ECG Alerts Powered by AI Could Reduce Deadly Cardiac Events

Credit: Unsplash / Tim Cooper

Electrocardiography (ECG) alerts powered by artificial intelligence (AI) may help identify patients at risk of a deadly cardiac event while in the hospital, according to findings from new research.

A study analyzing an AI-powered ECG alert across nearly 16,000 patients was linked to a significant reduction in mortality risk over the span of three months. The findings show another implementation of AI in modern health care that could provide more timely and strategic care to high-risk patients.

Early identification of vulnerable patients may improve hospital outcomes but could prove challenging for implementation into current clinical practice. This multi-site, randomized, controlled trial sought to assess the application of an AI-enable ECG system to identify hospitalized patients with a high mortality risk across 39 physicians attending to 15,965 patients.

Patients randomized to the AI-ECG alert intervention were provided the screening tool, which included an AI report and warning messages to specify patients at a high mortality risk. Once the AI-ECG warned of a high mortality risk, a message was immediately sent to the attending physician.

Notifications appeared in the recipient’s smartphone message system for prompt attention. The message relayed to the physician that “An ECG was received for patient X. An ECG indicates a high risk of mortality. Please intensively attend to the patient’s conditions.”

Physicians were allowed to click on a link to connect the ECG and the result of AI-ECG prediction to identify the ECG further.

Warning messages were actively sent for high-risk cases identified by the AI and the AI-ECG report of low-risk cases also presented the degree of risk. Physicians could check the relative severity by accessing the electronic health record (EHR) for patients in the intervention group.

The control group comprised no intervention and patients remained on routine clinical practice. Primary outcomes for the analysis included all-cause mortality within 90 days, with tracking by the AI-ECG. The secondary outcomes included cardiovascular cause mortality, arrhythmia medication, electrolyte examination, and cardiac examination.

Upon analysis, implementation of the AI-ECG alert was linked with a significant reduction in all-cause mortality within 90 days. Specifically, 3.6 percent of patients in the intervention cohort died within 90 days, compared with 4.3 percent in the control group.

According to a pre-specified analysis, the reduction in all-cause mortality associated with the AI-ECG alert was identified primarily among those with high-risk ECGs.

Regarding the secondary outcome analyses, investigators found those in the intervention group with high-risk ECGs received increased levels of intensive care compared with the control group.

Among the high-risk ECG cohort, implementation of the AI-ECG alert was associated with a significant reduction in the risk of cardiac death.

This article was originally published on sister site HCPLive.