Development and Validation of a Predictive Model for Short- and Medium-Term Hospital Readmission Following Heart Valve Surgery

Background: Although models exist for predicting hospital readmission after coronary artery bypass surgery, no such models exist for predicting readmission after heart valve surgery (HVS). Methods and Results: Using a geographically and structurally diverse sample of US hospitals (Premier Inpatient Database, January 2007-June 2011), we examined patient, hospital, and clinical factors predictive of short- and medium-term hospital readmission post-HVS. We set aside 20% of hospitals for model validation. A generalized estimating equation model accounted for clustering within hospitals. At 219 hospitals, we identified 38 532 patients (67 years, 56% male, 62% aortic valve surgery) who underwent HVS. A total of 3125 (7.8%) and 4943 (12.8%) patients were readmitted to the index hospital within 1 and 3 months, respectively. Our 3-month model predicted readmission rates between 3% and 61% with fair discrimination (C-statistic, 0.67) and good calibration (predicted vs observed differences in validation cohort averaged 1.9% across all deciles of predicted readmission risk). Results were similar for our 1-month model and our simplified 3-month model (suitable for clinical use), which used the 5 strongest predictors of readmission: transfused units of packed Red blood cells, presence of End-stage renal disease, type of Valve surgery, Emergency hospital admission, and hospital Length of stay (REVEaL). Conclusions: We described and validated key factors that predict short- and medium-term hospital readmission post-HVS. These models should enable clinicians to identify individuals with HVS who are at increased risk for hospital readmission and are most likely to benefit from improved postdischarge care and follow-up.