Fendler
- Department of Biostatistics and Translational Medicine, Medical University of Lodz, Lodz, Poland
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Karunathilaka SR, Arnold MA, Small GW. Nocturnal Hypoglycemic Alarm Based on Near-Infrared Spectroscopy: In Vivo Studies with a Rat Animal Model. Anal Chem 2019; 91:1855-1862. [PMID: 30605302 DOI: 10.1021/acs.analchem.8b03437] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
A noninvasive method for detecting episodes of nocturnal hypoglycemia is demonstrated with in vivo measurements made with a rat animal model. Employing spectra collected from the near-infrared combination region of 4000-5000 cm-1, piecewise linear discriminant analysis (PLDA) is used to classify spectra into alarm and nonalarm data classes on the basis of whether or not they correspond to glucose concentrations below a user-defined hypoglycemic threshold. A reference spectrum and corresponding glucose concentration are acquired at the start of the monitoring period, and spectra are then collected continuously and converted to absorbance units relative to the initial reference spectrum. The resulting differential spectra correspond to differential glucose concentrations that reflect the differences in concentration between each spectrum and the reference. Given an alarm threshold (e.g., 3.0 mM), a database of calibration differential spectra can be partitioned into two groups containing spectra above and below the threshold. A classification model is then computed with PLDA. The resulting model can be applied to the differential spectra collected during the monitoring period in order to identify spectra whose corresponding glucose concentrations lie in the hypoglycemic range. In this work, the alarm algorithm was tested in two single-day studies performed with anesthetized rats. Glucose concentrations spanned the range of 1.6 to 13.5 mM (29 to 244 mg/dL). For both rats, the alarm algorithm performed well. On average, 87.5% of alarm events were correctly detected, and the occurrence of false alarms was 7.2%. False alarms were restricted to times when the glucose concentrations were very close to the alarm threshold rather than at random times, thus demonstrating the potential of the approach for practical use.
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Affiliation(s)
- Sanjeewa R Karunathilaka
- Department of Chemistry & Optical Science and Technology Center , University of Iowa , Iowa City , Iowa 52242 , United States
| | - Mark A Arnold
- Department of Chemistry & Optical Science and Technology Center , University of Iowa , Iowa City , Iowa 52242 , United States
| | - Gary W Small
- Department of Chemistry & Optical Science and Technology Center , University of Iowa , Iowa City , Iowa 52242 , United States
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Garg SK, Akturk HK. A New Era in Continuous Glucose Monitoring: Food and Drug Administration Creates a New Category of Factory-Calibrated Nonadjunctive, Interoperable Class II Medical Devices. Diabetes Technol Ther 2018; 20:391-394. [PMID: 29901411 DOI: 10.1089/dia.2018.0142] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Satish K Garg
- Barbara Davis Center for Diabetes, University of Colorado Denver , Aurora, Colorado
| | - H Kaan Akturk
- Barbara Davis Center for Diabetes, University of Colorado Denver , Aurora, Colorado
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Sinha M, McKeon KM, Parker S, Goergen LG, Zheng H, El-Khatib FH, Russell SJ. A Comparison of Time Delay in Three Continuous Glucose Monitors for Adolescents and Adults. J Diabetes Sci Technol 2017; 11:1132-1137. [PMID: 28459159 PMCID: PMC5951038 DOI: 10.1177/1932296817704443] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND The physiologic delay in glucose diffusion from the blood to the interstitial fluid and instrumental factors contribute to the delay between changes in plasma glucose (PG) and measurements made by continuous glucose monitors (CGMs). This study compared the duration of this delay for three CGMs. METHODS A total of 24 healthy adolescent and adult subjects with type 1 diabetes wore three CGM devices simultaneously for 48 hours: Dexcom G4 Platinum, Abbott Navigator, and Medtronic Enlite. The time delay between PG and CGM-estimated plasma glucose (CGMG) was estimated by comparing time-shifted CGMG with reference PG taken every 15 minutes. RESULTS The delay estimated by our approach was larger for the Navigator than for the G4 Platinum in adolescents (7.7 ± 1.1 versus 5.6 ± 0.9 min, P = .0396) and adults (10.9 ± 1.1 versus 8.1 ± 0.7 min, P = .0107). The delay was nominally longer for the Navigator than for the Enlite in both the adolescent (7.7 ± 1.1 versus 4.3 ± 1.0 min, P = .0728) and adult (10.9 ± 1.1 versus 8.3 ± 0.9 min, P = .111) populations, but these differences were not statistically significant. There was no difference in the delay between G4 Platinum and Enlite. Adolescents had shorter delays than adults for all three devices. There was a significant correlation between longer delay and increasing age for the G4 Platinum and Navigator. CONCLUSIONS There are differences in the estimated PG to CGMG time delays between CGM devices in the same subjects. The delay between PG and CGMG is smaller for adolescents than for adults. The PG-to-CGMG time delay is influenced by both instrument and host factors.
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Affiliation(s)
- Manasi Sinha
- Diabetes Unit and Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Manasi Sinha, MD, MPH, Massachusetts General Hospital Diabetes Research Center, 50 Staniford St, Ste 340, Boston MA 02114, USA.
| | | | - Savan Parker
- Diabetes Unit and Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Laura G. Goergen
- Diabetes Unit and Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Hui Zheng
- Diabetes Unit and Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Firas H. El-Khatib
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Steven J. Russell
- Diabetes Unit and Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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8
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Uduku C, Oliver N. Pharmacological aspects of closed loop insulin delivery for type 1 diabetes. Curr Opin Pharmacol 2017; 36:29-33. [DOI: 10.1016/j.coph.2017.07.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2017] [Revised: 07/14/2017] [Accepted: 07/20/2017] [Indexed: 12/11/2022]
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Abstract
The artificial pancreas (closed-loop system) addresses the unmet clinical need for improved glucose control whilst reducing the burden of diabetes self-care in type 1 diabetes. Glucose-responsive insulin delivery above and below a preset insulin amount informed by sensor glucose readings differentiates closed-loop systems from conventional, threshold-suspend and predictive-suspend insulin pump therapy. Insulin requirements in type 1 diabetes can vary between one-third-threefold on a daily basis. Closed-loop systems accommodate these variations and mitigate the risk of hypoglycaemia associated with tight glucose control. In this review we focus on the progress being made in the development and evaluation of closed-loop systems in outpatient settings. Randomised transitional studies have shown feasibility and efficacy of closed-loop systems under supervision or remote monitoring. Closed-loop application during free-living, unsupervised conditions by children, adolescents and adults compared with sensor-augmented pumps have shown improved glucose outcomes, reduced hypoglycaemia and positive user acceptance. Innovative approaches to enhance closed-loop performance are discussed and we also present the outlook and strategies used to ease clinical adoption of closed-loop systems.
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Affiliation(s)
- Hood Thabit
- Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge, Level 4, Institute of Metabolic Science, Box 289, Addenbrooke's Hospital, Hills Rd, Cambridge, CB2 0QQ, UK
- Department of Diabetes & Endocrinology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Roman Hovorka
- Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge, Level 4, Institute of Metabolic Science, Box 289, Addenbrooke's Hospital, Hills Rd, Cambridge, CB2 0QQ, UK.
- Department of Paediatrics, University of Cambridge, Cambridge, UK.
