Abstract 17098: Detection of Multiple Structural Heart Diseases Using a Novel Artificial Intelligence-Driven Algorithm for Noisy Real-World Single-Lead ECGs

Abstract

Background: Wearable devices capable of ECG acquisition can help scale screening of structural heart disease (SHD) if diagnoses can be made with a single lead ECG. Algorithms for portable and wearable ECG require resilience to noisy acquisition but can enable detecting SHD in a community setting before symptom onset, ideal for public health management. Methods: Using Yale EHR data (2015-2021), we developed an artificial intelligence model to detect the presence of left ventricular systolic dysfunction, severe left-sided valvular disease, or hypertrophic cardiomyopathy using lead I of an ECG, which is commonly captured by wearable devices. We used 563,668 ECGs paired with echocardiograms obtained within 30 days of each other from 131,804 patients to develop the model. The dataset was split into training, validation, and test sets (85:5:10). For individual disease labels, we trained 6 convolutional neural network models using ECG signals augmented with random Gaussian real-world noise within 4 distinct frequency ranges and at 4 signal-to-noise ratios. Then, we included their prediction probabilities and age and sex as inputs to an XGBoost model to predict the composite SHD (Fig). Results: In the development set, the prevalence of composite SHD was 13%, encompassing left ventricular systolic dysfunction (9%), severe valvular disease (4%), and hypertrophic cardiomyopathy (1%). In the held-out test set of 13,181 patients (65±17 years, 49% women, 14% Black), AUROC and AUPRC of the ensemble model for prediction of SHD were 0.92 (0.90-0.93) and 0.61 (0.54-0.68), respectively (Fig). With optimized Youden's index, the model has a sensitivity of 0.83, specificity of 0.87, PPV of 0.41, and NPV of 0.98 to detect SHD. Conclusion: A novel wearable-adapted model demonstrated promising performance for detecting multiple SHD using single-lead ECG. This can enable community screening for SHD and improve patient outcomes through early initiation of guideline-directed therapies.