Key result
A night-based machine learning classifier using autonomic markers achieved the best performance in distinguishing symptomatic from asymptomatic Brugada syndrome patients with an AUC of 0.95.
Why the study?
The study was conducted to design classifiers using autonomic markers estimated overnight, during exercise, and during head-up tilt testing to identify autonomic protocols that distinguish symptomatic from asymptomatic Brugada syndrome patients.
Does autonomic response analysis during sleep, exercise, and head-up tilt testing accurately classify symptomatic versus asymptomatic patients with Brugada Syndrome?
Observational (n=44)
Yes
Does autonomic response analysis during sleep, exercise, and head-up tilt testing accurately classify symptomatic versus asymptomatic patients with Brugada Syndrome?
Nighttime autonomic response analysis, particularly between 4 a.m. and 5 a.m., provides high accuracy in distinguishing symptomatic from asymptomatic Brugada Syndrome patients, offering a potential tool for risk stratification.
Answers are grounded in this paper and cite their sources.
Nighttime autonomic markers may support Brugada syndrome risk stratification; hypothesis-generating and should not yet change practice.
Several autonomic markers were estimated overnight and during exercise and head-up tilt (HUT) testing for 44 BS patients, to design classifiers capable of distinguishing patients at different levels of risk. The classification performance of predictive models built from the optimization of a step-based machine-learning method were compared, so as to identify those autonomic protocols and markers best distinguishing between symptomatic and asymptomatic patients. Although exercise and HUT testing together led to better predictive results than when they were separately assessed, among all analyzed combinations, the night-based classifier presented the best performance (AU C = 95%), using the least amount of features. This optimal features subset was mostly composed of markers extracted between 4 a.m. -5 a.m. Thus, results provide further evidence for the role of nighttime analysis, mainly during the last hours of sleep, for risk stratification in BS.
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Calvo et al. (2019) conducted an observational in Brugada syndrome (n=44). Nighttime autonomic response analysis vs. Exercise and head-up tilt testing was evaluated on Area under the ROC curve (AUC) for distinguishing symptomatic from asymptomatic patients. A night-based machine learning classifier using autonomic markers achieved the best performance in distinguishing symptomatic from asymptomatic Brugada syndrome patients with an AUC of 0.95.
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