Machine learning to predict myocardial injury and death after non-cardiac surgery

Abstract Background/Introduction Myocardial injury after non-cardiac surgery (MINS) is defined as prognostically relevant myocardial injury due to ischaemia that occurs within 30-days of non-cardiac surgery. Purpose The aim of this study was to test whether machine learning, using neural networks, can accurately predict this frequent and important complication. Methods Using data from 24,589 participants in the Vascular Events in Noncardiac Surgery Patients Cohort Evaluation (VISION) study, who had non-cardiac surgery and post-operative high-sensitivity troponin T (hs-TnT) levels measured, a deep neural network was trained to predict the primary outcome of MINS and the secondary outcome of death within 30-days. Validation was performed on a separate, randomly selected, subset of the study population with model discrimination and accuracy (number of correct predictions) determined. Results Using only data available pre-operatively, the deep neural network predicted MINS with an area under the receiver operating characteristic curve (AUROC) of 0.75 (95% confidence interval [95% CI] 0.74-0.76) and death at 30-days with an AUROC of 0.83 (95% CI 0.79-0.86). Addition of basic intra-operative and early post-operative data increased the AUROC for MINS to 0.77 (95% CI 0.76-0.78) and death to 0.87 (95% CI 0.85-0.90). The deep neural network trained on the full dataset (pre-operative, intra-operative and early post-operative) predicted MINS with an accuracy of 70% and death within 30-days with an accuracy of 89%. Conclusions Neural networks can be trained to predict MINS and death within 30-days of non-cardiac surgery and the inclusion of intra-operative and early post-operative data improves predictive accuracy. These techniques may be useful clinically to predict adverse outcomes after non-cardiac surgery.MINS outcomesDeath Outcomes