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Hospital Readmissions within 30 days after discharge following CoronaryArtery Bypass Graft (CABG) Surgery are substantial contributors to healthcarecosts. Many predictive models were developed to identify risk factors forreadmissions. However, majority of the existing models use statistical analysistechniques with data available at discharge. We propose an ensembled model topredict CABG readmissions using pre-discharge perioperative data and machinelearning survival analysis techniques. Firstly, we applied fifty one potentialreadmission risk variables to Cox Proportional Hazard (CPH) survival regressionunivariate analysis. Fourteen of them turned out to be significant (with pvalue < 0.05), contributing to readmissions. Subsequently, we applied these 14predictors to multivariate CPH model and Deep Learning Neural Network (NN)representation of the CPH model, DeepSurv. We validated this new ensembledmodel with 453 isolated adult CABG cases. Nine of the fourteen perioperativerisk variables were identified as the most significant with Hazard Ratios (HR)of greater than 1.0. The concordance index metrics for CPH, DeepSurv, andensembled models were then evaluated with training and validation datasets. Ourensembled model yielded promising results in terms of c-statistics, as weraised the the number of iterations and data set sizes. 30 day all-causereadmissions among isolated CABG patients can be predicted more effectivelywith perioperative pre-discharge data, using machine learning survival analysistechniques. Prediction accuracy levels could be improved further with deeplearning algorithms.

Deep Learning Approach for Predicting 30 Day Readmissions after Coronary Artery Bypass Graft Surgery