Artificial intelligence powered by computers and machine learning could help reduce common cardiovascular complications after noncardiac surgery, such as heart attack and myocardial injury, in new research led by a postdoctoral fellow at the University of Western Australia. It turned out to be sexual.
Janice Norde, Ph.D., from the UWA School of Medicine and Royal Perth Hospital, and an international research team evaluated data from more than 24,000 participants in the Vascular Events Cohort Evaluation (VISION) Study in Non-Cardiac Surgery Patients, and the results were Anesthesia. magazine. .
Can researchers use machine learning and data to predict medical complications, particularly cardiovascular complications from surgery (other than cardiac surgery), before they occur, to better identify and treat vulnerable patients? I wanted to establish what
“Each year, more than 200 million patients worldwide undergo major noncardiac surgery, of which about 10 million experience a significant cardiovascular event within 30 days, leading to increased mortality, health It can lead to worsening of the condition and decreased long-term survival,” Dr. Norde said.
“The most common cardiovascular complications after surgery are heart attack and myocardial injury, but they are often difficult to detect because symptoms can be hidden or missed by routine testing.”
Using a sensitive laboratory test that measures a protein (troponin) that is released into the bloodstream when there is damage or injury to the myocardium, the research group found that 1 in 6 patients died during the first 3 days after surgery. was found to increase in level.
“This condition, known as post-noncardiac surgery myocardial injury, is associated with a significantly increased risk of death and other serious complications in the coming weeks, but the risk of death, age, physical fitness, underlying medical conditions, etc. It is difficult to predict because of the variables in , and we need to consider all the problems that arise during or early after surgery,” Dr. Norde said.
“Machine learning, and neural networks in particular, offer a promising approach because these techniques can analyze large amounts of data and identify complex patterns and relationships that are otherwise difficult to discover. It is highly adaptable to , so it can be implemented and adjusted for different settings.”
Graham Hillis, Professor of Medicine at UWA and Head of Cardiology at Royal Perth Hospital, said the results of the study combine machine learning techniques with data collected regularly before, during and after surgery. suggested that this could be a promising method to better distinguish between the most at-risk and at-risk patients. Risk may increase over time.
“This may allow medical professionals to catch problems early and intervene earlier to reduce potential complications,” said Professor Hillis.
“Further work is planned to fine-tune these methods and incorporate them into routine care.”
