02198nas a2200181   4500000000100000008004100001100001700042700001200059700001700071700002000088700001500108245012200123250001500245300000800260490000700268520169500275020004601970       2009                            d1 aFlabouris A.1 aChen J.1 aFinfer Simon1 aBellomo Rinaldo1 aHillman K.00aBaseline hospital performance and the impact of medical emergency teams: modelling vs. conventional subgroup analysis  a2009/12/22  a1170 v103 a<p>
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.
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