Original

Intensive Care Medicine

, Volume 36, Issue 9, pp 1556-1565

Open Access This content is freely available online to anyone, anywhere at any time.

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

  • Saeid EslamiAffiliated withDepartment of Medical Informatics, Academic Medical Center, University of Amsterdam Email author 
  • , Ameen Abu-HannaAffiliated withDepartment of Medical Informatics, Academic Medical Center, University of Amsterdam
  • , Nicolette F. de KeizerAffiliated withDepartment of Medical Informatics, Academic Medical Center, University of Amsterdam
  • , Rob J. BosmanAffiliated withDepartment of Intensive Care, OLVG Hospital
  • , Peter E. SpronkAffiliated withLaboratory of Experimental Intensive Care and Anesthesiology, Academic Medical Center, University of AmsterdamDepartment of Intensive Care, Gelre Hospital
  • , Evert de JongeAffiliated withDepartment of Intensive Care, Leiden University Medical Center
  • , Marcus J. SchultzAffiliated withDepartment of Intensive Care, Academic Medical Center, University of AmsterdamLaboratory of Experimental Intensive Care and Anesthesiology, Academic Medical Center, University of AmsterdamHERMES Critical Care Group

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.

Keywords

Glucose control Glucose regulation Critical care Clinical guideline Statistical process control Decision support system