Deriving the anticholinergic medication index for delirium and falls in older people: development and external validation of a risk prediction model
15 affiliations
- 1Academic Unit for Ageing and Stroke Research, Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
- 2Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
- 3Public Health, Policy and Systems, Institute of Population Health, University of Liverpool, Liverpool, UK
- 4Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
- 5School of Healthcare, University of Leeds, Leeds, UK
- 6NIHR Yorkshire and Humber Patient Safety Research Collaboration, Leeds, UK
- 7Institute of Population Health, University of Liverpool, Liverpool, UK
- 8Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK
- 9Edinburgh Clinical Trials Unit, University of Edinburgh, Edinburgh, UK
- 10School of Computer Science, University of Leeds, Leeds, UK
- 11School of Medicine and Population Health, University of Sheffield, Sheffield, UK
- 12Improvement Academy, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
- 13School of Psychology, University of Leeds, Leeds, UK
- 14Centre for Experimental Therapeutics, Institute of Systems, Molecular, and Integrative Biology, University of Liverpool, Liverpool, UK
- 15Wolfson Centre for Personalised Medicine, Institute of Systems, Molecular, and Integrative Biology, University of Liverpool, Liverpool, UK
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.
Design: Development and external validation of a risk prediction model.
Setting: UK primary and secondary care data from Connected Bradford and the Secure Anonymised Information Linkage (SAIL) databank, 1 January 2019 to 31 December 2019.
Participants: 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.
Main 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.
Results: 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).
Conclusions: 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.
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BibTeX
@article{best2026deriving,
title = {{Deriving the anticholinergic medication index for delirium and falls in older people: development and external validation of a risk prediction model}},
author = {Kate E Best and Olly Butters and Pieta Georgina Schofield and Oliver Todd and Sarah Alderson and David P Alldred and Laura Bonnett and Iain Buchan and Amanda Farrin and Owen Johnson and Ciarán McInerney and David Mehdizadeh and Tom Lawton and Rebecca Lawton and Farag Shuweidhi and Lauren E Walker and Robert West and Bridget Young and Munir Pirmohamed and Bethan Copsey and Aseel S Abuzour and Sarah E Rodgers and Andrew Clegg},
journal = {BMJ Medicine},
year = {2026},
month = nov,
doi = {10.1136/bmjmed-2026-002835},
url = {https://doi.org/10.1136/bmjmed-2026-002835},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Deriving the anticholinergic medication index for delirium and falls in older people: development and external validation of a risk prediction model
AU - Kate E Best
AU - Olly Butters
AU - Pieta Georgina Schofield
AU - Oliver Todd
AU - Sarah Alderson
AU - David P Alldred
AU - Laura Bonnett
AU - Iain Buchan
AU - Amanda Farrin
AU - Owen Johnson
AU - Ciarán McInerney
AU - David Mehdizadeh
AU - Tom Lawton
AU - Rebecca Lawton
AU - Farag Shuweidhi
AU - Lauren E Walker
AU - Robert West
AU - Bridget Young
AU - Munir Pirmohamed
AU - Bethan Copsey
AU - Aseel S Abuzour
AU - Sarah E Rodgers
AU - Andrew Clegg
PY - 2026
DA - 2026/11/04
JO - BMJ Medicine
DO - 10.1136/bmjmed-2026-002835
UR - https://doi.org/10.1136/bmjmed-2026-002835
AB - 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. Design: Development and external validation of a risk prediction model. Setting: UK primary and secondary care data from Connected Bradford and the Secure Anonymised Information Linkage (SAIL) databank, 1 January 2019 to 31 December 2019. Participants: 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. Main 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. Results: 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). Conclusions: 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.
SN - 2754-0413
ER -