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Comparing the performance of published risk scores in Brugada syndrome: a multi-center cohort study

Lee, Sharen; Zhou, Jiandong; Chung, Cheuk To; Lee, Rebecca On Yu; Bazoukis, George; Letsas, Konstantinos P; Wong, Wing Tak; ... Tse, Gary; + view all (2022) Comparing the performance of published risk scores in Brugada syndrome: a multi-center cohort study. Current Problems in Cardiology , Article 101381. 10.1016/j.cpcardiol.2022.101381. (In press). Green open access

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Abstract

INTRODUCTION: The management of Brugada Syndrome (BrS) patients at intermediate risk of arrhythmic events remains controversial. The present study evaluated the predictive performance of different risk scores in an Asian BrS population and its intermediate risk subgroup. METHODS: This retrospective cohort study included consecutive patients diagnosed with BrS from January 1st, 1997 to June 20th, 2020 from Hong Kong. The primary outcome is sustained ventricular tachyarrhythmias. Two novel risk risk scores and seven machine learning-based models (random survival forest, Ada boost classifier, Gaussian naïve Bayes, light gradient boosting machine, random forest classifier, gradient boosting classifier and decision tree classifier) were developed. The area under the receiver operator characteristic (ROC) curve (AUC) [95% confidence intervals] was compared between the different models. RESULTS: This study included 548 consecutive BrS patients (7% female, age at diagnosis: 50±16 years, follow-up: 84±55 months). For the whole cohort, the score developed by Sieira et al. showed the best performance (AUC: 0.806 [0.747-0.865]). A novel risk score was developed using the Sieira score and additional variables significant on univariable Cox regression (AUC: 0.855 [0.808-0.901]). A simpler score based on non-invasive results only showed a statistically comparable AUC (0.784 [0.724-0.845]), improved using random survival forests (AUC: 0.942 [0.913-0.964]). For the intermediate risk subgroup (N=274), a gradient boosting classifier model showed the best performance (AUC: 0.814 [0.791-0.832]). CONCLUSION: A simple risk score based on clinical and electrocardiographic variables showed a good performance for predicting VT/VF, improved using machine learning. Abstract: The management of Brugada Syndrome (BrS) patients at intermediate risk of arrhythmic events remains controversial. This study evaluated the predictive performance of published risk scores in a cohort of BrS patients from Hong Kong (N=548) and its intermediate risk subgroup (N=274). A novel risk score developed by modifying the best performing existing score (by. Sieira et al.) showed an area under the curve of 0.855 and 0.760 for the whole BrS cohort and the intermediate risk subgroup, respectively. The performance of the different scores was significantly improved machine learning-based methods, such as random survival forests and gradient boosting classifier.

Type: Article
Title: Comparing the performance of published risk scores in Brugada syndrome: a multi-center cohort study
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.cpcardiol.2022.101381
Publisher version: https://doi.org/10.1016/j.cpcardiol.2022.101381
Language: English
Additional information: © 2022 Elsevier B.V. Original content in this paper is licensed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/4.0/).
Keywords: Brugada syndrome, risk score, risk stratification, ventricular arrhythmias
UCL classification: UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > UCL School of Pharmacy > Practice and Policy
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > UCL School of Pharmacy
URI: https://discovery.ucl.ac.uk/id/eprint/10155528
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