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Artificial Intelligence Electrocardiogram and Left Ventricular Systolic Dysfunction in Kenya
Ambarish Pandey1, Neil Keshvani1,2,3, Matthew W Segar4
1Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas.
Insights
An AI-ECG algorithm shows high sensitivity and negative predictive value for detecting left ventricular systolic dysfunction (LVSD) risk. This artificial intelligence electrocardiogram tool is promising for scalable screening in resource-limited settings.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Diagnostics
Background:
- Early detection of heart failure with reduced ejection fraction is crucial but challenging in resource-limited settings due to limited echocardiography access.
- Artificial intelligence electrocardiogram (AI-ECG) algorithms show potential for identifying left ventricular systolic dysfunction (LVSD).
Purpose of the Study:
- To determine the frequency of patients with high probability of LVSD by AI-ECG in Kenya.
- To assess AI-ECG algorithm performance against echocardiography as the gold standard.
Main Methods:
- A cross-sectional study enrolled 1444 adult patients from 8 healthcare facilities in Kenya.
- Participants underwent 12-lead ECG, with a subset also completing echocardiography.
- AI-ECG (AiTiALVSD) was used to identify LVSD risk, compared against echocardiographic confirmation (LVEF <40%).
Main Results:
- LVSD was identified in 14.1% of participants.
- The AI-ECG algorithm demonstrated high sensitivity (95.6%) and negative predictive value (99.1%).
- The algorithm achieved an AUC of 0.96, with consistent performance across cardiovascular risk strata.
Conclusions:
- The AI-ECG algorithm shows potential clinical utility for screening LVSD risk.
- The algorithm's high sensitivity and negative predictive value make it suitable for resource-limited settings.
- This AI-ECG approach may offer a scalable solution for early detection of LVSD.
Importance:
Early detection of risk of heart failure with reduced ejection fraction remains challenging in resource-limited settings due to limited access to echocardiography. Artificial intelligence electrocardiogram (AI-ECG) algorithms have demonstrated promise for identifying left ventricular systolic dysfunction (LVSD), but their feasibility in resource-constrained settings remains unknown.
Objective:
To determine the frequency of patients in Kenya with a high probability of LVSD by AI-ECG and assess AI-ECG algorithm performance against the gold standard of echocardiography.
Design, Setting, And Participants:
This was a cross-sectional study with enrollment from June to December 2024. Participants underwent baseline assessment and 12-lead ECG, and a subset completed echocardiography within 7 days. The echocardiography subset included participants from 3 prespecified risk strata: those with prior cardiovascular disease, those at high cardiovascular risk (Framingham Risk Score [FRS] ≥10%), and those at low risk (FRS <10%). The study took place at 8 outpatient health care facilities across Kenya. A total of 1444 patients 18 years and older seeking routine care were enrolled and completed paired echocardiogram. Exclusion criteria included inability to provide informed consent.
Exposure:
Risk of LVSD was identified using a validated convolutional neural network AI-ECG algorithm (AiTiALVSD).
Main Outcomes And Measures:
Key outcomes were the diagnostic performance (sensitivity, specificity, and positive and negative predictive values) of the AI-ECG algorithm for detecting LVSD (LVEF <40%) when confirmed on echocardiography.
Results:
Among 1444 participants (mean [SD] age, 59.0 [16.7] years; 907 [62.8%] female; 1118 [77.4%] at high risk), LVSD was identified in 204 (14.1%). The AI-ECG algorithm had a sensitivity of 95.6% (95% CI, 91.8-97.7), specificity of 79.4% (95% CI, 77.0-81.5), positive predictive value of 43.2% (95% CI, 38.7-47.9), negative predictive value of 99.1% (95% CI, 98.3-99.5), and area under the receiver operating characteristic curve (AUC) of 0.96 (95% CI, 0.95-0.97). Performance remained consistent across cardiovascular risk strata (AUC, 0.96-0.98).
Conclusions And Relevance:
In this study, the AI-ECG algorithm demonstrated the potential clinical utility for screening of LVSD risk with high sensitivity and negative predictive value and may be particularly scalable in a resource-limited setting.
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