A heart attack, also known as a myocardial infarction, occurs when the flow of blood to the heart is blocked, which is usually caused by the narrowing of the coronary arteries, which feed the heart, due to a process of atherosclerosis resulting from the deposition of fats in the blood vessel wall. The symptoms of a heart attack are sometimes similar to other medical conditions, which can make diagnosis difficult.

Can we prevent heart attacks in the future? The Weizmann Institute in a scientific breakthrough

One of the best and most common ways to diagnose heart attacks is to measure troponin levels in the blood. Troponin is released when the heart muscle is damaged, and levels of the protein usually rise sharply within three to 12 hours after a heart attack, peaking about 24 hours later.

Many hospitals around the world have adopted diagnostic pathways that include evaluating troponin levels when a heart attack is suspected. Measuring troponin requires timely collection of blood samples, which can be challenging in emergency departments, because emergency department physicians only classify patients as having a low, moderate, or high risk of a heart attack without taking into account other important information such as when symptoms begin or EKG results , and they do not take into account the effect of sex, age and comorbidity.

The journal "Nature Medicine" reported that British researchers recently developed a fast and accurate algorithm based on artificial intelligence called CoDE-ACS, (Collaboration for the Diagnosis and Evaluation of Acute General Syndrome), and it is designed to calculate the likelihood of a heart attack among patients suspected of having one.

The researchers used data from 10,286,6 patients who had potential heart attacks worldwide. The algorithm is "learned" using the patient's gender, age, EKG results and medical history, as well as troponin levels, to determine the likelihood of a heart attack.

Compared to current methods, the researchers concluded that CoDE-ACS can rule out a heart attack in more than twice as many patients, with an accuracy of up to 99.6%.

The algorithm also accurately predicted a heart attack among subgroups, including men, women, the elderly, people with kidney damage or those who arrived at the hospital early after the onset of symptoms.

According to the researchers, the innovative algorithm can prevent unnecessary hospitalizations for patients who are unlikely to have had a heart attack or those at low risk of damage to the heart muscle or death after a heart attack, which can make urgent care more effective as a result of the speed and accuracy in identifying patients, those who will be able to return home and those who will have to stay for tests more

"For patients with severe chest pain due to a heart attack, early diagnosis and treatment save lives," said researcher Nicholas Mills, who noted that there are "many health conditions that cause these common symptoms, so the diagnosis is not clear in all cases."

"Harnessing data and artificial intelligence to support clinical decisions has huge potential to improve patient care and efficiency in overcrowded emergency departments," Mills added, revealing that "the CoDE-ACS algorithm is currently being piloted in Scotland to see if it can reduce pressure on overcrowded emergency departments."