ecg low ejection fraction detection and guiding in ai navigated treatment era

Conditions: Heart Failure; Ventricular Dysfunction, Left; Artificial Intelligence; Early Diagnosis; Asymptomatic Diseases; Cost-Benefit Analysis

Summary: asymptomatic left ventricular systolic dysfunction (alvsd), identified as a key component of stage b heart failure (hf) by aha/acc guidelines, is a common precursor to clinically overt hf. this progressive chronic disease affects over 23 million people worldwide and leads to significant morbidity, mortality, and healthcare costs. although alvsd presents a relatively lower risk compared to symptomatic reduced ejection fraction hf, it remains associated with a 1.6-fold increase in the risk of incident hf, a 2.13-fold increase in cardiovascular mortality, and a 1.46-fold increase in all-cause mortality. the prevalence of alvsd ranges from 3% to 6%, at least twice that of symptomatic hf. to prevent progression to symptomatic heart failure and associated morbidities and mortalities, guideline-directed medical therapy, including aceis/arbs or beta-blockers, is essential for patients with alvsd. however, distinguishing individuals with alvsd from the general population is challenging due to the lack of symptoms. effective screening methods are crucial to identify individuals with alvsd. traditionally, diagnosing alvsd involves screening asymptomatic populations using transthoracic echocardiography (tte), which is costly, time-consuming, and inconvenient for patients. other screening methods, such as laboratory tests for brain natriuretic peptide (bnp) or n- terminal pro-atrial natriuretic peptide (nt-probnp), have insufficient diagnostic performance. previous research proposed an ai-based alarm system (ai-s) to screen patients for alvsd, demonstrating greater accuracy than bnp screening and improved accessibility compared to widespread echocardiography. ai-s demonstrated a sensitivity of 92.6% (standard error \[se\] 0.042) for detecting medium-risk alvsd patients and 63% (se 0.154) for high-risk alvsd patients, with a specificity of 92.7% (se 0.003) for medium-risk patients and 98.7% (se 0.002) for high-risk patients. ai-s is accuracy, noninvasive, highly accessible in local medical clinics, less time-consuming, and cost-effective, making it a valuable screening tool for identifying alvsd prior to echocardiography or other confirmatory diagnostic methods. to date, no randomized controlled trial has assessed the cost-effectiveness and impact of ai-assisted screening tools for heart failure prevention in asians. the ecg ai-guided screening for low ejection fraction (eagle) trial reported a 32% increase in diagnosing of low left ventricular ejection fraction (defined as lvef ≤50%) within 90 days of the ecg. however, this population was not asian, and randomization involved primary care teams rather than participants. therefore, this randomized controlled trial is designed to evaluate the impact of ai-s on diagnosing low ejection fraction in asians, its cost-effectiveness, and the incidence of worsening hf (defined as admission for hf or hf-related emergency department visits).


Eligibility criteria:
inclusion criteria: * outpatients with at least one 12-lead ecg * age between 60-85 years exclusion criteria: * documented echocardiography within the previous 6 months * known severe lv dysfunction (lvef \<40%) * known heart failure history * scheduled echocardiography exam

Keywords: left ventricular dysfunction artificial intelligence electrocardiography