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Eichenlaub M, Stephan P, Waldenmaier D, Pleus S, Rothenbühler M, Haug C, Hinzmann R, Thomas A, Jendle J, Diem P, Freckmann G. Continuous Glucose Deviation Interval and Variability Analysis (CG-DIVA): A Novel Approach for the Statistical Accuracy Assessment of Continuous Glucose Monitoring Systems. J Diabetes Sci Technol 2024; 18:857-865. [PMID: 36329636 PMCID: PMC11307236 DOI: 10.1177/19322968221134639] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
BACKGROUND The accuracy of continuous glucose monitoring (CGM) systems is crucial for the management of glucose levels in individuals with diabetes mellitus. However, the discussion of CGM accuracy is challenged by an abundance of parameters and assessment methods. The aim of this article is to introduce the Continuous Glucose Deviation Interval and Variability Analysis (CG-DIVA), a new approach for a comprehensive characterization of CGM point accuracy which is based on the U.S. Food and Drug Administration requirements for "integrated" CGM systems. METHODS The statistical concept of tolerance intervals and data from two approved CGM systems was used to illustrate the CG-DIVA. RESULTS The CG-DIVA characterizes the expected range of deviations of the CGM system from a comparison method in different glucose concentration ranges and the variability of accuracy within and between sensors. The results of the CG-DIVA are visualized in an intuitive and straightforward graphical presentation. Compared with conventional accuracy characterizations, the CG-DIVA infers the expected accuracy of a CGM system and highlights important differences between CGM systems. Furthermore, it provides information on the incidence of large errors which are of particular clinical relevance. A software implementation of the CG-DIVA is freely available (https://github.com/IfDTUlm/CGM_Performance_Assessment). CONCLUSIONS We argue that the CG-DIVA can simplify the discussion and comparison of CGM accuracy and could replace the high number of conventional approaches. Future adaptations of the approach could thus become a putative standard for the accuracy characterization of CGM systems and serve as the basis for the definition of future CGM performance requirements.
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Affiliation(s)
- Manuel Eichenlaub
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | | | - Delia Waldenmaier
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Stefan Pleus
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | | | - Cornelia Haug
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Rolf Hinzmann
- Roche Diabetes Care GmbH, Mannheim, Germany
- IFCC Scientific Division — Working Group on Continuous Glucose Monitoring (WG-CGM)
| | - Andreas Thomas
- IFCC Scientific Division — Working Group on Continuous Glucose Monitoring (WG-CGM)
- Pirna, Germany
| | - Johan Jendle
- IFCC Scientific Division — Working Group on Continuous Glucose Monitoring (WG-CGM)
- Department of Medical Sciences, Örebro University, Örebro, Sweden
| | - Peter Diem
- IFCC Scientific Division — Working Group on Continuous Glucose Monitoring (WG-CGM)
- Endokrinologie Diabetologie Bern, Bern, Switzerland
| | - Guido Freckmann
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
- IFCC Scientific Division — Working Group on Continuous Glucose Monitoring (WG-CGM)
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Voglová Hagerf B, Protus M, Nemetova L, Mraz M, Kieslichova E, Uchytilova E, Indrova V, Lelito J, Girman P, Haluzík M, Franekova J, Svirlochova V, Klonoff DC, Kohn MA, Jabor A. Accuracy and Feasibility of Real-time Continuous Glucose Monitoring in Critically Ill Patients After Abdominal Surgery and Solid Organ Transplantation. Diabetes Care 2024; 47:956-963. [PMID: 38412005 PMCID: PMC11116916 DOI: 10.2337/dc23-1663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 01/25/2024] [Indexed: 02/28/2024]
Abstract
OBJECTIVE Glycemia management in critical care is posing a challenge in frequent measuring and adequate insulin dose adjustment. In recent years, continuous glucose measurement has gained accuracy and reliability in outpatient and inpatient settings. The aim of this study was to assess the feasibility and accuracy of real-time continuous glucose monitoring (CGM) in ICU patients after major abdominal surgery. RESEARCH DESIGN AND METHODS We included patients undergoing pancreatic surgery and solid organ transplantation (liver, pancreas, islets of Langerhans, kidney) requiring an ICU stay after surgery. We used a Dexcom G6 sensor, placed in the infraclavicular region, for real-time CGM. Arterial blood glucose measured by the amperometric principle (ABL 800; Radiometer, Copenhagen, Denmark) served as a reference value and for calibration. Blood glucose was also routinely monitored by a StatStrip bedside glucose meter. Sensor accuracy was assessed by mean absolute relative difference (MARD), bias, modified Bland-Altman plot, and surveillance error grid for paired samples of glucose values from CGM and acid-base analyzer (ABL). RESULTS We analyzed data from 61 patients and obtained 1,546 paired glucose values from CGM and ABL. Active sensor use was 95.1%. MARD was 9.4%, relative bias was 1.4%, and 92.8% of values fell in zone A, 6.1% fell in zone B, and 1.2% fell in zone C of the surveillance error grid. Median time in range was 78%, with minimum (<1%) time spent in hypoglycemia. StatStrip glucose meter MARD compared with ABL was 5.8%. CONCLUSIONS Our study shows clinically applicable accuracy and reliability of Dexcom G6 CGM in postoperative ICU patients and a feasible alternative sensor placement site.
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Affiliation(s)
- Barbora Voglová Hagerf
- Department of Diabetes, Diabetes Centre, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
- First Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Marek Protus
- First Faculty of Medicine, Charles University, Prague, Czech Republic
- Department of Anesthesiology, Resuscitation and Intensive Care, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Lenka Nemetova
- Department of Diabetes, Diabetes Centre, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Milos Mraz
- Department of Diabetes, Diabetes Centre, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
- First Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Eva Kieslichova
- First Faculty of Medicine, Charles University, Prague, Czech Republic
- Department of Anesthesiology, Resuscitation and Intensive Care, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Eva Uchytilova
- First Faculty of Medicine, Charles University, Prague, Czech Republic
- Department of Anesthesiology, Resuscitation and Intensive Care, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Veronika Indrova
- Department of Anesthesiology, Resuscitation and Intensive Care, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Jan Lelito
- Department of Anesthesiology, Resuscitation and Intensive Care, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Peter Girman
- Department of Diabetes, Diabetes Centre, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Martin Haluzík
- Department of Diabetes, Diabetes Centre, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
- First Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Janka Franekova
- Department of Laboratory Methods, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
- Third Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Veronika Svirlochova
- Department of Laboratory Methods, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - David C. Klonoff
- Diabetes Research Institute, Mills-Peninsula Medical Center, San Mateo, CA
| | | | - Antonin Jabor
- Department of Laboratory Methods, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
- Third Faculty of Medicine, Charles University, Prague, Czech Republic
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Friman O, Soltani N, Lind M, Zetterqvist P, Balintescu A, Perner A, Oldner A, Rooyackers O, Mårtensson J. Performance of Subcutaneous Continuous Glucose Monitoring in Adult Critically Ill Patients Receiving Vasopressor Therapy. Diabetes Technol Ther 2024. [PMID: 38758211 DOI: 10.1089/dia.2024.0035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/18/2024]
Abstract
Background: Subcutaneous continuous glucose monitoring (CGM) may facilitate glucose control in the ICU. We aimed to assess the accuracy of CGM (Dexcom G6) against arterial blood glucose (ABG) in adult critically ill patients receiving intravenous insulin infusion and vasopressor therapy. We also aimed to assess feasibility and tolerability of CGM in this setting. Methods: We included ICU patients receiving mechanical ventilation, insulin, and vasopressor therapy. Numerical accuracy was assessed by the mean absolute relative difference (MARD), overall, across arterial glucose strata, over different noradrenaline equivalent infusion rates, and over time since CGM start. MARD <14% was considered acceptable. Clinical accuracy was assessed using Clarke Error Grid (CEG) analysis. Feasibility outcome included number and duration of interrupted sensor readings due to signal loss. Tolerability outcome included skin reactions related to sensor insertion or sensor adhesives. Results: We obtained 2946 paired samples from 40 patients (18 with type 2 diabetes) receiving a median (IQR) maximum noradrenaline equivalent infusion rate of 0.18 (0.08-0.33) µg/kg/min during CGM. Overall, MARD was 12.7% (95% CI 10.7-15.3), and 99.8% of CGM readings were within CEG zones A and B. MARD values ≥14% were observed when ABG was outside target range (6-10 mmol/L [108-180 mg/dL]) and with noradrenaline equivalent infusion rates above 0.10 µg/kg/min. Accuracy improved with time after CGM start, reaching MARD values <14% after 36 h. We observed four episodes of interrupted sensor readings due to signal loss, ranging from 5 to 20 min. We observed no skin reaction related to sensor insertion or sensor adhesives. Conclusions: In our ICU cohort of patients receiving vasopressor infusion, subcutaneous CGM demonstrated acceptable overall numerical and clinical accuracy. However, suboptimal accuracy may occur outside glucose ranges of 6-10 mmol/L (108-180 mg/dL), during higher dose vasopressor infusion, and during the first 36 h after CGM start.
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Affiliation(s)
- Ola Friman
- Department of Physiology and Pharmacology, Section of Anaesthesia and Intensive Care, Karolinska Institutet, Stockholm, Sweden
- Department of Perioperative Medicine and Intensive Care, Karolinska University Hospital, Stockholm, Sweden
| | - Navid Soltani
- Department of Physiology and Pharmacology, Section of Anaesthesia and Intensive Care, Karolinska Institutet, Stockholm, Sweden
- Department of Perioperative Medicine and Intensive Care, Karolinska University Hospital, Stockholm, Sweden
| | - Marcus Lind
- Department of Medicine, NU-Hospital Group, Uddevalla, Sweden
- Department of Molecular and Clinical Medicine, University of Gothenburg, Gothenburg, Sweden
- Department of Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Pia Zetterqvist
- Department of Perioperative Medicine and Intensive Care, Karolinska University Hospital, Stockholm, Sweden
| | - Anca Balintescu
- Department of Clinical Science and Education, Section of Anaesthesia and Intensive Care, South General Hospital, Karolinska Institutet, Stockholm, Sweden
| | - Anders Perner
- Department of Intensive Care, Copenhagen University Hospital, Rigshospitalet, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Anders Oldner
- Department of Physiology and Pharmacology, Section of Anaesthesia and Intensive Care, Karolinska Institutet, Stockholm, Sweden
- Department of Perioperative Medicine and Intensive Care, Karolinska University Hospital, Stockholm, Sweden
| | - Olav Rooyackers
- Department of Perioperative Medicine and Intensive Care, Karolinska University Hospital, Stockholm, Sweden
- Division for Anesthesiology and Intensive Care, Department of Clinical Interventions and Technology CLINTEC, Karolinska Institutet Stockholm, Sweden
| | - Johan Mårtensson
- Department of Physiology and Pharmacology, Section of Anaesthesia and Intensive Care, Karolinska Institutet, Stockholm, Sweden
- Department of Perioperative Medicine and Intensive Care, Karolinska University Hospital, Stockholm, Sweden
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Qureshi MRA, Bain SC, Luzio S, Handy C, Fowles DJ, Love B, Wareham K, Barlow L, Dunseath GJ, Crane J, Masso IC, Ryan JAM, Chaudhry MS. Using Artificial Intelligence to Improve the Accuracy of a Wrist-Worn, Noninvasive Glucose Monitor: A Pilot Study. J Diabetes Sci Technol 2024:19322968241252819. [PMID: 38757895 DOI: 10.1177/19322968241252819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/18/2024]
Abstract
BACKGROUND Self-monitoring of glucose is important to the successful management of diabetes; however, existing monitoring methods require a degree of invasive measurement which can be unpleasant for users. This study investigates the accuracy of a noninvasive glucose monitoring system that analyses spectral variations in microwave signals. METHODS An open-label, pilot design study was conducted with four cohorts (N = 5/cohort). In each session, a dial-resonating sensor (DRS) attached to the wrist automatically collected data every 60 seconds, with a novel artificial intelligence (AI) model converting signal resonance output to a glucose prediction. Plasma glucose was measured in venous blood samples every 5 minutes for Cohorts 1 to 3 and every 10 minutes for Cohort 4. Accuracy was evaluated by calculating the mean absolute relative difference (MARD) between the DRS and plasma glucose values. RESULTS Accurate plasma glucose predictions were obtained across all four cohorts using a random sampling procedure applied to the full four-cohort data set, with an average MARD of 10.3%. A statistical analysis demonstrates the quality of these predictions, with a surveillance error grid (SEG) plot indicating no data pairs falling into the high-risk zones. CONCLUSIONS These findings show that MARD values approaching accuracies comparable to current commercial alternatives can be obtained from a multiparticipant pilot study with the application of AI. Microwave biosensors and AI models show promise for improving the accuracy and convenience of glucose monitoring systems for people with diabetes.
