title: Using aggregated, de-identified electronic health record data for multivariate pharmacosurveillance: a case study of azathioprine.
abstract: OBJECTIVE
  To demonstrate the use of aggregated and de-identified electronic health record (EHR) data for multivariate post-marketing pharmacosurveillance in a case study of azathioprine (AZA).
  
  
  METHODS
  Using aggregated, standardized, normalized, and de-identified, population-level data from the Explore platform (Explorys, Inc.) we searched over 10 million individuals, of which 14,580 were prescribed AZA based on RxNorm drug orders. Based on logical observation identifiers names and codes (LOINC) and vital sign data, we examined the following side effects: anemia, cell lysis, fever, hepatotoxicity, hypertension, nephrotoxicity, neutropenia, and neutrophilia. Patients prescribed AZA were compared to patients prescribed one of 11 other anti-rheumatologic drugs to determine the relative risk of side effect pairs.
  
  
  RESULTS
  Compared to AZA case report trends, hepatotoxicity (marked by elevated transaminases or elevated bilirubin) did not occur as an isolated event more frequently in patients prescribed AZA than other anti-rheumatic agents. While neutropenia occurred in 24% of patients (RR 1.15, 95% CI 1.07-1.23), neutrophilia was also frequent (45%) and increased in patients prescribed AZA (RR 1.28, 95% CI 1.22-1.34). After constructing a pairwise side effect network, neutropenia had no dependencies. A reduced risk of neutropenia was found in patients with co-existing elevations in total bilirubin or liver transaminases, supporting classic clinical knowledge that agranulocytosis is a largely unpredictable phenomenon. Rounding errors propagated in the statistically de-identified datasets for cohorts as small as 40 patients only contributed marginally to the calculated risk.
  
  
  CONCLUSIONS
  Our work demonstrates that aggregated, standardized, normalized and de-identified population level EHR data can provide both sufficient insight and statistical power to detect potential patterns of medication side effect associations, serving as a multivariate and generalizable approach to post-marketing drug surveillance.
authors.affiliation: Center for Clinical Informatics Research and Education, The MetroHealth System, Case Western Reserve University, Cleveland, OH, United States; Center for Proteomics and Bioinformatics, The MetroHealth System, Case Western Reserve University, Cleveland, OH, United States. Electronic address: vishal.patel2@case.edu.
authors.forename: Vishal N
authors.initials: VN
authors.lastname: Patel
authors.affiliation: Center for Clinical Informatics Research and Education, The MetroHealth System, Case Western Reserve University, Cleveland, OH, United States; Departments of Information Services, The MetroHealth System, Case Western Reserve University, Cleveland, OH, United States; Department of Internal Medicine, The MetroHealth System, Case Western Reserve University, Cleveland, OH, United States; Department of Pediatrics, The MetroHealth System, Case Western Reserve University, Cleveland, OH, United States; Departments of Epidemiology and Biostatistics, The MetroHealth System, Case Western Reserve University, Cleveland, OH, United States.
authors.forename: David C
authors.initials: DC
authors.lastname: Kaelber
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems; D001379:Azathioprine / Q000009:adverse effects* / Q000627:therapeutic use; D057225:Data Mining / Q000379:methods*; D064420:Drug-Related Side Effects and Adverse Reactions / Q000453:epidemiology*; D057286:Electronic Health Records; D062665:Epidemiological Monitoring; D005334:Fever; D006801:Humans; D006973:Hypertension; D015994:Incidence; D063305:Organ Dysfunction Scores
chemical_list: D001379:Azathioprine
keywords: Azathioprine; Clinical research informatics; De-identified data; Pharmacosurveillance; Postmarketing drug surveillance; Systems biology