Abstract
In this study we consider logistic regression models to predict mutation carriers in family members affected by Brugada Syndrome. This Syndrome is an inherited cardiopathy that predisposes individuals without structural heart disease to sudden cardiac death. We focused on five electrocardiographic markers, which have been explored as good discriminators between carriers and non-carriers of the genetic mutation responsible for this disease. Logistic regression models which combine some of the five markers were investigated. Our objective was to assess the predictive ability of these models through internal validation procedures. We also applied shrinkage methods to improve calibration of the models and future predictive accuracy. Validation of these models, using bootstrapping, point to some superiority of two models, for which fairly good measures of predictive accuracy were obtained. This study provides confidence in these models, which offer greater sensitivity than the usual screening by detecting a characteristic pattern in an electrocardiogram.
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Henriques, C., Matos, A., Santos, L.F. (2014). Predictive Models for Mutation Carriers in Brugada Syndrome Screening. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2014. ICCSA 2014. Lecture Notes in Computer Science, vol 8581. Springer, Cham. https://doi.org/10.1007/978-3-319-09150-1_36
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DOI: https://doi.org/10.1007/978-3-319-09150-1_36
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