Tse Gary, Lee Sharen, Zhou Jiandong, Liu Tong, Wong Ian Chi Kei, Mak Chloe, Mok Ngai Shing, Jeevaratnam Kamalan, Zhang Qingpeng, Cheng Shuk Han, Wong Wing Tak
Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China.
Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom.
Front Cardiovasc Med. 2021 Feb 5;8:608592. doi: 10.3389/fcvm.2021.608592. eCollection 2021.
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. 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. 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), = 0.007; optimum cut-off: 19 years], presentation with syncope [HR: 3.86 (1.43-10.42), = 0.008] or VT/VF [HR: 3.68 (1.62-8.37), = 0.002] and the presence of PVCs [HR: 2.89 (1.22-6.83), = 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), = 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. 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.
先天性长QT综合征（LQTS）是一种心脏离子通道病，使受影响个体易发生自发性室性心动过速/心室颤动（VT/VF）和心源性猝死（SCD）。该研究的主要目的是：（1）描述LQTS的当地流行病学情况，（2）确定该队列中心室心律失常的重要危险因素，（3）比较传统Cox回归与随机生存森林的性能。这是一项对1997年至2019年间诊断为先天性LQTS患者的全地区回顾性队列研究。主要结局是自发性VT/VF。该研究纳入了121例患者[首次就诊的中位年龄：20岁（四分位间距：8 - 44岁），62%为女性]，中位随访时间为88（51 - 143）个月。基因分析确定了KCNQ1、KCNH2、SCN5A、ANK2、CACNA1C、CAV3和AKAP9中的新突变。随访期间，23例患者发生了VT/VF。单变量Cox回归分析显示，年龄[风险比（HR）：1.02（1.01 - 1.04），P = 0.007；最佳截断值：19岁]、伴有晕厥[HR：3.86（1.43 - 10.42），P = 0.008]或VT/VF[HR：3.68（1.62 - 8.37），P = 0.002]以及存在室性早搏[HR：2.89（1.22 - 6.83），P = 0.015]是自发性VT/VF的显著预测因素。多变量调整后，只有首次伴有晕厥仍具有显著性[HR：3.58（1.32 - 9.71），P = 0.011]。随机生存森林（RSF）模型在预测性能上比Cox回归有显著改善（精确度：0.80对0.69；召回率：0.79对0.68；AUC：0.77对0.68；c统计量：0.79对0.67）。RSF模型生成了决策规则以预测诊断后的VT/VF。无论潜在的基因缺陷如何，通过临床病史、心电图指标和不同的检查结果可实现先天性LQTS的有效风险分层。使用RSF的机器学习方法比传统Cox回归模型能改善风险预测。
