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