Predicting Carbapenem-Resistant Enterobacteriaceae Carriage at the Time of Admission Using a State-Wide Hospital Discharge Database

Background Timely identification of patients likely to harbor carbapenem-resistant Enterobacteriaceae (CRE) can help healthcare facilities provide effective infection control and treatment. We evaluated whether a model utilizing prior healthcare information from a state hospital discharge database could predict a patient’s probability of CRE colonization at the time of hospital admission. Methods We performed a case-control study using the Illinois hospital discharge database. From a 2014-2015 patient cohort, we defined cases as index adult patient hospital encounters with a positive CRE culture collected within the first three days of hospitalization, as reported to the Illinois XDRO registry; controls were all patient admissions from the same hospital and month. We split the data into training (~60%) and validation (~40%) sets, and developed a logistic regression model to estimate coefficients for predictors of interest. Results We identified 486 index cases and 340,005 controls. Independent risk factors for CRE at time of admission were: age, number of short term acute care hospital (STACH) hospitalizations in the prior 365 days, mean STACH length of stay, number of long-term acute care hospital (LTACH) hospitalizations in the prior 365 days, mean LTACH length of stay, current admission to LTACH, and prior hospital admission with an infection diagnosis code. When applying the model to the validation dataset, the area under the receiver operating characteristic curve was 0.84. Conclusions A prediction model utilizing prior healthcare exposure information could discriminate patients who were likely to harbor CRE at the time of hospital admission.