From Molecules to Machines: An Integrative Framework Linking Molecular Pathogenesis, Multi‐Factorial Risk, Risk Stratification, Clinical Management, and Artificial Intelligence in QT Prolongation and Sudden Cardiac Death

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Abstract
QT prolongation causes torsades de pointes sudden death from heritable, pharmacologic, metabolic, nutritional triggers. Its dimensions have been studied separately. This integrative review synthesizes research on molecular pathogenesis, acquired/metabolic/nutritional risks, clinical stratification, therapy, and AI prediction. Dual‐function channel mutations and post‐translational defects underlie congenital LQTS beyond classic three genes. Drug–gene–metabolic interactions amplify acquired risk; insulin resistance, NAFLD, and adiposity are independent risk factors. Nutritional exposures (grapefruit juice, licorice, energy drinks) compound arrhythmic risk. QTc threshold alone is insufficient; T‐wave morphology, genotype, electromechanical window dynamics, and M‐FACT score add prognostic value. Nonpenetrant LQTS carries near‐population‐level event risk. Genotype‐targeted mexiletine and left cardiac sympathetic denervation are validated alternatives. Machine learning outperforms clinical scores; deep learning distinguishes congenital from acquired QT prolongation on ECG. Precision QT management requires integrated strategies including nutritional and metabolic determinants, QTc measurement, and AI‐enhanced prediction. Prospective data remain essential before algorithmic tools guide decisions.

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