{
  "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 literatures 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.",
  "abstract_shown": true,
  "affiliations": [
    "University of Brescia"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "A. Calzoni"
    },
    {
      "name": "A. Dei Rossi"
    },
    {
      "name": "G. Monachino"
    },
    {
      "name": "B. Zanchi"
    },
    {
      "name": "L. Fiorillo"
    },
    {
      "name": "M. Savardi"
    },
    {
      "name": "F. D. Faraci"
    },
    {
      "name": "A. Signoroni"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bwkm8n7e70mqe.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.08.26361626",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.09.08.26361626v1.full",
  "id": "bwkm8n7e70mqe",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.09.08.26361626v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "health informatics"
    ],
    [
      "openalex",
      "2737|Obstructive Sleep Apnea Research"
    ]
  ],
  "openalex": "W7212056391",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.09.08.26361626v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bwkm8n7e70mqe.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:11Z",
      "native_id": "10.64898/2026.09.08.26361626",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.09.08.26361626v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:health informatics",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "MambaSleepCVD for prediction of long term cardiovascular outcomes from polysomnography",
  "title_text": "MambaSleepCVD for prediction of long term cardiovascular outcomes from polysomnography",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:11Z",
  "url": "https://research.draconitic.com/paper/bwkm8n7e70mqe",
  "venue": "medRxiv",
  "versions": []
}