Drug Safety

, Volume 36, Issue 8, pp 651–661 | Cite as

Assessment of Case Definitions for Identifying Acute Liver Injury in Large Observational Databases

  • Aaron J. Katz
  • Patrick B. Ryan
  • Judith A. Racoosin
  • Paul E. Stang
Original Research Article

Abstract

Background

Determining the aetiology of acute liver injury (ALI) may be challenging to both clinicians and researchers. Observational research is particularly useful in studying rare medical outcomes such as ALI; however, case definitions for ALI in previous observational studies lack consistency and sensitivity. ALI is a clinically important condition with various aetiologies, including drug exposure.

Objective

The aim of this study was to evaluate four distinct case definitions for ALI across a diverse set of large observational databases, providing a better understanding of ALI prevalence and natural history.

Data Sources

Seven healthcare databases: GE Healthcare, MarketScan® Lab Database, Humana Inc., Partners HealthCare System, Regenstrief Institute, SDI Health (now IMS Health, Inc.), and the National Patient Care Database of the Veterans Health Administration.

Methods

We evaluated prevalence of ALI through the application of four distinct case definitions across seven observational healthcare databases. We described how laboratory and clinical characteristics of identified case populations varied across definitions and examined the prevalence of other hepatobiliary disorders among identified ALI cases that may decrease suspicion of drug-induced liver injury (DILI) in particular.

Results

This study demonstrated that increasing the restrictiveness of the case definition resulted in fewer cases, but greater prevalence of ALI clinical features. Considerable heterogeneity in the frequency of laboratory testing and results observed among cases meeting the most restrictive definition suggests that the clinical features, monitoring patterns and suspicion of ALI are highly variable among patients.

Conclusions

Creation of four distinct case definitions and application across a disparate set of observational databases resulted in significant variation in the prevalence of ALI. A greater understanding of the natural history of ALI through examination of electronic healthcare data can facilitate development of reliable and valid ALI case definitions that may enhance the ability to accurately identify associations between ALI and drug exposures. Considerable heterogeneity in laboratory values and frequency of laboratory testing among individuals meeting the criteria for ALI suggests that the evaluation of ALI is highly variable.

Supplementary material

40264_2013_60_MOESM1_ESM.pdf (87 kb)
Supplementary material 1 (PDF 87 kb)

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Aaron J. Katz
    • 1
  • Patrick B. Ryan
    • 2
    • 3
  • Judith A. Racoosin
    • 3
    • 4
  • Paul E. Stang
    • 2
    • 3
  1. 1.UNC Eshelman School of Pharmacy, Division of Pharmaceutical Policy and OutcomesUniversity of North Carolina at Chapel HillChapel HillUSA
  2. 2.Janssen Research and DevelopmentTitusvilleUSA
  3. 3.Observational Medical Outcomes PartnershipFoundation for the National Institutes of HealthBethesdaUSA
  4. 4.Center for Drug Evaluation and Research, USFDASilver SpringUSA

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