The pedQTNet deep learning model achieved higher sensitivity for detecting Long QT syndrome than pediatric electrophysiologists (100% vs. 71%, P<0.05) in a prospective pediatric ECG set.
Observational (n=37,992)
Does pedQTNet improve QTc estimation and LQTS detection compared to the Marquette 12SL algorithm and pediatric electrophysiologists in pediatric patients?
A deep learning model (pedQTNet) accurately estimates QTc intervals and detects Long QT syndrome in pediatric patients, performing on par with or better than expert electrophysiologists and commercial algorithms.
Absolute Event Rate: 100% vs 71%
p-value: p=< 0.05
Long QT syndrome (LQTS) is a primary risk factor for ventricular arrhythmias and sudden cardiac death in children. Accurate corrected QT intervals (QTc) measurement is imperative but challenging for non-heart-rhythm specialists, especially in children. We developed and evaluated pedQTNet, a deep neural network model for estimating QTc and detecting LQTS in pediatric patients. We analyzed a cohort of 37,992 patients aged 0-18 years with 65,370 ECGs annotated by pediatric electrophysiologists (PEPs) between 2010 and 2020. Using PEP-annotated QTc measurements as ground truth, pedQTNet was trained and calibrated on raw ECG waveforms to optimize QTc estimation and LQTS classification. Performance was compared to GE Healthcare's Marquette 12SL algorithm, and to PEPs in cross-validation, as well as an additional prospective set of 200 ECGs. In 10-fold cross-validation, pedQTNet estimated QTc's with a mean absolute error (MAE) of 18.8 ms (95% CI: 18.4-19.2) and predicted LQTS at 470 ms with 85% sensitivity (83%-87%), 87% specificity (87%-88%), positive likelihood ratio (PLR) of 6.7 (6.5-7.0), and negative likelihood ratio (NLR) of 0.17 (0.15-0.19), outperforming Marquette 12SL. In the prospective set, pedQTNet had higher sensitivity than PEPs (100% 69%-100% vs. 71% 53%-85%, P < 0.05), and a lower but not statistically significant NLR (0.00 0.00-0.70 vs. 0.30 0.18-0.50, P = 0.2). PedQTNet demonstrated high QTc estimation accuracy and reliable LQTS detection, outperforming a commercial tool and on par with expert interpretation. Its strong performance supports its clinical use for scalable, automated pediatric ECG screening and LQTS risk assessment, offering a practical tool for enhancing pediatric cardiac care.
Ruiz et al. (Wed,) conducted a observational in Long QT syndrome (LQTS) (n=37,992). pedQTNet vs. Pediatric electrophysiologists (PEPs) and Marquette 12SL was evaluated on Sensitivity for predicting LQTS at 470 ms in the prospective set (p=< 0.05). The pedQTNet deep learning model achieved higher sensitivity for detecting Long QT syndrome than pediatric electrophysiologists (100% vs. 71%, P<0.05) in a prospective pediatric ECG set.
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