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BACKGROUND: Studies often ignore time-varying confounding or may use inappropriate methodology to adjust for time-varying confounding. AIM: To estimate the effect of intensive care unit (ICU)-acquired bacteraemia on ICU mortality and discharge using appropriate methodology. METHODS: Marginal structural models with inverse probability weighting were used to estimate the ICU mortality and discharge associated with ICU-acquired bacteraemia among patients who stayed more than two days at the general ICU of a London teaching hospital and remained bacteraemia-free during those first two days. For comparison, the same associations were evaluated with (i) a conventional Cox model, adjusting only for baseline confounders and (ii) a Cox model adjusting for baseline and time-varying confounders. FINDINGS: Using the marginal structural model with inverse probability weighting, bacteraemia was associated with an increase in ICU mortality (cause-specific hazard ratio (CSHR): 1.29; 95% confidence interval (CI): 1.02-1.63) and a decrease in discharge (CSHR: 0.52; 95% CI: 0.45-0.60). By 60 days, among patients still in the ICU after two days and without prior bacteraemia, 8.0% of ICU deaths could be prevented by preventing all ICU-acquired bacteraemia cases. The conventional Cox model adjusting for time-varying confounders gave substantially different results [for ICU mortality, CSHR: 1.08 (95% CI: 0.88-1.32); for discharge, CSHR: 0.68 (95% CI: 0.60-0.77)]. CONCLUSION: In this study, even after adjusting for the timing of acquiring bacteraemia and time-varying confounding using inverse probability weighting for marginal structural models, ICU-acquired bacteraemia was associated with a decreased daily ICU discharge risk and an increased risk of ICU mortality.

Original publication





J Hosp Infect

Publication Date





42 - 47


Bacteraemia, Bias, Burden, Intensive care units, Inverse probability weighting, Adult, Aged, Aged, 80 and over, Bacteremia, Cohort Studies, Cross Infection, Female, Hospitals, Teaching, Humans, Intensive Care Units, London, Male, Middle Aged, Survival Analysis