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.
M, F., MH, Y., & N, F. (2026). 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.. Clinical cardiology. https://doi.org/10.1002/clc.70370
M F, MH Y, N F. 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.. Clinical cardiology. 2026; doi: 10.1002/clc.70370
M F, MH Y, N F. 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.[J]. Clinical cardiology. 2026. DOI: 10.1002/clc.70370.
@article{m2026,
author = {Farjam M and Yazdanpanah MH and Fereydouni N},
title = {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.},
journal = {Clinical cardiology},
year = {2026},
doi = {10.1002/clc.70370},
note = {PMID: 42274161},
}
TY - JOUR AU - Farjam M AU - Yazdanpanah MH AU - Fereydouni N TI - 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. T2 - Clinical cardiology PY - 2026 DO - 10.1002/clc.70370 AN - PMID:42274161 ER -
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.