TY - JOUR
T1 - Are Anticholinergic Symptoms a Risk Factor for Falls in Older General Practice Patients With Polypharmacy?
T2 - Study Protocol for the Development and Validation of a Prognostic Model
AU - Dinh, Truc Sophia
AU - González-González, Ana Isabel
AU - Meid, Andreas D.
AU - Snell, Kym I.E.
AU - Rudolf, Henrik
AU - Brueckle, Maria Sophie
AU - Blom, Jeanet W.
AU - Thiem, Ulrich
AU - Trampisch, Hans Joachim
AU - Elders, Petra J.M.
AU - Donner-Banzhoff, Norbert
AU - Gerlach, Ferdinand M.
AU - Harder, Sebastian
AU - van den Akker, Marjan
AU - Glasziou, Paul P.
AU - Haefeli, Walter E.
AU - Muth, Christiane
N1 - Funding Information:
PROPERmed was funded by the German Innovation Fund (grant number 01VSF16018). RIME was funded by the German Federal Ministry of Education and Research (grant number 01ET1005A). PRIMUM was funded by the German Federal Ministry of Education and Research (grant number 01GK0702). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Funding Information:
The authors would like to thank members of the original study groups of trials included in the PROPERmed IPD-MA: Wendy den Elzen, Wilbert van den Hout, Anne van Houwelingen, Margot Heijmans, Theo Stijnen for the ISCOPE study; Donna Bosch-Lenders, Prof. Andr? Knotterus, Jelle Stoffers and Bjorn Winkens for the PIL study; Prof. Greiner, Prof. Eva Hummers, Prof. Ulrike Junius-Walker, Prof. Petra Th?rmann and Prof. Stefan Wilm for the RIME study. The authors thank Phillip Elliot from Goethe-University for conducting a language review.
Publisher Copyright:
© Copyright © 2021 Dinh, González-González, Meid, Snell, Rudolf, Brueckle, Blom, Thiem, Trampisch, Elders, Donner-Banzhoff, Gerlach, Harder, van den Akker, Glasziou, Haefeli and Muth.
PY - 2021/1/14
Y1 - 2021/1/14
N2 - Background: Cumulative anticholinergic exposure, also known as anticholinergic burden, is associated with a variety of adverse outcomes. However, studies show that anticholinergic effects tend to be underestimated by prescribers, and anticholinergics are the most frequently prescribed potentially inappropriate medication in older patients. The grading systems and drugs included in existing scales to quantify anticholinergic burden differ considerably and do not adequately account for patients’ susceptibility to medications. Furthermore, their ability to link anticholinergic burden with adverse outcomes such as falls is unclear. This study aims to develop a prognostic model that predicts falls in older general practice patients, to assess the performance of several anticholinergic burden scales, and to quantify the added predictive value of anticholinergic symptoms in this context. Methods: Data from two cluster-randomized controlled trials investigating medication optimization in older general practice patients in Germany will be used. One trial (RIME, n = 1,197) will be used for the model development and the other trial (PRIMUM, n = 502) will be used to externally validate the model. A priori, candidate predictors will be selected based on a literature search, predictor availability, and clinical reasoning. Candidate predictors will include socio-demographics (e.g. age, sex), morbidity (e.g. single conditions), medication (e.g. polypharmacy, anticholinergic burden as defined by scales), and well-being (e.g. quality of life, physical function). A prognostic model including sociodemographic and lifestyle-related factors, as well as variables on morbidity, medication, health status, and well-being, will be developed, whereby the prognostic value of extending the model to include additional patient-reported symptoms will be also assessed. Logistic regression will be used for the binary outcome, which will be defined as “no falls” vs. “≥1 fall” within six months of baseline, as reported in patient interviews. Discussion: As the ability of different anticholinergic burden scales to predict falls in older patients is unclear, this study may provide insights into their relative importance as well as into the overall contribution of anticholinergic symptoms and other patient characteristics. The results may support general practitioners in their clinical decision-making and in prescribing fewer medications with anticholinergic properties.
AB - Background: Cumulative anticholinergic exposure, also known as anticholinergic burden, is associated with a variety of adverse outcomes. However, studies show that anticholinergic effects tend to be underestimated by prescribers, and anticholinergics are the most frequently prescribed potentially inappropriate medication in older patients. The grading systems and drugs included in existing scales to quantify anticholinergic burden differ considerably and do not adequately account for patients’ susceptibility to medications. Furthermore, their ability to link anticholinergic burden with adverse outcomes such as falls is unclear. This study aims to develop a prognostic model that predicts falls in older general practice patients, to assess the performance of several anticholinergic burden scales, and to quantify the added predictive value of anticholinergic symptoms in this context. Methods: Data from two cluster-randomized controlled trials investigating medication optimization in older general practice patients in Germany will be used. One trial (RIME, n = 1,197) will be used for the model development and the other trial (PRIMUM, n = 502) will be used to externally validate the model. A priori, candidate predictors will be selected based on a literature search, predictor availability, and clinical reasoning. Candidate predictors will include socio-demographics (e.g. age, sex), morbidity (e.g. single conditions), medication (e.g. polypharmacy, anticholinergic burden as defined by scales), and well-being (e.g. quality of life, physical function). A prognostic model including sociodemographic and lifestyle-related factors, as well as variables on morbidity, medication, health status, and well-being, will be developed, whereby the prognostic value of extending the model to include additional patient-reported symptoms will be also assessed. Logistic regression will be used for the binary outcome, which will be defined as “no falls” vs. “≥1 fall” within six months of baseline, as reported in patient interviews. Discussion: As the ability of different anticholinergic burden scales to predict falls in older patients is unclear, this study may provide insights into their relative importance as well as into the overall contribution of anticholinergic symptoms and other patient characteristics. The results may support general practitioners in their clinical decision-making and in prescribing fewer medications with anticholinergic properties.
KW - accidental falls [MeSH]
KW - aged [MesH]
KW - anticholinergic burden
KW - general practice
KW - multimorbidity [MeSH]
KW - polypharmacy
KW - prediction model
KW - prognosis research
UR - http://www.scopus.com/inward/record.url?scp=85100123837&partnerID=8YFLogxK
U2 - 10.3389/fphar.2020.577747
DO - 10.3389/fphar.2020.577747
M3 - Article
C2 - 33519441
VL - 11
JO - Frontiers in Pharmacology
JF - Frontiers in Pharmacology
SN - 1663-9812
M1 - 577747
ER -