Cargando…

Reducing the impact of insulin sensitivity variability on glycaemic outcomes using separate stochastic models within the STAR glycaemic protocol

BACKGROUND: The metabolism of critically ill patients evolves dynamically over time. Post critical insult, levels of counter-regulatory hormones are significantly elevated, but decrease rapidly over the first 12–48 hours in the intensive care unit (ICU). These hormones have a direct physiological im...

Descripción completa

Detalles Bibliográficos
Autores principales: Thomas, Felicity, Pretty, Christopher G, Fisk, Liam, Shaw, Geoffrey M, Chase, J Geoffrey, Desaive, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4013832/
https://www.ncbi.nlm.nih.gov/pubmed/24739335
http://dx.doi.org/10.1186/1475-925X-13-43
_version_ 1782315112232648704
author Thomas, Felicity
Pretty, Christopher G
Fisk, Liam
Shaw, Geoffrey M
Chase, J Geoffrey
Desaive, Thomas
author_facet Thomas, Felicity
Pretty, Christopher G
Fisk, Liam
Shaw, Geoffrey M
Chase, J Geoffrey
Desaive, Thomas
author_sort Thomas, Felicity
collection PubMed
description BACKGROUND: The metabolism of critically ill patients evolves dynamically over time. Post critical insult, levels of counter-regulatory hormones are significantly elevated, but decrease rapidly over the first 12–48 hours in the intensive care unit (ICU). These hormones have a direct physiological impact on insulin sensitivity (SI). Understanding the variability of SI is important for safely managing glycaemic levels and understanding the evolution of patient condition. The objective of this study is to assess the evolution of SI over the first two days of ICU stay, and using this data, propose a separate stochastic model to reduce the impact of SI variability during glycaemic control using the STAR glycaemic control protocol. METHODS: The value of SI was identified hourly for each patient using a validated physiological model. Variability of SI was then calculated as the hour-to-hour percentage change in SI. SI was examined using 6 hour blocks of SI to display trends while mitigating the effects of noise. To reduce the impact of SI variability on achieving glycaemic control a new stochastic model for the most variable period, 0–18 hours, was generated. Virtual simulations were conducted using an existing glycaemic control protocol (STAR) to investigate the clinical impact of using this separate stochastic model during this period of increased metabolic variability. RESULTS: For the first 18 hours, over 80% of all SI values were less than 0.5 × 10(-3) L/mU.min, compared to 65% for >18 hours. Using the new stochastic model for the first 18 hours of ICU stay reduced the number of hypoglycaemic measurements during virtual trials. For time spent below 4.4, 4.0, and 3.0 mmol/L absolute reductions of 1.1%, 0.8% and 0.1% were achieved, respectively. No severe hypoglycaemic events (BG < 2.2 mmol/L) occurred for either case. CONCLUSIONS: SI levels increase significantly, while variability decreases during the first 18 hours of a patients stay in ICU. Virtual trials, using a separate stochastic model for this period, demonstrated a reduction in variability and hypoglycaemia during the first 18 hours without adversely affecting the overall level of control. Thus, use of multiple models can reduce the impact of SI variability during model-based glycaemic control.
format Online
Article
Text
id pubmed-4013832
institution National Center for Biotechnology Information
language English
publishDate 2014
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-40138322014-05-23 Reducing the impact of insulin sensitivity variability on glycaemic outcomes using separate stochastic models within the STAR glycaemic protocol Thomas, Felicity Pretty, Christopher G Fisk, Liam Shaw, Geoffrey M Chase, J Geoffrey Desaive, Thomas Biomed Eng Online Research BACKGROUND: The metabolism of critically ill patients evolves dynamically over time. Post critical insult, levels of counter-regulatory hormones are significantly elevated, but decrease rapidly over the first 12–48 hours in the intensive care unit (ICU). These hormones have a direct physiological impact on insulin sensitivity (SI). Understanding the variability of SI is important for safely managing glycaemic levels and