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Development of phenotype algorithms for the detection of adverse events in electronic health record data: a
Louisa Redeker1, Annette Haerdtlein2, Anna Maria Wermund3
1Department of Clinical Pharmacology, School of Medicine, Faculty of Health, Witten/Herdecke University, Witten, Germany.
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.
