Shortcut-Aware Modeling of Serial ECGs for Cardiac Sarcoidosis

Cardiac sarcoidosis is difficult to diagnose, and serial ECG data may encode both physiologic signal and observation-process shortcuts. Here, observation-process shortcuts are predictive signals from ECG count and inter-ECG intervals rather than ECG waveform physiology. We study patient-level prediction of future CS diagnosis from serial ECG data collected before clinical presentation at a tertiary cardiac center. We compare single-ECG models, shortcut-only controls, shortcut-vulnerable serial ECG models, and shortcut-aware serial ECG models under unmatched evaluation and matched shortcut-control evaluation. We introduce RECAP, a shortcut-aware joint architecture-and-training design that represents prior ECGs relative to the patient’s latest eligible ECG, conditions these relationships on elapsed time, and uses matched-pair loss to reduce reliance on the measured shortcut variables. In the unmatched evaluation, shortcut-only controls are strongly predictive, showing that apparent serial gains can be driven by shortcut variables. In the matched shortcut-control evaluation, shortcut-only controls collapse to chance, and RECAP achieves the highest matched AUROC among the evaluated configurations. These results show that serial ECG waveforms retain predictive signal after targeted shortcut control and that rigorous evaluation in serial ECG settings requires explicit shortcut-only controls and matched shortcut-control evaluation.