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1638: READMISSION PREVENTION IN SEPSIS: DEVELOPMENT AND VALIDATION OF A PREDICTION MODEL: REPRESS

Introduction/Hypothesis: Hospital 30-day readmissions remain a significant quality and cost indicator for healthcare. Traditional readmission risk scores cannot be applied to all patient populations. Minimal data on sepsis bundle compliance in correlation to readmission risk is available. As sepsis survivorship continues to increase there is a need to determine which variables might be associated with a decrease in 30-day readmission, including bundle compliance during index admission, as outlined by the Surviving Sepsis Campaign (SSC). Methods: We conducted a retrospective analysis reviewing sepsis patients who had an unplanned 30-day readmission to our hospital. Characteristics reviewed included: age, sex, length of stay (LOS), Charlson comorbidity index (CCI), discharge disposition, ventilator days, Activity Measure for Post-Acute Care (AM-PAC) 6 click mobility score, renal replacement therapy (RRT) use, Richmond agitation sedation scale (RASS), malnutrition and the 3hr SSC bundle elements (lactate, blood cultures, antibiotics, fluid bolus, all-or-none compliance). A multivariate regression analysis was performed to identify risk factors most significant in predicting readmissions. Results: Sepsis patients discharged from Mayo Clinic Florida from July 1, 2015 to December 31, 2017 were reviewed to assess whether they had an unplanned readmission within 30 days. There were N=2578 patients with 467(18%) of the patients who had an unplanned readmission. A subgroup of 774 patients was evaluated to determine the association between 3hr SSC bundle compliance on index admission and readmission risk, no statistically significant association was found. A multivariate regression analysis was performed for REadmission PREvention in SepSis (REPRESS) which identified age, LOS, Charlson disease count, RASS score, discharge to a skilled nursing facility and mobility evaluated to have predictive significance in hospital readmission. Conclusions: Traditional readmission risk models are not a “one size fits all” tool. Our REPRESS model performed better when compared to the LACE model for predicting readmission risk in the sepsis population. Further research is needed to identify whether sepsis bundle compliance on index admission is associated with a decrease in hospital readmission.