{
  "abstract": "Persistent fever after initiating intravenous antibiotics is common in hospitalized patients with cancer and often leads to additional imaging and antimicrobial escalation. Early identification of patients unlikely to remain febrile could provide additional information during clinical reassessment. We developed and externally validated machine learning models to predict persistent fever 48–72 h after antibiotic initiation using electronic health records from two academic hospitals. Adult cancer patients with fever within 24–48 h of treatment were included. Models incorporated demographics, comorbidities, laboratory results, vital signs, and temperature patterns. The best-performing model achieved an AUROC of 0.82 (95% CI 0.79–0.84) internally and 0.78 (95% CI 0.77–0.79) externally. Temperature-related variables were the dominant predictors. Decision curve analysis demonstrated positive net benefit across much of the evaluated threshold range. Retrospective assessment suggested that approximately one quarter of antibiotic escalations and chest CT scans occurred in patients correctly predicted not to remain febrile, highlighting the potential contextual value of fever predictions. These findings indicate that short-term fever persistence can be predicted with clinically meaningful accuracy using routine data. However, clinical use should be limited to an adjunctive role rather than standalone rule-out decision-making, and prospective studies are required to evaluate safety, clinician uptake, and real-world impact.",
  "abstract_shown": true,
  "affiliations": [
    "Laboratory for Clinical Research and Real-World Evidence, Department of Hematology & Stem Cell Transplantation, West German Cancer Center, University Hospital Essen, Essen, Germany",
    "Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany",
    "National Center for Tumor Diseases (NCT-West), Campus Essen, Essen, Germany",
    "Department of Medical Microbiology and Infection Prevention, Amsterdam University Medical Centers, Amsterdam, The Netherlands",
    "Brigham and Women’s Hospital, Division of Infectious Diseases, Harvard Medical School, Boston, MA USA",
    "Department of Medical Oncology, West German Cancer Center, University Hospital Essen, Essen, Germany",
    "West German Cancer Center, University Hospital Essen, Essen, Germany",
    "DKTK Partner Site Essen/Düsseldorf, University Hospital Essen, Essen, Germany",
    "Department of Physics, TU Dortmund University, Dortmund, Germany",
    "Faculty of Medicine, University of Duisburg-Essen, Essen, Germany",
    "Department of Hematology & Stem Cell Transplantation, West German Cancer Center, University Hospital Essen, Essen, Germany",
    "Center for Molecular Biotechnology, University Hospital Essen, Essen, Germany"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
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        0,
        1,
        2
      ],
      "name": "Gernot Pucher"
    },
    {
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        3,
        4
      ],
      "name": "Sanjat Kanjilal",
      "orcid": "0000-0002-1221-5725"
    },
    {
      "affs": [
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        1,
        2
      ],
      "name": "Kevin Kopp"
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    {
      "affs": [
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        1,
        2
      ],
      "name": "Aman Deep"
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    {
      "affs": [
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      ],
      "name": "Till Rostalski"
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    {
      "affs": [
        2,
        5,
        6
      ],
      "name": "Martin Schuler"
    },
    {
      "affs": [
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        2
      ],
      "name": "Felix Nensa"
    },
    {
      "affs": [
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        2,
        7,
        8,
        9
      ],
      "name": "Jens Kleesiek"
    },
    {
      "affs": [
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        6,
        7,
        10,
        11
      ],
      "name": "H. Christian Reinhardt"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Christopher M. Sauer",
      "orcid": "0000-0002-2388-5919"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/d0b677x5wj4qx.bib",
  "cited_by": 0,
  "doi": "10.1038/s41746-026-03449-w",
  "fulltext_url": "https://doi.org/10.1038/s41746-026-03449-w",
  "id": "d0b677x5wj4qx",
  "issns": [
    "2398-6352"
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  "landing_url": "https://doi.org/10.1038/s41746-026-03449-w",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Article"
    ],
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      "lcc",
      "R858-859.7"
    ],
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      "openalex",
      "2730|Neutropenia and Cancer Infections"
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  "openalex": "W7225015510",
  "pdf_url": null,
  "pmcid": "PMC13723596",
  "pmid": "42966761",
  "published": "2026-10-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/d0b677x5wj4qx.ris",
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  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2730|Neutropenia and Cancer Infections",
      "subfield": "oncology"
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  ],
  "title": "Predicting persistent fever in patients with cancer receiving antibiotics: a machine learning approach",
  "title_text": "Predicting persistent fever in patients with cancer receiving antibiotics: a machine learning approach",
  "type": "peer-reviewed",
  "updated_at": "2026-11-05T12:20:23Z",
  "url": "https://research.draconitic.com/paper/d0b677x5wj4qx",
  "venue": "npj Digital Medicine",
  "versions": []
}