Abstract 231: Predicting Readmission Risk Following Coronary Revascularization at the Time of Admission

Abstract

Objective: Reducing readmissions following hospitalization for coronary revascularization is a national priority. Identifying patients at high risk for readmission early in a hospitalization would enable hospitals to target these individuals for enhanced discharge planning. Traditional risk models identify patients based on characteristics that may not be available until after discharge. We sought to compare the model performance for models based on data that is available earlier in the hospitalization. Methods: We developed models to predict 30-day inpatient readmission to our instititution for a cohort of patients who were revascularized at our institution between January 2010 and December 2013. We developed separate models for patients with percutaneous coronary revascularization (PCI) and coronary artery bypass graft surgery (CABG). We developed three models using data available at three different time points in the hospitalization: 1) at admission, 2) early in the hospitalization and 3) at discharge. Candidate variables for the admission model included demographics, comorbidities, and previous utilization within our system. The second model added initial vital signs and laboratory values. The discharge model added discharge vital signs and laboratory values, discharge medications, new comorbidities, and length of stay. We assessed each model using the c-index. Results: Our cohort included 4,941 PCI patients and 1,633 CABG patients. The readmission rate was 8.4% in the PCI group and 14.2% in the CABG group. For both populations, the discriminative ability of the admission model was high (0.805 for PCI, 0.824 for CABG). The addition of laboratory values and vital signs was associated with slight improvement in discrimination (0.812 for PCI, 0.829 for CABG). The addition of data available at the time of discharge further increased model discrimination (0.833 for PCI, 0.848 for CABG). Except for one model, the strongest predictor of readmission was carrying a diagnosis of hypertension at baseline (OR > 2 for all)). Previous hospitalization within six months was a strong predictor among PCI patients (OR 1.18, p<0.001), and having a previous acute myocardial infarction was the strongest predictor among CABG patients (OR 2.46, p = 0.002). Discussion: Risk prediction models based on data available only at discharge minimally improved the performance of models based solely on demographic and utilization data available at the time of admission. These simplified models may be sufficient to identify patients at highest risk of readmission following coronary revascularization early in the hospitlization. This would allow providers and health systems to target high-risk patients with enhanced discharge planning during the course of the hospitalization, and this may improve the ability to avoid readmissions.