AI-ECG may improve detection rate of underdiagnosed HF: EAGLE study

An artificial intelligence (AI)-enabled ECG algorithm integrated into routine care can increase the diagnosis of low ejection fraction (EF), according to new randomized trial data derived from EAGLE study published this week in Nature Medicine journal. The algorithm uses neural networks to predict a high likelihood of low EF, an often-missed predictor of adverse events, based on standard 12-lead electrocardiogram data.

"Because ECG is a low-cost test frequently performed for a variety of purposes, the algorithm could potentially improve early diagnosis and treatment in broad populations," write Xiaoxi Yao et al in their pragmatic randomised study.

For the EAGLE study, Yao and colleagues cluster randomized 120 primary care teams to treat patients either with access to their AI-ECG, which gives an indication of potential for low EF or with usual care. Overall, 11,573 patients without prior heart failure were assessed via the intervention pathway and 11,068 served as controls. The primary endpoint was newly discovered EF ≤50%

Six percent of patients in each group had positive AI-ECG results, indicating a high likelihood of low EF. More echocardiograms were obtained for patients with positive AI-ECG results in the intervention arm compared with patients whose clinicians did not have access to the technology (49.6% vs 38.1%; P < 0.001). However, echocardiogram use was similar for the cohorts in the overall population (19.2% vs 18.2%; P = 0.17).

Additionally, clinician access to AI-ECG data increased the diagnosis of low EF compared with usual care, both overall and especially among patients with positive results.

The AI-enabled EKG facilitated the diagnosis of patients with low ejection fraction in a real-world setting by identifying people who previously would have slipped through the cracks," senior author Peter Noseworthy, MD (Mayo Clinic), said in a press release.

These results indicate that use of an AI algorithm based on ECGs can enable the early diagnosis of low EF in patients in the setting of routine primary care.

The same tea of authors had previously published the utility of AI based algorithms in identifying otherwise missed cases of LongQT syndrome.(1)

Also Read:Use of Artificial Intelligence and Deep Neural Networks in Evaluation of Patients With Electrocardiographically Concealed Long QT Syndrome From the Surface 12-Lead Electrocardiogram

Source: Yao, X., Rushlow, D.R., Inselman, J.W. et al. Artificial intelligence–enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial. Nat Med (2021). https://doi.org/10.1038/s41591-021-01335-4