{
  "abstract": "Background: Laboratory-based frailty indices (FI-Lab) have shown promise in geriatric medicine research. We aimed, in diverse samples, to determine the optimal construction of an FI-Lab for acute care and evaluate its validity as a measure of latent health status across the adult life span. Methods and findings: Our retrospective multicohort study used emergency department encounters in Boston, USA (MIMIC-IV-ED; 2011–2019) and London, UK (King’s College Hospital (KCH); 2017–2020); and a community-based cohort in the UK (UK Biobank; 2006–2010). We evaluated FI-Lab configurations by varying test selection strategies, number of tests, and minimum test thresholds. Sensitivity analyses examined performance across age, ethnicities, sexes, and model types to identify potential blind spots. The primary outcome was 1-year all-cause mortality. Conclusions: The FI-Lab provides an automated, scalable measure of patient vulnerability that is robust across healthcare settings and populations. A core set of 20–40 commonly ordered tests is sufficient for signal capture. It offers a pragmatic complement to clinical judgement without requiring manual data entry or additional tests. Why was this study done?: What did the researchers do and find?: What do these findings mean?: In this multicohort study, Hugh Logan Ellis and colleagues optimize the laboratory-based frailty index for use as a general measure of illness severity across all adult ages and different healthcare settings.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Biostatistics and Health Informatics, King’s College London, London, United Kingdom",
    "Division of Geriatric Medicine, Department of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada",
    "Department of Endocrinology, St George's Healthcare NHS Trust, London, United Kingdom",
    "School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom",
    "Department of Clinical Gerontology, King’s College Hospital, London, United Kingdom",
    "Department of Diabetes, Kings College Hospital, London, United Kingdom",
    "School of Biosciences, University of Surrey, Guildford, United Kingdom",
    "Institute of Health Informatics, University College London, London, United Kingdom",
    "Faculty of Life Sciences & Medicine, King’s College London, London, United Kingdom",
    "Oncology DepartmentWestern General HospitalEdinburgh, United Kingdom",
    "Department of Neurology, King’s College Hospital, London, United Kingdom"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
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        0
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      "name": "Hugh Logan Ellis"
    },
    {
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        1
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      "name": "Peter Hanlon"
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    {
      "affs": [
        2
      ],
      "name": "Liam Dunnell"
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    {
      "affs": [
        3
      ],
      "name": "Martin Whyte"
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    {
      "affs": [
        4
      ],
      "name": "Daniel H. J. Davis"
    },
    {
      "affs": [
        5
      ],
      "name": "Josephine Bates"
    },
    {
      "affs": [
        6
      ],
      "name": "Adeel Jafri"
    },
    {
      "affs": [
        7
      ],
      "name": "Diana Shamsutdinova"
    },
    {
      "affs": [
        8
      ],
      "name": "James T. Teo"
    },
    {
      "affs": [
        9
      ],
      "name": "Zina Ibrahim"
    },
    {
      "affs": [
        10
      ],
      "name": "Kenneth Rockwood"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9cq4eemdrj89d.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pmed.1005004",
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      "2717|Frailty in Older Adults"
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  "pmcid": null,
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  "published": "2026-09-01",
  "publisher": "Public Library of Science",
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  "title": "Evaluation of the FI-Lab, a laboratory-based, automated frailty index for acute care: A multicohort study",
  "title_text": "Evaluation of the FI-Lab, a laboratory-based, automated frailty index for acute care: A multicohort study",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:02Z",
  "url": "https://research.draconitic.com/paper/9cq4eemdrj89d",
  "venue": "PLOS Medicine",
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