The prevalence of multimorbidity and polypharmacy increases significantly with age and are associated with negative health consequences. However, most current interventions to optimize medication... Show moreThe prevalence of multimorbidity and polypharmacy increases significantly with age and are associated with negative health consequences. However, most current interventions to optimize medication have failed to show significant effects on patient-relevant outcomes. This may be due to ineffectiveness of interventions themselves but may also reflect other factors: insufficient sample sizes, heterogeneity of population. To address this issue, the international PROPERmed collaboration was set up to obtain/synthesize individual participant data (IPD) from five cluster-randomized trials. The trials took place in Germany and The Netherlands and aimed to optimize medication in older general practice patients with chronic illness. PROPERmed is the first database of IPD to be drawn from multiple trials in this patient population and setting. It offers the opportunity to derive prognostic models with increased statistical power for prediction of patient-relevant outcomes resulting from the interplay of multimorbidity and polypharmacy. This may help patients from this heterogeneous group to be stratified according to risk and enable clinicians to identify patients that are likely to benefit most from resource/timeintensive interventions. The aim of this manuscript is to describe the rationale behind PROPERmed collaboration, characteristics of the included studies/participants, development of the harmonized IPD database and challenges faced during this process. Show less
Gonzalez-Gonzalez, A.I.; Meid, A.D.; Dinh, T.S.; Blom, J.W.; Akker, M. van den; Elders, P.J.M.; ... ; Muth, C. 2021
Objectives: To develop and validate a prognostic model to predict deterioration in health-related quality of life (dHRQoL) in older general practice patients with at least one chronic condition and... Show moreObjectives: To develop and validate a prognostic model to predict deterioration in health-related quality of life (dHRQoL) in older general practice patients with at least one chronic condition and one chronic prescription.Study Design and Setting: We used individual participant data from five cluster-randomized trials conducted in the Netherlands and Germany to predict dHRQoL, defined as a decrease in EQ-5D-3 L index score of > 5% after 6-month follow-up in logistic regression models with stratified intercepts to account for between-study heterogeneity. The model was validated internally and by using internal -external cross-validation (IECV).Results: In 3,582 patients with complete data, of whom 1,046 (29.2%) showed deterioration in HRQoL, and 12/87 variables were selected that were related to single (chronic) conditions, inappropriate medication, medication underuse, functional status, well-being, and HRQoL. Bootstrap internal validation showed a C-statistic of 0.71 (0.69 to 0.72) and a calibration slope of 0.88 (0.78 to 0.98). In the IECV loop, the model provided a pooled C-statistic of 0.68 (0.65 to 0.70) and calibration-in-the-large of 0 (-0.13 to 0.13). HRQoL/functionality had the strongest prognostic value.Conclusion: The model performed well in terms of discrimination, calibration, and generalizability and might help clinicians identify older patients at high risk of dHRQoL.Registration: PROSPERO ID: CRD42018088129. (c) 2020 Elsevier Inc. All rights reserved. Show less
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... Show moreBackground: 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. Show less
Objective: The objective of this study was to identify key features to be addressed in the reporting of deprescribing trials and to elab-orate and explain CONSORT items in this regard.Study Design... Show moreObjective: The objective of this study was to identify key features to be addressed in the reporting of deprescribing trials and to elab-orate and explain CONSORT items in this regard.Study Design and Setting: As a first step in a multistage process and based on a systematic review of deprescribing trials, we elab-orated variation in design, intervention, and reporting of the included trials of the review. We identified items that were missed or insufficiently described, using the CONSORT and TIDieR checklists. The resulting list of items, which we considered relevant to be reported in deprescribing trials, were discussed in a single-round Delphi exercise and subsequently in a full-day face-to-face meeting with an international panel of 14 experts. We agreed on CONSORT items for further elaboration with regard to design and reporting of deprescribing trials.Results: We identified seven CONSORT items on trial design, participants, intervention, outcomes, flowchart, and harms, where the investigators of deprescribing trials should take into consideration specific aspects, such as whether or not to use placebo or how to inform participants.Conclusion: This article presents an elaboration to the CONSORT statement for the reporting of deprescribing trials. It may also support investigators in motivated design choices. (c) 2020 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Show less
Muth, C.; Akker, M. van den; Blom, J.W.; Mallen, C.D.; Rochon, J.; Schellevis, F.G.; ... ; Glasziou, P.P. 2014
