INTRODUCTION Detection of clinical events from diverse electronic sources of information such as hospital discharge claims (HOSP), death registries (DEATH), laboratory values (LAB), and general practice databases (GP) may be useful for various epidemiological purposes. In particular, the EU-ADR project aims to detect adverse events deemed to be important in pharmacovigilance. Eight databases (DBs) from four countries, combining different sources of information, participate in the project. A common conceptual framework was lacking to describe harmoniously the algorithm by which each DB detected each event. OBJECTIVES Describe the algorithms that 8 different DBs used to identify 6 events: Acute myocardial infarction (AMI), Acute Renal Failure (ARF), Anaphylactic Shock (AS), Bullous Eruption (BE), Rhabdomyolysis (RHABD), Upper Gastrointestinal Bleeding (UGIB). Benchmark corresponding incidence rates (IRs). METHODS A list of medical concepts corresponding to each event of interest was provided and projected to the DSs different coding systems (ICD9, ICD10, READ, ICPC) and natural languages through the Unified Medical Language System (UMLS). Specific sources of information contained in each DB were classified in a common framework and DBs with similar structures were asked to search for concepts within the same sources. RESULTS Concepts were mainly searched for in GP diagnoses and primary diagnoses of HOSP, but for some events DEATH (AMI, ARF, AS) and LAB (RHABD) were independently used by DBs having them. Resulting age-adjusted IRs vary as follows across DBs: 1-2/1,000PY (AMI), 3-7/10,000PY (UGIB), 2-12/100,000PY (AS), 2-17/100,000PY (BE), 1-8/100,000PY (RHABD), 3-49/100,000PY (ARF). CONCLUSIONS It is possible to describe event extractions from heterogeneous DBs in a common conceptual framework. Residual differences in IRs may be due either to differences in the underlying populations or to differences in the characteristics (structure, coding system) of the DBs.
Gini, R., Avillach, P., Coloma, P., Mougin, F., Dufour, J., Thiessard, F., et al. (2010). Harmonising definitions of adverse events among 8 european healthcare databases participating in the EU-ADR project. Intervento presentato a: EuroEPI 2010 Epidemiology and Public Health in an Evolving Europe and XXXIV Congresso dell'associazione Italiana di Epidemiologia, Firenze.
Harmonising definitions of adverse events among 8 european healthcare databases participating in the EU-ADR project
Mazzaglia, G;Fornari, C;
2010
Abstract
INTRODUCTION Detection of clinical events from diverse electronic sources of information such as hospital discharge claims (HOSP), death registries (DEATH), laboratory values (LAB), and general practice databases (GP) may be useful for various epidemiological purposes. In particular, the EU-ADR project aims to detect adverse events deemed to be important in pharmacovigilance. Eight databases (DBs) from four countries, combining different sources of information, participate in the project. A common conceptual framework was lacking to describe harmoniously the algorithm by which each DB detected each event. OBJECTIVES Describe the algorithms that 8 different DBs used to identify 6 events: Acute myocardial infarction (AMI), Acute Renal Failure (ARF), Anaphylactic Shock (AS), Bullous Eruption (BE), Rhabdomyolysis (RHABD), Upper Gastrointestinal Bleeding (UGIB). Benchmark corresponding incidence rates (IRs). METHODS A list of medical concepts corresponding to each event of interest was provided and projected to the DSs different coding systems (ICD9, ICD10, READ, ICPC) and natural languages through the Unified Medical Language System (UMLS). Specific sources of information contained in each DB were classified in a common framework and DBs with similar structures were asked to search for concepts within the same sources. RESULTS Concepts were mainly searched for in GP diagnoses and primary diagnoses of HOSP, but for some events DEATH (AMI, ARF, AS) and LAB (RHABD) were independently used by DBs having them. Resulting age-adjusted IRs vary as follows across DBs: 1-2/1,000PY (AMI), 3-7/10,000PY (UGIB), 2-12/100,000PY (AS), 2-17/100,000PY (BE), 1-8/100,000PY (RHABD), 3-49/100,000PY (ARF). CONCLUSIONS It is possible to describe event extractions from heterogeneous DBs in a common conceptual framework. Residual differences in IRs may be due either to differences in the underlying populations or to differences in the characteristics (structure, coding system) of the DBs.File | Dimensione | Formato | |
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