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Affiliation(s)
| | - Stephen Charles Bain
- Joint Clinical Research Facility, Institute of Life Science 2, Swansea University, Swansea, UK
- Diabetes Research Group, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK
| | - Stephen Luzio
- Diabetes Research Group, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK
| | | | | | | | - Kathie Wareham
- Joint Clinical Research Facility, Institute of Life Science 2, Swansea University, Swansea, UK
| | - Lucy Barlow
- Joint Clinical Research Facility, Institute of Life Science 2, Swansea University, Swansea, UK
| | - Gareth J Dunseath
- Diabetes Research Group, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK
| | - Joel Crane
- Diabetes Research Group, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK
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Hanson K, Kipnes M, Tran H. Comparison of Point Accuracy Between Two Widely Used Continuous Glucose Monitoring Systems. J Diabetes Sci Technol 2024; 18:598-607. [PMID: 38189290 PMCID: PMC11089878 DOI: 10.1177/19322968231225676] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
BACKGROUND Safe and effective self-management of glucose levels requires immediate access to accurate data. We assessed the point accuracy of the Dexcom G7 Continuous Glucose Monitoring System (Dexcom, Inc., San Diego, CA, USA) and FreeStyle Libre 3 (Abbott Diabetes Care, Alameda, CA, USA) sensors in a head-to-head comparison. METHOD Multicenter, single-arm, prospective, nonsignificant risk evaluation enrolled adults (≥ 18 years) with diagnosed type 1 diabetes (T1D) or type 2 diabetes (T2D). Accuracy was assessed by comparing sensor data to laboratory reference values Yellow Springs Instrument [YSI] and capillary blood glucose values. Outcome measures were differences in mean absolute relative difference (MARD), number and percentage of matched glucose pairs within ±20 mg/dL/±20 of reference values within glucose ranges: < 54, 54 to 69, 70 to 180, 181 to 250, > 250 mg/dL, and combined. RESULTS Data from 55 adults were included in the analysis. Analysis showed significantly lower MARD with the FreeStyle Libre 3 sensor vs the Dexcom G7 sensor (8.9% vs 13.6%, respectively, P < .0001) with a higher percentage of glucose values within ±20 mg/dL/±20 of reference (91.4% vs 78.6%). The MARD values for both continuous glucose monitoring (CGM) sensors were similar during the first 12 hours; however, the FreeStyle Libre 3 MARD was notably lower than the Dexcom G7 MARD during the next 12 hours (10.0% vs 15.1%, respectively, P < .0001) and throughout the study period. CONCLUSIONS The FreeStyle Libre 3 sensor was more accurate than the Dexcom G7 sensor in all metrics evaluated throughout the study period. This is the first head-to-head study to our knowledge that compares the flagship products currently in widespread use of the two largest CGM manufacturers.
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Affiliation(s)
| | - Mark Kipnes
- Diabetes & Glandular Disease Clinic, San Antonio, TX, USA
| | - Hien Tran
- Texas Diabetes and Endocrinology, Round Rock, TX, USA
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Krouwer JS. Comparing Clinical Accuracy and Analytical Accuracy Between Continuous Glucose Monitors. J Diabetes Sci Technol 2024; 18:608-609. [PMID: 38314690 PMCID: PMC11089867 DOI: 10.1177/19322968241229102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Abstract
This study compares performance between two continuous glucose monitors (CGMs). The study design contains a mix of laboratory results (CGM vs YSI) and home results (CGM vs glucose meter). Analysis is provided for both clinical accuracy and analytical accuracy of CGM glucose measurements. Both types of accuracy are important. Error grid analysis informs about clinical accuracy. Analytical error is important as most users would prefer a CGM with a smaller spread of CGM versus reference differences. The authors provide the percentage of time that no result was obtained. Study design, data analysis, and editorial support were provided by a manufacturer of one of the products studied. This study provides a template for comparisons.
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Maytham K, Hagelqvist PG, Engberg S, Forman JL, Pedersen-Bjergaard U, Knop FK, Vilsbøll T, Andersen A. Accuracy of continuous glucose monitoring during exercise-related hypoglycemia in individuals with type 1 diabetes. Front Endocrinol (Lausanne) 2024; 15:1352829. [PMID: 38686202 PMCID: PMC11057372 DOI: 10.3389/fendo.2024.1352829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Accepted: 03/29/2024] [Indexed: 05/02/2024] Open
Abstract
Background Hypoglycemia is common in individuals with type 1 diabetes, especially during exercise. We investigated the accuracy of two different continuous glucose monitoring systems during exercise-related hypoglycemia in an experimental setting. Materials and methods Fifteen individuals with type 1 diabetes participated in two separate euglycemic-hypoglycemic clamp days (Clamp-exercise and Clamp-rest) including five phases: 1) baseline euglycemia, 2) plasma glucose (PG) decline ± exercise, 3) 15-minute hypoglycemia ± exercise, 4) 45-minute hypoglycemia, and 5) recovery euglycemia. Interstitial PG levels were measured every five minutes, using Dexcom G6 (DG6) and FreeStyle Libre 1 (FSL1). Yellow Springs Instruments 2900 was used as PG reference method, enabling mean absolute relative difference (MARD) assessment for each phase and Clarke error grid analysis for each day. Results Exercise had a negative effect on FSL1 accuracy in phase 2 and 3 compared to rest (ΔMARD = +5.3 percentage points [(95% CI): 1.6, 9.1] and +13.5 percentage points [6.4, 20.5], respectively). In contrast, exercise had a positive effect on DG6 accuracy during phase 2 and 4 compared to rest (ΔMARD = -6.2 percentage points [-11.2, -1.2] and -8.4 percentage points [-12.4, -4.3], respectively). Clarke error grid analysis showed a decrease in clinically acceptable treatment decisions during Clamp-exercise for FSL1 while a contrary increase was observed for DG6. Conclusion Physical exercise had clinically relevant impact on the accuracy of the investigated continuous glucose monitoring systems and their ability to accurately detect hypoglycemia.
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Affiliation(s)
- Kaisar Maytham
- Clinical Research, Copenhagen University Hospital - Steno Diabetes Center Copenhagen, Herlev, Copenhagen, Denmark
- Center for Clinical Metabolic Research, Gentofte Hospital, University of Copenhagen, Hellerup, Denmark
| | - Per G Hagelqvist
- Clinical Research, Copenhagen University Hospital - Steno Diabetes Center Copenhagen, Herlev, Copenhagen, Denmark
- Center for Clinical Metabolic Research, Gentofte Hospital, University of Copenhagen, Hellerup, Denmark
| | - Susanne Engberg
- Clinical Research, Copenhagen University Hospital - Steno Diabetes Center Copenhagen, Herlev, Copenhagen, Denmark
| | - Julie L Forman
- Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Ulrik Pedersen-Bjergaard
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Endocrinology and Nephrology, Nordsjællands Hospital Hillerød, University of Copenhagen, Hillerød, Denmark
| | - Filip K Knop
- Clinical Research, Copenhagen University Hospital - Steno Diabetes Center Copenhagen, Herlev, Copenhagen, Denmark
- Center for Clinical Metabolic Research, Gentofte Hospital, University of Copenhagen, Hellerup, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Tina Vilsbøll
- Clinical Research, Copenhagen University Hospital - Steno Diabetes Center Copenhagen, Herlev, Copenhagen, Denmark
- Center for Clinical Metabolic Research, Gentofte Hospital, University of Copenhagen, Hellerup, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Andreas Andersen
- Clinical Research, Copenhagen University Hospital - Steno Diabetes Center Copenhagen, Herlev, Copenhagen, Denmark
- Center for Clinical Metabolic Research, Gentofte Hospital, University of Copenhagen, Hellerup, Denmark
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Vigersky RA, Shin J. The Myth of MARD ( Mean Absolute Relative Difference): Limitations of MARD in the Clinical Assessment of Continuous Glucose Monitoring Data. Diabetes Technol Ther 2024; 26:38-44. [PMID: 38377323 DOI: 10.1089/dia.2023.0435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
The mean absolute relative difference (MARD) is a numerical metric that has been adopted by the diabetes technology community as the main indicator that describes the accuracy of a glucose sensor at a single point in time. The appropriateness of this adoption is questionable because there is limited evidence that MARD has meaningful clinical relevance in the current era of sensor technology. The calculation may be simple, but evaluation of MARD can be very complex because it is substantially impacted by the design of the data collection in an accuracy study. Factors that can influence the overall MARD include participant demographics such as type of diabetes and age, site of sensor wear, and the percentage of collected values in each glycemic range during the study that is, in turn, a function of the study design. MARD is only one of several important statistical metrics such as bias and precision that are relevant to assessing accuracy of a sensor. Furthermore, these analytic metrics convey little information about the safety and effectiveness of sensor use with an automated insulin delivery system or a standalone device. There are no clinical studies in people with diabetes (PWD) proving that MARD can accurately differentiate between a safe and unsafe sensor or between a more and less clinically effective sensor. Moreover, there are alternatives to MARD that can do this in a clinically meaningful way, which include error grid analyses and clinical studies in PWD. This review attempts to demythologize the status of MARD for the diabetes community in an effort to shift the focus from MARD to using clinically relevant assessments.
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Affiliation(s)
- Robert A Vigersky
- Medical Affairs, Medtronic Diabetes, Biostatistics, Northridge, California, USA
| | - John Shin
- Medical Affairs, Medtronic Diabetes, Biostatistics, Northridge, California, USA
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Jacobs PG, Herrero P, Facchinetti A, Vehi J, Kovatchev B, Breton MD, Cinar A, Nikita KS, Doyle FJ, Bondia J, Battelino T, Castle JR, Zarkogianni K, Narayan R, Mosquera-Lopez C. Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities. IEEE Rev Biomed Eng 2024; 17:19-41. [PMID: 37943654 DOI: 10.1109/rbme.2023.3331297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
OBJECTIVE Artificial intelligence and machine learning are transforming many fields including medicine. In diabetes, robust biosensing technologies and automated insulin delivery therapies have created a substantial opportunity to improve health. While the number of manuscripts addressing the topic of applying machine learning to diabetes has grown in recent years, there has been a lack of consistency in the methods, metrics, and data used to train and evaluate these algorithms. This manuscript provides consensus guidelines for machine learning practitioners in the field of diabetes, including best practice recommended approaches and warnings about pitfalls to avoid. METHODS Algorithmic approaches are reviewed and benefits of different algorithms are discussed including importance of clinical accuracy, explainability, interpretability, and personalization. We review the most common features used in machine learning applications in diabetes glucose control and provide an open-source library of functions for calculating features, as well as a framework for specifying data sets using data sheets. A review of current data sets available for training algorithms is provided as well as an online repository of data sources. SIGNIFICANCE These consensus guidelines are designed to improve performance and translatability of new machine learning algorithms developed in the field of diabetes for engineers and data scientists.
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Tian T, Aaron RE, Kohn MA, Klonoff DC. The Need for a Modern Error Grid for Clinical Accuracy of Blood Glucose Monitors and Continuous Glucose Monitors. J Diabetes Sci Technol 2024; 18:3-9. [PMID: 38124309 PMCID: PMC10899826 DOI: 10.1177/19322968231214281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Affiliation(s)
- Tiffany Tian
- Diabetes Technology Society, Burlingame, CA, USA
| | | | - Michael A. Kohn
- University of California, San Francisco, San Francisco, CA, USA
| | - David C. Klonoff
- Diabetes Research Institute, Mills-Peninsula Medical Center, San Mateo, CA, USA
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Mondal H, Biri SK, Pipil N, Mondal S. Accuracy of a Non-Invasive Home Glucose Monitor for Measurement of Blood Glucose. Indian J Endocrinol Metab 2024; 28:60-64. [PMID: 38533291 PMCID: PMC10962770 DOI: 10.4103/ijem.ijem_36_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 05/04/2023] [Accepted: 05/16/2023] [Indexed: 03/28/2024] Open
Abstract
Introduction Patients with diabetes mellitus monitor their blood glucose at home with monitors that require a drop of blood or use a continuous glucose monitoring device that implants a small needle in the body. However, both cause discomfort to the patients which may inhibit them for regular blood glucose checks. Photoplethysmogram (PPG) sensing technology is an approach for non-invasive blood glucose measurement and PPG sensors can be used to predict hypoglycaemic episodes. InChcek is a PPG-based non-invasive glucose monitor. However, its accuracy has not been checked yet. Hence, this study aimed to evaluate the accuracy of InCheck, a non-invasive glucose monitor for the estimation of blood glucose. Methods In a tertiary care hospital, patients who came for blood glucose estimation were tested for blood glucose non-invasively on the InCheck device and then by the laboratory method (glucose oxidase-peroxidase). These two readings were compared. We used International Organization for Standardization (ISO) 15197:2013 (95% of values should be within ± 15 mg/dL of reference reading if reference glucose <100 mg/dL or within ± 15% of reference reading if reference glucose ≥100 mg/dL and 99% of the values should be within zones A and B in consensus error grid), and Surveillance Error Grid for analyzing the accuracy. Results A total of 1223 samples were analyzed. There was a significant difference between the reference method glucose level (135 [Q1-Q3: 97 - 179] mg/dL) and monitor-measured glucose level (188.33 [Q1-Q3: 167.33-209.33] mg/dL) (P < 0.0001). A total of 18.5% of readings were following ISO 15197:2013 criteria and 67.25% of coordinates were within zone A and zone B of the consensus error grid. In the surveillance error grid analysis, about 29.4% of values were in the no-risk zone, 51.8% in slight risk, 18.6% in moderate risk, and 0.2% were in the severe risk zone. Conclusion The accuracy of the InCheck device for the estimation of blood glucose by PPG signal is not following the recommended guidelines. Hence, further research is necessary for programming or redesigning the hardware and software for a better result from this optical sensor-based non-invasive home glucose monitor.
