Artificial intelligence (AI) enhanced electrocardiograms are quicker and outperform current standard-of-care tests to identify patients presenting to emergency rooms with shortness of breath who have left ventricular systolic dysfunction (LVSD).

The study published in Circulation: Arrhythmia and Electrophysiology, an American Heart Association journal, found that AI-enhanced ECG was better than standard blood tests such as blood levels of natriuretic peptides in identifying which patients have LVSD.

Natriuretic peptides are elevated in the blood when heart failure is present. However, these biomarker levels are also affected by obesity, age, kidney disease, severe infection, high blood pressure in the vessels that bring blood to the lungs (pulmonary hypertension), abnormal heart rhythms and a specific heart failure medication.

The AI-enhanced ECG was also good at identifying patients with less severe but abnormally low pumping ability (50% or less of the blood leaving the heart with each contraction).

In addition, while several factors can influence blood test results, AI-enhanced ECG performed just as well in men and women and among patients in different age groups.

AI-enabled ECG provides a rapid and effective method to screen for LVSD

In LVSD, the left ventricle is weakened and must work harder to maintain adequate blood flow to the body. Approximately 1.2 million people go to emergency departments because they are short of breath. 

Demilade Adedinsewo, lead author of the study and chief fellow in the division of cardiovascular medicine at Mayo Clinic in Jacksonville, Florida, said: "Determining why someone has shortness of breath is challenging for emergency department physicians, and this AI-enabled ECG provides a rapid and effective method to screen these patients for left ventricular systolic dysfunction.

"An abnormal ECG raises concern about underlying cardiac abnormalities but are not specific for heart failure."

To create the AI-enhanced ECG, Mayo Clinic researchers used data on thousands of patients to train computers to distinguish between the ECG patterns of people ultimately diagnosed with LVSD and those without LVSD. In about 10 seconds, standard ECG recordings can be analyzed with the resulting AI software application to identify likely LVSD.

In this study, researchers tested the accuracy of the AI-enhanced ECG to identify LVSD in emergency room patients with shortness of breath compared to the results of biomarker blood tests. They applied the AI-enhancement to the ECGs of 1,606 patients (average age 68, 47% female, 91% white) who had received an ECG and blood testing in the emergency department, later followed by definitive testing using an echocardiogram.

AI-enhanced ECGs are not widely available. In May, the Food and Drug Administration granted emergency use authorisation of the AI-enhanced ECG algorithm to screen for LVSD in people with confirmed or suspected Covid-19 disease.

The current study is limited by being a retrospective analysis of previous emergency department visits.