Predicting adverse outcomes of cardiac surgery with the application of artificial neural networks

Abstract

Risk-stratification models based on pre-operative patient and disease characteristics are useful for providing individual patients with an insight into the potential risk of complications and mortality, for aiding the clinical decision for surgery vs non-surgical therapy, and for comparing the quality of care between different surgeons or hospitals. Our study aimed to apply artificial neural networks (ANN) models to predict mortality and morbidity after cardiac surgery, and also to compare the efficacy of this model to that of the logistic regression model and Parsonnet score. The accuracy of the ANN, logistic regression and Parsonnet score in predicting mortality was 83.8%, 87.9% and 78.4%. The accuracy of the ANN, logistic regression and Parsonnet score in predicting major morbidity was 79.0%, 74.3% and 68.6%. The area under the receiver operating characteristic curves (AUC) of the ANN, logistic regression and Parsonnet score in predicting in-hospital mortality were 0.873, 0.852 and 0.829. The AUCs of the ANN, logistic regression and Parsonnet score in predicting major morbidity were 0.852, 0.789 and 0.727. The results showed the ANN models have the best discriminating power in predicting in-hospital mortality and morbidity among these models.