Impact of sensor and measurement timing errors on model-based insulin sensitivity

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• 作者：Christopher G. Pretty^a ; ^{c.pretty@ulg.ac.be" class="auth_mail} ; ^{cgpresearch@gmail.com" class="auth_mail} ; Matthew Signal^b ; ^{matthew.signal@pg.canterbury.ac.nz" class="auth_mail} ; Liam Fisk^b ; ^{liam.fisk@pg.canterbury.ac.nz" class="auth_mail} ; Sophie Penning^a ; ^{sophie.penning@ulg.ac.be" class="auth_mail} ; Aaron Le Compte^b ; ^{aaron.lecompte@canterbury.ac.nz" class="auth_mail} ; Geoffrey M. Shaw^c ; ^{geoff.shaw@cdhb.govt.nz" class="auth_mail} ; Thomas Desaive^a ; ^{tdesaive@ulg.ac.be" class="auth_mail} ; J. Geoffrey Chase^b ; ^{geoff.chase@canterbury.ac.nz" class="auth_mail}
• 关键词：Biomedical system modelling ; Simulation and visualization ; Control of physiological and clinical variables
• 刊名：Computer Methods and Programs in Biomedicine
• 出版年：May, 2014
• 年：2014
• 卷：114
• 期：3
• 页码：e79-e86
• 全文大小：1552 K

文摘

A model-based insulin sensitivity parameter (SI) is often used in glucose-insulin system models to define the glycaemic response to insulin. As a parameter identified from clinical data, insulin sensitivity can be affected by blood glucose (BG) sensor error and measurement timing error, which can subsequently impact analyses or glycaemic variability during control. This study assessed the impact of both measurement timing and BG sensor errors on identified values of SI and its hour-to-hour variability within a common type of glucose-insulin system model.

Retrospective clinical data were used from 270 patients admitted to the Christchurch Hospital ICU between 2005 and 2007 to identify insulin sensitivity profiles. We developed error models for the Abbott Optium Xceed glucometer and measurement timing from clinical data. The effect of these errors on the re-identified insulin sensitivity was investigated by Monte-Carlo analysis.

The results of the study show that timing errors in isolation have little clinically significant impact on identified SI level or variability. The clinical impact of changes to SI level induced by combined sensor and timing errors is likely to be significant during glycaemic control. Identified values of SI were mostly (90th percentile) within 29% of the true value when influenced by both sources of error. However, these effects may be overshadowed by physiological factors arising from the critical condition of the patients or other under-modelled or un-modelled dynamics. Thus, glycaemic control protocols that are designed to work with data from glucometers need to be robust to these errors and not be too aggressive in dosing insulin.