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Implementing glucose control in intensive care: a multicenter trial using statistical process control

BACKGROUND: Glucose control (GC) with insulin decreases morbidity and mortality of critically ill patients. In this study we investigated GC performance over time during implementation of GC strategies within three intensive care units (ICUs) and in routine clinical practice. METHODS: All adult crit...

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Autores principales: Eslami, Saeid, Abu-Hanna, Ameen, de Keizer, Nicolette F., Bosman, Rob J., Spronk, Peter E., de Jonge, Evert, Schultz, Marcus J.
Formato: Texto
Lenguaje:English
Publicado: Springer-Verlag 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2926931/
https://www.ncbi.nlm.nih.gov/pubmed/20533024
http://dx.doi.org/10.1007/s00134-010-1924-3
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author Eslami, Saeid
Abu-Hanna, Ameen
de Keizer, Nicolette F.
Bosman, Rob J.
Spronk, Peter E.
de Jonge, Evert
Schultz, Marcus J.
author_facet Eslami, Saeid
Abu-Hanna, Ameen
de Keizer, Nicolette F.
Bosman, Rob J.
Spronk, Peter E.
de Jonge, Evert
Schultz, Marcus J.
author_sort Eslami, Saeid
collection PubMed
description BACKGROUND: Glucose control (GC) with insulin decreases morbidity and mortality of critically ill patients. In this study we investigated GC performance over time during implementation of GC strategies within three intensive care units (ICUs) and in routine clinical practice. METHODS: All adult critically ill patients who stayed for >24 h between 1999 and 2007 were included. Effects of implementing local GC guidelines and guideline revisions on effectiveness/efficiency-related indicators, safety-related indicators, and protocol-related indicators were measured. RESULTS: Data of 17,111 patient admissions were evaluated, with 714,141 available blood glucose levels (BGL) measurements. Mean BGL, time to reach target, hyperglycemia index, sampling frequency, percentage of hyperglycemia events, and in-range measurements statistically changed after introducing GC in all ICUs. The introduction of simple rules on GC had the largest effect. Subsequent changes in the protocol had a smaller effect than the introduction of the protocol itself. As soon as the protocol was introduced, in all ICUs the percentage of hypoglycemia events increased. Various revisions were implemented to reduce hypoglycemia events, but levels never returned to those from pre-implementation. More intensive implementation strategies including the use of a decision support system resulted in better control of the process. CONCLUSION: There are various strategies to achieve GC in routine clinical practice but with variable success. All of them were associated with an increase in hypoglycemia events, but GC was never stopped. Instead, these events have been accepted and managed. Statistical process control is a useful tool for monitoring phenomena over time and captures within-institution changes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00134-010-1924-3) contains supplementary material, which is available to authorized users.
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spelling pubmed-29269312010-08-27 Implementing glucose control in intensive care: a multicenter trial using statistical process control Eslami, Saeid Abu-Hanna, Ameen de Keizer, Nicolette F. Bosman, Rob J. Spronk, Peter E. de Jonge, Evert Schultz, Marcus J. Intensive Care Med Original BACKGROUND: Glucose control (GC) with insulin decreases morbidity and mortality of critically ill patients. In this study we investigated GC performance over time during implementation of GC strategies within three intensive care units (ICUs) and in routine clinical practice. METHODS: All adult critically ill patients who stayed for >24 h between 1999 and 2007 were included. Effects of implementing local GC guidelines and guideline revisions on effectiveness/efficiency-related indicators, safety-related indicators, and protocol-related indicators were measured. RESULTS: Data of 17,111 patient admissions were evaluated, with 714,141 available blood glucose levels (BGL) measurements. Mean BGL, time to reach target, hyperglycemia index, sampling frequency, percentage of hyperglycemia events, and in-range measurements statistically changed after introducing GC in all ICUs. The introduction of simple rules on GC had the largest effect. Subsequent changes in the protocol had a smaller effect than the introduction of the protocol itself. As soon as the protocol was introduced, in all ICUs the percentage of hypoglycemia events increased. Various revisions were implemented to reduce hypoglycemia events, but levels never returned to those from pre-implementation. More intensive implementation strategies including the use of a decision support system resulted in better control of the process. CONCLUSION: There are various strategies to achieve GC in routine clinical practice but with variable success. All of them were associated with an increase in hypoglycemia events, but GC was never stopped. Instead, these events have been accepted and managed. Statistical process control is a useful tool for monitoring phenomena over time and captures within-institution changes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00134-010-1924-3) contains supplementary material, which is available to authorized users. Springer-Verlag 2010-06-09 2010 /pmc/articles/PMC2926931/ /pubmed/20533024 http://dx.doi.org/10.1007/s00134-010-1924-3 Text en © The Author(s) 2010 https://creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
spellingShingle Original
Eslami, Saeid
Abu-Hanna, Ameen
de Keizer, Nicolette F.
Bosman, Rob J.
Spronk, Peter E.
de Jonge, Evert
Schultz, Marcus J.
Implementing glucose control in intensive care: a multicenter trial using statistical process control
title Implementing glucose control in intensive care: a multicenter trial using statistical process control
title_full Implementing glucose control in intensive care: a multicenter trial using statistical process control
title_fullStr Implementing glucose control in intensive care: a multicenter trial using statistical process control
title_full_unstemmed Implementing glucose control in intensive care: a multicenter trial using statistical process control
title_short Implementing glucose control in intensive care: a multicenter trial using statistical process control
title_sort implementing glucose control in intensive care: a multicenter trial using statistical process control
topic Original
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2926931/
https://www.ncbi.nlm.nih.gov/pubmed/20533024
http://dx.doi.org/10.1007/s00134-010-1924-3
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