Long-term clinical outcomes informed by artificial intelligence-enabled single-lead ECG detection of reduced ejection fraction

Abstract Background * Reduced left ventricular ejection fraction (LVEF) is linked to poor clinical outcomes (1, 2). * Despite its increasing prevalence, reduced LVEF is often undetected due to resource-limited echocardiography and lack of suitable point-of-care diagnostic tools. * Previous studies have shown that an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm can accurately identify patients with reduced LVEF, but may also offer novel insights on long-term prognosis (3, 4). Purpose We aimed to determine if an AI-ECG algorithm designed for detecting reduced LVEF can also independently predict 2-year major adverse cardiovascular events (MACE) and all-cause mortality. Method * A retrospective multicentre observational study investigating the ability of AI-ECG to predict long-term clinical outcomes (i.e. over two years' follow up). * Analysis included a total of 1,007 consecutive unselected patients attending for routine echocardiography, with a single-lead ECG recording performed at the same time. * An AI-ECG algorithm, designed to identify impaired LVEF, was applied to each single-lead ECG. * Occurrence of MACE and all-cause mortality associated with AI-ECG results (Figure 1) was investigated using Cox regression; evaluating performance of AI-ECG results as a classifier (0 or 1) and as a probability score (0 to 1). * The study was approved by the UK Health Research Authority (reference 21/LO/0051). Results * The mean age was 62.3 years. 52.4% of patients were male and 57.5% of patients were White. * Appropriately, patients with a positive AI-ECG had a higher MACE rate compared to those with a negative AI-ECG (34.2% vs. 11.9%; adjusted hazard ratio (aHR) 2.02; 95% CI, 1.46 – 2.81; p < 0.005), primarily driven by increased mortality (23% vs. 9.6%; p < 0.0001; aHR 1.65; 95% CI, 1.12 – 2.43) and heart failure hospitalization (14.5% vs. 1%; p < 0.0001) – Figure 2. * AI-ECG probability score (per 10% increase) was significantly associated with MACE (aHR 1.17; 95% CI, 1.09 – 1.25; p < 0.005) and all-cause mortality (aHR 1.11; 95% CI, 1.02 – 1.20; p = 0.01). * Importantly, sub-analysis of AI-ECG for those who have a normal LVEF (i.e. ≥50%) continued to show association between positive AI-ECG and MACE (27.2% vs. 11.9%; p < 0.0001; aHR 1.70, 95% CI 1.17 – 2.48) and all-cause mortality (20.4% vs. 9.6%; p < 0.0001; aHR 1.53, 95% CI 1.00 – 2.36). Conclusion An AI-ECG algorithm designed to identify impaired LVEF can identify patients at risk of M