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Viktoria S Wurmbach,1,2 Steffen J Schmidt,3 Anette Lampert,1,2 Simone Bernard,3 Christine K Faller,1,2 Petra A Thürmann,3,4 Walter E Haefeli,1,2 Hanna M Seidling1,2 On behalf of HIOPP-6 Consortium1Department of Clinical Pharmacology and Pharmacoepidemiology, Heidelberg University Hospital, Heidelberg, Germany; 2Cooperation Unit Clinical Pharmacy, Heidelberg University Hospital, Heidelberg, Germany; 3Department of Clinical Pharmacology, School of Medicine, Faculty of Health, Witten/Herdecke University, Witten, Germany; 4Philipp Klee-Institute for Clinical Pharmacology, HELIOS University Clinic Wuppertal, Wuppertal, GermanyCorrespondence: Hanna M SeidlingHeidelberg University Hospital, Department of Clinical Pharmacology and Pharmacoepidemiology, Im Neuenheimer Feld 410, Heidelberg, 69120, GermanyTel +49 6221/56-38736Email hanna.seidling@med.uni-heidelberg.dePurpose: The development and testing of key questions suitable to identify patients’ difficulties with medication administration.Materials and Methods: We used a consecutive five-step process to draft key questions regarding 43 aspects of medication administration that can be difficult for patients who manage a complex drug treatment: Step 1) Identification of potentially error-prone characteristics of drug treatment (such as certain dosage forms) and initial draft of key questions. Step 2) Assessment of how comprehensible the questions are for patients. Step 3) Pre-testing of exemplary key questions with patients and monitoring of patient’s actual medication administration behavior. Step 4) Evaluation by general practitioners of how well the questions may be integrated into actual patient visits. Step 5) Final approval of the questions in an expert panel. Thereafter, we pilot-tested exemplary questions with 36 patients (43 tests). In the course of this pilot-testing, the patients’ answers to the key questions were tested against both their actual behavior during medication administration and against their

Lucas Wirbka, Walter Emil Haefeli, Andreas Daniel Meid On behalf of ARMIN Study GroupDepartment of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg, 69120, GermanyCorrespondence: Andreas Daniel MeidDepartment of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Im Neuenheimer Feld 410, Heidelberg, 69120, GermanyTel +49 6221 56 37113Fax +49 6221 56 4642Email Andreas.Meid@med.uni-Heidelberg.deBackground: An essential contribution regarding the prevention of thromboembolic events in patients with (non-valvular) atrial fibrillation (AF) is good adherence to direct oral anticoagulants (DOACs). However, it is an open question what “good” adherence means for DOACs or below which threshold non-adherence is clinically relevant for AF patients. Ultimately, such a classification could prevent strokes and associated costs through adjusted treatment regimens or supportive measures.Methods: We selected 10,092 AF patients from health insurance claims data between 2014 and 2018 who were issued a majority (at least half of the number) of maximum approved strength prescriptions of one of the following DOACs, namely rivaroxaban, apixaban, or dabigatran. Due to the limited sample size, the prescriptions of dabigatran had to be finally excluded for the cut-off analysis. DOAC adherence was calculated as the proportion of days covered (PDC) by dividing the days of theoretical use (days covered) of the drug by the duration in days of the observation interval. PDC cut-off values were derived from stroke risk as a function of continuous PDC values in time-to-event analyses and corresponding dose-response models. The influence of adherence-promoting interventions (targeted and untargeted) on the occurrence of strokes and related costs was then projected, considering intervention costs per patient.Results: The population had a mean age of 74.5 years and 50% were female. The median PDC was 0.79 ± 0.28 with a median follow-up time

Objective To explore factors that potentially impact external validation performance while developing and validating a prognostic model for hospital admissions (HAs) in complex older general practice patients. Study design and setting Using individual participant data from four cluster-randomised trials conducted in the Netherlands and Germany, we used logistic regression to develop a prognostic model to predict all-cause HAs within a 6-month follow-up period. A stratified intercept was used to account for heterogeneity in baseline risk between the studies. The model was validated both internally and by using internal-external cross-validation (IECV). Results Prior HAs, physical components of the health-related quality of life comorbidity index, and medication-related variables were used in the final model. While achieving moderate discriminatory performance, internal bootstrap validation revealed a pronounced risk of overfitting. The results of the IECV, in which calibration was highly variable even after accounting for between-study heterogeneity, agreed with this finding. Heterogeneity was equally reflected in differing baseline risk, predictor effects and absolute risk predictions. Conclusions Predictor effect heterogeneity and differing baseline risk can explain the limited external performance of HA prediction models. With such drivers known, model adjustments in external validation settings (eg, intercept recalibration, complete updating) can be applied more purposefully. Trial registration number PROSPERO id: CRD42018088129.

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 base