gms | German Medical Science

29th Annual Meeting of the German Drug Utilisation Research Group (GAA)

Gesellschaft für Arzneimittelanwendungsforschung und Arzneimittelepidemiologie

24.11. - 25.11.2022, Münster

WOLGA: Further development, optimization and application of an algorithm for the detection of serious adverse drug reactions based on claims data - first results of a sub-analysis focusing on clostridium difficile infections

WOLGA - Weiterentwicklung, Optimierung und Anwendung eines Algorithmus zur Detektion schwerwiegender unerwünschter Arzneimittelwirkungen mit Routinedaten - Erste Ergebnisse einer Subanalyse mit Fokus auf Clostridium Difficile Infektionen

Meeting Abstract

  • corresponding author presenting/speaker Patrick Christ - Bundesinstitut für Arzneimittel und Medizinprodukte (BfArM), Bonn, Germany
  • author Nikolaj Rischke - Leibniz-Institut für Präventionsforschung und Epidemiologie (BIPS), Bremen, Germany
  • author Diana Dubrall - Bundesinstitut für Arzneimittel und Medizinprodukte (BfArM), Bonn, Germany
  • author Oliver Scholle - Leibniz-Institut für Präventionsforschung und Epidemiologie (BIPS), Bremen, Germany
  • author Jürgen Stausberg - Essen, Essen, Germany
  • author Ulrike Haug - Leibniz-Institut für Präventionsforschung und Epidemiologie (BIPS), Bremen, Germany
  • author Bernhardt Sachs - Bundesinstitut für Arzneimittel und Medizinprodukte (BfArM), Bonn, Germany

Gesellschaft für Arzneimittelanwendungsforschung und Arzneimittelepidemiologie e.V. (GAA). 29. Jahrestagung der Gesellschaft für Arzneimittelanwendungsforschung und Arzneimittelepidemiologie. Münster, 24.-25.11.2022. Düsseldorf: German Medical Science GMS Publishing House; 2022. Doc22gaa07

doi: 10.3205/22gaa07, urn:nbn:de:0183-22gaa070

Published: November 21, 2022

© 2022 Christ et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. See license information at http://creativecommons.org/licenses/by/4.0/.


Outline

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Background: Serious adverse drug reactions (ADRs) leading to hospitalizations are a problem for both patients and the health care system. ADR databases based on spontaneous reporting systems (e.g. EudraVigilance) contain valuable clinical information on ADRs but suffer from underreporting. There may be a potential use of German health claims data as complementary data source for the investigation of serious ADRs. Funded by the Innovation Fund of the Federal Joint Committee, the WOLGA project aims 1) to optimize the identification of serious ADRs in German health claims data and 2) to assess how data from spontaneous reporting systems and health claims data may complement each other. Here, we aim to illustrate this process based on the detection of ADR-related hospitalizations for enterocolitis due to clostridium difficile.

Materials and Methods: In the first step, an algorithm – previously developed by Stausberg et al. for the detection of ADR-related admissions in hospital data – was applied on the German Pharmacoepidemiological Research Database (GePaRD), which contains claims data from ~20% of the German population (here, data from 2010 to 2018 were used). The algorithm by Stausberg et al. is based on selected ICD-10 diagnoses associated with a possible to very high likelihood of reflecting an ADR. We selected all cases with a diagnosis of enterocolitis due to clostridium difficile (ICD 10 code: A04.7-) identified by this algorithm. In a next step, we used the claims data to comprehensively characterize these patients regarding demographics, the prescribing of drugs (before the hospital stay) known to be associated with this specific ADR as well as underlying diseases or conditions that increase the risk of enterocolitis due to clostridium difficile (i.e. irrespective of drug use).

In EudraVigilance, the Standardised MedDRA Query Pseudomembranous Colitis Narrow was used in order to create a comparative data set. Information about the demographical data, medical history of the patients and the suspected drugs were investigated. Furthermore, a causality assessment of the association between the suspected drugs and the clostridium difficile infection applying the WHO criteria was performed and only those cases with an at least possible causal relationship were considered for further analysis.

Results: Detailed results will be presented at the conference, so only an overview is provided here. In GePaRD, the algorithm identified 37.703 cases with a code for enterocolitis due to clostridium difficile. In EudraVigilance there were 181 cases matching the filter criteria. A considerable proportion of these cases from both datasets used relevant medication but also had comorbidities or prior hospitalizations (representing known risk factors described in the literature) that may have caused the diagnosis. Adapting the algorithm to avoid that these cases are classified as ADR-detected increases its specificity, although at the cost of sensitivity. In both datasets, there was only a small number of cases with none of the risk factors described in the literature for enterocolitis due to clostridium difficile mentioned above (drug-related, comorbidity, previous hospitalization).

Conclusion: These results suggest that consideration of all case-related data can optimize the existing algorithm to detect ADR-related enterocolitis due to clostridium difficile in terms of higher specificity. Although this results in a reduced sensitivity, it does not represent a relevant limitation for numerous questions and investigations. Further studies relating to the synergies of both datasets (EudraVigilance and GePaRD) are ongoing.