Key result
An ensembled machine learning model utilizing perioperative pre-discharge data predicted 30-day all-cause readmissions after CABG surgery with a validation concordance index of 0.571, compared to 0.631 for a traditional Cox Proportional Hazards model.
Why the study?
Most existing predictive models for 30-day readmissions after CABG surgery rely on standard statistical methods with data available only at discharge.
Does an ensembled deep learning model improve prediction of 30-day all-cause readmissions in adult CABG patients compared to standard Cox Proportional Hazard models?
Observational (n=2,293)
No
Does an ensembled deep learning model improve prediction of 30-day all-cause readmissions in adult CABG patients compared to standard Cox Proportional Hazard models?
Absolute Event Rate: 0.571% vs 0.631%
Machine learning survival analysis techniques, particularly deep learning algorithms, can effectively predict 30-day all-cause readmissions among isolated CABG patients using pre-discharge perioperative data.
ML model underperformed Cox PH for post-CABG readmission prediction; leaves open whether refined deep learning approaches add value in cardiac surgery.
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.
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Manyam et al. (2018) conducted an observational in Coronary Artery Bypass Graft (CABG) surgery (n=2,293). Ensembled machine learning model (DeepSurv + Cox Proportional Hazards) vs. Traditional Cox Proportional Hazards (CPH) model was evaluated on Concordance index (c-statistic) for predicting 30-day all-cause readmission on the validation dataset. An ensembled machine learning model utilizing perioperative pre-discharge data predicted 30-day all-cause readmissions after CABG surgery with a validation concordance index of 0.571, compared to 0.631 for a traditional Cox Proportional Hazards model.
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