understanding the evolution of patient condition. The objective of this study is to assess the evolution of SI over the first two days of ICU stay, and using this data, propose a separate stochastic model to reduce the impact of SI variability during glycaemic control using the STAR glycaemic control protocol. METHODS: The value of SI was identified hourly for each patient using a validated physiological model. Variability of SI was then calculated as the hour-to-hour percentage change in SI. SI was examined using 6 hour blocks of SI to display trends while mitigating the effects of noise. To reduce the impact of SI variability on achieving glycaemic control a new stochastic model for the most variable period, 0–18 hours, was generated. Virtual simulations were conducted using an existing glycaemic control protocol (STAR) to investigate the clinical impact of using this separate stochastic model during this period of increased metabolic variability. RESULTS: For the first 18 hours, over 80% of all SI values were less than 0.5 × 10(-3) L/mU.min, compared to 65% for >18 hours. Using the new stochastic model for the first 18 hours of ICU stay reduced the number of hypoglycaemic measurements during virtual trials. For time spent below 4.4, 4.0, and 3.0 mmol/L absolute reductions of 1.1%, 0.8% and 0.1% were achieved, respectively. No severe hypoglycaemic events (BG < 2.2 mmol/L) occurred for either case. CONCLUSIONS: SI levels increase significantly, while variability decreases during the first 18 hours of a patients stay in ICU. Virtual trials, using a separate stochastic model for this period, demonstrated a reduction in variability and hypoglycaemia during the first 18 hours without adversely affecting the overall level of control. Thus, use of multiple models can reduce the impact of SI variability during model-based glycaemic control. BioMed Central 2014-04-16 /pmc/articles/PMC4013832/ /pubmed/24739335 http://dx.doi.org/10.1186/1475-925X-13-43 Text en Copyright © 2014 Thomas et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Thomas, Felicity
Pretty, Christopher G
Fisk, Liam
Shaw, Geoffrey M
Chase, J Geoffrey
Desaive, Thomas
Reducing the impact of insulin sensitivity variability on glycaemic outcomes using separate stochastic models within the STAR glycaemic protocol
title Reducing the impact of insulin sensitivity variability on glycaemic outcomes using separate stochastic models within the STAR glycaemic protocol
title_full Reducing the impact of insulin sensitivity variability on glycaemic outcomes using separate stochastic models within the STAR glycaemic protocol
title_fullStr Reducing the impact of insulin sensitivity variability on glycaemic outcomes using separate stochastic models within the STAR glycaemic protocol
title_full_unstemmed Reducing the impact of insulin sensitivity variability on glycaemic outcomes using separate stochastic models within the STAR glycaemic protocol
title_short Reducing the impact of insulin sensitivity variability on glycaemic outcomes using separate stochastic models within the STAR glycaemic protocol
title_sort reducing the impact of insulin sensitivity variability on glycaemic outcomes using separate stochastic models within the star glycaemic protocol
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4013832/
https://www.ncbi.nlm.nih.gov/pubmed/24739335
http://dx.doi.org/10.1186/1475-925X-13-43
work_keys_str_mv AT thomasfelicity reducingtheimpactofinsulinsensitivityvariabilityonglycaemicoutcomesusingseparatestochasticmodelswithinthestarglycaemicprotocol
AT prettychristopherg reducingtheimpactofinsulinsensitivityvariabilityonglycaemicoutcomesusingseparatestochasticmodelswithinthestarglycaemicprotocol
AT fiskliam reducingtheimpactofinsulinsensitivityvariabilityonglycaemicoutcomesusingseparatestochasticmodelswithinthestarglycaemicprotocol
AT shawgeoffreym reducingtheimpactofinsulinsensitivityvariabilityonglycaemicoutcomesusingseparatestochasticmodelswithinthestarglycaemicprotocol
AT chasejgeoffrey reducingtheimpactofinsulinsensitivityvariabilityonglycaemicoutcomesusingseparatestochasticmodelswithinthestarglycaemicprotocol
AT desaivethomas reducingtheimpactofinsulinsensitivityvariabilityonglycaemicoutcomesusingseparatestochasticmodelswithinthestarglycaemicprotocol