DEVELOPMENT AND VALIDATION OF A NOVEL TOOL TO PREDICT HOSPITAL READMISSION RISK AMONG PATIENTS WITH DIABETES

Objective: To develop and validate a tool to predict the risk of all-cause readmission within 30 days (30-d readmission) among hospitalized patients with diabetes. Methods: A cohort of 44,203 discharges was retrospectively selected from the electronic records of adult patients with diabetes hospitalized at an urban academic medical center. Discharges of 60% of the patients (n = 26,402) were randomly selected as a training sample to develop the index. The remaining 40 (n = 17,801) were selected as a validation sample. Multi variable logistic regression with generalized estimating equations was used to develop the Diabetes Early Readmission Risk Indicator (DERRITM). Results: Ten statistically significant predictors were identified: employment status; living within 5 miles of the hospital; preadmission insulin use; burden of macrovascular diabetes complications; admission serum hematocrit, creatinine, and sodium; having a hospital discharge within 90 days before admission; most recent discharge status up to 1 year before admission; and a diagnosis of anemia. Discrimination of the model was acceptable (C statistic 0.70), and calibration was good. Characteristics of the validation and training samples were similar. Performance of the DERRITM in the validation sample was essentially unchanged (C statistic 0.69). Mean predicted 30-d readmission risks were also similar between the training and validation samples (39.3% and 38.7% in the highest quintiles). Conclusion: The DERRITM was found to he a valid tool to predict all-cause 30-d readmission risk of individual patients with diabetes. The identification of high-risk patients may encourage the use of interventions targeting those at greatest risk, potentially leading to better outcomes and lower healthcare costs.