This study was conducted at Barnes-Jewish Hospital (BJH), a 1,250-bed, tertiary-care academic hospital in St. Louis, Missouri. Eligibility was limited to nonsurgical patients admitted for >48 hours from January 1 through December 31, 2003. Nonsurgical patients were defined as those without operating room costs. Surgical patients were excluded because of their heterogeneity. Specifically, risk factors for length of stay, readmission to the hospital, and death were different in this population compared with other hospitalized patients. Data were primarily collected from the hospital’s Medical Informatics database. The database was queried to collect patient demographics; admission and discharge dates; International Classification of Diseases, 9th edition, Clinical Modification (ICD-9-CM), diagnosis and procedure codes (Appendix); inpatient medication orders; vital signs; and laboratory results, including C. difficile toxin assay results. The Medical Informatics database was also queried to ascertain date of death. Patients without a death date in the database were screened for death by reviewing the Social Security Death Index.

For each patient, a modified APACHE II Acute Physiology Score (APS) was calculated to adjust for severity of illness (15). The APS was based on laboratory results and vital signs collected within 24 hours of admission. The score was modified because data for respiratory rates and Glasgow coma scores were unavailable electronically. In addition, the Charlson-Deyo method was used to classify co-existing conditions (16,17). Albumin levels within 24 hours of admission were collected and categorized into normal (>3.5 g/dL), low (2.5–3.5 g/dL), and very low (<2.5 g/dL). Multiple imputation methods were used to impute albumin levels for patients without recorded values (18). For CDAD case-patients, only medication and intensive-care unit exposures before the patient’s first positive stool toxin assay were included in analyses.

Among 18,050 nonsurgical inpatients admitted during the 1-year study period, 390 had CDAD and 17,660 did not. Selected patient characteristics of the cohort are summarized in Table 1. CDAD patients were significantly older (median 66.0 vs. 52.7 years, p<0.001) more likely to be men, and more likely to be Caucasian than were noncase-patients. CDAD case-patients had a higher severity of illness on admission than noncases, as indicated by the modified APS. CDAD patients were more likely to have a diagnosis of congestive heart failure, chronic obstructive pulmonary disease, cancer, leukemia or lymphoma, and metastatic solid tumors.

Of 17,492 patients alive at the index hospitalization discharge, 4,207 (24%) were readmitted to BJH within 180 days. Fifty-two percent of CDAD patients were readmitted within 180 days versus 23% of noncases (log-rank p<0.001). Univariate and multivariable Cox regression results for time to readmission are presented in Table 2. The adjusted hazard ratio (HR) for readmission within 180 days was significantly higher for CDAD case-patients than noncases (HR 2.19, 95% CI 1.87–2.55) (Table 2).

Figure 1

Figure 1. Kaplan-Meier survival estimates for cohort (N = 18,050). CDAD, Clostridium difficile–associated disease.

By 180 days after hospital admission, 149 (38%) of 390 CDAD case-patients and 2,150 (12%) 17,660 noncase-patients had died. In the Kaplan-Meier analysis, the mortality rate was significantly higher for CDAD case-patients than noncases (log rank p<0.001) (Figure 1). Unadjusted and adjusted Cox regression results for death within 180 days of admission (“180-day mortality”) are presented in Table 3. The adjusted hazard ratio for 180-day mortality was significantly higher for CDAD case-patients than noncase patients (HR 1.23, 95% CI 1.03–1.46) (Table 3).

The propensity score matched-pairs analysis included 353 CDAD cases and 353 controls (N = 706). There were no significant differences between the matched cases and controls after correcting for multiple testing with the Bonferroni procedure. Thirty-seven CDAD case-patients were dropped because a nearest-neighbor control was not available. Unmatched CDAD patients had significantly higher modified APS (median = 7.0 vs. 5.0, p<0.001), longer median length of stay (13.6 days vs. 9.6 days, p = 0.01), and higher percentage of deaths at 180 days (59% vs. 36%, p = 0.01) than matched case-patients.

