1. Predicting readmission risk of patients with diabetes hospitalized for cardiovascular disease: a retrospective cohort study.
- Author
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Rubin DJ, Golden SH, McDonnell ME, and Zhao H
- Subjects
- Academic Medical Centers, Adult, Aged, Aged, 80 and over, Boston epidemiology, Cardiovascular Diseases epidemiology, Cardiovascular Diseases therapy, Cohort Studies, Diabetic Angiopathies epidemiology, Diabetic Angiopathies therapy, Diabetic Cardiomyopathies epidemiology, Diabetic Cardiomyopathies therapy, Electronic Health Records, Female, Humans, Logistic Models, Male, Middle Aged, Patient Readmission, Prognosis, Retrospective Studies, Risk, Young Adult, Cardiovascular Diseases diagnosis, Diabetic Angiopathies diagnosis, Diabetic Cardiomyopathies diagnosis
- Abstract
Objective: To develop and validate a tool that predicts 30d readmission risk of patients with diabetes hospitalized for cardiovascular disease (CVD), the Diabetes Early Readmission Risk Indicator-CVD (DERRI-CVD™)., Methods: A cohort of 8189 discharges was retrospectively selected from electronic records of adult patients with diabetes hospitalized for CVD. Discharges of 60% of the patients (n=4950) were randomly selected as a training sample and the remaining 40% (n=3219) were the validation sample., Results: Statistically significant predictors of all-cause 30d readmission risk were identified by multivariable logistic regression modeling: education level, employment status, living within 5miles of the hospital, pre-admission diabetes therapy, macrovascular complications, admission serum creatinine and albumin levels, having a hospital discharge within 90days pre-admission, and a psychiatric diagnosis. Model discrimination and calibration were good (C-statistic 0.71). Performance in the validation sample was comparable. Predicted 30d readmission risk was similar in the training and validation samples (38.6% and 35.1% in the highest quintiles)., Conclusions: The DERRI-CVD™ may be a valid tool to predict all-cause 30d readmission risk of patients with diabetes hospitalized for CVD. Identifying high-risk patients may encourage the use of interventions targeting those at greatest risk, potentially leading to better outcomes and lower healthcare costs., (Copyright © 2017 Elsevier Inc. All rights reserved.)
- Published
- 2017
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