Drotrecogin Alfa's Impact on Intensive Care Workload in Real Life Practice: A Propensity Score Approach
Abstract Objectives To estimate the impact of drotrecogin alfa (DA) on intensive care workload in an observational study while illustrating the use of propensity score (PS) matching to control for recruitment bias. Methods PREMISS is a prospective, multicenter pre–post study. Its goal was to evaluat...
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Veröffentlicht in: | Value in health 2008-12, Vol.11 (7), p.1051-1060 |
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Sprache: | eng |
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Zusammenfassung: | Abstract Objectives To estimate the impact of drotrecogin alfa (DA) on intensive care workload in an observational study while illustrating the use of propensity score (PS) matching to control for recruitment bias. Methods PREMISS is a prospective, multicenter pre–post study. Its goal was to evaluate DA in the treatment of severe sepsis with multiple organ failure. Inclusions took place before (control patients) and after (DA-treated patients) the drug's market authorization. Workload was measured in euros using the French classification of medical procedures. It was compared between the groups via random effects gamma regression using two techniques: 1) regression adjusting for the patients' initial characteristics on the whole population; and 2) PS matching. A structural equation model was used to explore the pathways leading to a workload increase. Results Drotrecogin alfa is estimated to increase intensive care unit (ICU) workload by 20% ( P = 0.045) according to the multivariate model and 34% ( P = 0.002) according to the PS-matched one. In the structural equation model fitted, only DA's direct effect on the occurrence of bleeding events reaches significance ( P = 0.024). Conclusions We found a significant effect of DA on ICU workload with both standard methods of adjustment and PS matching. This effect appears to be mainly due to DA's effect on bleeding events. The analysis illustrated the usefulness of PS methods in the analysis of observational data, as it leads to conclusions similar to the traditional multivariate regression approaches while avoiding making too many adjustments, allowing focusing on the treatment effect. |
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ISSN: | 1098-3015 1524-4733 |
DOI: | 10.1111/j.1524-4733.2008.00319.x |