Abstract
BackgroundIt is believed that Clostridium difficile infection (CDI) contributes to a prolongation of length of stay (LOS). Recent literature suggests that models previously used to determine LOS due to infection have overestimated LOS, compared to newer statistical models. The purpose of this review is to understand the impact that CDI has on LOS and in doing so, describe the methodological approaches used.AimFirst, to investigate and describe the reported prolongation of LOS in hospitalised patients with CDI. Second, to describe the methodologies used for determining excess LOS.MethodsAn integrative review method was used. Papers were reviewed and analysed individually and themes were combined using integrative methods.ResultsFindings from all studies suggested that CDI contributes to a longer LOS in hospital. In studies that compared persons with and without CDI, the difference in the LOS between the two groups ranged from 2.8days to 16.1days. Potential limitations with data analysis were identified, given that no study fully addressed the issue of a time-dependent bias when examining the LOS. Recent literature suggests that a multi-state model should be used to manage the issue of time-dependent bias.ConclusionStudies examining LOS attributed to CDI varied considerably in design and data collected. Future studies examining LOS related to CDI and other healthcare associated infections should consider capturing the timing of infection in order to be able to employ a multi-state model for data analysis.
Highlights
It is believed that Clostridium difficile infection (CDI) contributes to a prolongation of length of stay (LOS)
Findings from all studies suggested that CDI contributes to a longer LOS in hospital
In studies that compared persons with and without CDI, the difference in the LOS between the two groups ranged from 2.8 days to 16.1 days
Summary
It is believed that Clostridium difficile infection (CDI) contributes to a prolongation of length of stay (LOS). Determining the additional LOS due to an HAI, including CDI, is challenging due to the need to manage time-dependent bias—that is, the longer a person stays in hospital, the greater the risk of acquiring an infection. Time dependent bias is a term used to describe problem occurring when variables in the model change value after the start of patient observation. Such variables are called “time dependent,” because their value can change over time [11] One study demonstrating this bias examined readmission hospital and whether persons with a discharge summary were followed up by a physician after discharge. When the time dependent variable was analysed as a fixed variable, there were significantly lower readmissions in patients who saw physicians with the summary. There are numerous other publications which demonstrate this issue [7,8,9,13,14]
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