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Taylor M, Gregory R, Tomlins P, Jacob D, Hubble J, Sahota T. Closed-loop glycaemic control using an implantable artificial pancreas in diabetic domestic pig ( Sus scrofa domesticus ). Int J Pharm 2016; 500:371-8. [DOI: 10.1016/j.ijpharm.2015.12.024] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2015] [Revised: 11/30/2015] [Accepted: 12/09/2015] [Indexed: 01/30/2023]
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Caffarel-Salvador E, Brady AJ, Eltayib E, Meng T, Alonso-Vicente A, Gonzalez-Vazquez P, Torrisi BM, Vicente-Perez EM, Mooney K, Jones DS, Bell SEJ, McCoy CP, McCarthy HO, McElnay JC, Donnelly RF. Hydrogel-Forming Microneedle Arrays Allow Detection of Drugs and Glucose In Vivo: Potential for Use in Diagnosis and Therapeutic Drug Monitoring. PLoS One 2015; 10:e0145644. [PMID: 26717198 PMCID: PMC4699208 DOI: 10.1371/journal.pone.0145644] [Citation(s) in RCA: 100] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2015] [Accepted: 12/07/2015] [Indexed: 11/19/2022] Open
Abstract
We describe, for the first time the use of hydrogel-forming microneedle (MN) arrays for minimally-invasive extraction and quantification of drug substances and glucose from skin in vitro and in vivo. MN prepared from aqueous blends of hydrolysed poly(methyl-vinylether-co-maleic anhydride) (11.1% w/w) and poly(ethyleneglycol) 10,000 daltons (5.6% w/w) and crosslinked by esterification swelled upon skin insertion by uptake of fluid. Post-removal, theophylline and caffeine were extracted from MN and determined using HPLC, with glucose quantified using a proprietary kit. In vitro studies using excised neonatal porcine skin bathed on the underside by physiologically-relevant analyte concentrations showed rapid (5 min) analyte uptake. For example, mean concentrations of 0.16 μg/mL and 0.85 μg/mL, respectively, were detected for the lowest (5 μg/mL) and highest (35 μg/mL) Franz cell concentrations of theophylline after 5 min insertion. A mean concentration of 0.10 μg/mL was obtained by extraction of MN inserted for 5 min into skin bathed with 5 μg/mL caffeine, while the mean concentration obtained by extraction of MN inserted into skin bathed with 15 μg/mL caffeine was 0.33 μg/mL. The mean detected glucose concentration after 5 min insertion into skin bathed with 4 mmol/L was 19.46 nmol/L. The highest theophylline concentration detected following extraction from a hydrogel-forming MN inserted for 1 h into the skin of a rat dosed orally with 10 mg/kg was of 0.363 μg/mL, whilst a maximum concentration of 0.063 μg/mL was detected following extraction from a MN inserted for 1 h into the skin of a rat dosed with 5 mg/kg theophylline. In human volunteers, the highest mean concentration of caffeine detected using MN was 91.31 μg/mL over the period from 1 to 2 h post-consumption of 100 mg Proplus® tablets. The highest mean blood glucose level was 7.89 nmol/L detected 1 h following ingestion of 75 g of glucose, while the highest mean glucose concentration extracted from MN was 4.29 nmol/L, detected after 3 hours skin insertion in human volunteers. Whilst not directly correlated, concentrations extracted from MN were clearly indicative of trends in blood in both rats and human volunteers. This work strongly illustrates the potential of hydrogel-forming MN in minimally-invasive patient monitoring and diagnosis. Further studies are now ongoing to reduce clinical insertion times and develop mathematical algorithms enabling determination of blood levels directly from MN measurements.
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Affiliation(s)
- Ester Caffarel-Salvador
- School of Pharmacy, Queen’s University Belfast, 97 Lisburn Road, Belfast, BT97BL, United Kingdom
| | - Aaron J. Brady
- School of Pharmacy, Queen’s University Belfast, 97 Lisburn Road, Belfast, BT97BL, United Kingdom
| | - Eyman Eltayib
- School of Pharmacy, Queen’s University Belfast, 97 Lisburn Road, Belfast, BT97BL, United Kingdom
| | - Teng Meng
- School of Pharmacy, Queen’s University Belfast, 97 Lisburn Road, Belfast, BT97BL, United Kingdom
| | - Ana Alonso-Vicente
- School of Pharmacy, Queen’s University Belfast, 97 Lisburn Road, Belfast, BT97BL, United Kingdom
| | | | - Barbara M. Torrisi
- School of Pharmacy, Queen’s University Belfast, 97 Lisburn Road, Belfast, BT97BL, United Kingdom
| | - Eva Maria Vicente-Perez
- School of Pharmacy, Queen’s University Belfast, 97 Lisburn Road, Belfast, BT97BL, United Kingdom
| | - Karen Mooney
- School of Pharmacy, Queen’s University Belfast, 97 Lisburn Road, Belfast, BT97BL, United Kingdom
| | - David S. Jones
- School of Pharmacy, Queen’s University Belfast, 97 Lisburn Road, Belfast, BT97BL, United Kingdom
| | - Steven E. J. Bell
- School of Chemistry and Chemical Engineering, Queen’s University Belfast, Stranmillis Road, Belfast, BT9 5AG, United Kingdom
| | - Colin P. McCoy
- School of Pharmacy, Queen’s University Belfast, 97 Lisburn Road, Belfast, BT97BL, United Kingdom
| | - Helen O. McCarthy
- School of Pharmacy, Queen’s University Belfast, 97 Lisburn Road, Belfast, BT97BL, United Kingdom
| | - James C. McElnay
- School of Pharmacy, Queen’s University Belfast, 97 Lisburn Road, Belfast, BT97BL, United Kingdom
| | - Ryan F. Donnelly
- School of Pharmacy, Queen’s University Belfast, 97 Lisburn Road, Belfast, BT97BL, United Kingdom
- * E-mail:
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Cichosz SL, Johansen MD, Hejlesen O. Toward Big Data Analytics: Review of Predictive Models in Management of Diabetes and Its Complications. J Diabetes Sci Technol 2015; 10:27-34. [PMID: 26468133 PMCID: PMC4738225 DOI: 10.1177/1932296815611680] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Diabetes is one of the top priorities in medical science and health care management, and an abundance of data and information is available on these patients. Whether data stem from statistical models or complex pattern recognition models, they may be fused into predictive models that combine patient information and prognostic outcome results. Such knowledge could be used in clinical decision support, disease surveillance, and public health management to improve patient care. Our aim was to review the literature and give an introduction to predictive models in screening for and the management of prevalent short- and long-term complications in diabetes. Predictive models have been developed for management of diabetes and its complications, and the number of publications on such models has been growing over the past decade. Often multiple logistic or a similar linear regression is used for prediction model development, possibly owing to its transparent functionality. Ultimately, for prediction models to prove useful, they must demonstrate impact, namely, their use must generate better patient outcomes. Although extensive effort has been put in to building these predictive models, there is a remarkable scarcity of impact studies.
