Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/89273
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Type: Journal article
Title: Baseline hospital performance and the impact of medical emergency teams: modelling vs. conventional subgroup analysis
Author: Chen, J.
Flabouris, A.
Bellomo, R.
Hillman, K.
Finfer, S.
MERIT Investigators
Citation: Trials, 2009; 10(1):117-1-117-11
Publisher: BioMed Central
Issue Date: 2009
ISSN: 1745-6215
1745-6215
Statement of
Responsibility: 
Jack Chen, Arthas Flabouris, Rinaldo Bellomo, Ken Hillman, Simon Finfer, and MERIT investigators for the Simpson Centre and the ANZICS Clinical Trial Group
Abstract: BACKGROUND: To compare two approaches to the statistical analysis of the relationship between the baseline incidence of adverse events and the effect of medical emergency teams (METs). METHODS: Using data from a cluster randomized controlled trial (the MERIT study), we analysed the relationship between the baseline incidence of adverse events and its change from baseline to the MET activation phase using quadratic modelling techniques. We compared the findings with those obtained with conventional subgroup analysis. RESULTS: Using linear and quadratic modelling techniques, we found that each unit increase in the baseline incidence of adverse events in MET hospitals was associated with a 0.59 unit subsequent reduction in adverse events (95%CI: 0.33 to 0.86) after MET implementation and activation. This applied to cardiac arrests (0.74; 95%CI: 0.52 to 0.95), unplanned ICU admissions (0.56; 95%CI: 0.26 to 0.85) and unexpected deaths (0.68; 95%CI: 0.45 to 0.90). Control hospitals showed a similar reduction only for cardiac arrests (0.95; 95%CI: 0.56 to 1.32). Comparison using conventional subgroup analysis, on the other hand, detected no significant difference between MET and control hospitals. CONCLUSIONS: Our study showed that, in the MERIT study, when there was dependence of treatment effect on baseline performance, an approach based on regression modelling helped illustrate the nature and magnitude of such dependence while sub-group analysis did not. The ability to assess the nature and magnitude of such dependence may have policy implications. Regression technique may thus prove useful in analysing data when there is a conditional treatment effect.
Keywords: MERIT investigators for the Simpson Centre
ANZICS Clinical Trial Group
Humans
Heart Arrest
Incidence
Hospital Mortality
Models, Statistical
Linear Models
Cardiopulmonary Resuscitation
Intensive Care Units
Emergency Service, Hospital
Outcome Assessment (Health Care)
Rights: © 2009 Chen et al; licensee BioMed Central Ltd. 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 cited.
DOI: 10.1186/1745-6215-10-117
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