AI-ECG guidance in repaired TOF clinical need

Repaired Tetralogy Of Fallot is a lifelong condition with heterogeneous anatomic substrates, surgical histories, and right ventricular responses to chronic pulmonary regurgitation. The decision to undertake Pulmonary Valve Replacement balances the hazards of delayed intervention, such as progressive remodeling and Arrhythmia, against the risks of prosthetic valve dysfunction and reintervention. While guidelines reference composite assessments, they cannot fully capture patient-to-patient variation in the tempo of right heart changes. The 12-lead Electrocardiogram is ubiquitous and inexpensive, and it is collected far more frequently than advanced imaging, creating an opportunity for dense longitudinal risk signals.

Why timing of PVR remains difficult

Even within specialized congenital programs, thresholds for intervention are interpreted through the lens of anatomy, surgical history, exercise goals, and patient preferences. Conventional ECG markers such as QRS Duration and bundle branch patterns offer limited discrimination when applied in isolation. Imaging markers from Cardiac MRI provide structural and functional ground truth but are typically acquired episodically, which can miss inflection points in physiology. Serial ECGs, by contrast, are readily available and amenable to automated analysis. The central challenge is converting these waveforms into individualized probability estimates of near-term benefit from intervention.

Signals available in the 12-lead ECG

Right ventricular volume and pressure loading influence depolarization and repolarization patterns across precordial and limb leads. Subtle changes in QRS morphology, T-wave symmetry, conduction delays, and beat-to-beat variability can encode evolving Right Ventricular Remodeling. Such features are often below human visual resolution but are well suited to detection by Machine Learning and signal processing pipelines. By aggregating high-dimensional characteristics and temporal trajectories from routine ECGs, AI models can generate holistic risk profiles reflective of electrical, structural, and autonomic influences. The promise is not to replace imaging but to add a dynamic, low-burden monitoring channel.

How AI-ECG complements imaging and exercise

AI-derived ECG risk scores can be positioned as a screening signal between infrequent imaging assessments, flagging patients who may merit earlier reevaluation or escalation. When combined with physiologic testing and imaging, such signals may sharpen Risk Stratification by better aligning anatomy, electrophysiology, and symptoms. Importantly, an ECG-first approach can reduce reliance on episodic advanced imaging in low-risk intervals, while prioritizing timely imaging for those with deteriorating electrical profiles. This layered strategy aims to improve the timing of pulmonary valve replacement without increasing surveillance burdens. It also lends itself to automated alerts and multidisciplinary review, which are essential for consistent decision-making.

Development and validation of the AI-ECG model

The recent work detailed the creation of an ECG-based model trained to associate waveform-derived features with clinically relevant outcomes that inform timing of intervention. The investigators curated ECGs aligned with outcome windows that matter to patients and clinicians, such as near-term need for intervention or progression of right ventricular pathology. Waveform preprocessing, lead-level standardization, and noise handling were specified to accommodate congenital repair-related conduction heterogeneity. A transparent reporting approach for data sources, label definitions, and validation cohorts builds trust and facilitates replication. The PubMed record provides access to the abstract and metadata for further methodological detail https://pubmed.ncbi.nlm.nih.gov/40886753/.

Cohorts, labels, and preprocessing

Robust AI development begins with clear definitions of who, what, and when. The training cohort comprised individuals with repaired Tetralogy of Fallot and standardized 12-lead ECGs temporally linked to outcome windows of interest. Labels were constructed to reflect clinically actionable endpoints, such as the likelihood of meeting multidisciplinary criteria for intervention in a predefined horizon or evidence of progressive structural risk. Preprocessing addressed sampling rates, baseline wander, and lead inconsistencies to ensure comparability across systems and sites. To mitigate label leakage, events proximal to the ECG were handled carefully, and patients rather than ECGs were used as the unit of cross-validation where feasible.

Architecture, training, and feature attribution

Modeling approaches for ECG waveforms include one-dimensional convolutional networks, recurrent networks, and hybrid feature engineering that incorporates frequency-domain descriptors. The investigators emphasized generalizable architectures that can tolerate conduction variants common after right ventricular outflow tract surgery. Training pipelines balanced class representation and included holdout sets for integrity checks, while regularization tempered overfitting in the presence of high-dimensional inputs. Feature attribution techniques, ranging from saliency maps to perturbation analyses, provided qualitative insight into lead contributions and waveform segments associated with risk signals. Such interpretability is critical for clinician confidence and for detecting potential failure modes.

