RESUMEN
BACKGROUND: Hospital readmission is an adverse patient outcome that is serious, common, and costly. For hospitals, identifying patients at risk for hospital readmission is a priority to reduce costs and improve care. PURPOSE: The purposes were to validate a predictive algorithm to identify patients at a high risk for preventable hospital readmission within 30 days after discharge and determine if additional risk factors enhance readmission predictability. METHODS: A retrospective study was conducted on a randomized sample of 598 patients discharged from a Southeast community hospital. Data were collected from the organization's database and manually abstracted from the electronic medical record using a structured tool. Two separate logistic regression models were fit for the probability of readmission within 30 days after discharge. The first model used the LACE index as the predictor variable, and the second model used the LACE index with additional risk factors. The two models were compared to determine if additional risk factors increased the model's predictive ability. RESULTS: The results indicate both models have reasonable prognostic capability. The LACE index with additional risk factors did little to improve prognostication, while adding to the model's complexity. CONCLUSION: Findings support the use of the LACE index as a practical tool to identify patients at risk for readmission.