ArticleJournal of diabetes science and technology2014
Computing the surveillance error grid analysis: procedure and examples.
Article in Journal of diabetes science and technology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed, 42 citations in OpenAlex.
- Stability, Accuracy, and Risk Assessment of a Novel Subcutaneous Glucose Sensor.Diabetes technology & therapeutics · 2017Trial
- Feasibility and accuracy of continuous glucose monitoring in surgical intensive care unit patients : a single-center pilot study in South Korea.Acute and critical care · 2026Article
- Accuracy and Feasibility of Real-time Continuous Glucose Monitoring in Critically Ill Patients After Abdominal Surgery and Solid Organ Transplantation.Diabetes care · 2024Article
- Post-Market Surveillance Assessment of the Clinical Accuracy of a Blood Glucose Monitoring System with an Improved Algorithm for Enhanced Product Performance.Journal of diabetes science and technology · 2023Article
- The Accuracy of Continuous Glucose Monitoring in the Medical Intensive Care Unit.Journal of diabetes science and technology · 2022Article
- Use and Accuracy of Inpatient CGM During the COVID-19 Pandemic: An Observational Study of General Medicine and ICU Patients.Journal of diabetes science and technology · 2022Observational
- Evaluation of the FreeStyle Libre, a flash glucose monitoring system, in client-owned cats with diabetes mellitus.Journal of feline medicine and surgery · 2022Article
- Effect of intravenous vitamin C on arterial blood gas analyser and Accu-Chek point-of-care glucose monitoring in critically ill patients.Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine · 2022Article
- Evidence From a Long-Term, Systematic Post-Market Surveillance Program: Clinical Performance of a Hematocrit-Insensitive Blood Glucose Test Strip.Journal of diabetes science and technology · 2021Article
- 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.Journal of diabetes science and technology · 2020Article
- Review of Non-invasive Glucose Sensing Techniques: Optical, Electrical and Breath Acetone.Sensors (Basel, Switzerland) · 2020Review
- Diagnosis of Gestational Diabetes Mellitus with Point-of-Care Methods for Glucose versus Hospital Laboratory Method Using Isotope Dilution Gas Chromatography-Mass Spectrometry as Reference.Journal of diabetes research · 2020Article
- Toward a Framework for Outcome-Based Analytical Performance Specifications: A Methodology Review of Indirect Methods for Evaluating the Impact of Measurement Uncertainty on Clinical Outcomes.Clinical chemistry · 2019Review
- Head-to-head comparison of two continuous glucose monitoring systems on a cardio-surgical ICU.Journal of clinical monitoring and computing · 2019Article
- System Accuracy Assessment of a Blood Glucose Meter With Wireless Internet Access Associated With Unusual Hypoglycemia Patterns in Clinical Trials.Journal of diabetes science and technology · 2019Article
- Assessment of System Accuracy, Intermediate Measurement Precision, and Measurement Repeatability of a Blood Glucose Monitoring System Based on ISO 15197.Journal of diabetes science and technology · 2019Article
- Performance of a Factory-Calibrated Real-Time Continuous Glucose Monitoring System Utilizing an Automated Sensor Applicator.Diabetes technology & therapeutics · 2018Article
- Retrospective Analysis of Continuous Glucose Monitoring Data With the Surveillance Error Grid Supports Nonadjunctive Dosing Decisions.Journal of diabetes science and technology · 2017Article
- The Effects of Temperature and Relative Humidity on Point-of-Care Glucose Measurements in Hospital Practice in a Tropical Clinical Setting.Journal of diabetes science and technology · 2016Article
- Continuous Glucose Monitor Use and Accuracy in Hospitalized Patients.Diabetes technology & therapeutics · 2016Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
The surveillance error grid (SEG) analysis is a tool for analysis and visualization of blood glucose monitoring (BGM) errors, based on the opinions of 206 diabetes clinicians who rated 4 distinct treatment scenarios. Resulting from this large-scale inquiry is a matrix of 337 561 risk ratings, 1 for each pair of (reference, BGM) readings ranging from 20 to 580 mg/dl. The computation of the SEG is therefore complex and in need of automation. The SEG software introduced in this article automates the task of assigning a degree of risk to each data point for a set of measured and reference blood glucose values so that the data can be distributed into 8 risk zones. The software's 2 main purposes are to (1) distribute a set of BG Monitor data into 8 risk zones ranging from none to extreme and (2) present the data in a color coded display to promote visualization. Besides aggregating the data into 8 zones corresponding to levels of risk, the SEG computes the number and percentage of data pairs in each zone and the number/percentage of data pairs above/below the diagonal line in each zone, which are associated with BGM errors creating risks for hypo- or hyperglycemia, respectively. To illustrate the action of the SEG software we first present computer-simulated data stratified along error levels defined by ISO 15197:2013. This allows the SEG to be linked to this established standard. Further illustration of the SEG procedure is done with a series of previously published data, which reflect the performance of BGM devices and test strips under various environmental conditions. We conclude that the SEG software is a useful addition to the SEG analysis presented in this journal, developed to assess the magnitude of clinical risk from analytically inaccurate data in a variety of high-impact situations such as intensive care and disaster settings.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.