Process monitoring in intensive care with the use of cumulative expected minus observed mortality and risk-adjusted p charts

Cockings, Jerome G. L., Cook, David A. and Iqbal, Rehana K. (2006) Process monitoring in intensive care with the use of cumulative expected minus observed mortality and risk-adjusted p charts. Critical Care, 10 1: Article number R28. doi:10.1186/cc3996

Author Cockings, Jerome G. L.
Cook, David A.
Iqbal, Rehana K.
Title Process monitoring in intensive care with the use of cumulative expected minus observed mortality and risk-adjusted p charts
Journal name Critical Care   Check publisher's open access policy
ISSN 1364-8535
Publication date 2006-01-01
Sub-type Article (original research)
DOI 10.1186/cc3996
Open Access Status DOI
Volume 10
Issue 1
Start page Article number R28
Total pages 9
Place of publication United Kingdom
Publisher Biomed Central Ltd
Language eng
Subject 110310 Intensive Care
010401 Applied Statistics
Abstract Introduction A health care system is a complex adaptive system. The effect of a single intervention, incorporated into a complex clinical environment, may be different from that expected. A national database such as the Intensive Care National Audit & Research Centre (ICNARC) Case Mix Programme in the UK represents a centralised monitoring, surveillance and reporting system for retrospective quality and comparative audit. This can be supplemented with real-time process monitoring at a local level for continuous process improvement, allowing early detection of the impact of both unplanned and deliberately imposed changes in the clinical environment. Methods Demographic and UK Acute Physiology and Chronic Health Evaluation II (APACHE II) data were prospectively collected on all patients admitted to a UK regional hospital between 1 January 2003 and 30 June 2004 in accordance with the ICNARC Case Mix Programme. We present a cumulative expected minus observed (E-O) plot and the risk-adjusted p chart as methods of continuous process monitoring. We describe the construction and interpretation of these charts and show how they can be used to detect planned or unplanned organisational process changes affecting mortality outcomes. Results Five hundred and eighty-nine adult patients were included. The overall death rate was 0.78 of predicted. Calibration showed excess survival in ranges above 30% risk of death. The E-O plot confirmed a survival above that predicted. Small transient variations were seen in the slope that could represent random effects, or real but transient changes in the quality of care. The risk-adjusted p chart showed several observations below the 2 SD control limits of the expected mortality rate. These plots provide rapid analysis of risk-adjusted performance suitable for local application and interpretation. The E-O chart provided rapid easily visible feedback of changes in risk-adjusted mortality, while the risk-adjusted p chart allowed statistical evaluation. Conclusion Local analysis of risk-adjusted mortality data with an E-O plot and a risk-adjusted p chart is feasible and allows the rapid detection of changes in risk-adjusted outcome of intensive care patients. This complements the centralised national database, which is more archival and comparative in nature.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Unknown

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Medicine Publications
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Citation counts: TR Web of Science Citation Count  Cited 13 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 15 times in Scopus Article | Citations
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Created: Thu, 02 Apr 2009, 01:49:31 EST by Sophie Jordan on behalf of Anaesthesiology and Critical Care - PAH