Signal Detection of Potentially Drug-Induced Acute Liver Injury in Children Using a Multi-Country Healthcare Database Network
By C. Ferrajolo, P. Coloma, K. Verhamme, M. Schuemie, S. Bie, R. Gini, R. Herings, G. Mazzaglia, G. Picelli, C. Giaquinto and 9 more. 2014
33 citations recorded by the source corpus.
BackgroundData mining in spontaneous reporting databases has shown that drug-induced liver injury is infrequently reported in children.ObjectivesOur objectives were to (i) identify drugs potentially associated with acute liver injury (ALI) in children and adolescents using electronic healthcare record (EHR) data; and (ii) to evaluate the significance and novelty of these associations.MethodsWe identified potential cases of ALI during exposure to any prescribed/dispensed drug for individuals <18 years old from the EU-ADR network, which includes seven databases from three countries, covering the years 1996–2010. Several new methods for signal detection were applied to identify all statistically significant associations between drugs and ALI. A drug was considered statistically significantly associated with ALI, using all other time as a reference category, if the 95 % CI lower band of the relative risk was >1 and in the presence of at least three exposed cases of ALI. Potentially new signals were distinguished from already known associations concerning ALI (whether in adults and/or in the paediatric population) through manual review of published literature and drug product labels.ResultsThe study population comprised 4,838,146 individuals aged <18 years, who contributed an overall 25,575,132 person-years of follow-up. Within this population, we identified 1,015 potential cases of ALI. Overall, 20 positive drug–ALI associations were detected. The associations between ALI and domperidone, flunisolide and human insulin were considered as potentially new signals. Citalopram and cetirizine have been previously described as hepatotoxic in adults but not in children, while all remaining associations were already known in both adults and children.ConclusionsData mining of multiple EHR databases for signal detection confirmed known associations between ALI and several drugs, and identified some potentially new signals in children that require further investigation through formal epidemiologic studies. This study shows that EHRs may complement traditional spontaneous reporting systems for signal detection and strengthening.
Subfields: Emergency Medicine, Pharmacology, Toxicology
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- Zexian Zeng
- Yu Deng
- Xiaoyu Li
- Tristan Naumann
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- T. M. Herr
- Justin B. Starren
Citations in the corpus
- Natural Language Processing for EHR-Based Computational Phenotyping (2018, methodology): “explored other methods such as random forests [112], decision trees [100], [113], [131], [132], and the Longitudinal Gamma Poisson Shrinker [133], [134] for computational phe-”
- Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review (2017, methodology): “[29, 30] used Longitudinal Gamma Poisson Shrinker (LGPS) [83] to calculate incidence ratebased statistics and to identify drug-induced acute liver injury, where LGPS used person time rather than case counts for the estimation of the expected number of adverse events.”