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Affiliation(s)
- Simon Lebech Cichosz
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | | | - Ole Hejlesen
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
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13
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Andrus LP, Unruh R, Wisniewski NA, McShane MJ. Characterization of Lactate Sensors Based on Lactate Oxidase and Palladium Benzoporphyrin Immobilized in Hydrogels. BIOSENSORS 2015; 5:398-416. [PMID: 26198251 PMCID: PMC4600164 DOI: 10.3390/bios5030398] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2015] [Revised: 06/16/2015] [Accepted: 06/24/2015] [Indexed: 12/18/2022]
Abstract
An optical biosensor for lactate detection is described. By encapsulating enzyme-phosphor sensing molecules within permeable hydrogel materials, lactate-sensitive emission lifetimes were achieved. The relative amount of monomer was varied to compare three homo- and co-polymer materials: poly(2-hydroxyethyl methacrylate) (pHEMA) and two copolymers of pHEMA and poly(acrylamide) (pAam). Diffusion analysis demonstrated the ability to control lactate transport by varying the hydrogel composition, while having a minimal effect on oxygen diffusion. Sensors displayed the desired dose-variable response to lactate challenges, highlighting the tunable, diffusion-controlled nature of the sensing platform. Short-term repeated exposure tests revealed enhanced stability for sensors comprising hydrogels with acrylamide additives; after an initial "break-in" period, signal retention was 100% for 15 repeated cycles. Finally, because this study describes the modification of a previously developed glucose sensor for lactate analysis, it demonstrates the potential for mix-and-match enzyme-phosphor-hydrogel sensing for use in future multi-analyte sensors.
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Affiliation(s)
- Liam P Andrus
- Department of Biomedical Engineering, 5045 Emerging Technologies Building, 3120 TAMU, Texas A&M University, College Station, TX 77843, USA.
| | - Rachel Unruh
- Department of Biomedical Engineering, 5045 Emerging Technologies Building, 3120 TAMU, Texas A&M University, College Station, TX 77843, USA.
| | | | - Michael J McShane
- Department of Biomedical Engineering, 5045 Emerging Technologies Building, 3120 TAMU, Texas A&M University, College Station, TX 77843, USA.
- Department of Materials Science and Engineering, 3003 TAMU, Texas A&M University, College Station, TX 77843, USA.
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14
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Thabit H, Hovorka R, Evans M. Artificial pancreas: the bridge to a cure for type 1 diabetes. ACTA ACUST UNITED AC 2015. [DOI: 10.1002/edn.207] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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15
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Nacht B, Larndorfer C, Sax S, Borisov SM, Hajnsek M, Sinner F, List-Kratochvil EJ, Klimant I. Integrated catheter system for continuous glucose measurement and simultaneous insulin infusion. Biosens Bioelectron 2015; 64:102-10. [DOI: 10.1016/j.bios.2014.08.012] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2014] [Revised: 07/27/2014] [Accepted: 08/02/2014] [Indexed: 02/02/2023]
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16
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Cichosz SL, Frystyk J, Tarnow L, Fleischer J. Combining information of autonomic modulation and CGM measurements enables prediction and improves detection of spontaneous hypoglycemic events. J Diabetes Sci Technol 2015; 9:132-7. [PMID: 25216731 PMCID: PMC4495539 DOI: 10.1177/1932296814549830] [Citation(s) in RCA: 27] [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: 11/16/2022]
Abstract
We have previously tested, in a laboratory setting, a novel algorithm that enables prediction of hypoglycemia. The algorithm integrates information of autonomic modulation, based on heart rate variability (HRV), and data based on a continuous glucose monitoring (CGM) device. Now, we investigate whether the algorithm is suitable for prediction of hypoglycemia and for improvement of hypoglycemic detection during normal daily activities. Twenty-one adults (13 men) with T1D prone to hypoglycemia were recruited and monitored with CGM and a Holter device while they performed normal daily activities. We used our developed algorithm (a pattern classification method) to predict spontaneous hypoglycemia based on CGM and HRV. We compared 3 different models; (i) a model containing raw data from the CGM device; (ii) a CGM* model containing data derived from the CGM device signal; and (iii) a CGM+HRV model-combining model (ii) with HRV data. A total of 12 hypoglycemic events (glucose levels < 3.9 mmol/L, 70 mg/dL) and 237 euglycemic measurements were included. For a 20-minute prediction, model (i) resulted in a ROC AUC of 0.69. If a high sensitivity of 100% was chosen, the corresponding specificity was 69%. (ii) The CGM* model yielded a ROC AUC of 0.92 with a corresponding sensitivity of 100% and specificity of 71%. (iii) The CGM+HRV model yielded a ROC AUC of 0.96 with a corresponding sensitivity of 100% and specificity of 91%. Data shows that adding information of autonomic modulation to CGM measurements enables prediction and improves the detection of hypoglycemia.
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Affiliation(s)
- Simon Lebech Cichosz
- Department of Endocrinology and Internal Medicine and Medical Research Laboratory, Aarhus University Hospital, Denmark Department of Health Science and Technology, Aalborg University, Denmark
| | - Jan Frystyk
- Department of Endocrinology and Internal Medicine and Medical Research Laboratory, Aarhus University Hospital, Denmark
| | - Lise Tarnow
- Steno Diabetes Center, Department of Clinical Epidemiology, Aarhus University and Nordsjaellands Hospitaler Hilleroed, Denmark
| | - Jesper Fleischer
- Department of Endocrinology and Internal Medicine and Medical Research Laboratory, Aarhus University Hospital, Denmark
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17
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Shah VN, Shoskes A, Tawfik B, Garg SK. Closed-loop system in the management of diabetes: past, present, and future. Diabetes Technol Ther 2014; 16:477-90. [PMID: 25072271 DOI: 10.1089/dia.2014.0193] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Intensive insulin therapy (IIT) has been shown to reduce micro- and macrovascular complications in patients with type 1 diabetes mellitus (T1DM). However, IIT is associated with a significant increase in severe hypoglycemic events, resulting in increased morbidity and mortality. Optimization of glycemic control without hypoglycemia (especially nocturnal) should be the next major goal for subjects on insulin treatment. The use of insulin pumps along with continuous glucose monitors (CGMs) has made it easier but requires significant resources and patient education. Research is ongoing to close the loop by integrating the pump and the CGM using different algorithms. The currently available closed-loop system is the threshold suspend. Steps needed to achieve a near-perfect closed-loop are (1) a control-to-range system that will reduce the incidence and/or severity of hyper- and/or hypoglycemia by adjusting the insulin dose and (2) a control-to-target system, a fully automated or hybrid system that sets target glucose levels to individual needs and maintains glucose levels throughout the day using insulin (unihormonal) alone or with other hormones such as glucagon or possibly pramlintide (bihormonal). Future research is also focusing on better insulin delivery devices (pumps), more accurate CGMs, better predictive algorithms, and ultra-rapid-acting insulin analogs to make the closed-loop system as physiological as possible.
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Affiliation(s)
- Viral N Shah
- 1 Barbara Davis Center for Diabetes, University of Colorado Denver , Aurora, Colorado
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18
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Soto R, Privett BJ, Schoenfisch MH. In vivo analytical performance of nitric oxide-releasing glucose biosensors. Anal Chem 2014; 86:7141-9. [PMID: 24984031 PMCID: PMC4116185 DOI: 10.1021/ac5017425] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2014] [Accepted: 06/20/2014] [Indexed: 01/05/2023]
Abstract
The in vivo analytical performance of percutaneously implanted nitric oxide (NO)-releasing amperometric glucose biosensors was evaluated in swine for 10 d. Needle-type glucose biosensors were functionalized with NO-releasing polyurethane coatings designed to release similar total amounts of NO (3.1 μmol cm(-2)) for rapid (16.0 ± 4.4 h) or slower (>74.6 ± 16.6 h) durations and remain functional as outer glucose sensor membranes. Relative to controls, NO-releasing sensors were characterized with improved numerical accuracy on days 1 and 3. Furthermore, the clinical accuracy and sensitivity of rapid NO-releasing sensors were superior to control and slower NO-releasing sensors at both 1 and 3 d implantation. In contrast, the slower, extended, NO-releasing sensors were characterized by shorter sensor lag times (<4.2 min) in response to intravenous glucose tolerance tests versus burst NO-releasing and control sensors (>5.8 min) at 3, 7, and 10 d. Collectively, these results highlight the potential for NO release to enhance the analytical utility of in vivo glucose biosensors. Initial results also suggest that this analytical performance benefit is dependent on the NO-release duration.