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Affiliation(s)
- Himel Mondal
- Department of Physiology, All India Institute of Medical Sciences, Deoghar, Jharkhand, India
| | - Sairavi Kiran Biri
- Department of Biochemistry, Phulo Jhano Medical College, Dumka, Jharkhand, India
| | - Neha Pipil
- Department of Pharmacology, Rajshree Medical Research Institute, Bareilly, Uttar Pradesh, India
| | - Shaikat Mondal
- Department of Physiology, Raiganj Government Medical College and Hospital, Raiganj, West Bengal, India
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12
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Campos Lopes S, Brito AI, Barbosa M, Matos AC, Lopes Pereira M, Monteiro AM, Fernandes V. Flash glucose monitoring system in gestational diabetes: a study of accuracy and usability. Hormones (Athens) 2023; 22:703-713. [PMID: 37740861 DOI: 10.1007/s42000-023-00485-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Accepted: 09/01/2023] [Indexed: 09/25/2023]
Abstract
PURPOSE Studies of flash glucose monitoring systems (FGMSs) in pregnancy are insufficient, especially in gestational diabetes (GD). Our aim was to evaluate Freestyle Libre's usability and accuracy (compared to self-monitoring of blood glucose [SMBG]) for GD patients in real-life conditions. METHODS This is a prospective study with pregnant women diagnosed with GD (n = 24 for the usability analysis; n = 19 for the accuracy analysis). The study duration was up to 28 days (lifetime of two sensors). Participants executed a minimum of four daily FGMS readings obtained immediately after capillary SMBG. Analytical accuracy was assessed with mean absolute relative difference (MARD) and mean absolute difference (MAD); clinical accuracy was assessed with Surveillance Error Grid (SEG). Usability was evaluated with a user acceptability questionnaire. RESULTS The mean pregestational BMI was 25.21 ± 5.15 kg/m2 (mean ± SD), the mean gestational age was 30.31 ± 2.02 weeks, and the mean glucose values were 76.63 ± 7.49 mg/dL. A total of 1339 SMBG-FGMS pairs of values were obtained. Analytical accuracy was good with an overall MARD of 14.07% and an in-target MARD of 13.79%. The number of SMBG-FMGS pairs for above-target values was low (122 of 1339) with an associated MARD of 17.95%. Clinical accuracy of the FGMS was demonstrated, with 94.4% of values in the no-risk or slight, lower risk zones of the SEG. FGMS accuracy was unaffected by pregestational BMI or gestational age. The user acceptability questionnaire showed high levels of satisfaction, with 95.8-100% preferring FGMS to SMBG. No unexpected or severe adverse effects occurred. CONCLUSION FGMS showed good performance in GD regarding accuracy and usability. Larger studies are needed to corroborate our results, verify the analytical accuracy of above-target values as this glucose range might lead to initiation or adjustment of pharmacological therapy, and ultimately establish definitive recommendations regarding prescription of FGMS for GD patients.
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Affiliation(s)
| | | | - Mariana Barbosa
- Department of Endocrinology, Hospital de Braga, Braga, Portugal
| | | | | | | | - Vera Fernandes
- Department of Endocrinology, Hospital de Braga, Braga, Portugal
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13
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Freckmann G, Eichenlaub M, Waldenmaier D, Pleus S, Wehrstedt S, Haug C, Witthauer L, Jendle J, Hinzmann R, Thomas A, Eriksson Boija E, Makris K, Diem P, Tran N, Klonoff DC, Nichols JH, Slingerland RJ. Clinical Performance Evaluation of Continuous Glucose Monitoring Systems: A Scoping Review and Recommendations for Reporting. J Diabetes Sci Technol 2023; 17:1506-1526. [PMID: 37599389 PMCID: PMC10658695 DOI: 10.1177/19322968231190941] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/22/2023]
Abstract
The use of different approaches for design and results presentation of studies for the clinical performance evaluation of continuous glucose monitoring (CGM) systems has long been recognized as a major challenge in comparing their results. However, a comprehensive characterization of the variability in study designs is currently unavailable. This article presents a scoping review of clinical CGM performance evaluations published between 2002 and 2022. Specifically, this review quantifies the prevalence of numerous options associated with various aspects of study design, including subject population, comparator (reference) method selection, testing procedures, and statistical accuracy evaluation. We found that there is a large variability in nearly all of those aspects and, in particular, in the characteristics of the comparator measurements. Furthermore, these characteristics as well as other crucial aspects of study design are often not reported in sufficient detail to allow an informed interpretation of study results. We therefore provide recommendations for reporting the general study design, CGM system use, comparator measurement approach, testing procedures, and data analysis/statistical performance evaluation. Additionally, this review aims to serve as a foundation for the development of a standardized CGM performance evaluation procedure, thereby supporting the goals and objectives of the Working Group on CGM established by the Scientific Division of the International Federation of Clinical Chemistry and Laboratory Medicine.
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Affiliation(s)
- Guido Freckmann
- IFCC Scientific Division - Working Group on Continuous Glucose Monitoring
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Manuel Eichenlaub
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Delia Waldenmaier
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Stefan Pleus
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Stephanie Wehrstedt
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Cornelia Haug
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Lilian Witthauer
- Diabetes Center Berne, Bern, Switzerland
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital Bern, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Johan Jendle
- IFCC Scientific Division - Working Group on Continuous Glucose Monitoring
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Rolf Hinzmann
- IFCC Scientific Division - Working Group on Continuous Glucose Monitoring
- Roche Diabetes Care GmbH, Mannheim, Germany
| | - Andreas Thomas
- IFCC Scientific Division - Working Group on Continuous Glucose Monitoring
- Pirna, Germany
| | - Elisabet Eriksson Boija
- IFCC Scientific Division - Working Group on Continuous Glucose Monitoring
- Equalis AB, Uppsala, Sweden
| | - Konstantinos Makris
- IFCC Scientific Division - Working Group on Continuous Glucose Monitoring
- Clinical Biochemistry Department, KAT General Hospital, Athens, Greece
| | - Peter Diem
- IFCC Scientific Division - Working Group on Continuous Glucose Monitoring
- Endokrinologie Diabetologie Bern, Bern, Switzerland
| | - Nam Tran
- IFCC Scientific Division - Working Group on Continuous Glucose Monitoring
- Department of Pathology and Laboratory Medicine, University of California Davis Health, Sacramento, CA, USA
| | - David C. Klonoff
- IFCC Scientific Division - Working Group on Continuous Glucose Monitoring
- Diabetes Research Institute, Mills-Peninsula Medical Center, San Mateo, CA, USA
| | - James H. Nichols
- IFCC Scientific Division - Working Group on Continuous Glucose Monitoring
- Vanderbilt University Medical Center, Nashville, TN, USA
| | - Robbert J. Slingerland
- IFCC Scientific Division - Working Group on Continuous Glucose Monitoring
- Department of Clinical Chemistry, Isala Clinics, Zwolle, the Netherlands
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14
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Dziorny A, Jones C, Salant J, Kubis S, Zand MS, Wolfe H, Srinivasan V. Clinical and Analytic Accuracy of Simultaneously Acquired Hemoglobin Measurements: A Multi-Institution Cohort Study to Minimize Redundant Laboratory Usage. Pediatr Crit Care Med 2023; 24:e520-e530. [PMID: 37219964 PMCID: PMC10665541 DOI: 10.1097/pcc.0000000000003287] [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: 05/25/2023]
Abstract
OBJECTIVES Frequent diagnostic blood sampling contributes to anemia among critically ill children. Reducing duplicative hemoglobin testing while maintaining clinical accuracy can improve patient care efficacy. The objective of this study was to determine the analytical and clinical accuracy of simultaneously acquired hemoglobin measurements with different methods. DESIGN Retrospective cohort study. SETTING Two U.S. children's hospitals. PATIENTS Children (< 18 yr old) admitted to the PICU. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS We identified hemoglobin results from complete blood count (CBC) panels paired with blood gas (BG) panels and point-of-care (POC) devices. We estimated analytic accuracy by comparing hemoglobin distributions, correlation coefficients, and Bland-Altman bias. We measured clinical accuracy with error grid analysis and defined mismatch zones as low, medium, or high risk-based on deviance from unity and risk of therapeutic error. We calculated pairwise agreement to a binary decision to transfuse based on a hemoglobin value. Our cohort includes 49,004 ICU admissions from 29,926 patients, resulting in 85,757 CBC-BG hemoglobin pairs. BG hemoglobin was significantly higher (mean bias, 0.43-0.58 g/dL) than CBC hemoglobin with similar Pearson correlation ( R2 ) (0.90-0.91). POC hemoglobin was also significantly higher, but of lower magnitude (mean bias, 0.14 g/dL). Error grid analysis revealed only 78 (< 0.1%) CBC-BG hemoglobin pairs in the high-risk zone. For CBC-BG hemoglobin pairs, at a BG hemoglobin cutoff of greater than 8.0 g/dL, the "number needed to miss" a CBC hemoglobin less than 7 g/dL was 275 and 474 at each institution, respectively. CONCLUSIONS In this pragmatic two-institution cohort of greater than 29,000 patients, we show similar clinical and analytic accuracy of CBC and BG hemoglobin. Although BG hemoglobin values are higher than CBC hemoglobin values, the small magnitude is unlikely to be clinically significant. Application of these findings may reduce duplicative testing and decrease anemia among critically ill children.
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Affiliation(s)
- Adam Dziorny
- Department of Pediatrics, University of Rochester School of
Medicine, Rochester, NY
- Department of Biomedical Engineering, University of
Rochester, Rochester, NY
| | - Chloe Jones
- Department of Biomedical Engineering, University of
Rochester, Rochester, NY
| | - Jennifer Salant
- Department of Pediatrics, Weill Cornell Medicine, New York,
NY
| | - Sherri Kubis
- Department of Anesthesiology & Critical Care Medicine,
Children’s Hospital of Philadelphia, Philadelphia, PA
| | - Martin S. Zand
- Department of Internal Medicine, University of Rochester
School of Medicine, Rochester NY
| | - Heather Wolfe
- Department of Anesthesiology & Critical Care Medicine,
Children’s Hospital of Philadelphia, Philadelphia, PA
- Department of Anesthesiology, Critical Care and Pediatrics,
University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Vijay Srinivasan
- Department of Anesthesiology & Critical Care Medicine,
Children’s Hospital of Philadelphia, Philadelphia, PA
- Department of Anesthesiology, Critical Care and Pediatrics,
University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
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15
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Setford S, Phillips S, Cameron H, Grady M. Updated Post-Market Surveillance of the Clinical Accuracy of a Blood Glucose Test Strip Platform. J Diabetes Sci Technol 2023; 17:860-861. [PMID: 36825330 PMCID: PMC10210116 DOI: 10.1177/19322968231158394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
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16
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Khadem H, Nemat H, Elliott J, Benaissa M. Blood Glucose Level Time Series Forecasting: Nested Deep Ensemble Learning Lag Fusion. Bioengineering (Basel) 2023; 10:bioengineering10040487. [PMID: 37106674 PMCID: PMC10135844 DOI: 10.3390/bioengineering10040487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 04/12/2023] [Accepted: 04/17/2023] [Indexed: 04/29/2023] Open
Abstract
Blood glucose level prediction is a critical aspect of diabetes management. It enables individuals to make informed decisions about their insulin dosing, diet, and physical activity. This, in turn, improves their quality of life and reduces the risk of chronic and acute complications. One conundrum in developing time-series forecasting models for blood glucose level prediction is to determine an appropriate length for look-back windows. On the one hand, studying short histories foists the risk of information incompletion. On the other hand, analysing long histories might induce information redundancy due to the data shift phenomenon. Additionally, optimal lag lengths are inconsistent across individuals because of the domain shift occurrence. Therefore, in bespoke analysis, either optimal lag values should be found for each individual separately or a globally suboptimal lag value should be used for all. The former approach degenerates the analysis's congruency and imposes extra perplexity. With the latter, the fine-tunned lag is not necessarily the optimum option for all individuals. To cope with this challenge, this work suggests an interconnected lag fusion framework based on nested meta-learning analysis that improves the accuracy and precision of predictions for personalised blood glucose level forecasting. The proposed framework is leveraged to generate blood glucose prediction models for patients with type 1 diabetes by scrutinising two well-established publicly available Ohio type 1 diabetes datasets. The models developed undergo vigorous evaluation and statistical analysis from mathematical and clinical perspectives. The results achieved underpin the efficacy of the proposed method in blood glucose level time-series prediction analysis.
