{
  "abstract": "Objectives To develop phenotype algorithms for the detection of adverse events (AEs) or AE-proxies in electronic health records (EHRs), accounting for varying data availability.Design Multicentre study conducted as part of the Use Case POLAR_MI (POLypharmacy, drug interActions, Risks) of the German Medical Informatics Initiative (MII).Setting Germany.Participants Multidisciplinary teams from 10 German university sites within the MII.Interventions Not applicable.Main outcome measures Literature- and consensus-based development and operationalisation of AE algorithms using structured EHR data, including a standardised, multicentre expert review process. Data categories used: International Classification of Diseases, 10th Revision (ICD-10) codes for diagnoses; Anatomical Therapeutic Chemical (ATC) codes and ‘Pharmazentralnummern’ (PZN; German eight-digit identification code for pharmaceutical products) for medications (used in the treatment of AEs); Logical Observation Identifiers Names and Codes (LOINC) for laboratory values and medical findings; and ‘Operationen- und Prozedurenschlüssel’ (OPS; German procedure classification) codes for medical and surgical procedures.Results We developed 82 algorithms for 48 AEs. Algorithms for the same AE varied by data categories or code selections. At the AE level, 31 AEs were covered exclusively by newly developed algorithms, and 17 AEs by at least one modified algorithm.Overall, 52 algorithms were based on a single data category, while 30 required multiple categories. ICD-10 codes were most commonly used (n=65 AE algorithms), followed by LOINC (n=27), ATC codes (n=18), OPS codes (n=11) and PZN (n=2). All phenotype algorithms were semantically modelled and can be executed using the publicly available Terminology- and Ontology-based Phenotyping (TOP) Framework, which supports export in various formats.Conclusion We present a peer-reviewed set of algorithms for a large number of AEs, which can be implemented in structured routine electronic data sources and (pending validation studies) may support pharmacoepidemiologic research. The algorithms will be implementable across all 39 participating sites of the German MII. As a next step, we will empirically validate the algorithms against all information (including free text) contained in EHRs.Trial registration Not applicable.",
  "authors": [
    {
      "affiliations": [
        "Department of Clinical Pharmacology, School of Medicine, Faculty of Health, Witten/Herdecke University, Witten, Germany"
      ],
      "name": "Louisa Redeker"
    },
    {
      "affiliations": [
        "Institute of General Practice and Family Medicine, LMU University Hospital, LMU Munich, Munich, Germany"
      ],
      "name": "Annette Haerdtlein"
    },
    {
      "affiliations": [
        "Department of Clinical Pharmacy, Institute of Pharmacy, University of Bonn, Bonn, Germany"
      ],
      "name": "Anna Maria Wermund"
    },
    {
      "affiliations": [
        "Hospital Pharmacy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany"
      ],
      "name": "Beate Mussawy"
    },
    {
      "affiliations": [
        "Institute of General Practice and Family Medicine, LMU University Hospital, LMU Munich, Munich, Germany"
      ],
      "name": "Marietta Rottenkolber"
    },
    {
      "affiliations": [
        "Institute of Clinical Chemistry and Clinical Pharmacology, University Hospital Bonn, Bonn, Germany"
      ],
      "name": "Martin Coenen"
    },
    {
      "affiliations": [
        "Institute of Experimental and Clinical Pharmacology and Toxicology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany",
        "Pharmacy Department, Erlangen University Hospital, Erlangen, Germany"
      ],
      "name": "Pauline Dürr"
    },
    {
      "affiliations": [
        "Institute for Laboratory Medicine, University of Leipzig Medical Center, Leipzig, Germany"
      ],
      "name": "Martin Federbusch"
    },
    {
      "affiliations": [
        "Department of Clinical Pharmacology, University Hospital Tübingen, Tübingen, Germany"
      ],
      "name": "Christian Philipp Jüttner"
    },
    {
      "affiliations": [
        "Hospital Pharmacy, University Center for Pharmacotherapy and Pharmacoeconomics, Jena University Hospital, Jena, Germany"
      ],
      "name": "Anna Kathrin Schuster"
    },
    {
      "affiliations": [
        "Internal Medicine IX, Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Medical Faculty, University Hospital of Heidelberg, Heidelberg, Germany"
      ],
      "name": "Hanna Marita Seidling"
    },
    {
      "affiliations": [
        "Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Leipzig University, Leipzig, Germany"
      ],
      "name": "Alexandr Uciteli"
    },
    {
      "affiliations": [
        "Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Leipzig University, Leipzig, Germany"
      ],
      "name": "Christoph Beger"
    },
    {
      "affiliations": [
        "Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Leipzig University, Leipzig, Germany"
      ],
      "name": "Daniel Neumann"
    },
    {
      "affiliations": [
        "Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Leipzig University, Leipzig, Germany"
      ],
      "name": "Markus Loeffler"
    },
    {
      "affiliations": [
        "Institute of General Practice and Family Medicine, LMU University Hospital, LMU Munich, Munich, Germany"
      ],
      "name": "Tobias Dreischulte"
    },
    {
      "affiliations": [
        "Department of Clinical Pharmacology, School of Medicine, Faculty of Health, Witten/Herdecke University, Witten, Germany",
        "Philipp Klee-Institute for Clinical Pharmacology, Helios University Hospital Wuppertal, Wuppertal, Germany"
      ],
      "name": "Sven Schmiedl"
    },
    {
      "affiliations": [],
      "name": "on behalf of the POLAR_MI consortium"
    }
  ],
  "title": "Development of phenotype algorithms for the detection of adverse events in electronic health record data: a multicentre study",
  "uid": "868d5c1f-12a6-502a-b5d9-41b5dc500bb9"
}