Key result
The AI-enhanced electrocardiogram demonstrated excellent performance for detecting decreased left ventricular ejection fraction (<50%) with an AUC of 0.892 in an under-resourced African-American cohort.
Why the study?
AI-based health interventions are frequently developed without race-specific data analysis or validation, raising concerns about systemic racism and prompting evaluation in under-resourced African-American cohorts.
Does an AI-enhanced electrocardiogram accurately predict decreased LVEF, age, and sex in an under-resourced community-based African-American cohort?
Cross-Sectional (n=54)
No
Does an AI-enhanced electrocardiogram accurately predict decreased LVEF, age, and sex in an under-resourced community-based African-American cohort?
Effect estimate: AUC 0.892 (95% CI 0.708-1)
An AI-enhanced ECG showed strong diagnostic performance for detecting reduced ejection fraction in an under-resourced African-American community cohort, indicating its potential as an accessible screening tool to help mitigate healthcare disparities.
May support AI-ECG screening in under-resourced African-American cohorts; leaves open prospective validation before clinical adoption.
Objective: With the emergence of artificial intelligence (AI)-based health interventions, systemic racism remains a concern as these advancements are frequently developed without race-specific data analysis or validation. To evaluate the potential utility of an AI-based cardiovascular diseases (CVD) screening tool in an under-resourced African-American cohort, we reviewed the AI-enhanced electrocardiogram (ECG) data of participants enrolled in a community-based clinical trial as a proof-of-concept ancillary study for community-based screening. Methods: Enrollees completed cardiovascular testing including standard 12-lead ECG and a limited echocardiogram (TTE). All ECGs were analyzed using previously published institution-based AI algorithms. AI-ECG predictions were generated for age, sex, and decreased left ventricular ejection fraction (LVEF). Diagnostic accuracy of the AI-ECG for decreased LVEF and sex was quantified using area under the receiver operating characteristic curve (AUC). Correlation between actual age and AI-ECG predicted age was assessed using Pearson correlation coefficients. Results: Fifty-four participants completed both an ECG and TTE (mean age 55 years [range 31-87 years]; 66.7% female). All participants were in sinus rhythm, and the median LVEF of the cohort was 60-65%. The AI-ECG for decreased LVEF demonstrated excellent performance with an AUC of 0.892 (95% confidence interval [CI] 0.708-1); sensitivity=50% (95% CI 9.5-90.5%; n=1/2) and specificity=96% (95% CI 86.8-98.9%; n=49/51). The AI-ECG for participant sex demonstrated similar performance with AUC of 0.944 (95% CI 0.891-0.998); sensitivity=100% (95% CI 82.4-100.0%; n=18/18) and specificity=77.8% (95% CI 61.9-88.3%; n=28/36). The AI-ECG predicted mean age was 55 years (range 26.9-72.6 years) with a strong correlation to actual age (R=0.769; p<0.001). Conclusion: Our analyses of previously developed AI-ECG algorithms for prediction of age, sex, and decreased LVEF demonstrated reliable performance in this community-based, African-American cohort. This novel, community-centric delivery of AI could provide valuable screening resources and appropriate referrals for early detection of highly-morbid CVD for under-resourced patient populations.
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Harmon et al. (2022) conducted a cross-sectional in Cardiovascular disease screening (n=54). Artificial intelligence (AI)-enhanced electrocardiogram (AI-ECG) vs. Transthoracic echocardiogram (TTE) and chronological/demographic data was evaluated on Diagnostic accuracy (AUC) of AI-ECG for detecting decreased left ventricular ejection fraction (<50%) (AUC 0.892, 95% CI 0.708-1). The AI-enhanced electrocardiogram demonstrated excellent performance for detecting decreased left ventricular ejection fraction (<50%) with an AUC of 0.892 in an under-resourced African-American cohort.
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