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Meta-Analysis
. 2023 Sep 1;52(9):afad176.
doi: 10.1093/ageing/afad176.

Interventions for reducing anticholinergic medication burden in older adults-a systematic review and meta-analysis

Affiliations
Meta-Analysis

Interventions for reducing anticholinergic medication burden in older adults-a systematic review and meta-analysis

Eve Braithwaite et al. Age Ageing. .

Abstract

Introduction: Anticholinergic medications block the neurotransmitter acetylcholine in the brain and peripheral nervous system. Many medications have anticholinergic properties, and the cumulative effect of these medications is termed anticholinergic burden. Increased anticholinergic burden can have short-term side effects such as dry mouth, blurred vision and urinary retention as well as long-term effects including dementia, worsening physical function and falls.

Methods: We carried out a systematic review (SR) with meta-analysis (MA) looking at randomised controlled trials addressing interventions to reduce anticholinergic burden in older adults.

Results: We identified seven papers suitable for inclusion in our SR and MA. Interventions included multi-disciplinary involvement in medication reviews and deprescribing of AC medications. Pooled data revealed no significant difference in outcomes between control and intervention group for falls (OR = 0.76, 95% CI: 0.52-1.11, n = 647), cognition (mean difference = 1.54, 95% CI: -0.04 to 3.13, n = 405), anticholinergic burden (mean difference = 0.04, 95% CI: -0.11 to 0.18, n = 710) or quality of life (mean difference = 0.04, 95% CI: -0.04 to 0.12, n = 461).

Discussion: Overall, there was no significant difference with interventions to reduce anticholinergic burden. As we did not see a significant change in anticholinergic burden scores following interventions, it is likely other outcomes would not change. Short follow-up time and lack of training and support surrounding successful deprescribing may have contributed.

Keywords: anticholinergic medication; cognition; falls; meta-analysis; older adult; older people; systematic review.

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Conflict of interest statement

A.C. has led the development and validation of the Anticholinergic Medication Index (ACMI) in Health Data Research UK (HDR UK) funding. The ACMI will be supplied to UK providers of electronic health record systems and related software at no cost, on the basis that a premium charge is not applied to the end user. M.P. has received partnership funding for the following: MRC Clinical Pharmacology Training Scheme (co-funded by MRC and Roche, UCB, Eli Lilly and Novartis); and a PhD studentship jointly funded by EPSRC and Astra Zeneca. He has developed an HLA genotyping panel with MC Diagnostics, but does not benefit financially from this. He is part of the IMI Consortium ARDAT (www.ardat.org). None of this funding is related to this paper.

Figures

Figure 1
Figure 1
Flowchart of included studies.
Figure 2
Figure 2
Forest plots demonstrating at follow-up, in the intervention and the control groups: (a) pooled risk estimates of fall frequency; (b) pooled mean differences in Mini-Mental State Examination (MMSE) scores; (c) pooled mean differences in Drug Burden Index (DBI) scores; (d) pooled mean differences in EuroQol five-dimension health questionnaire (EQ-5D) scores. In (a), odds ratios less than 1 favour the intervention group; odds ratios more than 1 favour the control group. In (b, c, d), mean difference less than 0 favours the control group, mean difference more than 0 favours the intervention group. CI = confidence interval; I2 = I2 statistic; IV = inverse variance; M-H = Mantel–Haenszel method; P = P value; Random = random effects; Z = Z value.

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