Risk prediction algorithms for phenotype- and genotype-positive LQTS patients demonstrated good discrimination for 5-year life-threatening arrhythmic events, with external validation C-statistics of 0.700 and 0.711.
Cohort (n=4,278)
Yes
The developed risk prediction algorithms provide validated estimates of 5-year absolute risk for life-threatening arrhythmic events in LQTS patients, which can assist in clinical decision making regarding prophylactic ICD therapy.
Effect estimate: C-statistic 0.784 (phenotypic) and 0.785 (genotypic) (95% CI 0.740-0.827 and 0.721-0.849)
Background: Risk stratification in long QT syndrome (LQTS) patients is important for optimizing patient care and informing clinical decision making. We developed a risk prediction algorithm with prediction of 5-year absolute risk of the first life-threatening arrhythmic event defined as aborted cardiac arrest, sudden cardiac death, or appropriate implantable cardioverter defibrillator (ICD) shock in LQTS patients, accounting for individual risk factors and their changes over time. Methods: Rochester-based LQTS Registry included the phenotypic cohort consisting of 1,509 LQTS patients with a QTc ≥ 470 ms, and the genotypic cohort including 1,288 patients with single LQT1, LQT2, or LQT3 mutation. We developed two separate risk prediction models which included pre-specified time-dependent covariates of beta-blocker use, syncope (never, syncope while off beta blockers, and syncope while on beta blockers), and sex by age < and ≥13 years, baseline QTc, and genotype (for the genotypic cohort only). Follow-up started from enrollment in the registry and was censored at patients' 50s birthday, date of death due to reasons other than sudden cardiac death, or last contact, whichever occurred first. The predictive models were externally validated in an independent cohort of 1,481 LQTS patients from Pavia, Italy. Results: In Rochester dataset, there were 77 endpoints in the phenotypic cohort during a median follow-up of 9.0 years, and 47 endpoints in the genotypic cohort during a median follow-up of 9.8 years. The time-dependent extension of Harrell's generalized C-statistics for the phenotypic model and genotypic model were 0.784 (95% CI: 0.740-0.827) and 0.785 (95% CI: 0.721-0.849), respectively, in the Rochester cohort. The C-statistics obtained from external validation in the Pavia cohort were 0.700 (95% CI: 0.610-0.790) and 0.711 (95% CI: 0.631-0.792) for the two models, respectively. Based on the above models, an online risk calculator estimating a 5-year risk of life-threatening arrhythmic events was developed. Conclusion: This study developed two risk prediction algorithms for phenotype and genotype positive LQTS patients separately. The estimated 5-year absolute risk can be used to quantify a LQTS patient's risk of developing life-threatening arrhythmic events and thus assisting in clinical decision making regarding prophylactic ICD therapy.
Wang et al. (Fri,) conducted a cohort in Long QT syndrome (LQTS) (n=4,278). Risk prediction algorithms (phenotypic and genotypic models) was evaluated on First life-threatening arrhythmic event (aborted cardiac arrest, sudden cardiac death, or appropriate ICD shock) (C-statistic 0.784 (phenotypic) and 0.785 (genotypic), 95% CI 0.740-0.827 and 0.721-0.849). Risk prediction algorithms for phenotype- and genotype-positive LQTS patients demonstrated good discrimination for 5-year life-threatening arrhythmic events, with external validation C-statistics of 0.700 and 0.711.