scholarly journals Effects of Measurement Frequency on Analytical Quality Required for Glucose Measurements in Intensive Care Units: Assessments by Simulation Models

2014 ◽  
Vol 60 (4) ◽  
pp. 644-650 ◽  
Author(s):  
James C Boyd ◽  
David E Bruns

Abstract BACKGROUND Total error allowances have been proposed for glucose meters used in tight-glucose-control (TGC) protocols. It is unclear whether these proposed quality specifications are appropriate for continuous glucose monitoring (CGM). METHODS We performed Monte Carlo simulations of patients on TGC protocols. To simulate use of glucose meters, measurements were made hourly. To simulate CGM, glucose measurements were made every 5 min. Glucose was measured with defined bias (varied from −20% to 20%) and imprecision (0% to 20% CV). The measured glucose concentrations were used to alter insulin infusion rates according to established treatment protocols. Changes in true glucose were calculated hourly on the basis of the insulin infusion rate, the modeled patient's insulin sensitivity, and a model of glucose homeostasis. We modeled 18 000 patients, equally divided between the hourly and every-5-min measurement schemas and distributed among 45 combinations of bias and imprecision and 2 treatment protocols. RESULTS With both treatment protocols and both measurement frequencies, higher measurement imprecision increased the rates of hypoglycemia and hyperglycemia and increased glycemic variability (SD). These adverse effects of measurement imprecision were lower at the higher measurement frequency. The rate of hypoglycemia at an imprecision (CV) of 5% with hourly measurements was similar to the rate of hypoglycemia at 10% CV when measurements were made every 5 min. With measurements every 5 min, imprecision up to 10% had minimal effects on hyperglycemia or glycemic variability. Effects of simulated analytical bias on glycemia were unaffected by measurement frequency. CONCLUSIONS Quality specifications for imprecision of glucose meters are not transferable to CGM.

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 863-P
Author(s):  
YUAN ZHANG ◽  
EVAN A. OLAWSKY ◽  
ALISON C. ALVEAR ◽  
LYNN E. EBERLY ◽  
LISA S. CHOW

2018 ◽  
Vol 14 (4) ◽  
pp. 395-403 ◽  
Author(s):  
Karem Mileo Felício ◽  
Ana Carolina Contente Braga de Souza ◽  
Joao Felicio Abrahao Neto ◽  
Franciane Trindade Cunha de Melo ◽  
Carolina Tavares Carvalho ◽  
...  

2021 ◽  
pp. 193229682110289
Author(s):  
Evan Olawsky ◽  
Yuan Zhang ◽  
Lynn E Eberly ◽  
Erika S Helgeson ◽  
Lisa S Chow

Background: With the development of continuous glucose monitoring systems (CGMS), detailed glycemic data are now available for analysis. Yet analysis of this data-rich information can be formidable. The power of CGMS-derived data lies in its characterization of glycemic variability. In contrast, many standard glycemic measures like hemoglobin A1c (HbA1c) and self-monitored blood glucose inadequately describe glycemic variability and run the risk of bias toward overreporting hyperglycemia. Methods that adjust for this bias are often overlooked in clinical research due to difficulty of computation and lack of accessible analysis tools. Methods: In response, we have developed a new R package rGV, which calculates a suite of 16 glycemic variability metrics when provided a single individual’s CGM data. rGV is versatile and robust; it is capable of handling data of many formats from many sensor types. We also created a companion R Shiny web app that provides these glycemic variability analysis tools without prior knowledge of R coding. We analyzed the statistical reliability of all the glycemic variability metrics included in rGV and illustrate the clinical utility of rGV by analyzing CGM data from three studies. Results: In subjects without diabetes, greater glycemic variability was associated with higher HbA1c values. In patients with type 2 diabetes mellitus (T2DM), we found that high glucose is the primary driver of glycemic variability. In patients with type 1 diabetes (T1DM), we found that naltrexone use may potentially reduce glycemic variability. Conclusions: We present a new R package and accompanying web app to facilitate quick and easy computation of a suite of glycemic variability metrics.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fumi Uemura ◽  
Yosuke Okada ◽  
Keiichi Torimoto ◽  
Yoshiya Tanaka

AbstractTime in range (TIR) is an index of glycemic control obtained from continuous glucose monitoring (CGM). The aim was to compare the glycemic variability of treatment with sulfonylureas (SUs) in type 2 diabetes mellitus (T2DM) with well-controlled glucose level (TIR > 70%). The study subjects were 123 patients selected T2DM who underwent CGM more than 24 h on admission without changing treatment. The primary endpoint was the difference in glycemic variability, while the secondary endpoint was the difference in time below range < 54 mg/dL; TBR < 54, between the SU (n = 63) and non-SU (n = 60) groups. The standard deviation, percentage coefficient of variation (%CV), and maximum glucose level were higher in the SU group than in the non-SU group, and TBR < 54 was longer in the high-dose SU patients. SU treatment was identified as a significant factor that affected %CV (β: 2.678, p = 0.034). High-dose SU use contributed to prolonged TBR < 54 (β: 0.487, p = 0.028). Our study identified enlarged glycemic variability in sulfonylurea-treated well-controlled T2DM patients and high-dose SU use was associated with TBR < 54. The results highlight the need for careful adjustment of the SU dose, irrespective of glycated hemoglobin level or TIR value.


2021 ◽  
Vol 9 (1) ◽  
pp. e002032
Author(s):  
Marcela Martinez ◽  
Jimena Santamarina ◽  
Adrian Pavesi ◽  
Carla Musso ◽  
Guillermo E Umpierrez

Glycated hemoglobin is currently the gold standard for assessment of long-term glycemic control and response to medical treatment in patients with diabetes. Glycated hemoglobin, however, does not address fluctuations in blood glucose. Glycemic variability (GV) refers to fluctuations in blood glucose levels. Recent clinical data indicate that GV is associated with increased risk of hypoglycemia, microvascular and macrovascular complications, and mortality in patients with diabetes, independently of glycated hemoglobin level. The use of continuous glucose monitoring devices has markedly improved the assessment of GV in clinical practice and facilitated the assessment of GV as well as hypoglycemia and hyperglycemia events in patients with diabetes. We review current concepts on the definition and assessment of GV and its association with cardiovascular complications in patients with type 2 diabetes.


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