Abstract
BackgroundStudies estimating excess length of stay (LOS) attributable to nosocomial infections have failed to address time-varying confounding, likely leading to overestimation of their impact. We present a methodology based on inverse probability–weighted survival curves to address this limitation.MethodsA case study focusing on intensive care unit–acquired bacteremia using data from 2 general intensive care units (ICUs) from 2 London teaching hospitals were used to illustrate the methodology. The area under the curve of a conventional Kaplan-Meier curve applied to the observed data was compared with that of an inverse probability–weighted Kaplan-Meier curve applied after treating bacteremia as censoring events. Weights were based on the daily probability of acquiring bacteremia. The difference between the observed average LOS and the average LOS that would be observed if all bacteremia cases could be prevented was multiplied by the number of admitted patients to obtain the total excess LOS.ResultsThe estimated total number of extra ICU days caused by 666 bacteremia cases was estimated at 2453 (95% confidence interval [CI], 1803–3103) days. The excess number of days was overestimated when ignoring time-varying confounding (2845 [95% CI, 2276–3415]) or when completely ignoring confounding (2838 [95% CI, 2101–3575]).ConclusionsICU-acquired bacteremia was associated with a substantial excess LOS. Wider adoption of inverse probability–weighted survival curves or alternative techniques that address time-varying confounding could lead to better informed decision making around nosocomial infections and other time-dependent exposures.
Highlights
Studies estimating excess length of stay (LOS) attributable to nosocomial infections have failed to address time-varying confounding, likely leading to overestimation of their impact
intensive care unit (ICU)-acquired bacteremia was associated with a substantial excess LOS
Wider adoption of inverse probability– weighted survival curves or alternative techniques that address time-varying confounding could lead to better informed decision making around nosocomial infections and other time-dependent exposures
Summary
A case study focusing on intensive care unit–acquired bacteremia using data from 2 general intensive care units (ICUs) from 2 London teaching hospitals were used to illustrate the methodology. We used data from all patients admitted to two 15-bed general ICUs at St Thomas’ Hospitals (London, United Kingdom) between January 2002 and March 2006 [11, 18]. We excluded patients with a blood culture positive for bacteria during the first 2 days in the ICU, to exclude community-acquired cases After applying these exclusion criteria, a first blood culture positive for any bacteria obtained from the patient > 2 days after ICU admission was considered an ICU-acquired bacteremia. Onset of infection was defined utilizing the time of the specimen taken as a proxy for time of infection
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