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. © 2016 the authors. published on behalf of the american heart association,inc.,by wiley blackwell.