Tanja Mayer1, Andreas Daniel Meid2, Kai-Uwe Saum3, Hermann Brenner4, Ben Schöttker5, Hanna Marita Seidling1, Walter Emil Haefeli6. 1. Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg, Germany; Cooperation Unit Clinical Pharmacy, University of Heidelberg, Heidelberg, Germany. 2. Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg, Germany. 3. Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany. 4. Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Network Ageing Research, University of Heidelberg, Heidelberg, Germany. 5. Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Network Ageing Research, University of Heidelberg, Heidelberg, Germany; Institute of Health Care and Social Sciences, FOM University, Essen, Germany. 6. Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg, Germany; Cooperation Unit Clinical Pharmacy, University of Heidelberg, Heidelberg, Germany. Electronic address: walter.emil.haefeli@med.uni-heidelberg.de.
Abstract
OBJECTIVE: A patient's risk for anticholinergic adverse effects is frequently estimated by instruments evaluating the drugs included in his medication profile. It remains unknown, however, which characteristics should be included in such an assessment instrument aiming to reliably predict adverse anticholinergic outcomes. DESIGN: Cross-sectional study. SETTING: ESTHER cohort (Germany). PARTICIPANTS: Home-dwelling participants (N = 2,761) aged between 60 and 87 years. MEASUREMENTS: The association between anticholinergic load calculated with nine different instruments and four anticholinergic adverse outcomes was investigated in univariate and multivariate analyses. Therefore, linear models complemented with Kendall's tau rank correlation coefficients (ԏ) were applied for continuous outcomes and generalized linear models were used to derive odds ratios (ORs) with 95% confidence intervals (CIs) for binary endpoints. RESULTS: Based on the respective identification criteria for anticholinergic drugs, the nine instruments identified between 245 (9%) and 866 (31%) anticholinergic drug users (mean age ± SD: 73 ± 6 years; Mini-Mental State Examination [MMSE] score: 28.3 ± 2.07; Barthel Index: 97.1 ± 7.5; 291 reporting falls; 29 taking laxatives [surrogate for constipation]). In the multivariate analysis, only two instruments indicated a significant association between anticholinergic load and all four outcomes. The instrument considering the prescribed dose showed the strongest association with MMSE scores (ԏ = -0.10), falls (OR: 2.30; 95% CI: 1.50-3.52), and the use of laxatives (OR: 3.11; 95% CI: 1.04-9.36). CONCLUSIONS: Instruments most reliably predicted anticholinergicadverse events if they were either based on the drugs' serum anticholinergic activity and the suggestions of clinician experts or considered the actual prescribed dose.
OBJECTIVE: A patient's risk for anticholinergic adverse effects is frequently estimated by instruments evaluating the drugs included in his medication profile. It remains unknown, however, which characteristics should be included in such an assessment instrument aiming to reliably predict adverse anticholinergic outcomes. DESIGN: Cross-sectional study. SETTING: ESTHER cohort (Germany). PARTICIPANTS: Home-dwelling participants (N = 2,761) aged between 60 and 87 years. MEASUREMENTS: The association between anticholinergic load calculated with nine different instruments and four anticholinergic adverse outcomes was investigated in univariate and multivariate analyses. Therefore, linear models complemented with Kendall's tau rank correlation coefficients (ԏ) were applied for continuous outcomes and generalized linear models were used to derive odds ratios (ORs) with 95% confidence intervals (CIs) for binary endpoints. RESULTS: Based on the respective identification criteria for anticholinergic drugs, the nine instruments identified between 245 (9%) and 866 (31%) anticholinergic drug users (mean age ± SD: 73 ± 6 years; Mini-Mental State Examination [MMSE] score: 28.3 ± 2.07; Barthel Index: 97.1 ± 7.5; 291 reporting falls; 29 taking laxatives [surrogate for constipation]). In the multivariate analysis, only two instruments indicated a significant association between anticholinergic load and all four outcomes. The instrument considering the prescribed dose showed the strongest association with MMSE scores (ԏ = -0.10), falls (OR: 2.30; 95% CI: 1.50-3.52), and the use of laxatives (OR: 3.11; 95% CI: 1.04-9.36). CONCLUSIONS: Instruments most reliably predicted anticholinergicadverse events if they were either based on the drugs' serum anticholinergic activity and the suggestions of clinician experts or considered the actual prescribed dose.
Authors: Niko M Perttila; Hanna Öhman; Timo E Strandberg; Hannu Kautiainen; Minna Raivio; Marja-Liisa Laakkonen; Niina Savikko; Reijo S Tilvis; Kaisu H Pitkälä Journal: Drugs Aging Date: 2018-11 Impact factor: 3.923
Authors: Shahar Shmuel; Virginia Pate; Marc J Pepin; Janine C Bailey; Laura C Hanson; Til Stürmer; Rebecca B Naumann; Yvonne M Golightly; Danijela Gnjidic; Jennifer L Lund Journal: Pharmacoepidemiol Drug Saf Date: 2020-10-15 Impact factor: 2.890