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

Eslami S, Abu-Hanna A, de Keizer NF, Bosman RJ, Spronk PE, de Jonge E, Schultz MJ - Intensive Care Med (2010)

Bottom Line: Effects of implementing local GC guidelines and guideline revisions on effectiveness/efficiency-related indicators, safety-related indicators, and protocol-related indicators were measured.The introduction of simple rules on GC had the largest effect.All of them were associated with an increase in hypoglycemia events, but GC was never stopped.

View Article: PubMed Central - PubMed

Affiliation: Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Meibergdreef 15, 1105 AZ, Amsterdam, The Netherlands. s.eslami@amc.uva.nl

ABSTRACT

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.

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Control charts of mean BGL, time to reach target range, percentage of BGLs in range predefined in the protocols, and percentage of BGLs between 63 and 150 mg/dl (efficiency-related indicators). An asterisk means that the indicator was not only influenced by performance but also by definition of targets, and that because of the latter sharp changes over time could be recognized. When the data points are, without any special-cause variation, within the process control limits then the process is said to be “in control” and stable. Common rules for distinguishing a special-cause variation (i.e., a structural change): one or more points above or below the process control limit, a run of eight (or seven) or more points on one side of the center line, two out of three consecutive points appearing beyond 2 sigmas on the same side of the center line, a run of eight (or seven) or more points all trending up or down. Because the time of intervention (major changes) is known and because the process was stable (i.e., not “out of control” according to the SPC rules) before and after the intervention, the mean and process control limits are recalculated in the intervention period
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Fig1: Control charts of mean BGL, time to reach target range, percentage of BGLs in range predefined in the protocols, and percentage of BGLs between 63 and 150 mg/dl (efficiency-related indicators). An asterisk means that the indicator was not only influenced by performance but also by definition of targets, and that because of the latter sharp changes over time could be recognized. When the data points are, without any special-cause variation, within the process control limits then the process is said to be “in control” and stable. Common rules for distinguishing a special-cause variation (i.e., a structural change): one or more points above or below the process control limit, a run of eight (or seven) or more points on one side of the center line, two out of three consecutive points appearing beyond 2 sigmas on the same side of the center line, a run of eight (or seven) or more points all trending up or down. Because the time of intervention (major changes) is known and because the process was stable (i.e., not “out of control” according to the SPC rules) before and after the intervention, the mean and process control limits are recalculated in the intervention period

Mentions: Figure 1 shows the quality process control charts for the effectiveness/efficiency-related indicators of GC in the three ICUs. Mean BGL decreased and became “out of process control” (i.e., a change was detected) after implementing the GC guideline in all three ICUs. The introduction of simple rules on GC had the largest effect in ICU-A, since GC became more stable with less variation. Subsequent changes in the guidelines did not have effects as large as the introduction of the guideline itself. Mean BGL in ICU-B was reduced by the introduction of the guideline but the mean BGL still remained higher and less stable from quarter to quarter than in the other two ICUs. Similar to ICU-A, in ICU-C the implementation of the GC guideline had a large effect on BGL, but with the introduction of the computerized decision support system (CDSS) the mean BGL decreased further and GC stabilized.Fig. 1


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

Eslami S, Abu-Hanna A, de Keizer NF, Bosman RJ, Spronk PE, de Jonge E, Schultz MJ - Intensive Care Med (2010)

Control charts of mean BGL, time to reach target range, percentage of BGLs in range predefined in the protocols, and percentage of BGLs between 63 and 150 mg/dl (efficiency-related indicators). An asterisk means that the indicator was not only influenced by performance but also by definition of targets, and that because of the latter sharp changes over time could be recognized. When the data points are, without any special-cause variation, within the process control limits then the process is said to be “in control” and stable. Common rules for distinguishing a special-cause variation (i.e., a structural change): one or more points above or below the process control limit, a run of eight (or seven) or more points on one side of the center line, two out of three consecutive points appearing beyond 2 sigmas on the same side of the center line, a run of eight (or seven) or more points all trending up or down. Because the time of intervention (major changes) is known and because the process was stable (i.e., not “out of control” according to the SPC rules) before and after the intervention, the mean and process control limits are recalculated in the intervention period
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2926931&req=5

Fig1: Control charts of mean BGL, time to reach target range, percentage of BGLs in range predefined in the protocols, and percentage of BGLs between 63 and 150 mg/dl (efficiency-related indicators). An asterisk means that the indicator was not only influenced by performance but also by definition of targets, and that because of the latter sharp changes over time could be recognized. When the data points are, without any special-cause variation, within the process control limits then the process is said to be “in control” and stable. Common rules for distinguishing a special-cause variation (i.e., a structural change): one or more points above or below the process control limit, a run of eight (or seven) or more points on one side of the center line, two out of three consecutive points appearing beyond 2 sigmas on the same side of the center line, a run of eight (or seven) or more points all trending up or down. Because the time of intervention (major changes) is known and because the process was stable (i.e., not “out of control” according to the SPC rules) before and after the intervention, the mean and process control limits are recalculated in the intervention period
Mentions: Figure 1 shows the quality process control charts for the effectiveness/efficiency-related indicators of GC in the three ICUs. Mean BGL decreased and became “out of process control” (i.e., a change was detected) after implementing the GC guideline in all three ICUs. The introduction of simple rules on GC had the largest effect in ICU-A, since GC became more stable with less variation. Subsequent changes in the guidelines did not have effects as large as the introduction of the guideline itself. Mean BGL in ICU-B was reduced by the introduction of the guideline but the mean BGL still remained higher and less stable from quarter to quarter than in the other two ICUs. Similar to ICU-A, in ICU-C the implementation of the GC guideline had a large effect on BGL, but with the introduction of the computerized decision support system (CDSS) the mean BGL decreased further and GC stabilized.Fig. 1

Bottom Line: Effects of implementing local GC guidelines and guideline revisions on effectiveness/efficiency-related indicators, safety-related indicators, and protocol-related indicators were measured.The introduction of simple rules on GC had the largest effect.All of them were associated with an increase in hypoglycemia events, but GC was never stopped.

View Article: PubMed Central - PubMed

Affiliation: Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Meibergdreef 15, 1105 AZ, Amsterdam, The Netherlands. s.eslami@amc.uva.nl

ABSTRACT

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.

Show MeSH
Related in: MedlinePlus