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Affiliation(s)
- Robert
J. Soto
- Department
of Chemistry, University of North Carolina
at Chapel Hill, CB 3290, Chapel Hill, North Carolina 27599, United States
| | - Benjamin J. Privett
- Novan
Therapeutics, 4222 Emperor
Boulevard, Suite 200, Durham, North Carolina 27703, United States
| | - Mark H. Schoenfisch
- Department
of Chemistry, University of North Carolina
at Chapel Hill, CB 3290, Chapel Hill, North Carolina 27599, United States
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Rapsang AG, Shyam DC. Blood sugar control in the intensive care unit: time to relook. SOUTHERN AFRICAN JOURNAL OF ANAESTHESIA AND ANALGESIA 2014. [DOI: 10.1080/22201181.2015.959363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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20
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Wernerman J, Desaive T, Finfer S, Foubert L, Furnary A, Holzinger U, Hovorka R, Joseph J, Kosiborod M, Krinsley J, Mesotten D, Nasraway S, Rooyackers O, Schultz MJ, Van Herpe T, Vigersky RA, Preiser JC. Continuous glucose control in the ICU: report of a 2013 round table meeting. Crit Care 2014; 18:226. [PMID: 25041718 PMCID: PMC4078395 DOI: 10.1186/cc13921] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
Achieving adequate glucose control in critically ill patients is a complex but important part of optimal patient management. Until relatively recently, intermittent measurements of blood glucose have been the only means of monitoring blood glucose levels. With growing interest in the possible beneficial effects of continuous over intermittent monitoring and the development of several continuous glucose monitoring (CGM) systems, a round table conference was convened to discuss and, where possible, reach consensus on the various aspects related to glucose monitoring and management using these systems. In this report, we discuss the advantages and limitations of the different types of devices available, the potential advantages of continuous over intermittent testing, the relative importance of trend and point accuracy, the standards necessary for reporting results in clinical trials and for recognition by official bodies, and the changes that may be needed in current glucose management protocols as a result of a move towards increased use of CGM. We close with a list of the research priorities in this field, which will be necessary if CGM is to become a routine part of daily practice in the management of critically ill patients.
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Affiliation(s)
- Jan Wernerman
- Department of Anesthesiology and Intensive Care Medicine, K32, Karolinska University Hospital, Stockholm, Huddinge 14186, Sweden
| | - Thomas Desaive
- GIGA - Cardiovascular Sciences, University of Liege, Institute of Physics, B5, Allee du 6 aout, 17, Liege 4000, Belgium
| | - Simon Finfer
- The George Institute for Global Health and Royal North Shore Hospital, University of Sydney, St Leonards, Sydney, NSW 2065, Australia
| | - Luc Foubert
- Department of Anesthesia and Intensive Care Medicine, OLV Clinic, Aalst 9300, Belgium
| | - Anthony Furnary
- Starr-Wood Cardiac Group, 9155 SW Barnes Road, Portland, OR 97225-6629, USA
| | - Ulrike Holzinger
- Department of Medicine III - Division of Gastroenterology and Hepatology, Medical University of Vienna, Waehringer Guertel 18-20, Vienna 1090, Austria
| | - Roman Hovorka
- University of Cambridge Metabolic Research Laboratories, Level 4, Wellcome trust MRC Institute of Metabolic Science, Box 289, Addenbrooke’s Hospital, Hills Road, Cambridge CB2 0QQ, UK
| | - Jeffrey Joseph
- Jefferson Artificial Pancreas Center and Anesthesiology Program for Translational Research, Department of Anesthesiology, Jefferson Medical College of Thomas Jefferson University, 1020 Walnut Street, Philadelphia, PA 19107, USA
| | - Mikhail Kosiborod
- Saint-Luke’s Mid America Heart Institute, University of Missouri - Kansas City, 4401 Wornall Road, Kansas City, MO 64111, USA
| | - James Krinsley
- Division of Critical Care, Stamford Hospital and Columbia University College of Physicians and Surgeons, 30 Shelburne Road, Stamford, CT 06904, USA
| | - Dieter Mesotten
- Department of Intensive Care Medicine, University Hospitals Leuven, Herestraat 49, Leuven B-3000, Belgium
| | - Stanley Nasraway
- Surgical Intensive Care Units, Tufts Medical Center, 800 Washington Street, NEMC 4360, Boston, MA 02111, USA
| | - Olav Rooyackers
- Anesthesiology and Intensive Care Clinic, Karolinska Institute and University Hospital, Huddinge 14186, Sweden
| | - Marcus J Schultz
- Department of Intensive Care Medicine, Academic Medical Center at the University of Amsterdam, Meibergdreef 9, Amsterdam 1105 AZ, The Netherlands
| | - Tom Van Herpe
- Department of Intensive Care Medicine, University Hospitals Leuven, Herestraat 49, Leuven B-3000, Belgium
- Department of Electrical Engineering (STADIUS) - iMinds Future Health Department, Katholieke Universiteit Leuven, Leuven, Heverlee B-3001, Belgium
| | - Robert A Vigersky
- Diabetes Institute, Walter Reed National Military Medical Center, Bethesda, MD 20895, USA
| | - Jean-Charles Preiser
- Department of Intensive Care, Erasme Hospital, Université libre de Bruxelles, 808 route de Lennik, Brussels 1070, Belgium
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21
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Jina A, Tierney MJ, Tamada JA, McGill S, Desai S, Chua B, Chang A, Christiansen M. Design, development, and evaluation of a novel microneedle array-based continuous glucose monitor. J Diabetes Sci Technol 2014; 8:483-7. [PMID: 24876610 PMCID: PMC4455438 DOI: 10.1177/1932296814526191] [Citation(s) in RCA: 103] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The development of accurate, minimally invasive continuous glucose monitoring (CGM) devices has been the subject of much work by several groups, as it is believed that a less invasive and more user-friendly device will result in greater adoption of CGM by persons with insulin-dependent diabetes. This article presents the results of preliminary clinical studies in subjects with diabetes of a novel prototype microneedle-based continuous glucose monitor. In this device, an array of tiny hollow microneedles is applied into the epidermis from where glucose in interstitial fluid (ISF) is transported via passive diffusion to an amperometric glucose sensor external to the body. Comparison of 1396 paired device glucose measurements and fingerstick blood glucose readings for up to 72-hour wear in 10 diabetic subjects shows the device to be accurate and well tolerated by the subjects. Overall mean absolute relative difference (MARD) is 15% with 98.4% of paired points in the A+B region of the Clarke error grid. The prototype device has demonstrated clinically accurate glucose readings over 72 hours, the first time a microneedle-based device has achieved such performance.
