title: Drug Safety Monitoring in Children: Performance of Signal Detection Algorithms and Impact of Age Stratification.
abstract: Spontaneous reports of suspected adverse drug reactions (ADRs) can be analyzed to yield additional drug safety evidence for the pediatric population. Signal detection algorithms (SDAs) are required for these analyses; however, the performance of SDAs in the pediatric population specifically is unknown. We tested the performance of two SDAs on pediatric data from the US FDA Adverse Event Reporting System (FAERS) and investigated the impact of age stratification and age adjustment on the performance of SDAs.
  
  
  
  We tested the performance of two established SDAs: the proportional reporting ratio (PRR) and the empirical Bayes geometric mean (EBGM) on a pediatric dataset from FAERS (2004-2012). We compared the performance of the SDAs with a published pediatric-specific reference set by calculating diagnostic test-related statistics, including the area under the curve (AUC) of receiver operating characteristics. Impact of age stratification and age-adjustment on the performance of the SDAs was assessed. Age adjustment was performed by pooling (Mantel-Hanszel) stratum-specific estimates.
  
  
  
  A total of 115,674 pediatric reports (patients aged 0-18 years) comprising 893,587 drug-event combinations (DECs) were analysed. Crude values of the AUC were similar for both SDAs: 0.731 (PRR) and 0.745 (EBGM). Stratification unmasked four DECs, e.g., 'ibuprofen and thrombocytopenia'. Age adjustment did not improve performance.
  
  
  
  The performance of the two tested SDAs was similar in the pediatric population. Age adjustment does not improve performance and is therefore not recommended to be performed routinely. Stratification can reveal new associations, and therefore is recommended when either drug use is age-specific or when an age-specific risk is suspected.
authors.affiliation: Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, 3015 CN, The Netherlands.
authors.forename: Osemeke U
authors.initials: OU
authors.lastname: Osokogu
authors.affiliation: Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, 3015 CN, The Netherlands. c.dodd@erasmusmc.nl.
authors.forename: Caitlin
authors.initials: C
authors.lastname: Dodd
authors.affiliation: Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, 3015 CN, The Netherlands.|Dutch Medicines Evaluation Board, Utrecht, The Netherlands.
authors.forename: Alexandra
authors.initials: A
authors.lastname: Pacurariu
authors.affiliation: Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, 3015 CN, The Netherlands.|Department of Pediatric Pharmacology and Pharmacogenetics, Hôpital Robert Debré, APHP, Univ Paris 7-Diderot, Sorbonne Paris Cité, EA08, INSERM CIC1426, Paris, 75019, France.
authors.forename: Florentia
authors.initials: F
authors.lastname: Kaguelidou
authors.affiliation: Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, 3015 CN, The Netherlands.
authors.forename: Daniel
authors.initials: D
authors.lastname: Weibel
authors.affiliation: Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, 3015 CN, The Netherlands.
authors.forename: Miriam C J M
authors.initials: MC
authors.lastname: Sturkenboom
mesh_terms: D000293:Adolescent; D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data*; D017677:Age Distribution; D000367:Age Factors; D000465:Algorithms; D001499:Bayes Theorem; D002648:Child; D002675:Child, Preschool; D016208:Databases, Factual / Q000706:statistics & numerical data; D064420:Drug-Related Side Effects and Adverse Reactions / Q000453:epidemiology*; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D012372:ROC Curve; D014481:United States; D014486:United States Food and Drug Administration