Abstract 113: Predicting Short and Medium Term Hospital Readmission Following Heart Valve Surgery

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Background: Although models exist for predicting hospital readmission following coronary artery bypass surgery, no similar models exist for predicting hospital readmission following heart valve surgery (HVS). Given Medicare’s financial penalties for 30-day hospital readmission and the ongoing implementation of 90-day bundled payments for cardiac surgical procedures, we developed a clinical predictive model to estimate both short and medium-term readmission for patients with HVS.

Methods: Utilizing a geographically and structurally diverse sample of US hospitals (Premier Inpatient Database), we examined patient characteristics, hospital characteristics, and treatment factors predictive of short and medium term hospital readmission following HVS. The study period was from January 2007 to June 2011. A generalized estimating equation model accounted for clustering within hospitals. We set aside a random 20% of hospitals during model derivation for later use as an internal validation cohort.

Results: At 219 hospitals, we identified 38,532 patients with HVS (73 years old, 56% male, 71% non-Hispanic White, with 61% aortic valve, 29% mitral valve, and 10% combination valve surgery.) A total of 3084 (8%) and 4943 (12.9%) patients were readmitted within 1 month and 3 months, respectively, with the most common reasons for readmission being heart failure, atrial fibrillation, and pleural/pericardial effusions. Among the >60 factors examined, 18 independent factors were retained in the final model for predicting 3-month readmission (see table.) The strongest predictors of 3-month readmission were type of valve surgery, urgency of hospital admission, hospital length of stay, and discharge disposition. The final model had fair discrimination (C-statistic 0.667, p < 0.001) and good calibration (predicted vs. observed differences of <1-2% across all deciles of predicted readmission risk.)

Conclusions: We described key factors that predict 3-month hospital readmission following HVS. This model should enable clinicians to identify individuals with HVS who are at increased risk of hospital readmission and who are most likely to benefit from improved coordination of post-discharge care and follow-up.

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