In:
PLOS ONE, Public Library of Science (PLoS), Vol. 18, No. 1 ( 2023-1-23), p. e0280907-
Abstract:
Anticholinergic burden has been associated with adverse outcomes such as falls. To date, no gold standard measure has been identified to assess anticholinergic burden, and no conclusion has been drawn on which of the different measure algorithms best predicts falls in older patients from general practice. This study compared the ability of five measures of anticholinergic burden to predict falls. To account for patients’ individual susceptibility to medications, the added predictive value of typical anticholinergic symptoms was further quantified in this context. Methods and findings To predict falls, models were developed and validated based on logistic regression models created using data from two German cluster-randomized controlled trials. The outcome was defined as “≥ 1 fall” vs. “no fall” within a 6-month follow-up period. Data from the RIME study ( n = 1,197) were used in model development, and from PRIMUM ( n = 502) for external validation. The models were developed step-wise in order to quantify the predictive ability of anticholinergic burden measures, and anticholinergic symptoms. In the development set, 1,015 patients had complete data and 188 (18.5%) experienced ≥ 1 fall within the 6-month follow-up period. The overall predictive value of the five anticholinergic measures was limited, with neither the employed anticholinergic variable (binary / count / burden), nor dose-dependent or dose-independent measures differing significantly in their ability to predict falls. The highest c -statistic was obtained using the German Anticholinergic Burden Score (0.73), whereby the optimism-corrected c -statistic was 0.71 after interval validation using bootstrapping and 0.63 in the external validation. Previous falls and dizziness / vertigo had the strongest prognostic value in all models. Conclusions The ability of anticholinergic burden measures to predict falls does not appear to differ significantly, and the added value they contribute to risk classification in fall-prediction models is limited. Previous falls and dizziness / vertigo contributed most to model performance.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0280907
DOI:
10.1371/journal.pone.0280907.g001
DOI:
10.1371/journal.pone.0280907.g002
DOI:
10.1371/journal.pone.0280907.t001
DOI:
10.1371/journal.pone.0280907.t002
DOI:
10.1371/journal.pone.0280907.t003
DOI:
10.1371/journal.pone.0280907.t004
DOI:
10.1371/journal.pone.0280907.s001
DOI:
10.1371/journal.pone.0280907.s002
DOI:
10.1371/journal.pone.0280907.s003
DOI:
10.1371/journal.pone.0280907.s004
DOI:
10.1371/journal.pone.0280907.s005
DOI:
10.1371/journal.pone.0280907.s006
DOI:
10.1371/journal.pone.0280907.s007
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2023
detail.hit.zdb_id:
2267670-3
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