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

Link to article at PubMed

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

Endocr Pract. 2016 Jul 13;

Authors: Rubin DJ, Handorf EA, Golden SH, Nelson DB, McDonnell ME, Zhao H

Abstract
OBJECTIVE: To develop and validate a tool to predict the risk of all-cause readmission within 30 days (30d 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. Multivariable logistic regression with generalized estimating equations was used to develop the Diabetes Early Readmission Risk Indicator (DERRI(TM)).
RESULTS: Ten statistically significant predictors were identified: employment status, living within 5 miles of the hospital, pre-admission 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 DERRI(TM) in the validation sample was essentially unchanged (C-statistic 0.69). Predicted 30d readmission risk was also similar between the training and validation samples (39.3% and 38.7% in the highest quintiles).
CONCLUSION: The DERRI(TM) was found to be a valid tool to predict all-cause 30d 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.

PMID: 27409818 [PubMed - as supplied by publisher]

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