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Affiliation(s)
| | | | | | | | | | | | - Anna Chang
- John Muir Physician Network Clinical Research Center, Concord, CA USA
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22
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El-Khatib FH, Russell SJ, Magyar KL, Sinha M, McKeon K, Nathan DM, Damiano ER. Autonomous and continuous adaptation of a bihormonal bionic pancreas in adults and adolescents with type 1 diabetes. J Clin Endocrinol Metab 2014; 99:1701-11. [PMID: 24483160 PMCID: PMC4010702 DOI: 10.1210/jc.2013-4151] [Citation(s) in RCA: 75] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
CONTEXT A challenge for automated glycemic control in type 1 diabetes (T1D) is the large variation in insulin needs between individuals and within individuals at different times in their lives. OBJECTIVES The objectives of the study was to test the ability of a third-generation bihormonal bionic pancreas algorithm, initialized with only subject weight; to adapt automatically to the different insulin needs of adults and adolescents; and to evaluate the impact of optional, automatically adaptive meal-priming boluses. DESIGN This was a randomized controlled trial. SETTING The study was conducted at an inpatient clinical research center. PATIENTS Twelve adults and 12 adolescents with T1D participated in the study. INTERVENTIONS Subjects in each age group were randomized to automated glycemic control for 48 hours with or without automatically adaptive meal-priming boluses. MAIN OUTCOME MEASURES Mean plasma glucose (PG), time with PG less than 60 mg/dL, and insulin total daily dose were measured. RESULTS The 48-hour mean PG values with and without adaptive meal-priming boluses were 132 ± 9 vs 146 ± 9 mg/dL (P = .03) in adults and 162 ± 6 vs 175 ± 9 mg/dL (P = .01) in adolescents. Adaptive meal-priming boluses improved mean PG without increasing time spent with PG less than 60 mg/dL: 1.4% vs 2.3% (P = .6) in adults and 0.1% vs 0.1% (P = 1.0) in adolescents. Large increases in adaptive meal-priming boluses and shifts in the timing and size of automatic insulin doses occurred in adolescents. Much less adaptation occurred in adults. There was nearly a 4-fold variation in the total daily insulin dose across all cohorts (0.36-1.41 U/kg · d). CONCLUSIONS A single control algorithm, initialized only with subject weight, can quickly adapt to regulate glycemia in patients with TID and highly variable insulin requirements.
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23
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Del Favero S, Facchinetti A, Sparacino G, Cobelli C. Improving Accuracy and Precision of Glucose Sensor Profiles: Retrospective Fitting by Constrained Deconvolution. IEEE Trans Biomed Eng 2014; 61:1044-53. [DOI: 10.1109/tbme.2013.2293531] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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24
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Leelarathna L, English SW, Thabit H, Caldwell K, Allen JM, Kumareswaran K, Wilinska ME, Nodale M, Haidar A, Evans ML, Burnstein R, Hovorka R. Accuracy of subcutaneous continuous glucose monitoring in critically ill adults: improved sensor performance with enhanced calibrations. Diabetes Technol Ther 2014; 16:97-101. [PMID: 24180327 PMCID: PMC3894676 DOI: 10.1089/dia.2013.0221] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
OBJECTIVE Accurate real-time continuous glucose measurements may improve glucose control in the critical care unit. We evaluated the accuracy of the FreeStyle(®) Navigator(®) (Abbott Diabetes Care, Alameda, CA) subcutaneous continuous glucose monitoring (CGM) device in critically ill adults using two methods of calibration. SUBJECTS AND METHODS In a randomized trial, paired CGM and reference glucose (hourly arterial blood glucose [ABG]) were collected over a 48-h period from 24 adults with critical illness (mean±SD age, 60±14 years; mean±SD body mass index, 29.6±9.3 kg/m(2); mean±SD Acute Physiology and Chronic Health Evaluation score, 12±4 [range, 6-19]) and hyperglycemia. In 12 subjects, the CGM device was calibrated at variable intervals of 1-6 h using ABG. In the other 12 subjects, the sensor was calibrated according to the manufacturer's instructions (1, 2, 10, and 24 h) using arterial blood and the built-in point-of-care glucometer. RESULTS In total, 1,060 CGM-ABG pairs were analyzed over the glucose range from 4.3 to 18.8 mmol/L. Using enhanced calibration median (interquartile range) every 169 (122-213) min, the absolute relative deviation was lower (7.0% [3.5, 13.0] vs. 12.8% [6.3, 21.8], P<0.001), and the percentage of points in the Clarke error grid Zone A was higher (87.8% vs. 70.2%). CONCLUSIONS Accuracy of the Navigator CGM device during critical illness was comparable to that observed in non-critical care settings. Further significant improvements in accuracy may be obtained by frequent calibrations with ABG measurements.
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Affiliation(s)
- Lalantha Leelarathna
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Shane W. English
- Neurosciences Critical Care Unit, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Hood Thabit
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Karen Caldwell
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Janet M. Allen
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Kavita Kumareswaran
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Malgorzata E. Wilinska
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Marianna Nodale
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Ahmad Haidar
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Mark L. Evans
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Rowan Burnstein
- Neurosciences Critical Care Unit, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Roman Hovorka
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
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Mahmoudi Z, Dencker Johansen M, Christiansen JS, Hejlesen OK. A multistep algorithm for processing and calibration of microdialysis continuous glucose monitoring data. Diabetes Technol Ther 2013; 15:825-35. [PMID: 23944955 DOI: 10.1089/dia.2013.0041] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND The deviation of continuous subcutaneous glucose monitoring (CGM) data from reference blood glucose measurements is substantial, and adequate signal processing is required to reduce the discrepancy between subcutaneous glucose and blood glucose values. The purpose of this study was to develop a multistep algorithm for the processing and calibration of continuous subcutaneous glucose monitoring data with high accuracy and short delay. Algorithm PRESENTATION The algorithm comprises three steps: rate-limiting filtering, selective smoothing, and robust calibration. Initially, the algorithm detects nonphysiological glucose rate-of-change and corrects it with a weighted local polynomial. Noisy signal parts that require smoothing are then detected based on zero crossing count of the sensor signal first-order differences, and an exponentially weighted moving average smooths the noisy parts of the signal afterward. Finally, calibration is performed using a first-order polynomial as the conversion function, with coefficients being estimated using robust regression with a bi-square weight function. ALGORITHM PERFORMANCE: The performance of the algorithm was evaluated on 16 patients with type 1 diabetes mellitus. To compare the algorithm with state-of-the-art CGM data denoising and calibration, the rate-limiting filter and selective smoothing were replaced with an adaptive Kalman filter, and the calibration method was replaced with the calibration algorithm presented in one of the Medtronic (Northridge, CA) CGM patents. The median (mean) of the absolute relative deviation (ARD) of the sensor glucose values processed by the newly developed algorithm from capillary reference blood glucose measurements was 14.8% (22.6%), 10.6% (14.6%), and 8.9% (11.7%) in hypoglycemia, euglycemia, and hyperglycemia, respectively, whereas for the alternative algorithm, the median (mean) was 22.2% (26.9%), 12.1% (15.9%), and 8.8 (11.3%), respectively. The median (mean) ARD in all ranges was 10.3% (14.7%) for the new algorithm and 11.5% (15.8%) for the alternative algorithm. The new algorithm had an average delay of 2.1 min across the patients, and the alternative algorithm had an average delay of 2.9 min. CONCLUSIONS The presented algorithm may increase the accuracy of CGM data.
