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Abstract Background We previously built a patient-level prediction model to assess an individual’s risk of Carbapenem-resistant Enterobacteriaceae (CRE) carriage upon hospital admission based on the following factors: past hospital visits (short- and long-term acute care (STACHs and LTACHs)), endoscopic procedures, infection-related diagnosis codes, and patient age and sex. Our model discriminated CRE cases relatively well (c-statistic = 0.86). In the hopes of operationalizing our results, we evaluated the distribution of predicted probabilities on an updated dataset using existing model parameters. Methods We used Illinois Hospital discharge data (CYs 2015–2016) with ICD-10 diagnosis and procedure codes to establish baseline exposure history (2015) and to generate predicted probabilities (2016). We calculated the number of hospital visits and the average number of hospital days in the past year (STACH and LTACH). We identified infection-related diagnosis codes using prior knowledge, and included procedure codes for endoscopic retrograde cholangiopancreatography (ERCP). We then used the model parameters from our previous work to generate predicted probabilities corresponding to each hospital visit. Results Our study year (2016) included 1,229,158 visits by 816,500 unique adult patients. Sixty-two percent of patients had no inpatient visits in the previous year. Among those with a prior hospitalization, the median STACH length of stay was 4 days (IQR: 2–6). Three thousand five hundred and sixty-six patients (0.4%) had previous LTACH exposure upon admission, with a median length of stay of 25 days (IQR: 13–40). Thirty-two percent of hospital visits had an infection-related diagnosis code, and 0.5% had an ERCP procedure code. Of the more than 1.2 million visits, our model predicted 10,614 visits associated with a CRE risk of over 1%, 946 visits of over 10%, and 96 visits by 63 unique patients with over a 50% risk. On average, highest risk patients were exposed to (median) 15 (7–97) STACH, 104 LTACH (37–174) days; 83% had infection codes. Conclusion Using a large, de-identified statewide dataset, we were able to identify a small number of extremely high-risk individuals. Selective screening of these individuals upon admission could prove to be a valuable way to identify CRE-colonized patients in order to take proper precautions. Disclosures All authors: No reported disclosures.

2167. Predicting Carbapenem-Resistant Enterobacteriaceae (CRE) Carriage on Admission using Updated Statewide Hospital Discharge Data