Discrimination, calibration, and reclassification

Performance was characterized using standard metrics of discrimination and calibration appropriate for clinical risk tools. Beyond raw discrimination, calibration curves and probability error metrics helped demonstrate that predicted risks aligned with observed event rates across clinically relevant strata. Reclassification analyses assessed whether AI-ECG outputs improved assignment into action thresholds relative to conventional markers alone. Of equal importance, error analyses examined where the model underperformed, such as in specific conduction phenotypes or less represented surgical subgroups. Evaluation across multiple cohorts helps ensure that signal detection is not simply overfitting to one site or scanner profile.

Decision support and thresholding

Turning a continuous risk score into a decision aid requires explicit thresholds anchored to potential actions. The work explored operating points that align with multidisciplinary pathways, balancing sensitivity for early detection against specificity to avoid unnecessary imaging or premature intervention. Decision tools can expose several thresholds, for example one that triggers expedited imaging and another that prompts earlier clinic review. Where feasible, net benefit was contextualized using Decision Curve Analysis, emphasizing how AI-ECG would behave across plausible clinical preferences. Clear thresholding logic is essential for safe, consistent adoption and for prospective evaluation.

External validation generalizability and fairness

Generalization is a common stumbling block for biosignal models, so the project emphasized External Validation in independent cohorts. This helps ensure resilience to site-specific acquisition, patient mix, and follow-up patterns. Subgroup analyses probed performance across age at repair, valve conduits, and baseline conduction phenotypes to identify equity gaps. Where differences emerged, the authors considered strategies such as domain adaptation, stratified thresholds, or additional training data. Transparency about performance variance empowers local teams to set expectations and tailor implementation.

Translation implementation and future directions

AI-ECG risk tools can be embedded in routine congenital cardiology care to provide continuous, low-friction surveillance. A core use case is between-imaging monitoring, where elevated ECG-derived risk could prompt expedited imaging, exercise testing, or electrophysiology review. Over time, risk trajectories could inform the timing and sequence of interventions, including pulmonary valve replacement and arrhythmia management strategies. Feedback loops between modeled risk, clinician decisions, and outcomes enable learning health systems that refine thresholds and workflows. The key to impact is not just predictive accuracy but integration into the daily rhythms of congenital programs.

Workflow integration and monitoring

Practical adoption requires embedding inference within ECG acquisition or ECG repository systems, surfacing interpretable risk summaries alongside the tracing. Alerts should be routed to the congenital heart team with clear next-step suggestions that match existing pathways. To reduce alarm fatigue, thresholds can be tiered and configured to clinic schedules, and models can be gated by recent imaging or clinical events. Longitudinal visualization of risk alongside symptoms and exercise metrics aids shared decision-making with patients and families. Prospective pilots with pragmatic endpoints will be essential to demonstrate value and safety.

Governance transparency and safety

Governance should cover data provenance, change management, and performance surveillance with pre-specified action plans. Model cards and interpretability summaries help clinicians understand intended use, limitations, and known failure modes. Site onboarding should include a calibration check using local historical data to map scores to event rates before activating in care. Post-deployment monitoring can track calibration drift, subgroup performance, and alert-to-action timelines. Human oversight remains foundational, with multidisciplinary review preceding any high-stakes intervention decisions.

Limitations and research priorities

ECG-based signals are indirect proxies for structural heart changes and can be confounded by conduction abnormalities inherent to repaired Tetralogy of Fallot. Labeling for intervention intent may reflect local practice patterns as much as patient biology, which complicates transportability. Outcome windows and adjudication strategies must be transparent, and missing data mechanisms should be characterized. Serial ECG modeling will need to account for device therapy, medications, and intercurrent illness. Future work should compare AI-ECG plus standard care versus standard care alone in prospective pathways, measuring time to appropriate intervention and patient-centered outcomes.

Clinical takeaways

For congenital programs, the potential of AI-ECG lies in scalable, between-visit surveillance that augments imaging and exercise testing. Electrical signatures extracted from routine tracings can map to clinically relevant endpoints that matter for timing pulmonary valve replacement. Adoption should proceed with site-specific calibration, clear thresholds, and multidisciplinary oversight. If validated prospectively, AI-ECG may help align intervention timing with the unique trajectory of each patient with repaired Tetralogy of Fallot. The ultimate measure of success is whether such tools improve functional outcomes and reduce long-term rhythm and right heart complications.