Abstract 321: A Validated Model to Identify Patients at High Risk for 30-Day Readmission After Percutaneous Coronary Intervention

Abstract

Background: The Affordable Care Act (ACA) creates incentives within Medicare for hospitals that minimize readmissions shortly after discharge. Percutaneous coronary intervention (PCI) has among the highest rates of 30-day all-cause readmission. We developed and validated a prediction model to assist clinicians in identifying patients at high risk for 30-day readmission after discharge for PCI. Methods: We included all PCI admissions in non-federal hospitals in Massachusetts between October 1, 2005 and September 30, 2008. Readmissions within 30-days of discharge were identified via linkage with Massachusetts inpatient claims files. Within a 2/3 random sample, we developed 2 separate multivariable models to predict all-cause 30-day readmission, one incorporating only variables known prior to cardiac catheterization (Pre-PCI model), and a second incorporating variables known at discharge, including PCI-related complications and discharge disposition (Discharge model). In order to facilitate clinical use of the model via a web-based application, less influential variables were eliminated via stepwise selection, while retaining 95% of the predicted variability in the complete models. Models were validated within the remaining 1/3 sample, and model discrimination and calibration were assessed. Readmissions for staged PCIs were not considered as a readmission. Results: Of 36060 PCI patients surviving to discharge, 3760 (10.4%) were readmitted within 30 days. In the pre-PCI model, significant independent predictors of readmission included history of heart failure as well as heart failure status at time of PCI, gender, chronic lung disease, worse renal function, insurance status, admission status, previous CABG, peripheral vascular disease, presence of cardiogenic shock, and age. Additional predictors of readmission in the discharge model included length of stay, bleeding or vascular complications, use of drug-eluting stents, previous PCI, diabetes status, race, discharge location, and beta blocker being prescribed at discharge (Figure 1). Model discrimination was moderate for the pre-PCI model (C statistic = 0.67) and not substantially improved by the addition of post-PCI variables (C statistic = 0.69). Both models were well-calibrated within the validation dataset (Hosmer-Lemeshow goodness of fit P = NS for both). Conclusions: These validated models, developed in a large and broadly generalizable population, can be used to identify patients at high risk for readmission after PCI. Such a model could be used to target high risk patients for interventions to prevent readmission.