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Affiliation(s)
- Zeinab Mahmoudi
- 1 Department of Health Science and Technology, Aalborg University , Aalborg, Denmark
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Leelarathna L, Nodale M, Allen JM, Elleri D, Kumareswaran K, Haidar A, Caldwell K, Wilinska ME, Acerini CL, Evans ML, Murphy HR, Dunger DB, Hovorka R. Evaluating the accuracy and large inaccuracy of two continuous glucose monitoring systems. Diabetes Technol Ther 2013; 15:143-9. [PMID: 23256605 PMCID: PMC3558677 DOI: 10.1089/dia.2012.0245] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
OBJECTIVE This study evaluated the accuracy and large inaccuracy of the Freestyle Navigator (FSN) (Abbott Diabetes Care, Alameda, CA) and Dexcom SEVEN PLUS (DSP) (Dexcom, Inc., San Diego, CA) continuous glucose monitoring (CGM) systems during closed-loop studies. RESEARCH DESIGN AND METHODS Paired CGM and plasma glucose values (7,182 data pairs) were collected, every 15-60 min, from 32 adults (36.2±9.3 years) and 20 adolescents (15.3±1.5 years) with type 1 diabetes who participated in closed-loop studies. Levels 1, 2, and 3 of large sensor error with increasing severity were defined according to absolute relative deviation greater than or equal to ±40%, ±50%, and ±60% at a reference glucose level of ≥6 mmol/L or absolute deviation greater than or equal to ±2.4 mmol/L,±3.0 mmol/L, and ±3.6 mmol/L at a reference glucose level of <6 mmol/L. RESULTS Median absolute relative deviation was 9.9% for FSN and 12.6% for DSP. Proportions of data points in Zones A and B of Clarke error grid analysis were similar (96.4% for FSN vs. 97.8% for DSP). Large sensor over-reading, which increases risk of insulin over-delivery and hypoglycemia, occurred two- to threefold more frequently with DSP than FSN (once every 2.5, 4.6, and 10.7 days of FSN use vs. 1.2, 2.0, and 3.7 days of DSP use for Level 1-3 errors, respectively). At levels 2 and 3, large sensor errors lasting 1 h or longer were absent with FSN but persisted with DSP. CONCLUSIONS FSN and DSP differ substantially in the frequency and duration of large inaccuracy despite only modest differences in conventional measures of numerical and clinical accuracy. Further evaluations are required to confirm that FSN is more suitable for integration into closed-loop delivery systems.
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Affiliation(s)
- Lalantha Leelarathna
- Metabolic Research Laboratories, Institute of Metabolic Science, Cambridge, United Kingdom
| | - Marianna Nodale
- Metabolic Research Laboratories, Institute of Metabolic Science, Cambridge, United Kingdom
| | - Janet M. Allen
- Metabolic Research Laboratories, Institute of Metabolic Science, Cambridge, United Kingdom
| | - Daniela Elleri
- Metabolic Research Laboratories, Institute of Metabolic Science, Cambridge, United Kingdom
- Department of Paediatrics, University of Cambridge, Cambridge, United Kingdom
| | - Kavita Kumareswaran
- Metabolic Research Laboratories, Institute of Metabolic Science, Cambridge, United Kingdom
| | - Ahmad Haidar
- Metabolic Research Laboratories, Institute of Metabolic Science, Cambridge, United Kingdom
| | - Karen Caldwell
- Metabolic Research Laboratories, Institute of Metabolic Science, Cambridge, United Kingdom
| | - Malgorzata E. Wilinska
- Metabolic Research Laboratories, Institute of Metabolic Science, Cambridge, United Kingdom
- Department of Paediatrics, University of Cambridge, Cambridge, United Kingdom
| | - Carlo L. Acerini
- Metabolic Research Laboratories, Institute of Metabolic Science, Cambridge, United Kingdom
- Department of Paediatrics, University of Cambridge, Cambridge, United Kingdom
| | - Mark L. Evans
- Metabolic Research Laboratories, Institute of Metabolic Science, Cambridge, United Kingdom
| | - Helen R. Murphy
- Metabolic Research Laboratories, Institute of Metabolic Science, Cambridge, United Kingdom
| | - David B. Dunger
- Metabolic Research Laboratories, Institute of Metabolic Science, Cambridge, United Kingdom
- Department of Paediatrics, University of Cambridge, Cambridge, United Kingdom
| | - Roman Hovorka
- Metabolic Research Laboratories, Institute of Metabolic Science, Cambridge, United Kingdom
- Department of Paediatrics, University of Cambridge, Cambridge, United Kingdom
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Barry Keenan D, Mastrototaro JJ, Weinzimer SA, Steil GM. Interstitial fluid glucose time-lag correction for real-time continuous glucose monitoring. Biomed Signal Process Control 2013. [DOI: 10.1016/j.bspc.2012.05.007] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Hovorka R, Nodale M, Haidar A, Wilinska ME. Assessing performance of closed-loop insulin delivery systems by continuous glucose monitoring: drawbacks and way forward. Diabetes Technol Ther 2013; 15:4-12. [PMID: 23046396 PMCID: PMC3540898 DOI: 10.1089/dia.2012.0185] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
BACKGROUND We investigated whether continuous glucose monitoring (CGM) levels can accurately assess glycemic control while directing closed-loop insulin delivery. SUBJECTS AND METHODS Data were analyzed retrospectively from 33 subjects with type 1 diabetes who underwent closed-loop and conventional pump therapy on two separate nights. Glycemic control was evaluated by reference plasma glucose and contrasted against three methods based on Navigator (Abbott Diabetes Care, Alameda, CA) CGM levels. RESULTS Glucose mean and variability were estimated by unmodified CGM levels with acceptable clinical accuracy. Time when glucose was in target range was overestimated by CGM during closed-loop nights (CGM vs. plasma glucose median [interquartile range], 86% [65-97%] vs. 75% [59-91%]; P=0.04) but not during conventional pump therapy (57% [32-72%] vs. 51% [29-68%]; P=0.82) providing comparable treatment effect (mean [SD], 28% [29%] vs. 23% [21%]; P=0.11). Using the CGM measurement error of 15% derived from plasma glucose-CGM pairs (n=4,254), stochastic interpretation of CGM gave unbiased estimate of time in target during both closed-loop (79% [62-86%] vs. 75% [59-91%]; P=0.24) and conventional pump therapy (54% [33-66%] vs. 51% [29-68%]; P=0.44). Treatment effect (23% [24%] vs. 23% [21%]; P=0.96) and time below target were accurately estimated by stochastic CGM. Recalibrating CGM using reference plasma glucose values taken at the start and end of overnight closed-loop was not superior to stochastic CGM. CONCLUSIONS CGM is acceptable to estimate glucose mean and variability, but without adjustment it may overestimate benefit of closed-loop. Stochastic CGM provided unbiased estimate of time when glucose is in target and below target and may be acceptable for assessment of closed-loop in the outpatient setting.
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Affiliation(s)
- Roman Hovorka
- Metabolic Research Laboratories, Institute of Metabolic Science, Cambridge, United Kingdom.
