Researchers have developed an artificial intelligence model to extract valuable, predictive data from routine ECG tests.
The new model detected previously unnoticed signals in routine heart tests that strongly predict which patients are at risk of potentially fatal complications after surgery.
It greatly surpassed risk scores currently used by doctors.
The federally funded research by Johns Hopkins University in the US, which converts standard and inexpensive test results into a potentially lifesaving tool, could revolutionise decision-making and risk assessment for both patients and surgeons.
Senior author Robert D. Stevens, chief of the Division of Informatics, Integration, and Innovation at Johns Hopkins Medicine, said: ‘We demonstrate that a basic electrocardiogram contains important prognostic information not identifiable by the naked eye. We can only extract it with machine learning techniques.’
A significant number of people develop life-threatening complications after major surgery. The risk scores used by doctors to determine those at risk for complications are only accurate in around 60% of cases.
Aiming to develop a more precise method for predicting these health risks, the Johns Hopkins team examined the electrocardiogram, or ECG, a common pre-surgical heart test performed before major surgery.
It’s a fast, non-invasive way to evaluate cardiac activity through electric signals, and it can signal heart disease.
But ECG signals also pick up on other, more subtle physiological information,
Stevens said, and the Hopkins team suspected they might find a treasure trove of rich, predictive data – if AI could help them see it.
‘The ECG contains a lot of really interesting information, not just about the heart but about the cardiovascular system. Inflammation, the endocrine system, metabolism, fluids, electrolytes – all of these factors shape the morphology of the ECG. If we could get a really big dataset of ECG results and analyse it with deep learning, we reasoned we could get valuable information not currently available to clinicians.’
The team analysed preoperative ECG data from 37,000 patients who had surgery at Beth Israel Deaconess Medical Centre in Boston.
It trained two AI models to identify patients likely to have a heart attack, a stroke, or die within 30 days after their surgery. One model was trained on just ECG data. The other, which the team referred to as a fusion model, combined the ECG information with additional details from the patient’s medical records, such as age, gender, and existing medical conditions.
The ECG-only model predicted complications better than current risk scores, but the fusion model was even better, able to predict which patients would suffer post-surgical complications with 85% accuracy.
Lead author Carl Harris, a PhD student in biomedical engineering, said: ‘Surprising that we can take this routine diagnostic, this 10 seconds’ worth of data, and predict really well if someone will die after surgery. We have a really meaningful finding that can improve the assessment of surgical risk.’
The team also developed a method to explain which ECG features might be associated with a heart attack or a stroke after an operation.
Stevens said: ‘You can imagine if you're undergoing major surgery, instead of just having your ECG put in your records where no one will look at it, it’s run through a model and you get a risk assessment and can talk with your doctor about the risks and benefits of surgery. It’s a transformative step forward in how we assess risk for patients.’
Next, the team will further test the model on datasets from more patients. They would also like to test the model prospectively with patients about to undergo surgery.
The team would also like to determine what additional information can be extracted from ECG results through AI.
The findings are published in the British Journal of Anaesthesia.
Photo - Dr Robert Stevens, chief of the Division of Informatics, Integration and Innovation at Johns Hopkins Medicine, observes an electrocardiogram monitor.
Credit: Will Kirk/Johns Hopkins University
