[Clinical characteristics and treatment of congenital long QT syndrome in 58 children]

Territory-Wide Chinese Cohort of Long QT Syndrome: Random Survival Forest and Cox Analyses

Introduction: Congenital long QT syndrome (LQTS) is a cardiac ion channelopathy that predisposes affected individuals to spontaneous ventricular tachycardia/fibrillation (VT/VF) and sudden cardiac death (SCD). The main aims of the study were to: (1) provide a description of the local epidemiology of LQTS, (2) identify significant risk factors of ventricular arrhythmias in this cohort, and (3) compare the performance of traditional Cox regression with that of random survival forests.

Methods: This was a territory-wide retrospective cohort study of patients diagnosed with congenital LQTS between 1997 and 2019. The primary outcome was spontaneous VT/VF.

Results: This study included 121 patients [median age of initial presentation: 20 (interquartile range: 8–44) years, 62% female] with a median follow-up of 88 (51–143) months. Genetic analysis identified novel mutations in KCNQ1, KCNH2, SCN5A, ANK2, CACNA1C, CAV3, and AKAP9. During follow-up, 23 patients developed VT/VF. Univariate Cox regression analysis revealed that age [hazard ratio (HR): 1.02 (1.01–1.04), P = 0.007; optimum cut-off: 19 years], presentation with syncope [HR: 3.86 (1.43–10.42), P = 0.008] or VT/VF [HR: 3.68 (1.62–8.37), P = 0.002] and the presence of PVCs [HR: 2.89 (1.22–6.83), P = 0.015] were significant predictors of spontaneous VT/VF. Only initial presentation with syncope remained significant after multivariate adjustment [HR: 3.58 (1.32–9.71), P = 0.011]. Random survival forest (RSF) model provided significant improvement in prediction performance over Cox regression (precision: 0.80 vs. 0.69; recall: 0.79 vs. 0.68; AUC: 0.77 vs. 0.68; c-statistic: 0.79 vs. 0.67). Decision rules were generated by RSF model to predict VT/VF post-diagnosis.

Conclusions: Effective risk stratification in congenital LQTS can be achieved by clinical history, electrocardiographic indices, and different investigation results, irrespective of underlying genetic defects. A machine learning approach using RSF can improve risk prediction over traditional Cox regression models.