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Aufdeckung von Arzneimittelrisiken nach der Zulassung

Methodenentwicklung zur Nutzung von Routinedaten der gesetzlichen Krankenversicherungen

Detection of drug risks after approval

Methods development for the use of routine statutory health insurance data

  • Leitthema
  • Published:
Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz Aims and scope

Zusammenfassung

Unerwünschte Arzneimittelwirkungen zählen zu den häufigen Todesursachen. Aufgabe der Pharmakovigilanz ist es, Arzneimittel nach der Zulassung zu überwachen, um so mögliche Risiken aufzudecken. Zu diesem Zweck werden typischerweise Spontanmelderegister genutzt, an die u. a. Ärzte und pharmazeutische Industrie Berichte über unerwünschte Arzneimittelwirkungen (UAW) melden. Diese Register sind jedoch nur begrenzt geeignet, um potenzielle Sicherheitsrisiken zu identifizieren. Eine andere, möglicherweise informativere Datenquelle sind Abrechnungsdaten der gesetzlichen Krankenversicherungen (GKV), die nicht nur den Gesundheitszustand eines Patienten im Längsschnitt erfassen, sondern auch Informationen zu Begleitmedikationen und Komorbiditäten bereitstellen.

Um deren Potenzial nutzen zu können und so zur Verbesserung der Arzneimittelsicherheit beizutragen, sollen statistische Methoden weiterentwickelt werden, die sich in anderen Anwendungsgebieten bewährt haben. So steht eine große Bandbreite von Methoden für die Auswertung von Spontanmeldedaten zur Verfügung: Diese sollen zunächst umfassend verglichen und anschließend hinsichtlich ihrer Nutzbarkeit für longitudinale Daten erschlossen werden. Des Weiteren wird aufgezeigt, wie maschinelle Lernverfahren helfen könnten, seltene Risiken zu identifizieren. Zudem werden sogenannte Enrichment-Analysen eingesetzt, mit denen pharmakologische Arzneimittelgruppen und verwandte Komorbiditäten zusammengefasst werden können, um vulnerable Bevölkerungsgruppen zu identifizieren.

Insgesamt werden diese Methoden die Arzneimittelrisikoforschung anhand von GKV-Routinedaten vorantreiben, die aufgrund ihres Umfangs, der longitudinalen Erfassung sowie ihrer Aktualität eine vielversprechende Datenquelle bieten, um UAWs aufzudecken.

Abstract

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.

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Danksagung

Dieser Artikel entstand als Teil des Projekts „Nutzung von Routinedaten zur Pharmakovigilanz in Deutschland: Methodenentwicklung und erste Anwendungen“, kurz PV-Monitor, das im Rahmen des Innovationsfonds des Gemeinsamen Bundesausschusses unter dem Förderkennzeichen 01VSF16020 gefördert wird.

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Authors and Affiliations

  1. Leibniz-Institut für Präventionsforschung und Epidemiologie – BIPS, Achterstr. 30, 28359, Bremen, Deutschland

    Ronja Foraita, Louis Dijkstra, Mariam R. Rizkallah, Marvin N. Wright & Iris Pigeot

  2. Wissenschaftliches Institut der Techniker Krankenkasse für Nutzen und Effizienz im Gesundheitswesen (WINEG TK), Hamburg, Deutschland

    Felix Falkenberg, Marco Garling & Roland Linder

  3. Institut für Experimentelle und Klinische Pharmakologie und Toxikologie, Universität zu Lübeck, Lübeck, Deutschland

    René Pflock & Markus Schwaninger

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  1. Ronja Foraita
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Correspondence to Ronja Foraita.

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Interessenkonflikt

R. Foraita, L. Dijkstra, F. Falkenberg, M. Garling, R. Linder, R. Pflock, M. R. Rizkallah, M. Schwaninger, M. N. Wright und I. Pigeot geben an, dass kein Interessenkonflikt besteht.

Dieser Beitrag beinhaltet keine von den Autoren durchgeführten Studien an Menschen oder Tieren.

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Foraita, R., Dijkstra, L., Falkenberg, F. et al. Aufdeckung von Arzneimittelrisiken nach der Zulassung. Bundesgesundheitsbl 61, 1075–1081 (2018). https://doi.org/10.1007/s00103-018-2786-z

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