Abstract

Background 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.

Objectives To develop a refined risk stratification model for P/LP variants by applying machine learning classification to electrophysiological data measured in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs).

Methods Eleven patient-specific hiPSC lines carrying six P/LP variants in KCNQ1 or KCNH2 were differentiated to cardiomyocytes (hiPSC-CMs). Electrophysiological responses from multielectrode array recordings at baseline and after application of selective ion channel blockers or pro-arrhythmic compounds were used to train a machine learning model to classify variant-specific risk levels based on in vitro electrophysiological readouts.

Results Our findings revealed a correlation between variant risk level, hiPSC-CM electrophysiological profiles, and drug responses. The machine learning classifier, trained on multielectrode array recordings, achieved 89% accuracy in classification of P/LP genetic variants according to the associated risk levels.

Conclusions This study demonstrates that integrating hiPSC-CM electrophysiological profiling with machine learning provides a robust method to improve variant-specific risk stratification for LQTS patients.

Clinical Aspects Understanding which patients may be at risk of cardiac events or sudden cardiac death is crucial to implement appropriate preventive measures. This study leverages patient-specific in vitro models and machine learning to improve the risk stratification of pathogenic/likely pathogenic variants associated with LQTS, better supporting clinical decisions related to risk assessment and management of LQTS patients. This scalable approach can be implemented across multiple centres, enhancing the risk stratification of LQTS variants beyond what is currently possible when clinical data are limited.

Translational Outlook Machine learning-based variant risk stratification is a novel approach for integrating hiPSC-CM-derived electrophysiological data into clinical workflows. While this study demonstrates the feasibility of our approach, further research is required to validate these findings across larger and more diverse patient cohorts. Additionally, efforts to standardize the pipeline and adapt it for multicentric implementation are necessary.

Discriminating LQTS patients at high or low risk for sudden death is a clinical challenge.

Improved stratification of pathogenic/likely pathogenic variants is achievable through machine learning classification on in vitro electrophysiological data.

Integration of the clinical workflow with data from patient-specific in vitro models will enhance risk stratification.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This project has received funding from the Horizon Europe EU (HORIZON-MSCA-2022-PF-01 PREPARE No. 101105561 to AK), Horizon 2020 (H2020-MSCA-IF-2017 No. 795209 to LS), Fondazione CARIPLO grant No. 2019-1691 to LS, Leducq Foundation grant 18CVD05 to PJS. Italian Ministry of University and Research within Mission 4, Education and Research, Component 2, From Research to Business, Investment 1.2, Funding projects presented by young researchers of the National Recovery and Resilience Plan. Project No. 2022-NAZ-0485 (H45E22001210006) to LS.

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Ethics committee of Istituto Auxologico Italiano IRCCS gave ethical approval for this work (Approval number: 2020_10_20_07).

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

Data Availability

The datasets, models and R code used for the analyses are available in the lab GitHub repository.

Abbreviations and acronymsAPDAction Potential DurationFPDField Potential DurationiPSC-CMsCardiomyocytes derived from human induced pluripotent stem cellsJLNSJervell and Lange-Nielsen SyndromeLQTSLong QT SyndromeMEAMultielectrode ArraysMLMachine LearningP/LPPathogenic/Likely PathogenicTdPTorsades de PointesQTcQT interval corrected for heart rateWTWild type