title: A Method to Combine Signals from Spontaneous Reporting Systems and Observational Healthcare Data to Detect Adverse Drug Reactions.
abstract: BACKGROUND
  Observational healthcare data contain information useful for hastening detection of adverse drug reactions (ADRs) that may be missed by using data in spontaneous reporting systems (SRSs) alone. There are only several papers describing methods that integrate evidence from healthcare databases and SRSs. We propose a methodology that combines ADR signals from these two sources.
  
  
  OBJECTIVE
  The aim of this study was to investigate whether the proposed method would result in more accurate ADR detection than methods using SRSs or healthcare data alone.
  
  
  METHODS
  We applied the method to four clinically serious ADRs, and evaluated it using three experiments that involve combining an SRS with a single facility small-scale electronic health record (EHR), a larger scale network-based EHR, and a much larger scale healthcare claims database. The evaluation used a reference standard comprising 165 positive and 234 negative drug-ADR pairs.
  
  
  METHODS
  Area under the receiver operator characteristics curve (AUC) was computed to measure performance.
  
  
  RESULTS
  There was no improvement in the AUC when the SRS and small-scale HER were combined. The AUC of the combined SRS and large-scale EHR was 0.82 whereas it was 0.76 for each of the individual systems. Similarly, the AUC of the combined SRS and claims system was 0.82 whereas it was 0.76 and 0.78, respectively, for the individual systems.
  
  
  CONCLUSIONS
  The proposed method resulted in a significant improvement in the accuracy of ADR detection when the resources used for combining had sufficient amounts of data, demonstrating that the method could integrate evidence from multiple sources and serve as a tool in actual pharmacovigilance practice.
authors.affiliation: Department of Biomedical Informatics, Columbia University Medical Center, 622 W. 168th Street, Presbyterian Building 20th Floor, New York, NY, 10032, USA. yl2565@columbia.edu.
authors.forename: Ying
authors.initials: Y
authors.lastname: Li
authors.affiliation: Department of Biomedical Informatics, Columbia University Medical Center, 622 W. 168th Street, Presbyterian Building 20th Floor, New York, NY, 10032, USA.|Janssen Research and Development, 1125 Trenton Harbourton Rd, Titusville, NJ, 08560, USA.|Observational Health Data Sciences and Informatics (OHDSI), New York, NY, 10032, USA.
authors.forename: Patrick B
authors.initials: PB
authors.lastname: Ryan
authors.affiliation: Department of Biostatistics, Columbia University, New York, NY, 10032, USA.
authors.forename: Ying
authors.initials: Y
authors.lastname: Wei
authors.affiliation: Department of Biomedical Informatics, Columbia University Medical Center, 622 W. 168th Street, Presbyterian Building 20th Floor, New York, NY, 10032, USA.
authors.forename: Carol
authors.initials: C
authors.lastname: Friedman
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems; D016208:Databases, Factual; D003695:Delivery of Health Care / Q000379:methods*; D064420:Drug-Related Side Effects and Adverse Reactions / Q000453:epidemiology*; D057286:Electronic Health Records; D005260:Female; D006282:Health Personnel; D006801:Humans; D008297:Male; D060735:Pharmacovigilance