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Affiliation(s)
- Heydar Khadem
- Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S10 2TN, UK
| | - Hoda Nemat
- Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S10 2TN, UK
| | - Jackie Elliott
- Department of Oncology and Metabolism, University of Sheffield, Sheffield S10 2TN, UK
- Department of Diabetes and Endocrinology, Sheffield Teaching Hospitals, Sheffield S5 7AU, UK
| | - Mohammed Benaissa
- Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S10 2TN, UK
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17
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Weber MR, Diebold M, Wiesli P, Kistler AD. Accuracy of Flash Glucose Monitoring in Hemodialysis Patients With and Without Diabetes Mellitus. Exp Clin Endocrinol Diabetes 2023; 131:132-141. [PMID: 36377191 PMCID: PMC9998185 DOI: 10.1055/a-1978-0226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
AIMS Glucose and insulin metabolism are altered in hemodialysis patients, and diabetes management is difficult in these patients. We aimed to validate flash glucose monitoring (FGM) in hemodialysis patients with and without diabetes mellitus as an attractive option for glucose monitoring not requiring regular self-punctures. METHODS We measured interstitial glucose using a FreeStyle Libre device in eight hemodialysis patients with and seven without diabetes mellitus over 14 days and compared the results to simultaneously performed self-monitoring of capillary blood glucose (SMBG). RESULTS In 720 paired measurements, mean flash glucose values were significantly lower than self-measured capillary values (6.17±2.52 vs. 7.15±2.41 mmol/L, p=1.3 E-86). Overall, the mean absolute relative difference was 17.4%, and the mean absolute difference was 1.20 mmol/L. The systematic error was significantly larger in patients without vs. with diabetes (- 1.17 vs. - 0.82 mmol/L) and on dialysis vs. interdialytic days (-1.09 vs. -0.90 mmol/L). Compared to venous blood glucose (72 paired measurements), the systematic error of FGM was even larger (5.89±2.44 mmol/L vs. 7.78±7.25 mmol/L, p=3.74E-22). Several strategies to reduce the systematic error were evaluated, including the addition of +1.0 mmol/L as a correction term to all FGM values, which significantly improved accuracy. CONCLUSIONS FGM systematically underestimates blood glucose in hemodialysis patients but, taking this systematic error into account, the system may be useful for glucose monitoring in hemodialysis patients with or without diabetes.
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Affiliation(s)
- Michèle R Weber
- Department of Medicine, Cantonal Hospital Frauenfeld, Frauenfeld, Switzerland
| | - Matthias Diebold
- Clinic for Transplantation Immunology and Nephrology, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Peter Wiesli
- Department of Medicine, Cantonal Hospital Frauenfeld, Frauenfeld, Switzerland
| | - Andreas D Kistler
- Department of Medicine, Cantonal Hospital Frauenfeld, Frauenfeld, Switzerland
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18
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Nemat H, Khadem H, Elliott J, Benaissa M. Causality analysis in type 1 diabetes mellitus with application to blood glucose level prediction. Comput Biol Med 2023; 153:106535. [PMID: 36640530 DOI: 10.1016/j.compbiomed.2022.106535] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 12/05/2022] [Accepted: 12/31/2022] [Indexed: 01/05/2023]
Abstract
Effective control of blood glucose level (BGL) is the key factor in the management of type 1 diabetes mellitus (T1D). BGL prediction is an important tool to help maximise the time BGL is in the target range and thus minimise both acute and chronic diabetes-related complications. To predict future BGL, histories of variables known to affect BGL, such as carbohydrate intake, injected bolus insulin, and physical activity, are utilised. Due to these identified cause and effect relationships, T1D management can be examined via the causality context. In this respect, this work initially investigates these relations and quantifies the causality strengths of each variable with BGL using the convergent cross mapping method (CCM). Then, considering the extended CCM, the causality strengths of each variable for different lags are quantified. After that, the optimal time lag for each variable is determined according to the quantified causality effects. Subsequently, the feasibility of leveraging causality information as prior knowledge for BGL prediction is investigated by proposing two approaches. In the first approach, causality strengths are used as weights for relevant affecting variables. In the second approach, the optimal causal lags and the corresponding causality strengths are considered the shifts and weights for the variables, respectively. Overall, the evaluation criteria and statistical analysis used for comparing results show the effectiveness of using causality analysis in T1D management.
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Affiliation(s)
- Hoda Nemat
- Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield, S1 4DE, UK.
| | - Heydar Khadem
- Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield, S1 4DE, UK.
| | - Jackie Elliott
- Department of Oncology and Metabolism, University of Sheffield, Sheffield S10 2RX, UK; Sheffield Teaching Hospitals, Diabetes and Endocrine Centre, Northern General Hospital, Sheffield S5 7AU, UK.
| | - Mohammed Benaissa
- Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield, S1 4DE, UK.
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19
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Setford S, Liu Z, McColl D, Phillips S, Cameron H, Grady M. Post-Market Surveillance Assessment of the Clinical Accuracy of a Blood Glucose Monitoring System with an Improved Algorithm for Enhanced Product Performance. J Diabetes Sci Technol 2023; 17:133-140. [PMID: 34463143 PMCID: PMC9846413 DOI: 10.1177/19322968211039465] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
BACKGROUND On-going manufacturer-led post-market surveillance (PMS), assessing the clinical accuracy of blood glucose monitoring (BGM) systems, is critical to substantiate the performance of such products for people with diabetes. MATERIALS AND METHODS Batches of Verio test-strip product were randomly and routinely selected over the period from launch of an improved-algorithm product to reporting date and sent to 3 clinic sites for clinician-led accuracy assessment. Accuracy is reported as per recently adopted FDA guidance for BGM systems, EN ISO 15197:2015 and MARD/MAD (Mean absolute relative difference/Mean absolute difference). RESULTS Thirty-three individual test-strip batches were evaluated corresponding to 506 unique donors. Accuracy performance - FDA: 98.9% of values within ±15% of comparator; ISO: 99.0% within ±15 mg/dL or ±15% at <100 mg/dL (<5.55 mmol/L) or ≥100 mg/dL (≥5.55 mmol/L) glucose, respectively. Overall MARD was 4.19% with a MARD range of 3.54%-5.73% across all test strip batches. CONCLUSIONS This post-market surveillance program demonstrates the new BGM system consistently meets measures of clinical accuracy specified by regulators. This program supports a growing demand by regulators for real-world evidence demonstrating consistent in-market product efficacy as opposed to the current largely passive approach that relies on assessment of reports filed by device users.
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Affiliation(s)
- Steven Setford
- LifeScan Scotland Ltd., Inverness,
UK
- Steven Setford, PhD, LifeScan Scotland Ltd,
Beechwood Park North, Inverness, Highland IV2 3ED, UK.
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20
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Della Cioppa A, De Falco I, Koutny T, Scafuri U, Ubl M, Tarantino E. Reducing high-risk glucose forecasting errors by evolving interpretable models for Type 1 diabetes. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.110012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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21
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Phillips S, Setford S, Grady M, Liu Z, Cameron H. Post-Market Surveillance of a Blood Glucose Test Strip Demonstrates No Evidence of Interference on Clinical Accuracy in a Large Cohort of People with Type 1 or Type 2 Diabetes. J Diabetes Sci Technol 2023; 17:141-151. [PMID: 34486429 PMCID: PMC9846393 DOI: 10.1177/19322968211042352] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
BACKGROUND Regulations and industry guidance relating to testing for interference in blood glucose monitoring (BGM) systems continue to focus on in vitro laboratory bench tests. Post-market surveillance (PMS) in a clinical setting allows for BGM accuracy assessments to evaluate the impact of real-world exposure to polypharmacy in people with diabetes. This study evaluated the OneTouch Select Plus® BGM test-strip accuracy with respect to polypharmacy using a clinical registry dataset. METHODS Medication profiles were analysed for 1023 subjects (425 with type 1 (T1D) and 598 with type 2 diabetes (T2D)) attending 3 UK hospitals. Blood samples were analysed to determine clinical accuracy of the BGM test-strip against a laboratory comparator. RESULTS 538 different medications (48 diabetes and 490 non-diabetes) were recorded across the 1023 subjects. Patients took on average 6.9 (n = 1-36) individual medications and 4.1 (n = 1-13) unique medication classes. Clinical accuracy to EN ISO 15197:2015 criteria were met irrespective of increasing average number of individual medications, categorized from 1-3, 4-6, 7-9, 10-12 and >12 taken per subject (97.7%, 97.7%, 97.8%, 97.8%, and 98.4%, respectively). Clinical accuracy criteria were met across 15 classes of medication using the combined dataset (97.9%; 29784/30433). Surveillance Error Grid (SEG) analysis showed 98.7% (29959/30368) of readings presented no clinical risk. No individual class or combination of medication classes impacted clinical accuracy of the BGM test-strip. CONCLUSIONS Clinical performance for the test strip under assessment demonstrated no evidence of interference from over 500 prescription medications, with clinical accuracy maintained across a range of polypharmacy conditions in people with diabetes.
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Affiliation(s)
- Stuart Phillips
- LifeScan Scotland Ltd, Inverness,
UK
- Stuart Phillips M.Sc., LifeScan Scotland
Ltd, Beechwood Park North, Inverness, IV2 3ED, UK.
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22
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Battelino T, Alexander CM, Amiel SA, Arreaza-Rubin G, Beck RW, Bergenstal RM, Buckingham BA, Carroll J, Ceriello A, Chow E, Choudhary P, Close K, Danne T, Dutta S, Gabbay R, Garg S, Heverly J, Hirsch IB, Kader T, Kenney J, Kovatchev B, Laffel L, Maahs D, Mathieu C, Mauricio D, Nimri R, Nishimura R, Scharf M, Del Prato S, Renard E, Rosenstock J, Saboo B, Ueki K, Umpierrez GE, Weinzimer SA, Phillip M. Continuous glucose monitoring and metrics for clinical trials: an international consensus statement. Lancet Diabetes Endocrinol 2023; 11:42-57. [PMID: 36493795 DOI: 10.1016/s2213-8587(22)00319-9] [Citation(s) in RCA: 175] [Impact Index Per Article: 175.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 10/27/2022] [Accepted: 10/28/2022] [Indexed: 12/12/2022]
Abstract
Randomised controlled trials and other prospective clinical studies for novel medical interventions in people with diabetes have traditionally reported HbA1c as the measure of average blood glucose levels for the 3 months preceding the HbA1c test date. The use of this measure highlights the long-established correlation between HbA1c and relative risk of diabetes complications; the change in the measure, before and after the therapeutic intervention, is used by regulators for the approval of medications for diabetes. However, with the increasing use of continuous glucose monitoring (CGM) in clinical practice, prospective clinical studies are also increasingly using CGM devices to collect data and evaluate glucose profiles among study participants, complementing HbA1c findings, and further assess the effects of therapeutic interventions on HbA1c. Data is collected by CGM devices at 1-5 min intervals, which obtains data on glycaemic excursions and periods of asymptomatic hypoglycaemia or hyperglycaemia (ie, details of glycaemic control that are not provided by HbA1c concentrations alone that are measured continuously and can be analysed in daily, weekly, or monthly timeframes). These CGM-derived metrics are the subject of standardised, internationally agreed reporting formats and should, therefore, be considered for use in all clinical studies in diabetes. The purpose of this consensus statement is to recommend the ways CGM data might be used in prospective clinical studies, either as a specified study endpoint or as supportive complementary glucose metrics, to provide clinical information that can be considered by investigators, regulators, companies, clinicians, and individuals with diabetes who are stakeholders in trial outcomes. In this consensus statement, we provide recommendations on how to optimise CGM-derived glucose data collection in clinical studies, including the specific glucose metrics and specific glucose metrics that should be evaluated. These recommendations have been endorsed by the American Association of Clinical Endocrinologists, the American Diabetes Association, the Association of Diabetes Care and Education Specialists, DiabetesIndia, the European Association for the Study of Diabetes, the International Society for Pediatric and Adolescent Diabetes, the Japanese Diabetes Society, and the Juvenile Diabetes Research Foundation. A standardised approach to CGM data collection and reporting in clinical trials will encourage the use of these metrics and enhance the interpretability of CGM data, which could provide useful information other than HbA1c for informing therapeutic and treatment decisions, particularly related to hypoglycaemia, postprandial hyperglycaemia, and glucose variability.
