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Paediatric/young versus adult patients with long QT syndrome

Lee, Sharen, Zhou, Jiandong, Jeevaratnam, Kamalan, Wong, Wing Tak, Wong, Ian Chi Kei, Mak, Chloe, Mok, Ngai Shing, Liu, Tong, Zhang, Qingpeng, Tse, Gary and others. (2021) Paediatric/young versus adult patients with long QT syndrome. Open Heart, 8 (2). Article Number e001671. ISSN 2053-3624. (doi:10.1136/openhrt-2021-001671) (KAR id:98736)

Abstract

Introduction: 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.

Methods: 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).

Results: 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.

Conclusion: Clinical and ECG presentation varies between the paediatric/young and adult LQTS population. Machine learning models achieved more accurate VT/VF prediction.

Item Type: Article
DOI/Identification number: 10.1136/openhrt-2021-001671
Subjects: R Medicine
Divisions: Divisions > Division of Natural Sciences > Kent and Medway Medical School
Depositing User: Manfred Gschwandtner
Date Deposited: 06 Dec 2022 11:19 UTC
Last Modified: 07 Dec 2022 17:08 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/98736 (The current URI for this page, for reference purposes)

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