MambaSleepCVD for prediction of long term cardiovascular outcomes from polysomnography

Abstract Long-term cardiovascular disease (CVD) risk prediction remains a major global clinical priority. This study investigates how much prognostic information full-night polysomnography (PSG) carries after explicitly controlling demographic confounding, and whether combining specialized unimodal representations with continuous fusion mechanisms can effectively leverage it. We adopt a modular framework based on Coupled Mamba for simultaneous cross-modal and temporal fusion, capturing long-range dependencies and dynamic interactions between physiological modalities throughout sleep. With this capability we assess whether specialized supervised representations from task-specific unimodal encoders offer prognostic value comparable to task-agnostic multimodal self-supervised pre-training. Validated on the Sleep Heart Health Study (SHHS) dataset, our approach achieves performance comparable to or better than current state-of-the-art architectures in the fully supervised, data-abundant regime, although the cardiac channel alone accounts for most of the discriminative signal. More importantly, we identify a significant age-related bias in the reference dataset that, if left unaddressed, creates a shortcut leading to inflated risk predictions, accounting for a large part of the literature’s reported performance. Ultimately, our findings suggest that supervised unimodal experts integrated through a fusion engine provide a flexible pathway for prognostic risk ranking from sleep recordings, while reliable CVD screening will require independent external cohorts and explicit confounder control.