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Affiliation(s)
- Tadej Battelino
- Department of Pediatric Endocrinology, Diabetes and Metabolism, University Children's Hospital, University Medical Centre Ljubljana, and Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.
| | | | | | - Guillermo Arreaza-Rubin
- Division of Diabetes, Endocrinology and Metabolic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, USA
| | - Roy W Beck
- Jaeb Center for Health Research, Tampa, FL, USA
| | | | - Bruce A Buckingham
- Division of Endocrinology and Diabetes, Department of Pediatrics, Stanford Medical Center, Stanford, CA, USA
| | | | | | - Elaine Chow
- Phase 1 Clinical Trial Centre, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Pratik Choudhary
- Leicester Diabetes Research Centre, University of Leicester, Leicester, UK
| | - Kelly Close
- diaTribe Foundation, San Francisco, CA, USA; Close Concerns, San Francisco, CA, USA
| | - Thomas Danne
- Diabetes Centre for Children and Adolescents, Auf der Bult, Hanover, Germany
| | | | - Robert Gabbay
- American Diabetes Association, Arlington, VA, USA; Harvard Medical School, Harvard University, Boston, MA, USA
| | - Satish Garg
- Barbara Davis Centre for Diabetes, University of Colorado Denver, Aurora, CO, USA
| | | | - Irl B Hirsch
- Division of Metabolism, Endocrinology and Nutrition, University of Washington School of Medicine, University of Washington, Seattle, WA, USA
| | - Tina Kader
- Jewish General Hospital, Montreal, QC, Canada
| | | | - Boris Kovatchev
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
| | - Lori Laffel
- Pediatric, Adolescent and Young Adult Section, Joslin Diabetes Center, Harvard Medical School, Harvard University, Boston, MA, USA
| | - David Maahs
- Department of Pediatrics, Stanford Diabetes Research Center, Stanford, CA, USA
| | - Chantal Mathieu
- Clinical and Experimental Endocrinology, KU Leuven, Leuven, Belgium
| | - Dídac Mauricio
- Department of Endocrinology and Nutrition, CIBERDEM (Instituto de Salud Carlos III), Hospital de la Santa Creu i Sant Pau, Autonomous University of Barcelona, Barcelona, Spain
| | - Revital Nimri
- National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Petah Tikva, Israel
| | - Rimei Nishimura
- The Jikei University School of Medicine, Jikei University, Tokyo, Japan
| | - Mauro Scharf
- Centro de Diabetes Curitiba and Division of Pediatric Endocrinology, Hospital Nossa Senhora das Graças, Curitiba, Brazil
| | - Stefano Del Prato
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Eric Renard
- Department of Endocrinology, Diabetes and Nutrition, Montpellier University Hospital, Montpellier, France; Institute of Functional Genomics, University of Montpellier, Montpellier, France; INSERM Clinical Investigation Centre, Montpellier, France
| | - Julio Rosenstock
- Velocity Clinical Research, Medical City, Dallas, TX; University of Texas Southwestern Medical Center, University of Texas, Dallas, TX, USA
| | - Banshi Saboo
- Dia Care, Diabetes Care and Hormone Clinic, Ahmedabad, India
| | - Kohjiro Ueki
- Diabetes Research Center, National Center for Global Health and Medicine, Tokyo, Japan
| | | | - Stuart A Weinzimer
- Department of Pediatrics, Yale University School of Medicine, Yale University, New Haven, CT, USA
| | - Moshe Phillip
- National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Petah Tikva, Israel; Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
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Tripathy HP, Pattanaik P, Mishra DK, Kamilla SK, Holderbaum W. Experimental and probabilistic model validation of ultrasonic MEMS transceiver for blood glucose sensing. Sci Rep 2022; 12:21259. [PMID: 36481774 PMCID: PMC9732296 DOI: 10.1038/s41598-022-25717-x] [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: 06/30/2022] [Accepted: 12/05/2022] [Indexed: 12/13/2022] Open
Abstract
In contrast to traditional laboratory glucose monitoring, recent developments have focused on blood glucose self-monitoring and providing patients with a self-monitoring device. This paper proposes a system based on ultrasound principles for quantifying glucose levels in blood by conducting an in-vitro experiment with goat blood before human blood. The ultrasonic transceiver is powered by a frequency generator that operates at 40 kHz and 1.6 V, and variations in glucose level affect the ultrasonic transceiver readings. The RVM probabilistic model is used to determine the variation in glucose levels in a blood sample. Blood glucose levels are measured simultaneously using a commercial glucose metre for confirmation. The experimental data values proposed are highly correlated with commercial glucose metre readings. The proposed ultrasonic MEMS-based blood glucometer measures a glucose level of [Formula: see text] mg/dl. In the near future, the miniature version of the experimental model may be useful to human society.
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Affiliation(s)
- Hara Prasada Tripathy
- grid.412612.20000 0004 1760 9349Semiconductor Research Laboratory, Faculty of Engineering and Technology (ITER), Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, 751030 India
| | - Priyabrata Pattanaik
- grid.412612.20000 0004 1760 9349Semiconductor Research Laboratory, Faculty of Engineering and Technology (ITER), Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, 751030 India
| | - Dilip Kumar Mishra
- grid.412612.20000 0004 1760 9349Semiconductor Research Laboratory, Faculty of Engineering and Technology (ITER), Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, 751030 India
| | - Sushanta Kumar Kamilla
- grid.412612.20000 0004 1760 9349Semiconductor Research Laboratory, Faculty of Engineering and Technology (ITER), Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, 751030 India
| | - William Holderbaum
- grid.9435.b0000 0004 0457 9566School of Biological Science, Biomedical Engineering, University of Reading, Whiteknights, RG6 6AY UK
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24
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Longo RR, Joshi R. The Devil Is in the Details: Use, Limitations, and Implementation of Continuous Glucose Monitoring in the Inpatient Setting. Diabetes Spectr 2022; 35:405-419. [PMID: 36561647 PMCID: PMC9668728 DOI: 10.2337/dsi22-0009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Until recently, continuous glucose monitoring (CGM) systems were reserved for use in the outpatient setting or for investigational purposes in hospitalized patients. However, during the coronavirus disease 2019 pandemic, use of CGM in the inpatient setting has grown rapidly. This review outlines important details related to the accuracy, limitations, and implementation of, as well as necessary staff education for, inpatient CGM use and offers a glimpse into the future of CGM in the inpatient setting.
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Affiliation(s)
- Rebecca Rick Longo
- Lahey Hospital and Medical Center–Beth Israel Lahey Health, Burlington, MA
| | - Renu Joshi
- University of Pittsburgh Medical Center, Harrisburg, PA
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25
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Worth C, Dunne MJ, Salomon-Estebanez M, Harper S, Nutter PW, Dastamani A, Senniappan S, Banerjee I. The hypoglycaemia error grid: A UK-wide consensus on CGM accuracy assessment in hyperinsulinism. Front Endocrinol (Lausanne) 2022; 13:1016072. [PMID: 36407313 PMCID: PMC9666389 DOI: 10.3389/fendo.2022.1016072] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 10/20/2022] [Indexed: 01/24/2023] Open
Abstract
Objective Continuous Glucose Monitoring (CGM) is gaining in popularity for patients with paediatric hypoglycaemia disorders such as Congenital Hyperinsulinism (CHI), but no standard measures of accuracy or associated clinical risk are available. The small number of prior assessments of CGM accuracy in CHI have thus been incomplete. We aimed to develop a novel Hypoglycaemia Error Grid (HEG) for CGM assessment for those with CHI based on expert consensus opinion applied to a large paired (CGM/blood glucose) dataset. Design and methods Paediatric endocrinology consultants regularly managing CHI in the two UK centres of excellence were asked to complete a questionnaire regarding glucose cutoffs and associated anticipated risks of CGM errors in a hypothetical model. Collated information was utilised to mathematically generate the HEG which was then approved by expert, consensus opinion. Ten patients with CHI underwent 12 weeks of monitoring with a Dexcom G6 CGM and self-monitored blood glucose (SMBG) with a Contour Next One glucometer to test application of the HEG and provide an assessment of accuracy for those with CHI. Results CGM performance was suboptimal, based on 1441 paired values of CGM and SMBG showing Mean Absolute Relative Difference (MARD) of 19.3% and hypoglycaemia (glucose <3.5mmol/L (63mg/dL)) sensitivity of only 45%. The HEG provided clinical context to CGM errors with 15% classified as moderate risk by expert consensus when data was restricted to that of practical use. This provides a contrasting risk profile from existing diabetes error grids, reinforcing its utility in the clinical assessment of CGM accuracy in hypoglycaemia. Conclusions The Hypoglycaemia Error Grid, based on UK expert consensus opinion has demonstrated inadequate accuracy of CGM to recommend as a standalone tool for routine clinical use. However, suboptimal accuracy of CGM relative to SMBG does not detract from alternative uses of CGM in this patient group, such as use as a digital phenotyping tool. The HEG is freely available on GitHub for use by other researchers to assess accuracy in their patient populations and validate these findings.
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Affiliation(s)
- Chris Worth
- Department of Paediatric Endocrinology, Royal Manchester Children’s Hospital, Manchester, United Kingdom
- Department of Computer Science, University of Manchester, Manchester, United Kingdom
| | - Mark J. Dunne
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Maria Salomon-Estebanez
- Department of Paediatric Endocrinology, Royal Manchester Children’s Hospital, Manchester, United Kingdom
| | - Simon Harper
- Department of Computer Science, University of Manchester, Manchester, United Kingdom
| | - Paul W. Nutter
- Department of Computer Science, University of Manchester, Manchester, United Kingdom
| | - Antonia Dastamani
- Department of Paediatric Endocrinology, Great Ormond Street Hospital for Children, London, United Kingdom
| | - Senthil Senniappan
- Department of Paediatric Endocrinology, Alder Hey Children’s Hospital, Liverpool, United Kingdom
| | - Indraneel Banerjee
- Department of Paediatric Endocrinology, Royal Manchester Children’s Hospital, Manchester, United Kingdom
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
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26
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Freckmann G, Mende J, Pleus S, Waldenmaier D, Baumstark A, Jendrike N, Haug C. Mean Absolute Relative Difference of Blood Glucose Monitoring Systems and Relationship to ISO 15197. J Diabetes Sci Technol 2022; 16:1089-1095. [PMID: 33759584 PMCID: PMC9445334 DOI: 10.1177/19322968211001402] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND The analytical quality of a blood glucose monitoring system (BGMS) is often assessed according to the requirements described in the international standard ISO 15197. However, the mean absolute relative difference (MARD) is sometimes used as well. This analysis aims at providing empirical data from BGMS evaluation studies conducted according to ISO 15197 and at providing an estimation of how MARD and percentage of measurement results within ISO accuracy limits are related. METHODS Results of 77 system accuracy evaluations conducted according to ISO 15197 were used to calculate MARD between BGMS and a laboratory comparison method's results (glucose oxidase or hexokinase method). Additionally, bias and 95%-limits of agreement (LoA) using the Bland and Altman method were calculated. RESULTS MARD results ranged from 2.3% to 20.5%. The lowest MARD of a test strip lot that showed <95% of results within ISO limits was 6.1%. The distribution of MARD results shows that only 3.6% of test strip lots with a MARD equal to or below 7% showed <95% of results within ISO limits (2.2% of all test strip lots). Bias of test strip lots that showed ≥95% of results within the limits ranged from -10.3% to +7.4%. The half-width of the 95%-LoA of test strip lots that showed ≥95% of results within the limits ranged from 4.8% to 24.0%. CONCLUSION There is a threshold MARD that may allow an estimate whether ISO 15197 requirements are fulfilled, but this statement cannot be made with certainty.
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Affiliation(s)
- Guido Freckmann
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Jochen Mende
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Stefan Pleus
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Delia Waldenmaier
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Annette Baumstark
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Nina Jendrike
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Cornelia Haug
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
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27
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Naraba H, Goto T, Tokuda M, Sonoo T, Nakano H, Takahashi Y, Hashimoto H, Nakamura K. Accuracy and Stability of a Subcutaneous Flash Glucose Monitoring System in Critically Ill Patients. J Diabetes Sci Technol 2022; 16:1128-1135. [PMID: 34116614 PMCID: PMC9445337 DOI: 10.1177/19322968211017203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Flash glucose monitoring (FGM) systems can reduce glycemic variability and facilitate blood glucose management within the target range. However, in critically ill patients, only small (n < 30) studies have examined the accuracy of FGM and none have assessed the stability of FGM accuracy. We evaluated the accuracy and stability of FGM in critically ill patients. METHOD This was a single-center, retrospective observational study. We included a total of 116 critically ill patients who underwent FGM for glycemic control. The accuracy of FGM was assessed as follows using blood gas glucose values as a reference: (1) numerical accuracy using the mean absolute relative difference, (2) clinical accuracy using consensus error grid analysis, and (3) stability of accuracy assessing 14-day trends in consensus error grid distribution. RESULTS FGM sensors remained in situ for a median of 6 [4, 11] days. We compared 2014 pairs of measurements between the sensor and blood gas analysis. Glucose values from the sensor were consistently lower, with a mean absolute relative difference of 13.8% (±16.0%), than those from blood gas analysis. Consensus error grid analysis demonstrated 99.4% of the readings to be in a clinically acceptable accuracy zone. The accuracy of FGM was stable across the 14 days after device insertion. CONCLUSIONS FGM had acceptable reliability and accuracy to arterial blood gas analysis in critically ill patients. In addition, the accuracy of FGM persisted for at least 14 days. Our study promotes the potential usefulness of FGM for glycemic monitoring in critically ill patients.
