Predicting Myocardial Injury After Noncardiac Surgery

Abstract: Myocardial injury is the leading cause of death in Europe following non-cardiac surgery. Its causes, diagnosis, and treatment are still under investigation by the scientific community. Some research groups have hypothesized a connection between myocardial injury and hypotension during surgery. This thesis investigated the development of a machine learning binary classifier to make a diagnosis of myocardial injury from a set of patients undergoing non-cardiac surgery. Furthermore, it aimed to clarify the potential of hypotension as a predictor of the pathology. After an evaluation of the requirements that the model had to meet, it was decided to use a decision tree. 4 features were selected combining ANOVA and the domain knowledge of the doctors. The classifier obtained a F1 macro-score of 0.68, showing to have potential in classifying patients as positive or negative. Among the selected features, hypotension obtained the lowest predictive power. Despite the performance of the model, further research is needed to validate the results across different populations and to investigate the use of hypotension as a predictor of myocardial injury.