Every year, more than 200 million people undergo major non-cardiac surgery worldwide. These operations save and improve lives, but they are not without risks. The most common complications after surgery are cardiovascular (problems with the heart or the blood vessels). About one in three patients suffers some damage to the heart after non-cardiac surgery. Whilst this is often very minor, it is associated with an increased risk of death and long-term health adverse outcomes for the patient.  

Complications after surgery are generally difficult to predict and hard to prevent. This research project aims to address this difficult problem using an artificial intelligence technique called machine learning, in which computers 'learn' from data and improve their predictive accuracy. These techniques can continually update risk estimates as new data becomes available. 

Using state-of-the-art technology within Royal Perth Hospital’s Health in a Virtual Environment (HIVE), the team will leverage HIVE’s machine learning and monitoring capabilities to test and improve these innovative risk prediction techniques. The team will develop a system that not only predicts a patient's risk before surgery but also continuously updates it during and after the operation. This will also help identify patients whose risk is increasing and warn doctors and nurses looking after them. 

This project aims to produce a user-friendly, clinically useful software program that can be adapted for use in any hospital, automatically predicts the risk of heart and other complications after surgery, continuously updates this as new information becomes available, and warns clinicians if the risk is increasing. Most importantly, this first-of-its-kind research could provide an effective, low-cost means to improve outcomes for the many millions of patients undergoing surgery every year and help save lives.