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Leelarathna L, English SW, Thabit H, Caldwell K, Allen JM, Kumareswaran K, Wilinska ME, Nodale M, Mangat J, Evans ML, Burnstein R, Hovorka R. Feasibility of fully automated closed-loop glucose control using continuous subcutaneous glucose measurements in critical illness: a randomized controlled trial. Crit Care 2013; 17:R159. [PMID: 23883613 PMCID: PMC4056260 DOI: 10.1186/cc12838] [Citation(s) in RCA: 78] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2013] [Accepted: 07/24/2013] [Indexed: 02/07/2023] Open
Abstract
INTRODUCTION Closed-loop (CL) systems modulate insulin delivery according to glucose levels without nurse input. In a prospective randomized controlled trial, we evaluated the feasibility of an automated closed-loop approach based on subcutaneous glucose measurements in comparison with a local sliding-scale insulin-therapy protocol. METHODS Twenty-four critically ill adults (predominantly trauma and neuroscience patients) with hyperglycemia (glucose, ≥10 mM) or already receiving insulin therapy, were randomized to receive either fully automated closed-loop therapy (model predictive control algorithm directing insulin and 20% dextrose infusion based on FreeStyle Navigator continuous subcutaneous glucose values, n = 12) or a local protocol (n = 12) with intravenous sliding-scale insulin, over a 48-hour period. The primary end point was percentage of time when arterial blood glucose was between 6.0 and 8.0 mM. RESULTS The time when glucose was in the target range was significantly increased during closed-loop therapy (54.3% (44.1 to 72.8) versus 18.5% (0.1 to 39.9), P = 0.001; median (interquartile range)), and so was time in wider targets, 5.6 to 10.0 mM and 4.0 to 10.0 mM (P ≤ 0.002), reflecting a reduced glucose exposure >8 and >10 mM (P ≤ 0.002). Mean glucose was significantly lower during CL (7.8 (7.4 to 8.2) versus 9.1 (8.3 to 13.0] mM; P = 0.001) without hypoglycemia (<4 mM) during either therapy. CONCLUSIONS Fully automated closed-loop control based on subcutaneous glucose measurements is feasible and may provide efficacious and hypoglycemia-free glucose control in critically ill adults. TRIAL REGISTRATION ClinicalTrials.gov Identifier, NCT01440842.
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Affiliation(s)
- Lalantha Leelarathna
- Wellcome Trust-MRC Institute of Metabolic Science, Metabolic Research Laboratories, University of Cambridge, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 0QQ, UK
| | - Shane W English
- Neurosciences Critical Care Unit, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 0QQ, UK
| | - Hood Thabit
- Wellcome Trust-MRC Institute of Metabolic Science, Metabolic Research Laboratories, University of Cambridge, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 0QQ, UK
| | - Karen Caldwell
- Wellcome Trust-MRC Institute of Metabolic Science, Metabolic Research Laboratories, University of Cambridge, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 0QQ, UK
| | - Janet M Allen
- Wellcome Trust-MRC Institute of Metabolic Science, Metabolic Research Laboratories, University of Cambridge, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 0QQ, UK
| | - Kavita Kumareswaran
- Wellcome Trust-MRC Institute of Metabolic Science, Metabolic Research Laboratories, University of Cambridge, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 0QQ, UK
| | - Malgorzata E Wilinska
- Wellcome Trust-MRC Institute of Metabolic Science, Metabolic Research Laboratories, University of Cambridge, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 0QQ, UK
| | - Marianna Nodale
- Wellcome Trust-MRC Institute of Metabolic Science, Metabolic Research Laboratories, University of Cambridge, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 0QQ, UK
| | - Jasdip Mangat
- Wellcome Trust-MRC Institute of Metabolic Science, Metabolic Research Laboratories, University of Cambridge, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 0QQ, UK
| | - Mark L Evans
- Wellcome Trust-MRC Institute of Metabolic Science, Metabolic Research Laboratories, University of Cambridge, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 0QQ, UK
| | - Rowan Burnstein
- Neurosciences Critical Care Unit, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 0QQ, UK
| | - Roman Hovorka
- Wellcome Trust-MRC Institute of Metabolic Science, Metabolic Research Laboratories, University of Cambridge, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 0QQ, UK
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Weinstock RS, Bristol S, Armenia A, Gesswein AC, Bequette BW, Willis JP. Pilot study of a prototype minimally invasive intradermal continuous glucose monitor. J Diabetes Sci Technol 2012; 6:1454-63. [PMID: 23294793 PMCID: PMC3570888 DOI: 10.1177/193229681200600627] [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: 11/17/2022]
Abstract
INTRODUCTION The purpose of this study was to assess point accuracy, rate-of-change accuracy, and safety of a prototype, minimally invasive continuous glucose monitoring (CGM) device over a 12 h in-clinic study. The CGM system consisted of a wireless electronics module with a disposable glucose sensor attached to the bottom. The electronics module was affixed to the abdomen using an adhesive pad on the bottom of the disposable sensor housing. METHODS Two CGM sensors were inserted into the abdominal tissue (left and right) of 15 adults aged 26-67 years, 5 with normoglycemia, 5 with type 1 diabetes, and 5 with type 2 diabetes. Over a 12 h period, each participant was fed three meals. Reference venous blood samples were drawn at periodic intervals (12.4 ± 5.3 min), and glucose was measured at the bedside using a laboratory reference method. For each participant, a single plasma equivalent glucose concentration was used for retrospective sensor calibration. RESULTS A total of 1082 paired data points were obtained from 15 subjects and 25 of 30 sensors. Statistical analysis yielded a mean absolute relative difference of 12.6% and a mean absolute difference of 16.0 mg/dl. Continuous glucose error grid analysis showed the combined point and rate-of-change accuracy was 97.4% in zone A and 1.5% in zone B (98.9% A+B), with 1.1% erroneous readings. CONCLUSIONS The prototype CGM system provided clinically accurate results 98.9% of the time and was well tolerated by participants, with little or no pain and no adverse events.
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Affiliation(s)
- Ruth S. Weinstock
- Department of Medicine, State University of New York Upstate Medical University, Syracuse, New York
| | - Suzan Bristol
- Department of Medicine, State University of New York Upstate Medical University, Syracuse, New York
| | | | | | - B. Wayne Bequette
- Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, New York
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Signal M, Le Compte A, Harris DL, Weston PJ, Harding JE, Chase JG. Impact of retrospective calibration algorithms on hypoglycemia detection in newborn infants using continuous glucose monitoring. Diabetes Technol Ther 2012; 14:883-90. [PMID: 22856622 PMCID: PMC3459024 DOI: 10.1089/dia.2012.0111] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
BACKGROUND Neonatal hypoglycemia is common and may cause serious brain injury. Diagnosis is by blood glucose (BG) measurements, often taken several hours apart. Continuous glucose monitoring (CGM) could improve hypoglycemia detection, while reducing the number of BG measurements. Calibration algorithms convert sensor signals into CGM output. Thus, these algorithms directly affect measures used to quantify hypoglycemia. This study was designed to quantify the effects of recalibration and filtering of CGM data on measures of hypoglycemia (BG <2.6 mmol/L) in neonates. SUBJECTS AND METHODS CGM data from 50 infants were recalibrated using an algorithm that explicitly recognized the high-accuracy BG measurements available in this study. CGM data were analyzed as (1) original CGM output, (2) recalibrated CGM output, (3) recalibrated CGM output with postcalibration median filtering, and (4) recalibrated CGM output with precalibration median filtering. Hypoglycemia was classified by number of episodes, duration, severity, and hypoglycemic index. RESULTS Recalibration increased the number of hypoglycemic events (from 161 to 193), hypoglycemia duration (from 2.2% to 2.6%), and hypoglycemic index (from 4.9 to 7.1 μmol/L). Median filtering postrecalibration reduced hypoglycemic events from 193 to 131, with little change in duration (from 2.6% to 2.5%) and hypoglycemic index (from 7.1 to 6.9 μmol/L). Median filtering prerecalibration resulted in 146 hypoglycemic events, a total duration of hypoglycemia of 2.6%, and a hypoglycemic index of 6.8 μmol/L. CONCLUSIONS Hypoglycemia metrics, especially counting events, are heavily dependent on CGM calibration BG error, and the calibration algorithm. CGM devices tended to read high at lower levels, so when high accuracy calibration measurements are available it may be more appropriate to recalibrate the data.
