{
  "abstract": "Objective: To develop the anticholinergic medication index (ACMI), a new anticholinergic burden measure derived from a clinical prediction model for falls and delirium, scoring 88 anticholinergic medicines prescribed in primary care.\n\nDesign: Development and external validation of a risk prediction model.\n\nSetting: UK primary and secondary care data from Connected Bradford and the Secure Anonymised Information Linkage (SAIL) databank, 1 January 2019 to 31 December 2019.\n\nParticipants: For development cohort, 71 757 Bradford and Craven residents aged >65 years on 1 January 2019 who contributed data to Connected Bradford between 1 January 2019 and 31 December 2019. For external validation cohort, 678 134 Welsh residents aged >65 years on 1 January 2019 who contributed data to SAIL between 1 January 2019 and 31 December 2019.\n\nMain outcome measures: Composite outcome of hospital admission for delirium or hospital admission for a fall or fracture (as an indicator of an injurious fall) during the study period.\n\nResults: 71 757 patients (4147 (5.8%) with the composite outcome) comprised the development cohort and 678 134 patients (43 675 (6.4%) with the composite outcome) comprised the external validation cohort. Based on anticholinergic medications recorded in electronic health records as predictors, a prediction model for falls and delirium was developed and validated. Model coefficients for anticholinergic medications were transformed into ACMI scores. Of 88 anticholinergic medications, codeine, prednisolone, and furosemide were the most commonly prescribed (8.3%, 6.5%, and 6.0% of patients prescribed these anticholinergic drugs at least once in the development cohort, respectively). Model discrimination and calibration in the development cohort was good (C index=0.794, R2=0.06, calibration slope=0.97). The model performed well in external validation with good discrimination (C index=0.721, 95% confidence interval (CI) 0.718 to 0.734, R2=0.04) but with some evidence of miscalibration (calibration slope=0.69). The ACMI medications were scored 0.1 (desloratadine) to 2.3 (amantadine).\n\nConclusions: The ACMI was designed to be implemented in primary care to automatically calculate anticholinergic burden. Combined with the model predicted risk of delirium or falls, the ACMI allows clinicians to prioritise patients for structured medication reviews to reduce overprescribing.",
  "abstract_shown": true,
  "affiliations": [
    "Academic Unit for Ageing and Stroke Research, Leeds Institute of Health Sciences, University of Leeds, Leeds, UK",
    "Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK",
    "Public Health, Policy and Systems, Institute of Population Health, University of Liverpool, Liverpool, UK",
    "Leeds Institute of Health Sciences, University of Leeds, Leeds, UK",
    "School of Healthcare, University of Leeds, Leeds, UK",
    "NIHR Yorkshire and Humber Patient Safety Research Collaboration, Leeds, UK",
    "Institute of Population Health, University of Liverpool, Liverpool, UK",
    "Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK",
    "Edinburgh Clinical Trials Unit, University of Edinburgh, Edinburgh, UK",
    "School of Computer Science, University of Leeds, Leeds, UK",
    "School of Medicine and Population Health, University of Sheffield, Sheffield, UK",
    "Improvement Academy, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK",
    "School of Psychology, University of Leeds, Leeds, UK",
    "Centre for Experimental Therapeutics, Institute of Systems, Molecular, and Integrative Biology, University of Liverpool, Liverpool, UK",
    "Wolfson Centre for Personalised Medicine, Institute of Systems, Molecular, and Integrative Biology, University of Liverpool, Liverpool, UK"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 23,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Kate E Best",
      "orcid": "0000-0002-4663-7141"
    },
    {
      "affs": [
        2
      ],
      "name": "Olly Butters",
      "orcid": "0000-0003-0354-8461"
    },
    {
      "affs": [
        2
      ],
      "name": "Pieta Georgina Schofield"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Oliver Todd"
    },
    {
      "affs": [
        3
      ],
      "name": "Sarah Alderson",
      "orcid": "0000-0002-5418-0495"
    },
    {
      "affs": [
        4,
        5
      ],
      "name": "David P Alldred"
    },
    {
      "affs": [
        6
      ],
      "name": "Laura Bonnett"
    },
    {
      "affs": [
        6
      ],
      "name": "Iain Buchan"
    },
    {
      "affs": [
        7,
        8
      ],
      "name": "Amanda Farrin"
    },
    {
      "affs": [
        9
      ],
      "name": "Owen Johnson"
    },
    {
      "affs": [
        10
      ],
      "name": "Ciarán McInerney"
    },
    {
      "affs": [
        5
      ],
      "name": "David Mehdizadeh"
    },
    {
      "affs": [
        11
      ],
      "name": "Tom Lawton",
      "orcid": "0000-0003-1351-8127"
    },
    {
      "affs": [
        1,
        12
      ],
      "name": "Rebecca Lawton",
      "orcid": "0000-0002-5832-402X"
    },
    {
      "affs": [
        3
      ],
      "name": "Farag Shuweidhi"
    },
    {
      "affs": [
        13
      ],
      "name": "Lauren E Walker"
    },
    {
      "affs": [
        3
      ],
      "name": "Robert West"
    },
    {
      "affs": [
        2
      ],
      "name": "Bridget Young"
    },
    {
      "affs": [
        13,
        14
      ],
      "name": "Munir Pirmohamed",
      "orcid": "0000-0002-7534-7266"
    },
    {
      "affs": [
        7
      ],
      "name": "Bethan Copsey"
    },
    {
      "affs": [
        0
      ],
      "name": "Aseel S Abuzour"
    },
    {
      "affs": [
        2
      ],
      "name": "Sarah E Rodgers"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Andrew Clegg",
      "orcid": "0000-0001-5972-1097"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/50gycvxgxdm5t.bib",
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  "doi": "10.1136/bmjmed-2026-002835",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13735312/",
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  "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": [
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      "heading",
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  "pdf_url": "https://bmjmedicine.bmj.com/content/5/1/e002835.full.pdf",
  "pmcid": "PMC13735312",
  "pmid": "42982602",
  "published": "2026-11-04",
  "publisher": "BMJ Publishing Group",
  "ris": "https://research.draconitic.com/paper/50gycvxgxdm5t.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T12:21:01Z",
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      "url": "https://doi.org/10.1136/bmjmed-2026-002835"
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  "title": "Deriving the anticholinergic medication index for delirium and falls in older people: development and external validation of a risk prediction model",
  "title_text": "Deriving the anticholinergic medication index for delirium and falls in older people: development and external validation of a risk prediction model",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T12:21:01Z",
  "url": "https://research.draconitic.com/paper/50gycvxgxdm5t",
  "venue": "BMJ Medicine",
  "versions": []
}