Background: Reducing preventable hospital readmissions after coronary artery bypass graft (CABG) surgery has become a national priority. Predictive models can be used to identify patients at high risk for readmission. However, the majority of the existing models are based on data available at discharge. We sought to develop a model to predict hospital readmission using data available soon after admission for isolated CABG surgery.
Methods: Fifty risk factors were included in a bivariate analysis, 16 of which were significantly associated (P < 0.05) with readmissions and were entered into a multivariate logistic regression and removed stepwise, using backward elimination procedures. The derived model was then validated on 896 prospective isolated CABG cases.
Results: Of 2589 isolated CABG patients identified between December 1, 2010, and June 30, 2014, 237(9.15%) were readmitted within 30 days. Five risk factors were predictive of 30-day all-cause readmission: age (odds ratio [OR] = 1.03; 95% confidence interval [CI]: 1.01-1.05; P = 0.004), prior heart failure (OR = 1.55; 95%CI: 1.07-2.24; P = 0.020), total albumin prior to surgery (OR = 0.68; 95%CI: 0.05-0.94; P = 0.021), previous myocardial infarction (OR = 1.44; 95%CI: 1.00-2.08; P = 0.50), and history of diabetes (OR = 1.54; 95%CI: 1.09-2.19; P = 0.015). The area under the curve c-statistic was 0.63 in the derivation sample and 0.65 in the validation sample showing good discrimination.
Conclusions: A 30-day all-cause readmission among isolated CABG patients can be predicted soon after admission with a small number of risk factors.
Keywords: CABG readmission; quality improvement; risk prediction.
© 2018 Wiley Periodicals, Inc.