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Affiliation(s)
- Hiromu Naraba
- Department of Emergency and Critical Care Medicine, Hitachi General Hospital, Hitachi, Ibaraki, Japan
- TXP Medical Co., Ltd., University of Tokyo, Bunkyo, Tokyo, Japan
- Hiromu Naraba, MD, Department of Emergency and Critical Care Medicine, Hitachi General Hospital, 2-1-1 Jonan, Hitachi, Ibaraki, 317-0077, Japan.
| | - Tadahiro Goto
- TXP Medical Co., Ltd., University of Tokyo, Bunkyo, Tokyo, Japan
- Department of Clinical Epidemiology and Health Economics, School of Public Health, The University of Tokyo, Bunkyo, Tokyo, Japan
| | - Mitsuhiro Tokuda
- Department of Emergency and Critical Care Medicine, Hitachi General Hospital, Hitachi, Ibaraki, Japan
| | - Tomohiro Sonoo
- Department of Emergency and Critical Care Medicine, Hitachi General Hospital, Hitachi, Ibaraki, Japan
- TXP Medical Co., Ltd., University of Tokyo, Bunkyo, Tokyo, Japan
| | - Hidehiko Nakano
- Department of Emergency and Critical Care Medicine, Hitachi General Hospital, Hitachi, Ibaraki, Japan
| | - Yuji Takahashi
- Department of Emergency and Critical Care Medicine, Hitachi General Hospital, Hitachi, Ibaraki, Japan
| | - Hideki Hashimoto
- Department of Emergency and Critical Care Medicine, Hitachi General Hospital, Hitachi, Ibaraki, Japan
| | - Kensuke Nakamura
- Department of Emergency and Critical Care Medicine, Hitachi General Hospital, Hitachi, Ibaraki, Japan
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28
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Fiedorova K, Augustynek M, Kubicek J, Kudrna P, Bibbo D. Review of present method of glucose from human blood and body fluids assessment. Biosens Bioelectron 2022; 211:114348. [DOI: 10.1016/j.bios.2022.114348] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 03/22/2022] [Accepted: 05/05/2022] [Indexed: 12/15/2022]
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29
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Knies M, Teske E, Kooistra H. Evaluation of the FreeStyle Libre, a flash glucose monitoring system, in client-owned cats with diabetes mellitus. J Feline Med Surg 2022; 24:e223-e231. [PMID: 35762266 PMCID: PMC9315169 DOI: 10.1177/1098612x221104051] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Objectives Home blood glucose monitoring using a portable blood glucose meter is
important in the management of feline diabetes mellitus, but taking blood
samples may be stressful for owners and cats. A flash glucose monitoring
system measuring interstitial glucose, such as the FreeStyle Libre,
overcomes some of these drawbacks. The aim of this study was to evaluate the
practical use and analytical and clinical accuracy of the FreeStyle Libre in
41 client-owned diabetic cats. Methods In this prospective study, interstitial glucose concentrations were measured
with the FreeStyle Libre and compared with blood glucose concentrations
measured with a portable blood glucose meter (AlphaTRAK) on days 1, 7 or 8
and 14 after application of the device. Cat behaviour during application,
location, skin reaction at the attachment site and owner satisfaction were
assessed. Accuracy was determined by fulfilment of ISO 15197:2013 criteria,
including Bland–Altman plotting and error grid analysis. Results Placing the device was easy, with 70% of cats showing no reaction. Most
sensors were placed on the thoracic wall. Skin reactions at the attachment
site were not present or mild in almost all cats. Owners were very satisfied
with the use of the FreeStyle Libre. Median functional life of the sensor
was 10 days (range 1–14). Good correlation was found between interstitial
and blood glucose measurements (rho[r] = 0.88, P
<0.0001). Fifty-three percent of interstitial glucose concentrations were
within a maximum deviation of 15% from blood glucose concentrations and
92.7% were within the safe risk zones 0 and 1 of the surveillance error
grid. Conclusions and relevance The flash glucose monitoring system was easy to use and owners of diabetic
cats were satisfied with its use. Although the device did not completely
fulfil ISO requirements, it is sufficiently accurate for glucose monitoring
in diabetic cats.
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Affiliation(s)
- Marieke Knies
- AniCura Veterinary Referral Centre Haaglanden, Rijswijk, The Netherlands.,Department of Clinical Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands
| | - Erik Teske
- Department of Clinical Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands
| | - Hans Kooistra
- Department of Clinical Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands
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30
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Rosengrave PC, Wohlrab C, Spencer E, Williman J, Shaw G, Carr AC. Effect of intravenous vitamin C on arterial blood gas analyser and Accu-Chek point-of-care glucose monitoring in critically ill patients. CRIT CARE RESUSC 2022; 24:175-182. [PMID: 38045598 PMCID: PMC10692626 DOI: 10.51893/2022.2.oa7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: Intravenous vitamin C is known to interfere with some point-of-care blood glucose meters. We aimed to determine the concentrations at which ascorbate interferes with glucose concentrations measured using a point-of-care blood glucose meter. We also compared the point-of-care meter and an arterial blood gas (ABG) analyser in the intensive care unit with laboratory glucose monitoring in septic patients receiving intravenous vitamin C infusions. Methods: Blood samples containing normal, depleted and supplemented glucose and increasing concentrations of ascorbate (0.1-1.0 mmol/L) were tested using an Accu-Chek Inform II (Roche Diagnostics, USA) glucometer. For the in vivo study, 41 individual blood samples were drawn daily from septic patients (n = 16) receiving infusions of 25 mg/kg of vitamin C every 6 hours. The glucose values of matched blood samples were assessed using Accu-Chek, ABG and laboratory glucose methods. Results: For every 1 mmol/L of ascorbate added, the glucose concentration measured by the point-of-care monitor increased by 1.4 mmol/L (95% CI, 1.0-1.8; P < 0.001). Analysis of matched blood samples collected following intravenous vitamin C infusion indicated that 98% of the ABG and 83% of the Accu-Chek values met the International Organization for Standardization (ISO) 15197:2013 accuracy criteria. One patient had severe renal impairment, which contributed to elevated plasma vitamin C concentrations (median, 0.95 mmol/L; range, 0.64-1.10 mmol/L), resulting in elevated Accu-Chek readings and presenting a moderate clinical risk for the highest value. Conclusions: Vitamin C concentrations < 0.8 mmol/L do not interfere with point-of-care glucose monitoring. Intravenous vitamin C infusion of 25 mg/kg every 6 hours does not interfere with point-of-care glucose monitoring unless the patient has renal impairment, in which case laboratory glucose tests should be used.
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Affiliation(s)
- Patrice C. Rosengrave
- Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand
- Centre for Postgraduate Nursing Studies, University of Otago, Christchurch, New Zealand
| | - Christina Wohlrab
- Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand
| | - Emma Spencer
- Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand
| | - Jonathan Williman
- Department of Population Health, University of Otago, Christchurch, New Zealand
| | - Geoff Shaw
- Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand
| | - Anitra C. Carr
- Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand
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31
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Koutny T, Mayo M. Predicting glucose level with an adapted branch predictor. Comput Biol Med 2022; 145:105388. [DOI: 10.1016/j.compbiomed.2022.105388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 02/22/2022] [Accepted: 03/04/2022] [Indexed: 11/15/2022]
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32
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Mondal H, Mondal S. Analyse Accuracy of Glucose Monitors without any Dedicated Software Package. Indian J Endocrinol Metab 2022; 26:284-288. [PMID: 36248042 PMCID: PMC9555376 DOI: 10.4103/ijem.ijem_500_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 04/09/2022] [Accepted: 04/22/2022] [Indexed: 11/04/2022] Open
Affiliation(s)
- Himel Mondal
- Department of Physiology, Saheed Laxman Nayak Medical College and Hospital, Koraput, Odisha, India
| | - Shaikat Mondal
- Department of Physiology, Raiganj Government Medical College and Hospital, West Bengal, India
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33
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Nemat H, Khadem H, Eissa MR, Elliott J, Benaissa M. Blood Glucose Level Prediction: Advanced Deep-Ensemble Learning Approach. IEEE J Biomed Health Inform 2022; 26:2758-2769. [PMID: 35077372 DOI: 10.1109/jbhi.2022.3144870] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Optimal and sustainable control of blood glucose levels (BGLs) is the aim of type-1 diabetes management. The automated prediction of BGL using machine learning (ML) algorithms is considered as a promising tool that can support this aim. In this context, this paper proposes new advanced ML architectures to predict BGL leveraging deep learning and ensemble learning. The deep-ensemble models are developed with novel meta-learning approaches, where the feasibility of changing the dimension of a univariate time series forecasting task is investigated. The models are evaluated regression-wise and clinical-wise. The performance of the proposed ensemble models are compared with benchmark non-ensemble models. The results show the superior performance of the developed ensemble models over developed non-ensemble benchmark models and also show the efficacy of the proposed meta-learning approaches.
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34
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Wen X, Zeng N, Zhang N, Ou T, Li X, Li X, Li W, Xu K, Du T. Diabetes Complications and Related Comorbidities Impair the Accuracy of FreeStyle Libre, a Flash Continuous Glucose Monitoring System, in Patients with Type 2 Diabetes. Diabetes Metab Syndr Obes 2022; 15:3437-3445. [PMID: 36353669 PMCID: PMC9639390 DOI: 10.2147/dmso.s381565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 09/16/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Although flash continuous glucose monitoring systems (FCGM) accuracy has been extensively studied in diabetes, its accuracy is still not fully evaluated in type 2 diabetes (T2D) patients in real-world settings. In the present study, we aim to assess the effects of diabetes complications and related comorbidities on FCGM accuracy in T2D patients with diabetes complications and related comorbidities in the real world. METHODS FCGM data were collected at eight-time points daily (3 AM, 7 AM, 9 AM, 11 AM, 1 PM, 5 PM, 7 PM, and 9 PM) from 742 patients with T2D and compared with simultaneous fingertip capillary blood glucose (reference blood glucose, REF), and the difference was evaluated using Parkes error grid (PEG), surveillance error grid (SEG), and logistic regression analysis. RESULTS In total, 25,579 FCGM/REF data pairs were included in the study. The FCGM values were lower than the paired REF values in 75% of the pairs. The maximum bias (-23.0%) and maximum mean absolute relative difference (24.5%) were observed at 3 AM among eight-time points. SEG analysis also demonstrated the highest percentage of paired readings in moderate and great risk zone (C and D) at 3 AM than PEG analysis (7.33% vs 0.43%, P<0.001). According to the SEG classification, hypoglycemia, infection, diabetic foot, diabetic ketoacidosis, and hypertension were independent risk factors that impaired FCGM accuracy in patients. CONCLUSION FCGM commonly underestimates blood glucose levels. Compared with PEG, SEG analysis seems more conducive to the analysis of FCGM performance. The present data highlights the impairment of diabetes complications and related comorbidities on the FCGM accuracy in T2D patients.
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Affiliation(s)
- Xiaofang Wen
- Department of Endocrinology, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510260, People’s Republic of China
| | - Nan Zeng
- Department of Endocrinology, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510260, People’s Republic of China
| | - Ningbo Zhang
- Department of Endocrinology, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510260, People’s Republic of China
- Department of Endocrinology, Peking University Shenzhen Hospital, Shenzhen, 518036, People’s Republic of China
| | - Tingting Ou
- Department of Endocrinology, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510260, People’s Republic of China
| | - Xiaowei Li
- Department of Endocrinology, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510260, People’s Republic of China
| | - Xiaoying Li
- Department of Endocrinology, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510260, People’s Republic of China
| | - Wangen Li
- Department of Endocrinology, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510260, People’s Republic of China
| | - Kang Xu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, People’s Republic of China
| | - Tao Du
- Department of Endocrinology, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510260, People’s Republic of China
- Correspondence: Tao Du; Kang Xu, Email ;
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35
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Bergenstal RM, Simonson GD, Heinemann L. More Green, Less Red: How Color Standardization May Facilitate Effective Use of CGM Data. J Diabetes Sci Technol 2022; 16:3-6. [PMID: 34711063 PMCID: PMC8875060 DOI: 10.1177/19322968211053341] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
| | - Gregg D. Simonson
- International Diabetes Center, HealthPartners Institute, Minneapolis, MN, USA
| | - Lutz Heinemann
- Science-Consulting in Diabetes GmbH, Kaarst, Germany
- Lutz Heinemann, PhD, Science-Consulting in Diabetes GmbH, Geranienweg 7A, 41564 Kaarst, Germany.
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36
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Shang T, Zhang JY, Thomas A, Arnold MA, Vetter BN, Heinemann L, Klonoff DC. Products for Monitoring Glucose Levels in the Human Body With Noninvasive Optical, Noninvasive Fluid Sampling, or Minimally Invasive Technologies. J Diabetes Sci Technol 2022; 16:168-214. [PMID: 34120487 PMCID: PMC8721558 DOI: 10.1177/19322968211007212] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND Conventional home blood glucose measurements require a sample of blood that is obtained by puncturing the skin at the fingertip. To avoid the pain associated with this procedure, there is high demand for medical products that allow glucose monitoring without blood sampling. In this review article, all such products are presented. METHODS In order to identify such products, four different sources were used: (1) PubMed, (2) Google Patents, (3) Diabetes Technology Meeting Startup Showcase participants, and (4) experts in the field of glucose monitoring. The information obtained were filtered by using two inclusion criteria: (1) regulatory clearance, and/or (2) significant coverage in Google News starting in the year 2016, unless the article indicated that the product had been discontinued. The identified bloodless monitoring products were classified into three categories: (1) noninvasive optical, (2) noninvasive fluid sampling, and (3) minimally invasive devices. RESULTS In total, 28 noninvasive optical, 6 noninvasive fluid sampling, and 31 minimally invasive glucose monitoring products were identified. Subsequently, these products were characterized according to their regulatory, technological, and consumer features. Products with regulatory clearance are described in greater detail according to their advantages and disadvantages, and with design images. CONCLUSIONS Based on favorable technological features, consumer features, and other advantages, several bloodless products are commercially available and promise to enhance diabetes management. Paths for future products are discussed with an emphasis on understanding existing barriers related to both technical and non-technical issues.