In the matched-pairs analysis, median length of stay was 9.6 days for CDAD patients compared with 5.8 days for controls, and the increased attributable length of stay for CDAD patients was 2.8 days (Wilcoxon signed-rank p<0.001). Among the 706 patients in the matched-pairs analysis, 445 (63%) were discharged to home and 188 (27%) were discharged to a long-term-care facility. Only 7 (1%) patients were discharged to an outside hospital; therefore, these patients were combined with patients discharged to a long-term-care facility in the analysis. CDAD patients were significantly more likely than controls to be discharged to a long-term-care facility or outside hospital (32% vs. 23%, odds ratio 1.62, 95% CI 1.15–2.28, McNemar p = 0.01).

Figure 2

Figure 2. Kaplan-Meier estimates of time until hospital readmission for matched pairs (n = 580). CDAD, Clostridium difficile–associated disease.

Among 290 matched-pairs with both patient and control alive at index hospitalization discharge, 148 CDAD patients were readmitted to BJH within 180 days compared with 92 controls, for an attributable readmission of 19.3% (11.4%–27.2%). In the Kaplan-Meier and Cox model analyses, CDAD patients were significantly more likely than controls to be readmitted to the hospital within 180 days (Figure 2, Table 4).

Figure 3

Figure 3. Kaplan-Meier survival estimates for matched pairs (n = 706). CDAD, Clostridium difficile–associated disease.

By 180 days after hospital admission, 127 CDAD patients died compared with 107 controls, for an attributable mortality of 5.7% (95% CI –1.3%–12.6%). Although CDAD case-patients were no more likely than controls to die within 60 days of hospital admission, a divergence in survival between CDAD case-patients and controls began 60 days after hospital admission (Figure 3, Table 4). The HR for death from 61–180 days was significantly higher for CDAD patients than controls (HR 2.00, 95% CI 1.47–2.72) (Table 4). Among 223 matched-pairs with both case-patients and controls alive after day 60, 19.7% of CDAD patients and 12.6% of controls died within 180 days for an attributable mortality between 61–180 days of 7.2% (95% CI 0.4%–14.0%).

The results of this study indicate that CDAD is a major contributor to death even in nonoutbreak settings. In this CDAD-endemic setting, the disease was associated with a 23% higher hazard of death within 180 days after hospital admission in the multivariable cohort analysis and a 7.2% attributable mortality 61–180 days after hospital admission in the matched-pairs analysis. Historically, endemic CDAD has been reported to be associated with minimal increased risk in mortality although NAP1 strain CDAD outbreaks have been associated with much higher attributable mortality (10,11,13). Two studies of CDAD in endemic settings reported 1.2%–1.5% inhospital mortality rates from CDAD (10,13). Using a multivariable Cox proportional hazards model, Kyne et al. found no association between CDAD and 1-year mortality, although that study was quite small (47 CDAD patients) (11). In contrast, several studies have identified increased deaths associated with outbreaks of the NAP1 strain. Pepin et al. estimated the 1-year attributable mortality of CDAD during an outbreak with the NAP1 strain to be 16.7% (9). Hubert et al. reported that CDAD was the attributable or contributive cause of death in 8.4% of patients infected with a strain of C. difficile that had the binary toxin and tcdC deletion (21). Loo et al. found CDAD to be the attributable cause of death within 30 days in 6.9% of CDAD patients and suspected that CDAD contributed to death in another 7.5% of CDAD patients (12). The estimate of 6.9% attributable mortality, however, was determined through chart review, not through multivariable analyses, and medical chart review may not be an adequate method to determine attributable mortality because of subjectivity (22).

Although the 5.7% 180-day attributable mortality determined in the propensity score matched-pairs analysis in our study was not statistically significant, the estimate is substantially higher than estimates reported from other CDAD-endemic settings. The attributable mortality we report is more consistent with estimates from outbreaks of the NAP1 strain and is likely clinically significant. The NAP1 strain was first identified at BJH during 2005, but the strain may have been present during the study period (23). During the years 2000–2006 at BJH, there were no apparent increases in hospital-onset CDAD incidence rates or severity of CDAD (as measured by the number of colectomies per CDAD case per year and by the percentage of patients with CDAD who died during hospitalization) (data not shown). Thus, the high attributable mortality found in this study has important implications for patients: CDAD remains an important cause of patient death even in a CDAD-endemic setting.

