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Statistical algorithms for clinical decisions and prevention of genetic-related heart disease

Abstract Drug-induced long QT syndrome (diLQTS) can lead to seemingly healthy patients experiencing cardiac arrest, specifically Torsades de Pointes (TdP). A goal of this study is to identify genetic risk markers for diLQTS and TdP for prevention of genetic-related heart disease. We use data that were made available for general research use by National Center for Biotechnology Information, focused on subjects with a history of diLQTS or TdP after taking medication. Statistical learning algorithms were used to build a predictive model. The proposed approach can provide the likelihood of benefit for the treatment based on a set of genetic markers.