Making C difficile Less Difficult to “C”
Reviewed By: Emily Martin, MPH, PhD, Wayne State University
Two recent papers highlight the burden of CDI-associated mortality and propose a potential automated tool to guide CDItreatment decisions in patients most at risk of CDI relapse.
Mortality rates due to C. difficile infections (CDI) are difficult to define, especially in non-outbreak settings, because of the high rates of CDI among people with serious health conditions and a history of health care contacts. Studies that have tried to determine CDI attributable mortality, apart from the mortality risk of underlying conditions, have found conflicting results. A study by Hensgens et al in the April 15 issue of Clinical Infectious Diseases has tried to define mortality due to endemic CDI by conducting a matched cohort study in 13 Danish hospitals over almost 3 years. This large study included 1366 individuals with CDI, detected by Immunocard, cytotoxicity assay, or Vidas toxin A and/or B enzyme immunoassay. The majority of CDI detections (86%) were healthcare-associated and the most frequent ribotype was 014 (16.3%). Ribotype 027 (a subtype suspected to have hypervirilent properties) was found in only 8%; however, patients with 027 had a mortality rate that was twice that of other types. 317 CDI patients were matched to both non-CDI controls with diarrhea and controls without diarrhea. The 30-day mortality rate in the CDI patients was 2.5 times that in the non-diarrhea controls and 1.6 times that in the controls with diarrhea. The increased mortality rate persisted over time with the one-year mortality rate being 1.5 times higher in the CDI cases compared to patients without diarrhea. As noted by the authors, the use of enzyme immunoassay to identify CDI may have resulted in an underestimate of the overall CDI rate over the study period. Nevertheless, this study emphasizes the significant contribution of CDI to increased mortality, even in the absence of an outbreak.
Among those who survive a C difficile infection, the risk is not over. Patients frequently relapse within months of their initial infection. A recent study by Hebert et al. looked for risk factors for CDI relapse using electronic health record data. By using electronic health records, their findings could support the future development of automated electronic identification of those at highest risk of CDI relapse. The study gathered health record data from 4 metropolitan Chicago hospitals over nearly 5 years. They generated a risk-factor model using all patient data. Risk factors for relapse included age, fluoroquinolone exposure, ICU stay, and the use of cephalosporin, proton pump inhibitor, and metronidazole therapies after diagnosis. The authors also examined only that information available to the physician at diagnosis, finding length of stay, age, and ICU stay to be predictors of future relapse. Overall, the prediction model was able to identify the 15% of patients with CDI who had at least a 50% risk of relapse. In the future, the use of CDI models such as the model in this study as automatic electronic alerts may help to better guide treatment decisions in patients most at risk of relapse.