Guillaume Bouzillé1,2,3,4, Marie-Noëlle Osmont5, Louise Triquet5, Natalia Grabar6,7, Cécile Rochefort-Morel8, Emmanuel Chazard9, Elisabeth Polard5, Marc Cuggia1,2,3,4. 1. INSERM, Rennes, France. 2. Université de Rennes 1, LTSI, Rennes, France. 3. CHU Rennes, CIC Inserm 1414, Rennes, France. 4. CHU Rennes, Centre de Données Cliniques, Rennes, France. 5. Centre Régional de Pharmacovigilance, CHU Rennes, Rennes, France. 6. UMR 8163, CNRS, Lille, France. 7. Université de Lille, UMR 8163-STL-Savoirs Textes Langage, Lille, France. 8. CHU Rennes, Service de pneumologie et d'allergologie, Rennes, France. 9. Département de Santé Publique, Université de Lille EA 2694, Lille, France.
Abstract
RATIONALE, AIMS, AND OBJECTIVES: The spontaneous reporting system currently used in pharmacovigilance is not sufficiently exhaustive to detect all adverse drug reactions (ADRs). With the widespread use of electronic health records, biomedical data collected during the clinical care process can be reused and analysed to better detect ADRs. The aim of this study was to assess whether querying a Clinical Data Warehouse (CDW) could increase the detection of drug-induced anaphylaxis. METHODS: All known cases of drug-induced anaphylaxis that occurred or required hospitalization at Rennes Academic Hospital in 2011 (n = 19) were retrieved from the French pharmacovigilance database, which contains all reported ADR events. Then, from the Rennes Academic Hospital CDW, a training set (all patients hospitalized in 2011) and a test set (all patients hospitalized in 2012) were extracted. The training set was used to define an optimized query, by building a set of keywords (based on the known cases) and exclusion criteria to search structured and unstructured data within the CDW in order to identify at least all known cases of drug-induced anaphylaxis for 2011. Then, the real performance of the optimized query was tested in the test set. RESULTS: Using the optimized query, 59 cases of drug-induced anaphylaxis were identified among the 253 patient records extracted from the test set as possible anaphylaxis cases. Specifically, the optimal query identified 41 drug-induced anaphylaxis cases that were not detected by searching the French pharmacovigilance database but missed 7 cases detected only by spontaneous reporting. DISCUSSION: We proposed an information retrieval-based method for detecting drug-induced anaphylaxis, by querying structured and unstructured data in a CDW. CDW queries are less specific than spontaneous reporting and Diagnosis-related Groups queries, although their sensitivity is much higher. CDW queries can facilitate monitoring by pharmacovigilance experts. Our method could be easily incorporated in the routine practice.
RATIONALE, AIMS, AND OBJECTIVES: The spontaneous reporting system currently used in pharmacovigilance is not sufficiently exhaustive to detect all adverse drug reactions (ADRs). With the widespread use of electronic health records, biomedical data collected during the clinical care process can be reused and analysed to better detect ADRs. The aim of this study was to assess whether querying a Clinical Data Warehouse (CDW) could increase the detection of drug-induced anaphylaxis. METHODS: All known cases of drug-induced anaphylaxis that occurred or required hospitalization at Rennes Academic Hospital in 2011 (n = 19) were retrieved from the French pharmacovigilance database, which contains all reported ADR events. Then, from the Rennes Academic Hospital CDW, a training set (all patients hospitalized in 2011) and a test set (all patients hospitalized in 2012) were extracted. The training set was used to define an optimized query, by building a set of keywords (based on the known cases) and exclusion criteria to search structured and unstructured data within the CDW in order to identify at least all known cases of drug-induced anaphylaxis for 2011. Then, the real performance of the optimized query was tested in the test set. RESULTS: Using the optimized query, 59 cases of drug-induced anaphylaxis were identified among the 253 patient records extracted from the test set as possible anaphylaxis cases. Specifically, the optimal query identified 41 drug-induced anaphylaxis cases that were not detected by searching the French pharmacovigilance database but missed 7 cases detected only by spontaneous reporting. DISCUSSION: We proposed an information retrieval-based method for detecting drug-induced anaphylaxis, by querying structured and unstructured data in a CDW. CDW queries are less specific than spontaneous reporting and Diagnosis-related Groups queries, although their sensitivity is much higher. CDW queries can facilitate monitoring by pharmacovigilance experts. Our method could be easily incorporated in the routine practice.
Keywords:
adverse drug reaction reporting systems; drug-related side effects and adverse reactions; electronic health records; information storage and retrieval