Khudiakov, Mura, Giannetti, Leonov, Alberio, Eskandr, Lonati, Borghi, Brink, Crotti, Gnecchi, Schwartz, & Sala (2026). Machine learning-guided risk stratification for Long QT Syndrome genetic variants with hiPSC-derived cardiomyocytes.. Cardiovascular research.
Khudiakov, Mura, Giannetti, Leonov, Alberio, Eskandr, et al. Machine learning-guided risk stratification for Long QT Syndrome genetic variants with hiPSC-derived cardiomyocytes.. Cardiovascular research. 2026; PMID: 42133816
Khudiakov, Mura, Giannetti, et al. Machine learning-guided risk stratification for Long QT Syndrome genetic variants with hiPSC-derived cardiomyocytes.[J]. Cardiovascular research. 2026.
@article{khudiakov2026,
author = {Khudiakov and Mura and Giannetti and Leonov and Alberio and Eskandr and Lonati and Borghi and Brink and Crotti and Gnecchi and Schwartz and Sala},
title = {Machine learning-guided risk stratification for Long QT Syndrome genetic variants with hiPSC-derived cardiomyocytes.},
journal = {Cardiovascular research},
year = {2026},
note = {PMID: 42133816},
}
TY - JOUR AU - Khudiakov AU - Mura AU - Giannetti AU - Leonov AU - Alberio AU - Eskandr AU - Lonati AU - Borghi AU - Brink AU - Crotti AU - Gnecchi AU - Schwartz AU - Sala TI - Machine learning-guided risk stratification for Long QT Syndrome genetic variants with hiPSC-derived cardiomyocytes. T2 - Cardiovascular research PY - 2026 AN - PMID:42133816 ER -
Long QT syndrome (LQTS) is a life-threatening genetic disorder characterized by prolonged QT intervals on electrocardiograms. Congenital forms are mostly associated with variants in the KCNQ1 and KCNH2 genes. Among pathogenic or likely pathogenic (P/LP) variants, some are associated with a significantly higher incidence of cardiac events compared to others. While therapies have significantly reduced mortality, some patients are unresponsive or intolerant to therapy, perpetuating their arrhythmic risk, including sudden cardiac death. Current approaches for risk stratification are insufficient, highlighting the critical need for more accurate identification and management of patients carrying high risk genetic variants.Here, we aimed to develop a refined risk stratification model for P/LP variants by applying machine learning classification to electrophysiological data measured in patient-specific human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs).