Cardiovascular Analytics Group, Hong Kong, China-UK Collaboration.
School of Data Science, City University of Hong Kong, Hong Kong, People's Republic of China.
Open Heart. 2021 Sep;8(2). doi: 10.1136/openhrt-2021-001671.
Long QT syndrome (LQTS) is a less prevalent cardiac ion channelopathy than Brugada syndrome in Asia. The present study compared the outcomes between paediatric/young and adult LQTS patients.
This was a population-based retrospective cohort study of consecutive patients diagnosed with LQTS attending public hospitals in Hong Kong. The primary outcome was spontaneous ventricular tachycardia/ventricular fibrillation (VT/VF).
A total of 142 LQTS (mean onset age=27±23 years old) were included. Arrhythmias other than VT/VF (HR 4.67, 95% CI (1.53 to 14.3), p=0.007), initial VT/VF (HR=3.25 (95% CI 1.29 to 8.16), p=0.012) and Schwartz score (HR=1.90 (95% CI 1.11 to 3.26), p=0.020) were predictive of the primary outcome for the overall cohort, while arrhythmias other than VT/VF (HR=5.41 (95% CI 1.36 to 21.4), p=0.016) and Schwartz score (HR=4.67 (95% CI 1.48 to 14.7), p=0.009) were predictive for the adult subgroup (>25 years old; n=58). A random survival forest model identified initial VT/VF, Schwartz score, initial QTc interval, family history of LQTS, initially asymptomatic and arrhythmias other than VT/VF as the most important variables for risk prediction.
Clinical and ECG presentation varies between the paediatric/young and adult LQTS population. Machine learning models achieved more accurate VT/VF prediction.
长 QT 综合征(LQTS)在亚洲的心脏离子通道病中比 Brugada 综合征少见。本研究比较了儿科/年轻和成年 LQTS 患者的结局。
这是一项基于人群的回顾性队列研究,连续纳入在香港公立医院就诊的 LQTS 确诊患者。主要结局是自发性室性心动过速/心室颤动(VT/VF)。
共纳入 142 例 LQTS 患者(平均发病年龄=27±23 岁)。除 VT/VF 以外的心律失常(HR 4.67,95%CI(1.53 至 14.3),p=0.007)、初始 VT/VF(HR=3.25,95%CI(1.29 至 8.16),p=0.012)和 Schwartz 评分(HR=1.90,95%CI(1.11 至 3.26),p=0.020)均为整个队列的主要结局预测因素,而除 VT/VF 以外的心律失常(HR=5.41,95%CI(1.36 至 21.4),p=0.016)和 Schwartz 评分(HR=4.67,95%CI(1.48 至 14.7),p=0.009)则为成年亚组(>25 岁;n=58)的预测因素。随机生存森林模型确定初始 VT/VF、Schwartz 评分、初始 QTc 间隔、LQTS 家族史、最初无症状和除 VT/VF 以外的心律失常为风险预测的最重要变量。
儿科/年轻和成年 LQTS 人群的临床表现不同。机器学习模型可实现更准确的 VT/VF 预测。