A new machine learning model uses electrocardiogram (ECG) readings to diagnose and classify heart attacks faster and more accurately than current approaches, according to a study led by University of Pittsburgh researchers.
Through ECG, The new tool helps detect subtle clues in the ECG that are difficult for clinicians to spot and improves the classification of patients with chest pain.
The researchers compared their model to three gold standards for assessing cardiac events: experienced clinician interpretation of ECG, commercial ECG algorithms, and the HEART score, which considers medical history, age, risk factors such as smoking, diabetes, high cholesterol and blood levels of a protein called troponin.
The model outperformed all three, accurately reclassifying 1 in 3 patients with chest pain as low, intermediate, or high risk.
According to co-author Christian Martin-Gill, the algorithm will help emergency department providers identify people having a heart attack and those with reduced blood flow to the heart in a much more robust way compared with traditional ECG analysis.
“When a patient comes into the hospital with chest pain, the first question we ask is whether the patient is having a heart attack or not. It seems like that should be straightforward, but when its not clear from the ECG, it can take up to 24 hours to complete additional tests,” said associate professor in the Pitt School of Nursing and of Emergency Medicine and Cardiology in the School of Medicine Salah Al-Zaiti.
“Our model helps address this major challenge by improving risk assessment so that patients can get appropriate care without delay,” he added.
Source: Qatar News Agency