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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.
Objectives: Investigate the effectiveness of a complex intervention aimed at improving the appropriateness of medication in older patients with multimorbidity in general practice.
Design: Pragmatic, cluster randomised controlled trial with general practice as unit of randomisation.
Setting: 72 general practices in Hesse, Germany.
Participants: 505 randomly sampled, cognitively intact patients (≥60 years, ≥3 chronic conditions under pharmacological treatment, ≥5 long-term drug prescriptions with systemic effects); 465 patients and 71 practices completed the study.
Interventions: Intervention group (IG): The healthcare assistant conducted a checklist-based interview with patients on medication-related problems and reconciled their medications. Assisted by a computerised decision support system, the general practitioner optimised medication, discussed it with patients and adjusted it accordingly. The control group (CG) continued with usual care.
Outcome measures: The primary outcome was a modified Medication Appropriateness Index (MAI, excluding item 10 on cost-effectiveness), assessed in blinded medication reviews and calculated as the difference between baseline and after 6 months; secondary outcomes after 6 and 9 months’ follow-up: quality of life, functioning, medication adherence, and so on.
Results: At baseline, a high proportion of patients had appropriate to mildly inappropriate prescriptions (MAI 0–5 points: n=350 patients). Randomisation revealed balanced groups (IG: 36 practices/252 patients; CG: 36/253). Intervention had no significant effect on primary outcome: mean MAI sum scores decreased by 0.3 points in IG and 0.8 points in CG, resulting in a non-significant adjusted mean difference of 0.7 (95% CI −0.2 to 1.6) points in favour of CG. Secondary outcomes showed non-significant changes (quality of life slightly improved in IG but continued to decline in CG) or remained stable (functioning, medication adherence).
Conclusions: The intervention had no significant effects. Many patients already received appropriate prescriptions and enjoyed good quality of life and functional status. We can therefore conclude that in our study, there was not enough scope for improvement.
Trial registration number: ISRCTN99526053. NCT01171339; Results.
Evidence-based clinical guidelines generally consider single conditions, and rarely multimorbidity. We developed an evidence-based guideline for a structured care program to manage polypharmacy in multimorbidity by using a realist synthesis to update the German polypharmacy guideline including the following five methods: formal prioritization in focus groups; systematic guideline review of evidence-based multimorbidity/polypharmacy guidelines; evidence search/synthesis and recommendation development; multidisciplinary consent of recommendations; feasibility test of updated guideline. We identified the need for a better description of the target group, decision support, prioritization of medication, consideration of patient preferences and anticholinergic properties, and of healthcare interfaces. We conducted a systematic guideline review of eight guidelines and extracted and synthesized recommendations using the Ariadne principles. We also included 48 systematic reviews. We formulated and agreed upon 34 recommendations for the revised guideline. During the feasibility test, guideline use enabled 57% of GPs to identify problems, leading to medication changes in 49% and self-assessed improvement in 56% of patients. Although 58% of GPs felt that it was too long, 92% recommended it. Polypharmacy should be systematically reviewed at least annually. Patients, family members, and healthcare professionals should monitor and adjust it using prospective process validation, taking into account patient preferences and agreed treatment goals.