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Affiliation(s)
- Trisha Shang
- Diabetes Technology Society, Burlingame, California, USA
| | | | - Andreas Thomas
- AGDT (Working group of Diabetes Technology), Germany, Ulm, Germany
| | - Mark A. Arnold
- University of Iowa, Department of Chemistry, Iowa City, Iowa, USA
| | | | | | - David C. Klonoff
- Mills-Peninsula Medical Center, San Mateo, California, USA
- David C. Klonoff, MD, FACP, FRCP (Edin), Fellow AIMBE, Mills-Peninsula Medical Center, 100 South San Mateo Drive, Room 5147, San Mateo, California 94401, USA.
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Baker M, Musselman ME, Rogers R, Hellman R. Practical implementation of remote continuous glucose monitoring in hospitalized patients with diabetes. Am J Health Syst Pharm 2021; 79:452-458. [PMID: 34849550 PMCID: PMC8767852 DOI: 10.1093/ajhp/zxab456] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Purpose Inpatient diabetes management involves frequent assessment of glucose levels for treatment decisions. Here we describe a program for inpatient real-time continuous glucose monitoring (rtCGM) at a community hospital and the accuracy of rtCGM-based glucose estimates. Methods Adult inpatients with preexisting diabetes managed with intensive insulin therapy and a diagnosis of coronavirus disease 2019 (COVID-19) were monitored via rtCGM for safety. An rtCGM system transmitted glucose concentration and trending information at 5-minute intervals to nearby smartphones, which relayed the data to a centralized monitoring station. Hypoglycemia alerts were triggered by rtCGM values of ≤85 mg/dL, but rtCGM data were otherwise not used in management decisions; insulin dosing adjustments were based on blood glucose values measured via fingerstick blood sampling. Accuracy was evaluated retrospectively by comparing rtCGM values to contemporaneous point-of-care (POC) blood glucose values. Results A total of 238 pairs of rtCGM and POC data points from 10 patients showed an overall mean absolute relative difference (MARD) of 10.3%. Clarke error grid analysis showed 99.2% of points in the clinically acceptable range, and surveillance error grid analysis showed 89.1% of points in the lowest risk category. It was determined that for 25% of the rtCGM values, discordances in rtCGM and POC values would likely have resulted in different insulin doses. Insulin dose recommendations based on rtCGM values differed by 1 to 3 units from POC-based recommendations. Conclusion rtCGM for inpatient diabetes monitoring is feasible. Evaluation of individual rtCGM-POC paired values suggested that using rtCGM data for management decisions poses minimal risks to patients. Further studies to establish the safety and cost implications of using rtCGM data for inpatient diabetes management decisions are warranted.
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Affiliation(s)
- Matt Baker
- North Kansas City Hospital, North Kansas City, MO, USA
| | | | - Rachel Rogers
- North Kansas City Hospital, North Kansas City, MO, USA
| | - Richard Hellman
- Heart of America Research Foundation, North Kansas City MO, USA
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A Review of Non-Invasive Optical Systems for Continuous Blood Glucose Monitoring. SENSORS 2021; 21:s21206820. [PMID: 34696033 PMCID: PMC8537963 DOI: 10.3390/s21206820] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 10/06/2021] [Accepted: 10/11/2021] [Indexed: 12/15/2022]
Abstract
The prevalence of diabetes is increasing globally. More than 690 million cases of diabetes are expected worldwide by 2045. Continuous blood glucose monitoring is essential to control the disease and avoid long-term complications. Diabetics suffer on a daily basis with the traditional glucose monitors currently in use, which are invasive, painful, and cost-intensive. Therefore, the demand for non-invasive, painless, economical, and reliable approaches to monitor glucose levels is increasing. Since the last decades, many glucose sensing technologies have been developed. Researchers and scientists have been working on the enhancement of these technologies to achieve better results. This paper provides an updated review of some of the pioneering non-invasive optical techniques for monitoring blood glucose levels that have been proposed in the last six years, including a summary of state-of-the-art error analysis and validation techniques.
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Dudukcu HV, Taskiran M, Yildirim T. Blood glucose prediction with deep neural networks using weighted decision level fusion. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.08.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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van Doorn WPTM, Foreman YD, Schaper NC, Savelberg HHCM, Koster A, van der Kallen CJH, Wesselius A, Schram MT, Henry RMA, Dagnelie PC, de Galan BE, Bekers O, Stehouwer CDA, Meex SJR, Brouwers MCGJ. Machine learning-based glucose prediction with use of continuous glucose and physical activity monitoring data: The Maastricht Study. PLoS One 2021; 16:e0253125. [PMID: 34166426 PMCID: PMC8224858 DOI: 10.1371/journal.pone.0253125] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 05/31/2021] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND Closed-loop insulin delivery systems, which integrate continuous glucose monitoring (CGM) and algorithms that continuously guide insulin dosing, have been shown to improve glycaemic control. The ability to predict future glucose values can further optimize such devices. In this study, we used machine learning to train models in predicting future glucose levels based on prior CGM and accelerometry data. METHODS We used data from The Maastricht Study, an observational population-based cohort that comprises individuals with normal glucose metabolism, prediabetes, or type 2 diabetes. We included individuals who underwent >48h of CGM (n = 851), most of whom (n = 540) simultaneously wore an accelerometer to assess physical activity. A random subset of individuals was used to train models in predicting glucose levels at 15- and 60-minute intervals based on either CGM data or both CGM and accelerometer data. In the remaining individuals, model performance was evaluated with root-mean-square error (RMSE), Spearman's correlation coefficient (rho) and surveillance error grid. For a proof-of-concept translation, CGM-based prediction models were optimized and validated with the use of data from individuals with type 1 diabetes (OhioT1DM Dataset, n = 6). RESULTS Models trained with CGM data were able to accurately predict glucose values at 15 (RMSE: 0.19mmol/L; rho: 0.96) and 60 minutes (RMSE: 0.59mmol/L, rho: 0.72). Model performance was comparable in individuals with type 2 diabetes. Incorporation of accelerometer data only slightly improved prediction. The error grid results indicated that model predictions were clinically safe (15 min: >99%, 60 min >98%). Our prediction models translated well to individuals with type 1 diabetes, which is reflected by high accuracy (RMSEs for 15 and 60 minutes of 0.43 and 1.73 mmol/L, respectively) and clinical safety (15 min: >99%, 60 min: >91%). CONCLUSIONS Machine learning-based models are able to accurately and safely predict glucose values at 15- and 60-minute intervals based on CGM data only. Future research should further optimize the models for implementation in closed-loop insulin delivery systems.
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Affiliation(s)
- William P. T. M. van Doorn
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Department of Clinical Chemistry, Central Diagnostic Laboratory, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Yuri D. Foreman
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Department of Internal Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Nicolaas C. Schaper
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Division of Endocrinology and Metabolic Disease, Department of Internal Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
- CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, The Netherlands
| | - Hans H. C. M. Savelberg
- Department of Human Biology and Movement Science, NUTRIM School for Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Annemarie Koster
- CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, The Netherlands
- Department of Social Medicine, Maastricht University, Maastricht, The Netherlands
| | - Carla J. H. van der Kallen
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Department of Internal Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Anke Wesselius
- Department of Complex Genetics and Epidemiology, NUTRIM School for Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Miranda T. Schram
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Department of Internal Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
- Heart and Vascular Centre, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Ronald M. A. Henry
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Department of Internal Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
- Heart and Vascular Centre, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Pieter C. Dagnelie
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Department of Internal Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Bastiaan E. de Galan
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Division of Endocrinology and Metabolic Disease, Department of Internal Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
- Department of Internal Medicine, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Otto Bekers
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Department of Clinical Chemistry, Central Diagnostic Laboratory, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Coen D. A. Stehouwer
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Department of Internal Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Steven J. R. Meex
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Department of Clinical Chemistry, Central Diagnostic Laboratory, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Martijn C. G. J. Brouwers
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Division of Endocrinology and Metabolic Disease, Department of Internal Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
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Lee I, Probst D, Klonoff D, Sode K. Continuous glucose monitoring systems - Current status and future perspectives of the flagship technologies in biosensor research -. Biosens Bioelectron 2021; 181:113054. [DOI: 10.1016/j.bios.2021.113054] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 01/23/2021] [Accepted: 01/27/2021] [Indexed: 12/14/2022]
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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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Grady M, Cameron H, Phillips S, Smith G, Setford S. Analysis of a Unique Postmarket Surveillance Dataset That a Glucose Test-Strip Demonstrates no Evidence of Interference and Robust Clinical Accuracy Irrespective of the Prescription Medication Status of a Large Cohort of Patients With Diabetes. J Diabetes Sci Technol 2021; 15:82-90. [PMID: 31478385 PMCID: PMC7783023 DOI: 10.1177/1932296819873053] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND Despite a marked increase in polypharmacy in patients with diabetes there have been no thorough evaluations of the impact of polypharmacy on the accuracy of any current blood glucose monitoring (BGM) system. This study evaluated the accuracy of a BGM test-strip with respect to polypharmacy using a large clinical registry dataset. METHODS Medication profiles were analyzed for 830 subjects (334 with type 1 [T1D] and 496 with type 2 diabetes [T2D]) attending three hospitals. Blood samples were analyzed to determine clinical accuracy of the BGM test-strip compared to a laboratory comparator. RESULTS Across the 830 subjects, 473 different medications (41 diabetes and 432 nondiabetes) were recorded. Patients took on average 6.5 (n = 1-23) individual medications and 4 (n = 1-11) unique classes of medication. Clinical accuracy to EN ISO 15197:2015 criteria was met irrespective of increasing average number of individual medication, categorized from 1 to 4, 5 to 8, 9 to 12, and >12 taken per subject (97.7%, 98.4%, 98.1%, and 98.5%, respectively). Clinical accuracy to EN ISO 15197:2015 criteria was also met across 15 classes of medication using the combined dataset (98.1%; 13 003/13 253). Surveillance error grid analysis showed 98.8% (13 079/13 232) of readings presented no clinical risk. No individual class or combination of medication classes impacted clinical accuracy of the BGM test-strip. CONCLUSIONS This comprehensive analysis for this specific test-strip platform demonstrated no evidence of interference and robust clinical accuracy of this test strip, irrespective of the prescription medication status of patients with diabetes.
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Affiliation(s)
- Mike Grady
- LifeScan Scotland Ltd, Inverness, UK
- Mike Grady, PhD, LifeScan Scotland Ltd, Beechwood Park North, Inverness IV2 3ED, UK.
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Setford S, Phillips S, Grady M. Evidence From a Long-Term, Systematic Post-Market Surveillance Program: Clinical Performance of a Hematocrit-Insensitive Blood Glucose Test Strip. J Diabetes Sci Technol 2021; 15:67-75. [PMID: 30730221 PMCID: PMC7783001 DOI: 10.1177/1932296819826968] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
BACKGROUND Described is a manufacturer's systematic post-market evaluation of the long-term clinical accuracy of a commercially available blood glucose monitoring (BGM) test strip product. METHODS Production batches of test strips were routinely and regularly sampled and evaluated in a clinical setting to assess product accuracy. Evaluations were performed on capillary blood samples from a minimum of 100 subjects with diabetes, by clinical staff according to instructions for use. Readings were compared against capillary blood samples collected at the same time and measured by a standard laboratory reference method. Clinical accuracy was calculated according to EN ISO 15197:2015. RESULTS A total of 21 115 paired results were obtained, equating to 209 production batches over the >3-year period since test strip launch. Of the results, 97.6% met the accuracy criterion (range: 97.1-98.1% by year), with 98.1% of values presenting zero risk as defined by the surveillance error grid. At the <5th (21.0-33.8%) and >95th (48.3-59.4%) percentile extremes of hematocrit distribution, 97.9% and 96.4% of values were clinically accurate. The product also demonstrated clinical accuracy across all seven glucose ranges ("bins") as defined by the standard. Under conditions of combined hematocrit and glucose (<80 mg/dL and ≥300 mg/dL) extremes, 97.7% of values were clinically accurate. CONCLUSIONS Methodologies and results from a manufacturer's self-imposed clinical accuracy surveillance program of a BGM product is presented. Given the publication of sometimes-conflicting data presented within ad hoc BGM clinical accuracy evaluations, usually of limited size, it is advocated that BGM manufacturers adopt similarly robust and systematic surveillance programs to safeguard patients.
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Affiliation(s)
- Steven Setford
- LifeScan Scotland, Inverness, UK
- Steven Setford, PhD, LifeScan Scotland Ltd, Beechwood Park North, Inverness, IV2 3ED, UK.
