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

Abstract

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.

Keywords

• aortic valve
• mitral valve
• model
• prediction statistics
• readmission
• surgery