title: Pediatric drug safety signal detection: a new drug-event reference set for performance testing of data-mining methods and systems.
abstract: BACKGROUND
  Better evidence regarding drug safety in the pediatric population might be generated from existing data sources such as spontaneous reporting systems and electronic healthcare records. The Global Research in Paediatrics (GRiP)-Network of Excellence aims to develop pediatric-specific methods that can be applied to these data sources. A reference set of positive and negative drug-event associations is required.
  
  
  OBJECTIVE
  The aim of this study was to develop a pediatric-specific reference set of positive and negative drug-event associations.
  
  
  METHODS
  Considering user patterns and expert opinion, 16 drugs that are used in individuals aged 0-18 years were selected and evaluated against 16 events, regarded as important safety outcomes. A cross-table of unique drug-event pairs was created. Each pair was classified as potential positive or negative control based on information from the drug's Summary of Product Characteristics and Micromedex. If both information sources consistently listed the event as an adverse event, the combination was reviewed as potential positive control. If both did not, the combination was evaluated as potential negative control. Further evaluation was based on published literature.
  
  
  RESULTS
  Selected drugs include ibuprofen, flucloxacillin, domperidone, methylphenidate, montelukast, quinine, and cyproterone/ethinylestradiol. Selected events include bullous eruption, aplastic anemia, ventricular arrhythmia, sudden death, acute kidney injury, psychosis, and seizure. Altogether, 256 unique combinations were reviewed, yielding 37 positive (17 with evidence from the pediatric population and 20 with evidence from adults only) and 90 negative control pairs, with the remainder being unclassifiable.
  
  
  CONCLUSIONS
  We propose a drug-event reference set that can be used to compare different signal detection methods in the pediatric population.
authors: Department of Medical Informatics, Erasmus University Medical Center, PO Box 2040, 3000 CA, Rotterdam, The Netherlands, o.osokogu@erasmusmc.nl.
authors: Osemeke U
authors: OU
authors: Osokogu
authors: Federica
authors: F
authors: Fregonese
authors: Carmen
authors: C
authors: Ferrajolo
authors: Katia
authors: K
authors: Verhamme
authors: Sandra
authors: S
authors: de Bie
authors: Geert
authors: G
authors: 't Jong
authors: Mariana
authors: M
authors: Catapano
authors: Daniel
authors: D
authors: Weibel
authors: Florentia
authors: F
authors: Kaguelidou
authors: Wichor M
authors: WM
authors: Bramer
authors: Yingfen
authors: Y
authors: Hsia
authors: Ian C K
authors: IC
authors: Wong
authors: Madlen
authors: M
authors: Gazarian
authors: Jan
authors: J
authors: Bonhoeffer
authors: Miriam
authors: M
authors: Sturkenboom
mesh_terms: D000293:Adolescent; D016907:Adverse Drug Reaction Reporting Systems / Q000458:organization & administration / Q000706:statistics & numerical data; D002648:Child; D002675:Child, Preschool; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual / Q000706:statistics & numerical data; D064420:Drug-Related Side Effects and Adverse Reactions / Q000453:epidemiology / Q000209:etiology; D057286:Electronic Health Records / Q000706:statistics & numerical data; D006801:Humans; D007223:Infant; D010372:Pediatrics / Q000592:standards*