Advancing Athlete Risk Stratification with Predictive Frameworks

Athlete risk stratification now integrates epidemiological evidence, explainable artificial intelligence, and mechanistic cardiac models to better protect young competitors. This convergence offers a path to distinguish healthy athletic remodeling from hidden substrates that can trigger fatal arrhythmias during competition.

Sports-related sudden cardiac death and its precursor, sudden cardiac arrest, remain the leading medical cause of mortality in adolescent and young adult athletes. Incidence ranges from 0.1 to 0.6 cases per 100,000 participant-years, with striking male predominance (83.7–100 %) and clear age-dependent shifts in underlying pathology—from sudden arrhythmic death syndrome in younger athletes to coronary disease after age 35.

Evidence Built on 84 Rigorous Studies

A systematic review (see Nature article, linked below) registered on PROSPERO and conducted to PRISMA 2020 standards screened more than 6,800 records before including 84 studies. Bias assessment with PROBAST showed most carried low risk of bias and high applicability, providing a credible foundation for synthesizing incidence data, xAI techniques, and electrophysiological modeling. The resulting framework directly strengthens athlete risk stratification by linking real-world risk factors with transparent AI decisions and biologically grounded simulations.

When AI Explains Its Reasoning

Thirty recent studies applied explainable AI—primarily convolutional neural networks often paired with LSTM layers—to life-threatening arrhythmias. Gradient-weighted class activation mapping emerged as the dominant interpretability method. While performance on public datasets such as MIT-BIH and CPSC-2018 can be impressive, models trained on non-athletic populations struggle with class imbalance and lack the pathology-specific focus required for confident use in sports cardiology.

Models That Mirror the Living Heart

Thirty-eight modeling papers demonstrated steady progress toward physiological fidelity, employing monodomain and bidomain equations, FitzHugh-Nagumo and Van der Pol oscillators, cellular automata, and fully coupled electromechanical whole-heart simulations. These approaches can now generate synthetic athlete-specific ECG data, potentially overcoming the scarcity of real-world events that has long hampered rare-outcome prediction.

Real-World Value for Screening Programs

For health economists and policymakers, the persistent variability in sudden cardiac death definitions and reliance on heterogeneous datasets represent clear obstacles to accurate cost-effectiveness modeling. Embedding epidemiological risk modifiers (age, sex, ethnicity) into AI explanations and electrophysiological simulations could sharpen effectiveness estimates and support stronger value propositions for advanced screening technologies.

By closing these translational gaps, integrated athlete risk stratification promises earlier identification of at-risk individuals while delivering the mechanistic transparency and synthetic data needed for robust reimbursement and market-access decisions in sports medicine.

Source: npj Digital Medicine