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Galindo RJ, Migdal AL, Davis GM, Urrutia MA, Albury B, Zambrano C, Vellanki P, Pasquel FJ, Fayfman M, Peng L, Umpierrez GE. Comparison of the FreeStyle Libre Pro Flash Continuous Glucose Monitoring (CGM) System and Point-of-Care Capillary Glucose Testing in Hospitalized Patients With Type 2 Diabetes Treated With Basal-Bolus Insulin Regimen. Diabetes Care 2020; 43:2730-2735. [PMID: 32641372 PMCID: PMC7809713 DOI: 10.2337/dc19-2073] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Accepted: 05/10/2020] [Indexed: 02/03/2023]
Abstract
OBJECTIVE We compared the performance of the FreeStyle Libre Pro continuous glucose monitoring (CGM) and point-of-care capillary glucose testing (POC) among insulin-treated hospitalized patients with type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS This was a prospective study in adult patients with T2D admitted to general medicine and surgery wards. Patients were monitored with POC before meals and bedtime and with CGM during the hospital stay. Study end points included differences between POC and CGM in mean daily blood glucose (BG), hypoglycemia <70 and <54 mg/dL, and nocturnal hypoglycemia. We also calculated the mean absolute relative difference (MARD), ±15%/15 mg/dL, ±20%/20 mg/dL, and ±30%/30 mg/dL and error grid analysis between matched glucose pairs. RESULTS Mean daily glucose was significantly higher by POC (188.9 ± 37.3 vs. 176.1 ± 46.9 mg/dL) with an estimated mean difference of 12.8 mg/dL (95% CI 8.3-17.2 mg/dL), and proportions of patients with glucose readings <70 mg/dL (14% vs. 56%) and <54 mg/dL (4.1% vs. 36%) detected by POC BG were significantly lower compared with CGM (all P < 0.001). Nocturnal and prolonged CGM hypoglycemia <54 mg/dL were 26% and 12%, respectively. The overall MARD was 14.8%, ranging between 11.4% and 16.7% for glucose values between 70 and 250 mg/dL and higher for 51-69 mg/dL (MARD 28.0%). The percentages of glucose readings within ±15%/15 mg/dL, ±20%/20 mg/dL, and ±30%/30 mg/dL were 62%, 76%, and 91%, respectively. Error grid analysis showed 98.8% of glucose pairs within zones A and B. CONCLUSIONS Compared with POC, FreeStyle Libre CGM showed lower mean daily glucose and higher detection of hypoglycemic events, particularly nocturnal and prolonged hypoglycemia in hospitalized patients with T2D. CGM's accuracy was lower in the hypoglycemic range.
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Affiliation(s)
- Rodolfo J Galindo
- Division of Endocrinology, Metabolism, and Lipids, Emory University School of Medicine, Atlanta, GA
| | - Alexandra L Migdal
- Division of Endocrinology, Metabolism, and Lipids, Emory University School of Medicine, Atlanta, GA
| | - Georgia M Davis
- Division of Endocrinology, Metabolism, and Lipids, Emory University School of Medicine, Atlanta, GA
| | - Maria A Urrutia
- Division of Endocrinology, Metabolism, and Lipids, Emory University School of Medicine, Atlanta, GA
| | - Bonnie Albury
- Division of Endocrinology, Metabolism, and Lipids, Emory University School of Medicine, Atlanta, GA
| | - Cesar Zambrano
- Division of Endocrinology, Metabolism, and Lipids, Emory University School of Medicine, Atlanta, GA
| | - Priyathama Vellanki
- Division of Endocrinology, Metabolism, and Lipids, Emory University School of Medicine, Atlanta, GA
| | - Francisco J Pasquel
- Division of Endocrinology, Metabolism, and Lipids, Emory University School of Medicine, Atlanta, GA
| | - Maya Fayfman
- Division of Endocrinology, Metabolism, and Lipids, Emory University School of Medicine, Atlanta, GA
| | - Limin Peng
- Rollins School of Public Health, Emory University, Atlanta, GA
| | - Guillermo E Umpierrez
- Division of Endocrinology, Metabolism, and Lipids, Emory University School of Medicine, Atlanta, GA
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Sopfe J, Vigers T, Pyle L, Giller RH, Forlenza GP. Safety and Accuracy of Factory-Calibrated Continuous Glucose Monitoring in Pediatric Patients Undergoing Hematopoietic Stem Cell Transplantation. Diabetes Technol Ther 2020; 22:727-733. [PMID: 32105513 PMCID: PMC7591371 DOI: 10.1089/dia.2019.0521] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Background: Pediatric patients undergoing hematopoietic stem cell transplantation (HSCT) may be at risk for malglycemia and adverse outcomes, including infection, prolonged hospital stays, organ dysfunction, graft-versus-host-disease, delayed hematopoietic recovery, and increased mortality. Continuous glucose monitoring (CGM) may aid in describing and treating malglycemia in this population. However, no studies have demonstrated safety, tolerability, or accuracy of CGM in this uniquely immunocompromised population. Materials and Methods: A prospective observational study was conducted, using the Abbott Freestyle Libre Pro, in patients aged 2-30 undergoing HSCT at Children's Hospital Colorado to evaluate continuous glycemia in this population. CGM occurred up to 7 days before and 60 days after HSCT, during hospitalization only. In a secondary analysis of this data, blood glucoses collected during routine HSCT care were compared with CGM values to evaluate accuracy. Adverse events and patient refusal to wear CGM device were monitored to assess safety and tolerability. Results: Participants (n = 29; median age 13.1 years, [interquartile range] [4.7, 16.6] years) wore 84 sensors for an average of 25 [21.5, 30.0] days per participant. Paired serum-sensor values (n = 893) demonstrated a mean absolute relative difference of 20% ± 14% with Clarke Error Grid analysis showing 99% of pairs in the clinically acceptable Zones (A+B). There were four episodes of self-limited bleeding (4.8% of sensors); no other adverse events occurred. Six patients (20.7%) refused subsequent CGM placements. Conclusions: CGM use appears safe and feasible although with suboptimal accuracy in the hospitalized pediatric HSCT population. Few adverse events occurred, all of which were low grade.
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Affiliation(s)
- Jenna Sopfe
- Department of Pediatrics, Center for Cancer and Blood Disorders, University of Colorado School of Medicine, Aurora, Colorado
- Address correspondence to: Jenna Sopfe, MD, Department of Pediatrics, Center for Cancer and Blood Disorders, University of Colorado School of Medicine, 13123 E 16th Avenue, B115, Aurora, CO 80045
| | - Tim Vigers
- Department of Biostatistics and Informatics, University of Colorado Denver, Aurora, Colorado
- Barbara Davis Center, University of Colorado Denver, Aurora, Colorado
| | - Laura Pyle
- Department of Biostatistics and Informatics, University of Colorado Denver, Aurora, Colorado
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, Colorado
| | - Roger H. Giller
- Department of Pediatrics, Center for Cancer and Blood Disorders, University of Colorado School of Medicine, Aurora, Colorado
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Cembrowski G, Jung J, Mei J, Xu E, Curic T, Gibney RTN, Jacka M, Sadrzadeh H. Five-Year Two-Center Retrospective Comparison of Central Laboratory Glucose to GEM 4000 and ABL 800 Blood Glucose: Demonstrating the (In)adequacy of Blood Gas Glucose. J Diabetes Sci Technol 2020; 14:535-545. [PMID: 31686527 PMCID: PMC7576946 DOI: 10.1177/1932296819883260] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
PURPOSE To evaluate the glucose assays of two blood gas analyzers (BGAs) in intensive care unit (ICU) patients by comparing ICU BGA glucoses to central laboratory (CL) glucoses of almost simultaneously drawn specimens. METHODS Data repositories provided five years of ICU BGA glucoses and contemporaneously drawn CL glucoses from a Calgary, Alberta ICU equipped with IL GEM 4000 and CL Roche Cobas 8000-C702, and an Edmonton, Alberta ICU equipped with Radiometer ABL 800 and CL Beckman-Coulter DxC. Blood glucose analyzer and CL glucose differences were evaluated if they were both drawn either within ±15 or ±5 minutes. Glucose differences were assessed graphically and quantitatively with simple run charts and the surveillance error grid (SEG) and quantitatively with the 2016 Food and Drug Administration guidance document, with ISO 15197 and SEG statistical summaries. As the GEM glucose exhibits diurnal variation, CL-arterial blood gas (ABG) differences were evaluated according to time of day. RESULTS Compared to the GEM glucoses measured between 0200 and 0800, the run charts of (GEM-CL) glucose demonstrate significant outliers between 0800 and 0200 which are identified as moderate to severe clinical outliers by SEG analysis (P < .002 and P < .0005 for 5- and 15-minute intervals). Over the entire 24-hour period, the rates of moderate to severe glucose clinical outliers are 3.5/1000 (GEM) and 0.6/1000 glucoses (ABL), respectively, using the 15-minute interval (P < .0001). DISCUSSION The GEM ABG glucose is associated with a higher frequency of moderate to severe glucose clinical outliers, especially between 0800 and 0200, increased CL testing and higher average patient glucoses.
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Affiliation(s)
- George Cembrowski
- Laboratory Medicine and Pathology,
University of Alberta, Edmonton, AB, Canada
- CC Quality Control Consulting,
Laboratory Concision, Edmonton, AB, Canada
- George Cembrowski, MD, PhD, Laboratory
Medicine and Pathology, University of Alberta, Edmonton, AB, Canada T5N 3M7.
| | - Joanna Jung
- Laboratory Medicine and Pathology,
University of Alberta, Edmonton, AB, Canada
| | - Junyi Mei
- College of Medicine, University of
Manitoba, Winnipeg, MB, Canada
| | - Eric Xu
- College of Medicine, University of
Manitoba, Winnipeg, MB, Canada
| | | | - RT Noel Gibney
- Critical Care, School of Medicine,
University of Alberta, Edmonton, AB, Canada
| | - Michael Jacka
- Critical Care, School of Medicine,
University of Alberta, Edmonton, AB, Canada
| | - Hossein Sadrzadeh
- Calgary Laboratory Services, AB,
Canada
- Cumming School Medicine, University of
Calgary, Calgary, AB, Canada
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Abstract
When used in hospital settings, glucose meter performance issues involve analytic comparability to lab-based testing, patient and sample variables, and clinical affects such as insulin treatment protocol outcomes and morbidity or outcome risk factors. Different tools are available to assess these issues, including accuracy and precision statistics along with clinical risk measures such as error grids or simulation testing. Regulatory, guidance, and professional bodies have advocated a number of varying recommendations for glucose meter performance in different situations and under different patient conditions. These are summarized and compared, but reconciling these guidelines can be confusing or difficult for providers. Blood glucose meters are useful in the management of patients in acute or assisted care facilities, but users must appreciate the variables that affect measurements and provide for oversight that can manage risk factors and maintain meter performance expectations.
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Affiliation(s)
- Cynthia Foss Bowman
- VA New Jersey Health Care Service, Pathology and Laboratory Medicine Service, East Orange, NJ, USA
- Cynthia Foss Bowman, MD, VANJ HCS, Department of Pathology and Lab Medicine Services, 385 Tremont St. A-135A, East Orange, NJ 07018, USA.
| | - James H. Nichols
- Vanderbilt University School of Medicine, Department of Pathology, Microbiology, Immunology, Nashville, TN
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Ober RA, Geist GE. Assessment of a Noninvasive Chronic Glucose Monitoring System in Euglycemic and Diabetic Swine (Sus scrofa). JOURNAL OF THE AMERICAN ASSOCIATION FOR LABORATORY ANIMAL SCIENCE : JAALAS 2020; 59. [PMID: 32284091 PMCID: PMC7338878 DOI: 10.30802/aalas-jaalas-19-000140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 10/18/2019] [Accepted: 11/07/2019] [Indexed: 06/11/2023]
Abstract
Models of type-I diabetes are well-characterized and commonly used in the preclinical evaluation of drugs and medical devices. The diabetic minipig is an excellent example of a translational model. However, chronic glucose monitoring in this species can be challenging; frequent blood sampling can be technically difficult and poorly tolerated in conscious swine. Skin-patch continuous blood glucose monitors are FDA-approved for human use and offer a potential refinement to cageside blood collection. However, this modality has not been evaluated in pigs. In this study, young adult male STZ-induced diabetic Yucatan minipigs (n = 4) and healthy York pigs (n = 4) were implanted with a 14-d skin-patch continuous glucose monitor. Readings from continuous glucose monitors were time-matched to whole blood samples, with glucose measurements performed using point-of-care blood glucose monitors, serum chemistry or both. The aims of the study were to assess if a continuous glucose monitoring system could accurately detect glucose levels in swine, and to compare the readings toboth point-of-care glucometers and serum chemistry results. We hypothesized that a continuous glucose monitoring system would accurately detect glucose levels in swine in comparison with a validated analyzer and could serve as an animal welfarerefinement for studies of diabetes. We found that the continuous glucose monitor used in this study provided an adequateadjunct for clinical management in the stable diabetic pig and a minimally invasive and inexpensive option for colony maintenanceof chronically diabetic swine.
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Affiliation(s)
- Rebecca A Ober
- Institute of Comparative Medicine, Columbia University, New York, New York
| | - Gail E Geist
- Center for Comparative Medicine, Northwestern University, Chicago, Illinois
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50
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Affiliation(s)
- Prakesh S Shah
- Department of Pediatrics, Mount Sinai Hospital, Toronto, Canada. .,Department of Pediatrics, University of Toronto, Toronto, Canada.
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