Abstract
BACKGROUND: Clostridium difficile infection (CDI) severity has increased, especially among hospitalized older adults. We evaluated clinical factors to predict mortality after CDI. METHODS: We collected data from inpatients diagnosed with CDI at a U.S. academic medical center (HSR-IRB#13630). We evaluated age, Charlson comorbidity index (CCI), whether patients were admitted from a long-term care facility, whether patients were in an intensive care unit (ICU) at the time of diagnosis, white blood cell count (WBC), blood urea nitrogen (BUN), low body mass index, and delirium as possible predictors. A parsimonious predictive model was chosen using the Akaike information criterion (AIC) and a best subsets model selection algorithm. The area under the receiver operating characteristic curve was used to assess the model's comparative, with the AIC as the selection criterion for all subsets to measure fit and control for overfitting. RESULTS: From the 362 subjects, the selected model included CCI, WBC, BUN, ICU, and delirium. The logistic regression coefficients were converted to a points scale and calibrated so that each unit on the CCI contributed 2 points, ICU admission contributed 5 points, each unit of WBC (natural log scale) contributed 3 points, each unit of BUN contributed 5 points, and delirium contributed 11 points.Our model shows substantial ability to predict short-term mortality in patients hospitalized with CDI. CONCLUSION: Patients who were diagnosed in the ICU and developed delirium are at the highest risk for dying within 30 days of CDI diagnosis.