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

Drug Safety volume 36, pages 651–661 (2013)Cite this 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.

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Acknowledgements

We would like to thank Jesse A. Berlin, ScD, of the OMOP Scientific Advisory Board and Craig Cheetham, PharmD, of Kaiser Permanente Southern California for their critical and thorough review of the manuscript.

Financial support

The OMOP is funded by the Foundation for the National Institutes of Health through generous contributions from the following: Abbott, Amgen Inc., AstraZeneca, Bayer Healthcare Pharmaceuticals, Inc., Bristol-Myers Squibb, Eli Lilly & Company, GlaxoSmithKline, Johnson & Johnson, Lundbeck, Inc., Merck & Co., Inc., Novartis Pharmaceuticals Corporation, Pfizer Inc., Pharmaceutical Research Manufacturers of America (PhRMA), Roche, Sanofi, Schering-Plough Corporation, and Takeda.

Conflict of interest

Patrick Ryan and Paul Stang are employees of Janssen Research and Development, which provides funding for the OMOP, and have received OMOP-funded travel support. They both hold stock in multiple pharmaceutical companies. Aaron Katz and Judith Racoosin have no conflicts of interest to declare that are directly relevant to the content of this study.

Author information

Authors and Affiliations

  1. UNC Eshelman School of Pharmacy, Division of Pharmaceutical Policy and Outcomes, University of North Carolina at Chapel Hill, CB# 7573, Chapel Hill, NC, 27599, USA

    Aaron J. Katz

  2. Janssen Research and Development, Titusville, NJ, USA

    Patrick B. Ryan & Paul E. Stang

  3. Observational Medical Outcomes Partnership, Foundation for the National Institutes of Health, Bethesda, MD, USA

    Patrick B. Ryan, Judith A. Racoosin & Paul E. Stang

  4. Center for Drug Evaluation and Research, USFDA, Silver Spring, MD, USA

    Judith A. Racoosin

Authors
  1. Aaron J. Katz
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  2. Patrick B. Ryan
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  3. Judith A. Racoosin
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  4. Paul E. Stang
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Corresponding author

Correspondence to Aaron J. Katz.

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Katz, A.J., Ryan, P.B., Racoosin, J.A. et al. Assessment of Case Definitions for Identifying Acute Liver Injury in Large Observational Databases. Drug Saf 36, 651–661 (2013). https://doi.org/10.1007/s40264-013-0060-8

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Keywords

  • Index Date
  • Case Definition
  • Acute Liver Injury
  • Observational Database
  • Observational Medical Outcome Partnership