Estimating excess length of stay due to central line-associated bloodstream infection: separating the wheat from the chaff.

From the University of Wisconsin School of Medicine and Public Health and the William S. Middleton Veterans Affairs Hospital, Madison, Wisconsin. Received July 21, 2010; accepted July 23, 2010; electronically published October 4, 2010. Infect Control Hosp Epidemiol 2010; 31(11):1115-1117 2010 by The Society for Healthcare Epidemiology of America. All rights reserved. 0899-823X/2010/3111-0003$15.00. DOI: 10.1086/656594 The central line is an instrumental tool of modern health care with great potential to heal and a corresponding potential for harm. Central line–associated bloodstream infection (CLABSI), once thought to be unavoidable in many patients, is now recognized as largely preventable. In recent years, a number of studies have documented impressive reductions in rates of CLABSI through standardization of insertion practices. Reductions in rates of CLABSI have also been achieved through the judicious application of novel technologies. Nevertheless, implementation of interventions to prevent CLABSI requires a considerable investment in resources and manpower that hospitals could deploy alternatively on other quality improvement projects. Therefore, it is not enough to know whether an intervention to prevent CLABSI works; it is equally important to know whether the intervention is cost-effective. A starting point for any cost-effectiveness study is an accurate understanding of the outcomes attributable to the complication. As the primary driver of costs, length of stay (LOS) is the most important outcome that must be addressed in cost-effectiveness studies of interventions for prevention of CLABSI. Unfortunately, published estimates of LOS attributable to CLABSI vary widely—from 5 to 20 days—and have, on at least one occasion, contributed to the uncertainty surrounding the cost-effectiveness of interventions to prevent CLABSI. Although contextual differences due to patient population, microbial profile of causative pathogens, and therapeutic management of infection may explain some of the variation in LOS in published studies, bias deriving from methods used to estimate LOS play a far greater role. A number of factors associated with accurate attribution of LOS and the bias that can arise with different methodological approaches are highlighted in Figure 1. In Figure 1, a hypothetical patient is admitted to an intensive care unit (ICU), develops CLABSI on day 4, and has a total hospital stay of 10 days, with the black area representing the LOS that is attributable to CLABSI. Although not explicitly shown in Figure 1, it is assumed that there are a variety of patient and contextual characteristics that also influence the total LOS (eg, severity of illness and age) and that imperfect adjustment will lead to misattribution of hospital days that would have occurred anyway (even if the CLABSI event had never occurred). The accuracy of the estimate of attributable LOS is therefore contingent on the extent to which these characteristics can be measured and adjusted for as well as the total number of days that are eligible for analyses. In example A, an investigator elects to treat CLABSI as a time-independent event and therefore all days of hospitalization are included in the analysis. Because methods used to adjust for confounding are imperfect and because the timedependency of CLABSI is ignored, this type of analysis will lead to the largest overestimation of attributable LOS. In example B, the investigator recognizes that CLABSI is a timedependent event and considers only those days after CLABSI as eligible for the analysis but does not adjust for confounding variables, leading to exaggerated estimates of attributable LOS. In example C, the investigator attempts to deal with confounding in the design phase of the study by matching to make control subjects more like case patients on a variety of known confounding variables. However, this process is imperfect at best and counterproductive at worst (eg, if the process used for matching leads to the introduction of selection bias). Example D demonstrates the problem of competing events. In this example, the CLABSI event actually leads to death on day 5. If competing events are prevalent in the study sample, then estimates of attributable LOS may actually be biased toward the null. Finally, in example E, the investigator uses advanced statistical methods to explicitly model LOS attributable to CLABSI. The qualifier “advanced” is important in this example, because use of the standard regression methods with which we are most familiar will likely not result in estimates