FactorECG demonstrated superior discriminative ability for predicting death, LVAD, or heart transplant compared to QRSAREA (c-statistic 0.69 vs 0.61; P<0.001).
Cohort (n=1,306)
Yes
Does FactorECG improve prediction of the combined clinical endpoint of death, left ventricular assist device, or heart transplantation in CRT patients compared to guideline ECG criteria and QRSAREA?
An explainable deep learning algorithm using standard 12-lead ECGs (FactorECG) significantly improves the prediction of clinical outcomes in CRT patients compared to current guideline criteria and QRSAREA.
Effect estimate: c-statistic 0.69 (95% CI 0.66-0.72)
Absolute Event Rate: 0.69% vs 0.61%
p-value: p=<0.001
AIMS: This study aims to identify and visualize electrocardiogram (ECG) features using an explainable deep learning-based algorithm to predict cardiac resynchronization therapy (CRT) outcome. Its performance is compared with current guideline ECG criteria and QRSAREA. METHODS AND RESULTS: A deep learning algorithm, trained on 1.1 million ECGs from 251 473 patients, was used to compress the median beat ECG, thereby summarizing most ECG features into only 21 explainable factors (FactorECG). Pre-implantation ECGs of 1306 CRT patients from three academic centres were converted into their respective FactorECG. FactorECG predicted the combined clinical endpoint of death, left ventricular assist device, or heart transplantation c-statistic 0.69, 95% confidence interval (CI) 0.66-0.72, significantly outperforming QRSAREA and guideline ECG criteria c-statistic 0.61 (95% CI 0.58-0.64) and 0.57 (95% CI 0.54-0.60), P < 0.001 for both. The addition of 13 clinical variables was of limited added value for the FactorECG model when compared with QRSAREA (Δ c-statistic 0.03 vs. 0.10). FactorECG identified inferolateral T-wave inversion, smaller right precordial S- and T-wave amplitude, ventricular rate, and increased PR interval and P-wave duration to be important predictors for poor outcome. An online visualization tool was created to provide interactive visualizations (https://crt.ecgx.ai). CONCLUSION: Requiring only a standard 12-lead ECG, FactorECG held superior discriminative ability for the prediction of clinical outcome when compared with guideline criteria and QRSAREA, without requiring additional clinical variables. End-to-end automated visualization of ECG features allows for an explainable algorithm, which may facilitate rapid uptake of this personalized decision-making tool in CRT.
Wouters et al. (Mon,) conducted a cohort in Cardiac resynchronization therapy (n=1,306). FactorECG (deep learning-based algorithm) vs. Guideline ECG criteria and QRSAREA was evaluated on Combined clinical endpoint of death, left ventricular assist device, or heart transplantation (c-statistic 0.69, 95% CI 0.66-0.72, p=<0.001). FactorECG demonstrated superior discriminative ability for predicting death, LVAD, or heart transplant compared to QRSAREA (c-statistic 0.69 vs 0.61; P<0.001).