{
  "abstract": "Background\n\nDelirium is a complex neuropsychiatric syndrome whose severe and lasting consequences in hospitalized patients are largely considered preventable. Prevention primarily relies on the early identification of high-risk patients prior to syndrome onset. However, this remains challenging in clinical practice due to the absence of a diagnostic gold standard and the considerable administrative burden associated with established screening tools such as the 4AT score.Objective\n\nThe aim of this study is to compare the 4AT score with an established AI-supported prognostic tool in applied clinical practice. Our goal is to demonstrate that shifting from a purely diagnostic approach toward AI-supported delirium risk prediction enables more efficient clinical workflows, reduces misclassification rates, facilitates earlier and more targeted interventions, and ultimately improves long-term patient outcomes.Methods\n\nIn this prospective study, a total of 2002 patients from a trauma ward were screened and assessed for delirium risk using the 4AT score and the AI-based tool at admission, on the evening of admission, and on the following day, with each assessment predicting the risk of delirium occurrence at any point during the remainder of the hospital stay. The AI tool based its predictions on clinical data automatically derived from electronic health records. The AI-based tool applied a previously developed and validated random forest model (not developed as part of this study).Results\n\nOf the 2002 processed patients, the AI prediction tool correctly predicted 87.3% (165/189) of patients with delirium onset as at-risk, compared to an early detection rate of 28.0% (5/189) achieved by the 4AT score. Moreover, the AI-based tool correctly classified 73.9% (1340/1813) patients without delirium as not-at-risk, compared to 96.3% (1746/1813) for the 4AT score. Therefore, the AI-based tool achieved very good delirium predictions, with an area under the receiver operating characteristic curve of 0.884 (95% CI 0.8634-0.9039).Conclusions\n\nThe AI-based delirium prognostic tool demonstrated superior early predictive capacity for delirium risk at admission, compared with the 4AT score. The AI-based tool enables proactive risk management and targeted risk reduction.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Orthopaedics and Trauma, Medical University of Graz, Graz, Styria, Austria, 43 31638581719.",
    "Directorate for Digitalisation, IT, Organisational Development and Innovations, Styrian Hospital Holding, Graz, Austria.",
    "Department of Psychiatry, Psychosomatics and Psychotherapeutic Medicine, Medical University of Graz, Graz, Austria.",
    "Department of Neurology, Medical University of Graz, Graz, Austria.",
    "Hospital Pharmacy, University Hospital of Graz, Styrian Hospital Holding, Graz, Austria.",
    "Research Unit for Safety and Sustainability in Health Care, c/o Division of Plastic, Aesthetic and Reconstructive Surgery, Department of Surgery, Medical University of Graz, Graz, Austria."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Klaus Franceschini",
      "orcid": "0009-0004-7837-4624"
    },
    {
      "affs": [
        0
      ],
      "name": "Marisa Valentini",
      "orcid": "0000-0002-8480-9434"
    },
    {
      "affs": [
        0
      ],
      "name": "Andreas Leithner",
      "orcid": "0000-0002-2598-2325"
    },
    {
      "affs": [
        1
      ],
      "name": "Diether Kramer",
      "orcid": "0009-0005-7580-9220"
    },
    {
      "affs": [
        1
      ],
      "name": "Stefanie Jauk",
      "orcid": "0000-0003-1287-594X"
    },
    {
      "affs": [
        1
      ],
      "name": "Lena Delia Lorenzer",
      "orcid": "0009-0008-2021-685X"
    },
    {
      "affs": [
        2
      ],
      "name": "Karin Hasiba-Seebacher",
      "orcid": "0009-0009-2960-7360"
    },
    {
      "affs": [
        3
      ],
      "name": "Stefan Sumerauer",
      "orcid": "0009-0009-0692-0450"
    },
    {
      "affs": [
        4
      ],
      "name": "Viktoria Christin Godschachner",
      "orcid": "0009-0007-7121-9252"
    },
    {
      "affs": [
        4
      ],
      "name": "Magdalena Hellauer",
      "orcid": "0009-0001-4395-781X"
    },
    {
      "affs": [
        5
      ],
      "name": "Gerald Sendlhofer",
      "orcid": "0000-0002-6538-3116"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2exrxgn5cspna.bib",
  "cited_by": 0,
  "doi": "10.2196/104907",
  "fulltext_url": "https://europepmc.org/articles/PMC13686404",
  "id": "2exrxgn5cspna",
  "issns": [
    "2561-326X"
  ],
  "landing_url": "https://europepmc.org/article/MED/42906075",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2706|Intensive Care Unit Cognitive Disorders"
    ]
  ],
  "openalex": "W7223130371",
  "pdf_url": "https://europepmc.org/articles/PMC13686404?pdf=render",
  "pmcid": "PMC13686404",
  "pmid": "42906075",
  "published": "2026-10-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2exrxgn5cspna.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:53Z",
      "native_id": "MED:42906075",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42906075"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2706|Intensive Care Unit Cognitive Disorders",
      "subfield": "general"
    }
  ],
  "title": "AI-Based Delirium Risk Prediction Compared With the Validated 4AT-Score Screening Tool in Patients With Trauma: Prospective Cohort Study.",
  "title_text": "AI-Based Delirium Risk Prediction Compared With the Validated 4AT-Score Screening Tool in Patients With Trauma: Prospective Cohort Study.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:53Z",
  "url": "https://research.draconitic.com/paper/2exrxgn5cspna",
  "venue": "JMIR formative research",
  "versions": []
}