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Affiliation(s)
- Matthew Signal
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Aaron Le Compte
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Deborah L. Harris
- Liggins Institute, University of Auckland, Auckland, New Zealand
- Newborn Intensive Care Unit, Waikato District Health Board, Hamilton, New Zealand
| | - Philip J. Weston
- Newborn Intensive Care Unit, Waikato District Health Board, Hamilton, New Zealand
| | - Jane E. Harding
- Liggins Institute, University of Auckland, Auckland, New Zealand
| | - J. Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
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Abstract
Advances in diabetes technology have led to significant improvements in the quality of life and care received by individuals with diabetes. Despite this, achieving tight glycemic control through intensive insulin therapy and modern insulin regimens is challenging because of the barrier of hypoglycemia, the most feared complication of insulin therapy as reported by patients, caregivers, and physicians. This article outlines the individual components of the closed-loop system together with the existing clinical evidence. The artificial pancreas prototypes currently used in clinical studies are reviewed as well as obstacles and limitations facing the technology.
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Affiliation(s)
- Hood Thabit
- Clinical Research Fellow, Institute of Metabolic Science, University of Cambridge, Addenbrookes Hospital, Cambridge, United Kingdom
| | - Roman Hovorka
- Principal Research Associate, Institute of Metabolic Science, University of Cambridge, Addenbrookes Hospital, Cambridge, United Kingdom
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Keenan DB, Grosman B, Clark HW, Roy A, Weinzimer SA, Shah RV, Mastrototaro JJ. Continuous glucose monitoring considerations for the development of a closed-loop artificial pancreas system. J Diabetes Sci Technol 2011; 5:1327-36. [PMID: 22226249 PMCID: PMC3262698 DOI: 10.1177/193229681100500603] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND Commercialization of a closed-loop artificial pancreas system that employs continuous subcutaneous insulin infusion and interstitial fluid glucose sensing has been encumbered by state-of-the-art technology. Continuous glucose monitoring (CGM) devices with improved accuracy could significantly advance development efforts. However, the current accuracy of CGM devices might be adequate for closed-loop control. METHODS The influence that known CGM limitations have on closed-loop control was investigated by integrating sources of sensor inaccuracy with the University of Virginia Padova Diabetes simulator. Non-glucose interference, physiological time lag and sensor error measurements, selected from 83 Enlite™ glucose sensor recordings with the Guardian® REAL-Time system, were used to modulate simulated plasma glucose signals. The effect of sensor accuracy on closed-loop controller performance was evaluated in silico, and contrasted with closed-loop clinical studies during the nocturnal control period. RESULTS Based on n = 2472 reference points, a mean sensor error of 14% with physiological time lags of 3.28 ± 4.62 min (max 13.2 min) was calculated for simulation. Sensor bias reduced time in target for both simulation and clinical experiments. In simulation, additive error increased time <70 mg/dl and >180 mg/dl by 0.2% and 5.6%, respectively. In-clinic, the greatest low blood glucose index values (max = 5.9) corresponded to sensor performance. CONCLUSION Sensors have sufficient accuracy for closed-loop control, however, algorithms are necessary to effectively calibrate and detect erroneous calibrations and failing sensors. Clinical closed-loop data suggest that control with a higher target of 140 mg/dl during the nocturnal period could significantly reduce the risk for hypoglycemia.
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Affiliation(s)
- D Barry Keenan
- Medtronic MiniMed, Northridge, California 91325, USA. barry.keenan@ medtronic.com
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Blevins TC, Bode BW, Garg SK, Grunberger G, Hirsch IB, Jovanovič L, Nardacci E, Orzeck EA, Roberts VL, Tamborlane WV, Rothermel C. Statement by the American Association of Clinical Endocrinologists Consensus Panel on continuous glucose monitoring. Endocr Pract 2011; 16:730-45. [PMID: 21356637 DOI: 10.4158/ep.16.5.730] [Citation(s) in RCA: 74] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Muchmore D, Sharp M, Vaughn D. Benefits of blinded continuous glucose monitoring during a randomized clinical trial. J Diabetes Sci Technol 2011; 5:676-80. [PMID: 21722582 PMCID: PMC3192633 DOI: 10.1177/193229681100500321] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Real-time, personal continuous glucose monitoring (CGM) is a validated technology that can help patients improve glycemic control. Blinded CGM is a promising technology for obtaining retrospective data in clinical research where the quantity and quality of blood glucose information is important. This study was designed to investigate the use of novel procedures to enhance data capture from blinded CGM. METHODS Following a 4-week run-in, 46 patients with type 1 diabetes were randomized to one of two prandial insulins for a 12-week treatment period, after which they were crossed over to the alternate treatment for 12 weeks. Continuous glucose monitoring was implemented at the end of run-in (practice only) and during the last 2 weeks of each treatment period. Eighty percent of 288 possible daily glucose values were required for at least three days. Continuous glucose monitoring was extended for an additional week if these criteria were not met, and patients were allowed to insert sensors at home when necessary. Continuous glucose monitoring results were compared to reference eight-point self-monitoring of blood glucose (SMBG). RESULTS Higher than expected sensor failure rate was approximately 25%. During run-in, 12 of 45 attempted profiles failed adequacy criteria. However, treatment periods had only 1 of 82 attempted profiles considered inadequate (6 cases required an additional week of CGM). Using SMBG as reference, 93.7% of 777 CGM values were in Clarke error grid zones A+B. CONCLUSIONS With appropriate training, adequate practice, and opportunity to repeat blinded CGM as needed, nearly 100% of attempted profiles can be obtained successfully.
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Abstract
Automated closed-loop insulin delivery, also referred to as the 'artificial pancreas', has been an important but elusive goal of diabetes treatment for many decades. Research milestones include the conception of continuous glucose monitoring in the early 1960s, followed by the production of the first commercial hospital-based artificial pancreas in the late 1970s that combined intravenous glucose sensing and insulin delivery. In the past 10 years, research into the artificial pancreas has gained substantial momentum and focused on the subcutaneous route for glucose measurement and insulin delivery, which reflects technological advances in interstitial glucose monitoring and the increasing use of the continuous subcutaneous insulin infusion. This Review discusses the design of an artificial pancreas, its components and clinical results, as well as the advantages and disadvantages of different types of automated closed-loop systems and potential future advances. The introduction of the artificial pancreas into clinical practice will probably occur gradually, starting with simpler approaches, such as overnight control of blood glucose concentration and temporary pump shut-off, that are adapted to more complex situations, such as glycemic control during meals and exercise.
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Affiliation(s)
- Roman Hovorka
- Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0QQ, UK.
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