Our study showed that CDAD had a delayed impact on death. In the matched-pairs analysis, the divergence in survival between CDAD cases-patients and controls did not begin until >60 days after hospital admission. Within 60 days of admission, survival was not significantly different between CDAD patients and controls, when all but 4 (1%) patients had been discharged from the hospital. This finding is consistent with those of 2 recent nested matched case–control studies in nonoutbreak settings, in which no significant excess deaths were reported after 30 days (24) or at discharge (25). Although CDAD can be acutely life-threatening, delayed death caused by CDAD may not be easily recognized as related to the initial CDAD episode. CDAD may contribute to a decline in patient function and overall illness over time, ultimately leading to death in many patients.

The results of the time-to-readmission and discharge location analyses further emphasize the negative impact of CDAD. CDAD patients were more than twice as likely to be readmitted to BJH within 180 days compared with controls. This finding is consistent with our prior findings that CDAD contributes to an increase in hospital costs extending out to at least 180 days (26). CDAD patients were also significantly more likely to be discharged to a long-term-care facility or outside hospital. Few data are available on the health of CDAD patients after hospital discharge, and future studies following CDAD patients as outpatients over an extended period are needed.

Data on the excess length of hospital stay attributable to CDAD are limited. Wilcox et al. found that CDAD patients stayed in the hospital, on average, 21.3 days longer than non-CDAD patients; however, the attributable length of stay was not calculated (14). O’Brien et al. reported that the mean increase in hospitalization among CDAD patients was 2.9 days (27). Kyne et al. calculated the attributable length of stay at 3.6 days (11), which was comparable to the attributable length of stay estimate found in our study (2.8 days).

Our study has several limitations, including the retrospective study design. Use of electronic data from the hospital’s Medical Informatics database has limitations, although use of these data made analysis of such a large cohort feasible. Differences seen in observational studies may be due to unmeasured confounders. We attempted to address this issue by using 2 methods to control for confounding: multivariable regression analyses and propensity score matched-pairs analyses. As evident from the Kaplan-Meier mortality analyses, the matched-pairs population is a more homogeneous population than the cohort. This design allows more precise effect estimation because the association between CDAD and the propensity score variables among the study participants is eliminated. A strength of the multivariable regression analyses is the use of all available data in the cohort. In the propensity score matched-pairs analyses, 37 CDAD cases were excluded because of lack of a suitable control. Unmatched case-patients were more severely ill than matched case-patients, and their exclusion is a limitation of the propensity-score matched-pairs analyses. In the time-to-readmission analyses, we were unable to identify readmissions to hospitals other than our institution. Finally, surgical patients were excluded from these analyses. Because of this exclusion, the most severely ill CDAD patients requiring colectomies (n = 3) were not represented in the dataset. The absence of these patients, as well as the 37 unmatched case-patients, may have resulted in estimates of attributable length of stay and death that are biased low.

Data on attributable outcomes associated with CDAD are scarce. As previously mentioned, some data on attributable mortality and length of stay exist; however, these findings are limited by lack of adequate controls, small sample size, or outbreak settings. Our study provided detailed analysis on the effect of CDAD on time-to-readmission. Another key strength of this study is the combination of 2 analytical methods: Cox proportional hazards regression in the primary cohort and propensity score matched-pairs analysis. Mortality and time-to-readmission analyses, which were conducted in both the cohort and matched-pairs populations, had remarkably similar results. The results of this study suggest that endemic CDAD can lead to significantly poorer patient outcomes, including increased hospital length of stay, death, risk for admission to a long-term-care facility, and risk for hospital readmission. Even when the most severe CDAD cases are not considered, the detrimental effect of CDAD on patient health appears to extend beyond hospital discharge. Although prospective validation of these findings is needed, proper allocation of healthcare resources toward prevention of this infection is necessary to prevent further illness and death attributable to CDAD.