AI Insight
Researchers developed and validated an enhanced adaptive QT correction formula (QTcAd) for pediatric patients that accounts for age-specific demographic variables to improve detection of congenital long QT syndrome (LQTS). Tested across over 8,300 ECGs, QTcAd achieved substantially higher sensitivity than the standard Bazett correction (92% vs 46.7%) while maintaining high specificity (96.9%), as measured against a clinically confirmed LQTS cohort of 137 patients. The formula also reduced unnecessary follow-up testing in an emergency department cohort, generating 270 fewer borderline or prolonged QTc classifications compared to the traditional method.
Why it matters
Congenital LQTS is a potentially fatal arrhythmia condition, and more accurate pediatric screening tools could reduce both missed diagnoses and unnecessary repeat testing, easing clinical burden on patients, families, and healthcare systems. The authors have made the formula publicly accessible through an online calculator, which could facilitate broader implementation across clinical settings.
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Background: Traditional heart rate (HR) adjusted QT correction (QTc) formulae often fail to eliminate the inverse HR-QT interval relationship, particularly in pediatric patients. In this study, we optimized our previously published adaptive QTc (QTcAd) formula by including additional demographic variables and broadening the pediatric age range. We tested the hypothesis that QTcAd improves congenital long QT syndrome (congenital LQTS) detection performance and reduces erroneous classifications across pediatric cohorts. Methods: We retrospectively analyzed 8,306 ECGs from 4,556 cardiovascular disease (CVD)-free pediatric patients. For neonatal patients (1-30 days old), we derived daily QTcAd parameter values. For older patients, we developed regression models to estimate QTcAd parameters (mean Heart Rate (HR) = -15.9ln(days) + 219; |m| = 0.0001(days) + 1, where |m|=absolute HR-QT regression slope). To support LQTS screening, we constructed dynamic QTcAd thresholds by estimating age-specific reference limits. Diagnostic performance was tested in a clinically confirmed LQTS cohort (n=137), and further evaluated in the Pediatric Heart Network (PHN; n=2,394) and Emergency Department (ED; n=2,002) cohorts. Results: Using the confirmed LQTS cohort as the event population and the CVD-free cohort as the non-event population, QTcAd demonstrated higher sensitivity than QTcB (92% vs 46.7%). QTcAd maintained high specificity (96.9% vs 98.9%), which resulted in a higher Youden index (0.889 vs 0.456). In the PHN healthy cohort, both QTc formulae classified the majority of individuals as normal (QTcAd 95%; QTcB 98.2%) indicating few false-positives. In the ED cohort, QTcAd reduced borderline/prolonged QTc classifications requiring follow-up, yielding 270 fewer repeat-testing triggers than QTcB. We developed a publicly accessible calculator to compute QTcAd and classify congenital LQTS risk. Conclusion: We developed and validated an enhanced QTcAd formula for pediatric patients. QTcAd-based-age-adjusted dynamic thresholding improved performance for congenital LQTS screening, while maintaining high specificity. This reduces false-positive LQTS classifications and repeat ECGs, thereby decreasing unnecessary downstream clinical evaluation.