A Model of Self-Monitoring Blood Glucose Measurement Error

Background: A reliable model of the probability density function (PDF) of self-monitoring of blood glucose (SMBG) measurement error would be important for several applications in diabetes, like testing in silico insulin therapies. In the literature, the PDF of SMBG error is usually described by a Gaussian function, whose symmetry and simplicity are unable to properly describe the variability of experimental data. Here, we propose a new methodology to derive more realistic models of SMBG error PDF. Methods: The blood glucose range is divided into zones where error (absolute or relative) presents a constant standard deviation (SD). In each zone, a suitable PDF model is fitted by maximum-likelihood to experimental data. Model validation is performed by goodness-of-fit tests. The method is tested on two databases collected by the One Touch Ultra 2 (OTU2; Lifescan Inc, Milpitas, CA) and the Bayer Contour Next USB (BCN; Bayer HealthCare LLC, Diabetes Care, Whippany, NJ). In both cases, skew-normal and exponential models are used to describe the distribution of errors and outliers, respectively. Results: Two zones were identified: zone 1 with constant SD absolute error; zone 2 with constant SD relative error. Goodness-of-fit tests confirmed that identified PDF models are valid and superior to Gaussian models used so far in the literature. Conclusions: The proposed methodology allows to derive realistic models of SMBG error PDF. These models can be used in several investigations of present interest in the scientific community, for example, to perform in silico clinical trials to compare SMBG-based with nonadjunctive CGM-based insulin treatments.

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An in silico method to identify computer-based protocols worthy of clinical study: An insulin infusion protocol use case

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocv067 ◽

2015 ◽

Vol 23 (2) ◽

pp. 283-288 ◽

Cited By ~ 3

Author(s):

Anthony F Wong ◽

Ulrike Pielmeier ◽

Peter J Haug ◽

Steen Andreassen ◽

Alan H Morris

Keyword(s):

Blood Glucose ◽

In Silico ◽

Insulin Infusion ◽

Blood Glucose Measurement ◽

Glucose Measurement ◽

Rank Test ◽

Insulin Infusion Rate ◽

Signed Rank ◽

Signed Rank Test ◽

Computer Based

Abstract Objective Develop an efficient non-clinical method for identifying promising computer-based protocols for clinical study. An in silico comparison can provide information that informs the decision to proceed to a clinical trial. The authors compared two existing computer-based insulin infusion protocols: eProtocol-insulin from Utah, USA, and Glucosafe from Denmark. Materials and Methods The authors used eProtocol-insulin to manage intensive care unit (ICU) hyperglycemia with intravenous (IV) insulin from 2004 to 2010. Recommendations accepted by the bedside clinicians directly link the subsequent blood glucose values to eProtocol-insulin recommendations and provide a unique clinical database. The authors retrospectively compared in silico 18 984 eProtocol-insulin continuous IV insulin infusion rate recommendations from 408 ICU patients with those of Glucosafe, the candidate computer-based protocol. The subsequent blood glucose measurement value (low, on target, high) was used to identify if the insulin recommendation was too high, on target, or too low. Results Glucosafe consistently provided more favorable continuous IV insulin infusion rate recommendations than eProtocol-insulin for on target (64% of comparisons), low (80% of comparisons), or high (70% of comparisons) blood glucose. Aggregated eProtocol-insulin and Glucosafe continuous IV insulin infusion rates were clinically similar though statistically significantly different (Wilcoxon signed rank test P = .01). In contrast, when stratified by low, on target, or high subsequent blood glucose measurement, insulin infusion rates from eProtocol-insulin and Glucosafe were statistically significantly different (Wilcoxon signed rank test, P < .001), and clinically different. Discussion This in silico comparison appears to be an efficient nonclinical method for identifying promising computer-based protocols. Conclusion Preclinical in silico comparison analytical framework allows rapid and inexpensive identification of computer-based protocol care strategies that justify expensive and burdensome clinical trials.

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Improving the Quality of Self-Monitoring Blood Glucose Measurement: A Study in Reducing Calibration Errors

Diabetes Technology & Therapeutics ◽

10.1089/dia.2006.8.347 ◽

2006 ◽

Vol 8 (3) ◽

pp. 347-357 ◽

Cited By ~ 33

Author(s):

John M. Baum ◽

Nanette M. Monhaut ◽

Donald R. Parker ◽

Christopher P. Price

Keyword(s):

Blood Glucose ◽

Blood Glucose Measurement ◽

Glucose Measurement ◽

Self Monitoring

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Hemoglobin A1C as an indicator of the accuracy of blood glucose measurement: comparative study of three technologies used for blood glucose measurements

JOURNAL OF ADVANCES IN BIOTECHNOLOGY ◽

10.24297/jbt.v6i3.6262 ◽

2017 ◽

Vol 6 (3) ◽

pp. 919-923

Author(s):

Hassan Almarshad

Keyword(s):

Blood Glucose ◽

Comparative Study ◽

Hemoglobin A1c ◽

Glucose Monitoring ◽

Blood Glucose Measurement ◽

Blood Glucose Monitoring ◽

Glucose Measurement ◽

Self Monitoring ◽

Blood Glucose Self Monitoring ◽

Monitoring Devices

In previous studies, the accuracy of glucose measurements were found with significant variations in different self-monitoring devices. This study suggests Hemoglobin a1c (Hba1c) to be used as as an indicator for the accuracy of blood-glucose monitoring devices. In this study, the association between the readings of glycohematoglobin HbA1C and the hyperglycemic readings of thirty hyperglycemic patients is used as an indicator of the accuracy of three types of glucometer devices. The association between hyperglycemic readings and the percentage of HbA1C for the same patients was investigated. The results showed significant association between levels of blood glucose and the percentage of HbA1C in three devices with statistically significant ( p < 0.05). Such relationship is suggested to be used as a relative accuracy of various types of blood glucose self-monitoring devices.

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