Influence of metabolic profiles on the safety of drug therapy in routine care in Germany: protocol of the cohort study EMPAR.

INTRODUCTION: Pre-emptive testing of pharmacogenetically relevant single-nucleotide polymorphisms can be an effective tool in the prevention of adverse drug reactions and therapy resistance. However, most of the tests are not used as standard in routine care in Germany because of lacking evidence for the clinical and economical benefit and their impact on the usage of healthcare services. We address this issue by investigating the influence of pharmacogenetic profiles on the use of healthcare services over an extended period of several years using routine care data from a statutory health insurance company. The goal is to provide clinical evidence whether pre-emptive pharmacogenetic testing of metabolic profiles in routine care in Germany is beneficial and cost-effective. METHODS AND ANALYSIS: The EMPAR (Einfluss metabolischer Profile auf die Arzneimitteltherapiesicherheit in der Routineversorgung) study is a non-interventional cohort study conducted to analyse pharmacogenetic risk factors that are important for drug therapy by means of endpoints relevant for healthcare. The analysis is based on pharmacogenetic profiles and statutory health insurance data. We perform pharmacogenetic, pharmacoepidemiological and pharmacoeconomic analyses using health care utilisation scores and machine learning techniques. Therefore, we aim to include about 10 000 patients (≥18 years) insured by the health insurance provider Techniker Krankenkasse. The study focuses on patients with prescriptions of anticoagulants and prescriptions of cholesterol-lowering drugs. Also, a screening for special pharmacogenetic characteristics will be performed in patients with at least one Y57.9! diagnosis (Complication of medical and surgical care: drug or medicament, unspecified). Outcomes include the utilisation of health insurance services, the incidence of incapacity for work and costs for drugs and treatment. ETHICS AND DISSEMINATION: The protocol was approved by the Ethics Committee of the Medical Faculty, University of Bonn (Lfd. Nr. 339/17). The results of this research project will be published in scientific open access journals and at conferences. TRIAL REGISTRATION NUMBER: German Clinical Trials Register, DRKS00013909.

Adverse drug reaction or innocent bystander? A systematic comparison of statistical discovery methods for spontaneous reporting systems.

PURPOSE: Spontaneous reporting systems (SRSs) are used to discover previously unknown relationships between drugs and adverse drug reactions (ADRs). A plethora of statistical methods have been proposed over the years to identify these drug-ADR pairs. The objective of this study is to compare a wide variety of methods in their ability to detect these signals, especially when their detection is complicated by the presence of innocent bystanders (drugs that are mistaken to be associated with the ADR, since they are prescribed together with the drug that is the ADR's actual cause). METHODS: Twelve methods, 24 measures in total, ranging from simple disproportionality measures (eg, the reporting odds ratio), hypothesis tests (eg, test of the Poisson mean), Bayesian shrinkage estimates (eg, the Bayesian confidence propagation neural network, BCPNN) to sparse regression (LASSO), are compared in their ability to detect drug-ADR pairs in a large number of simulated SRSs with varying numbers of innocent bystanders and effect sizes. The area under the precision-recall curve is used to assess the measures' performance. RESULTS: Hypothesis tests (especially the test of the Poisson mean) perform best when the associations are weak and there is little to no confounding by other drugs. When the level of confounding increases and/or the effect sizes become larger, Bayesian shrinkage methods should be preferred. The LASSO proves to be the most robust against the innocent bystander effect. CONCLUSIONS: There is no absolute "winner". Which method to use for a particular SRS depends on the effect sizes and the level of confounding present in the data.

[Detection of drug risks after approval : Methods development for the use of routine statutory health insurance data]. / Aufdeckung von Arzneimittelrisiken nach der Zulassung : Methodenentwicklung zur Nutzung von Routinedaten der gesetzlichen Krankenversicherungen.

Adverse drug reactions are among the leading causes of death. Pharmacovigilance aims to monitor drugs after they have been released to the market in order to detect potential risks. Data sources commonly used to this end are spontaneous reports sent in by doctors or pharmaceutical companies. Reports alone are rather limited when it comes to detecting potential health risks. Routine statutory health insurance data, however, are a richer source since they not only provide a detailed picture of the patients' wellbeing over time, but also contain information on concomitant medication and comorbidities.To take advantage of their potential and to increase drug safety, we will further develop statistical methods that have shown their merit in other fields as a source of inspiration. A plethora of methods have been proposed over the years for spontaneous reporting data: a comprehensive comparison of these methods and their potential use for longitudinal data should be explored. In addition, we show how methods from machine learning could aid in identifying rare risks. We discuss these so-called enrichment analyses and how utilizing pharmaceutical similarities between drugs and similarities between comorbidities could help to construct risk profiles of the patients prone to experience an adverse drug event.Summarizing these methods will further push drug safety research based on healthcare claim data from German health insurances which form, due to their size, longitudinal coverage, and timeliness, an excellent basis for investigating adverse effects of drugs.

Exploring conflict- and target-related movement of visual attention.

Intermixing trials of a visual search task with trials of a modified flanker task, the authors investigated whether the presentation of conflicting distractors at only one side (left or right) of a target stimulus triggers shifts of visual attention towards the contralateral side. Search time patterns provided evidence for lateral attention shifts only when participants performed the flanker task under an instruction assumed to widen the focus of attention, demonstrating that instruction-based control settings of an otherwise identical task can impact performance in an unrelated task. Contrasting conditions with response-related and response-unrelated distractors showed that shifting attention does not depend on response conflict and may be explained as stimulus-conflict-related withdrawal or target-related deployment of attention.