Wav2Arrest 2.0: Long-Horizon Cardiac Arrest Prediction with Time-to-Event Modeling, Identity-Invariance, and Pseudo-Lab Alignment

• URL: http://arxiv.org/abs/2509.21695v1
• Date: Thu, 25 Sep 2025 23:46:39 GMT
• Title: Wav2Arrest 2.0: Long-Horizon Cardiac Arrest Prediction with Time-to-Event Modeling, Identity-Invariance, and Pseudo-Lab Alignment
• Authors: Saurabh Kataria, Davood Fattahi, Minxiao Wang, Ran Xiao, Matthew Clark, Timothy Ruchti, Mark Mai, Xiao Hu,
• Abstract summary: High-frequency physiological waveform modality offers deep, real-time insights into patient status.<n>Recently, physiological foundation models have been shown to predict critical events, including Cardiac Arrest.<n>We offer three improvements to improve PPG-only CA systems by using minimal auxiliary information.
• Score: 5.706374608871095
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-frequency physiological waveform modality offers deep, real-time insights into patient status. Recently, physiological foundation models based on Photoplethysmography (PPG), such as PPG-GPT, have been shown to predict critical events, including Cardiac Arrest (CA). However, their powerful representation still needs to be leveraged suitably, especially when the downstream data/label is scarce. We offer three orthogonal improvements to improve PPG-only CA systems by using minimal auxiliary information. First, we propose to use time-to-event modeling, either through simple regression to the event onset time or by pursuing fine-grained discrete survival modeling. Second, we encourage the model to learn CA-focused features by making them patient-identity invariant. This is achieved by first training the largest-scale de-identified biometric identification model, referred to as the p-vector, and subsequently using it adversarially to deconfound cues, such as person identity, that may cause overfitting through memorization. Third, we propose regression on the pseudo-lab values generated by pre-trained auxiliary estimator networks. This is crucial since true blood lab measurements, such as lactate, sodium, troponin, and potassium, are collected sparingly. Via zero-shot prediction, the auxiliary networks can enrich cardiac arrest waveform labels and generate pseudo-continuous estimates as targets. Our proposals can independently improve the 24-hour time-averaged AUC from the 0.74 to the 0.78-0.80 range. We primarily improve over longer time horizons with minimal degradation near the event, thus pushing the Early Warning System research. Finally, we pursue multi-task formulation and diagnose it with a high gradient conflict rate among competing losses, which we alleviate via the PCGrad optimization technique.

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