The effect of hospital-acquired infection with Clostridium difficile on length of stay in hospital

Eligibility criteria

We included admissions to hospital that occurred after July 1, 2002, for which discharges took place before Mar. 31, 2009. We excluded stays of less than 72 hours, since infection with C. difficile deemed to have been acquired in hospital requires a positive test for the organism more than 72 hours after admission.10^,11 We also excluded admissions of patients younger than 15 years of age and patients admitted to an obstetric service, because the model we used to predict baseline risk in this study was not validated in these populations.12 The unit of analysis was the stay in hospital.

We abstracted the following information from the admissions to hospital included in the study: age and sex of patient, dates of admission and discharge, admitting service, comorbidities (for assigning scores on the Charlson comorbidity index) and results of laboratory investigations. We also measured patients’ use of the health care system in the year before admission by calculating the number of previous admissions to hospital, visits to the emergency department and total number of days spent as an inpatient at our hospital.

Determination of hospital-acquired C. difficile

As per guidelines for the surveillance of C. difficile,10^,11 patients were classified as having a hospital-acquired infection if a toxin assay from a sample of liquid stool taken at least 72 hours after admission returned a positive result. Tests with positive results that occurred within two months of a previous positive result were considered a relapse and were not counted.

To determine if C. difficile had been acquired in hospital, we applied text-searching techniques to electronic laboratory reports of assays for C. difficile toxins to identify positive tests. We then compared the date/time of the positive test with the date/time of the patient’s admission to determine if the organism was acquired in hospital. We validated the results from this electronic algorithm by comparing them to the results of reports classified manually by infection-control staff; we determined that our algorithm had a sensitivity of 100%.

Outcome

The primary outcome was time to discharge from hospital. Observation of patient time was censored at death.13

Statistical analyses

We summarized characteristics of included admissions to hospital by C. difficile status. We calculated the baseline risk of death for each admission using a logistic regression model12 validated in our hospital’s population.14 This model calculates a patient’s risk of dying while in hospital using data available at admission. The model includes age, sex, comorbities,15^,16 acuity of admission, admitting service and severity of acute disease (summarized using the laboratory-based acute physiology score).12

We described times to discharge from hospital by C. difficile status using Kaplan–Meier curves that accounted for the time-varying nature of C. difficile. We did so by rearranging our data in the “counting process format.”17 Data in this format use multiple records per admission to hospital to correspond with a change in status. A patient who acquires C. difficile would have two lines of data; the first corresponding to the time between admission and acquisition of C. difficile and the second corresponding to the time to discharge or death. Acquisition of C. difficile would always be censored in the first observation. Using this format, we tabulated the median times to discharge and the probabilities of remaining in hospital at 7 and 28 days (i.e., the 50th and 90th percentiles of length of stay). We calculated the crude median length of stay by C. difficile status. This analysis does not account for the time-varying nature of C. difficile.

To calculate the median times to discharge for patients with C. difficile, only the days between C. difficile acquisition and discharge are attributed to the group with C. difficile; the time from admission to C. difficile acquisition is attributed to the group that did acquire C. difficile in hospital. For admissions to hospital that did not acquire C. difficile, all days between admission and discharge are counted. To control for the effect of baseline risk of death in these analyses, we stratified patients by deciles of risk.

The unadjusted and adjusted associations between hospital-acquired C. difficile and time to discharge from hospital were estimated using Cox proportional hazards regression models.18 Baseline risk of death, hospital-acquired C. difficile (specified as a time-varying covariable)19 and the interaction between these two variables were the primary predictors in the model. We included the interaction term to account for effect modification by baseline risk of death. The model also included three covariables that influence risk of in-hospital mortality: the number of admissions to hospital in the previous year, the total number of days spent as an inpatient in the previous year and the year of admission to hospital. The number of visits to the emergency department in the previous year was excluded, as it was collinear with the number of admissions to hospital in the previous year. Because we modelled time to discharge, the hazard ratios (HRs) in the models represent the risk of being discharged associated with a particular factor. Thus, an HR of less than 1.00 suggests a decreased risk of discharge, which in turn implies an increased length of stay.

We used fractional polynomial functions to determine the best functional form for baseline risk of death.20^–22 Covariables were centred about their means to reduce the risk of multicollinearity.23 We accounted for clustering of admissions of the same patient using a marginal approach with robust sandwich estimates for the covarince matrix.24 To account for nonproportional hazards, we included interaction terms between log-time and hospital-acquired C. difficile and between log-time and the fractional polynomial terms for baseline risk of death. We eliminated nonsignificant terms from the model in a stepwise fashion, starting with the highest-order interactions, until all terms remaining in the model were significant at α = 0.05.

We used the Cox model to generate the time-to-discharge curve that would be expected for patients with hospital-acquired C. difficile, had they not acquired the organism. We used the actual covariable values of the patients with C. difficile — changing their hospital-acquired C. difficile statuses to “absent” — to generate the expected time to discharge. The difference between the observed and expected lengths of stay is the increase in length of stay attributed to hospital-acquired C. difficile.