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Risk prediction model successfully identified patients at high risk for C. difficile infection

Dubberke E. Infect Control Hosp Epidemiol.2011;32:360-366.

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A risk prediction model identified patients at increased risk for Clostridium difficile infection in a hospital setting. However, further research is needed to determine whether the model could be used in a clinical setting to reduce costs and prevent outcomes associated with Clostridium difficile, according to Erik R. Dubberke, MD, MSPH, and colleagues.

Risk prediction modeling has not frequently been used in infectious disease epidemiology. Therefore, Dubberke, assistant professor of medicine at Washington University School of Medicine, and colleagues developed a model to ultimately use in real time to prevent C. difficile infections in the hospital setting.

In the retrospective cohort study, the researchers pooled electronic data for 35,350 patients admitted to the Barnes-Jewish Hospital in St. Louise, for at least 48 hours during 2003.

Significant differences were observed between patients with (n=329) and without C. difficile infection (P<.05). Those with C. difficile were more likely to be white (71% vs. 62%), less likely to be female (51% vs. 58%), and were older (median 66 years vs. 56 years) when compared with those without C. difficile infection.

Moreover, patients with C. difficile infection had a higher acuity of illness on admission to the hospital and higher mean C. difficile infection pressure scores, according to the researchers.

“We are in the process of validating these findings in a second group of patients and we will model the cost-effectiveness of potential interventions based on this risk prediction index,” Dubberke told Infectious Disease News. “Once these studies are completed, we plan to trial the most cost-effective intervention to see if we can successfully prevent C. difficile infection in high-risk patients.”

Disclosures: Dr. Dubberke has performed research for Viropharma and Merck and has served as a consultant for Merck, Becton-Dickinson, Optimer, Meridian, and Steris.

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