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Open Access Research article

Benefits of ICU admission in critically ill patients: Whether instrumental variable methods or propensity scores should be used

Romain Pirracchio12*, Charles Sprung4, Didier Payen3 and Sylvie Chevret1

Author Affiliations

1 Département de Biostatistique et Informatique Médicale, Unité INSERM UMR 717, Hôpital Saint Louis, APHP; Université Paris 7 Diderot; 1 Avenue Claude Vellefaux, Paris, 75010, France

2 Service d'Anesthésie Réanimation, Hôpital Européen Georges Pompidou, APHP; Université Paris V Descartes, Sorbonne Paris Cité; 20 rue Leblanc, Paris, 75015, France

3 Département d'Anesthésie Réanimation SMUR, Hôpital Lariboisière, APHP; Université Paris 7 Diderot; 2 rue Ambroise Paré, Paris, 75010, France

4 Department of Anesthesiology and Critical Care Medicine, Hadassah University Hospital, Ein-Karem; Kiryat Hadassah, Jerusalem, 91120, Israel

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BMC Medical Research Methodology 2011, 11:132  doi:10.1186/1471-2288-11-132

Published: 21 September 2011



The assessment of the causal effect of Intensive Care Unit (ICU) admission generally involves usual observational designs and thus requires controlling for confounding variables. Instrumental variable analysis is an econometric technique that allows causal inferences of the effectiveness of some treatments during situations to be made when a randomized trial has not been or cannot be conducted. This technique relies on the existence of one variable or "instrument" that is supposed to achieve similar observations with a different treatment for "arbitrary" reasons, thus inducing substantial variation in the treatment decision with no direct effect on the outcome. The objective of the study was to assess the benefit in terms of hospital mortality of ICU admission in a cohort of patients proposed for ICU admission (ELDICUS cohort).


Using this cohort of 8,201 patients triaged for ICU (including 6,752 (82.3%) patients admitted), the benefit of ICU admission was evaluated using 3 different approaches: instrumental variables, standard regression and propensity score matched analyses. We further evaluated the results obtained using different instrumental variable methods that have been proposed for dichotomous outcomes.


The physician's main specialization was found to be the best instrument. All instrumental variable models adequately reduced baseline imbalances, but failed to show a significant effect of ICU admission on hospital mortality, with confidence intervals far higher than those obtained in standard or propensity-based analyses.


Instrumental variable methods offer an appealing alternative to handle the selection bias related to nonrandomized designs, especially when the presence of significant unmeasured confounding is suspected. Applied to the ELDICUS database, this analysis failed to show any significant beneficial effect of ICU admission on hospital mortality. This result could be due to the lack of statistical power of these methods.

Causal inference; Instrumental variables; Dichotomous outcome; Mortality; ICU