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Drug Safety

, Volume 40, Issue 6, pp 465–474 | Cite as

Adverse Drug Event Causality Analysis (ADECA): A Process for Evaluating Evidence and Assigning Drugs to Risk Categories for Sudden Death

  • Raymond L. Woosley
  • Klaus Romero
  • Craig W. Heise
  • Tyler Gallo
  • Jared Tate
  • Raymond David Woosley
  • Sophie Ward
Leading Article

Abstract

Growing evidence indicates that many drugs have the ability to cause a potentially lethal cardiac arrhythmia, torsades de pointes (TdP). This necessitates the development of a compilation of drugs that have this potential toxicity. Such a list is helpful in identifying the etiology of TdP in patients taking multiple drugs and assists decision making by those caring for patients at high risk of TdP. The Arizona Center for Education and Research on Therapeutics (AZCERT) has developed a process to standardize the identification of drugs and place them in risk categories for their clinical ability to cause TdP and QT prolongation. AZCERT’s Adverse Drug Event Causality Analysis (ADECA) utilizes 16 types of data drawn from four sources to compile an open-source knowledge base, QTdrugs, which is maintained on the CredibleMeds.org website. Because the evidence for most drugs is incomplete, the ADECA process is used to place drugs into one of three categories that represent different levels of certainty: known TdP risk, possible TdP risk, and conditional TdP risk. Each category has strict evidentiary requirements for clinical evidence of TdP and/or QT prolongation. These are described in this paper. Because evidence can evolve over time, the ADECA process includes the continuous gathering and analysis of newly emerging evidence to revise the lists. The QTdrugs lists have proven to be a valued, readily available, commercial influence-free resource for healthcare providers, patients, researchers, and authors of consensus guidelines for the safe use of medicines.

Keywords

Risk Category Clinical Decision Support System Potential DDIs Conditional Risk Component Pair 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The authors gratefully acknowledge the contributions of Kristin Black and Marius Petriu to the CredibleMeds.org website and the QTdrugs knowledge base.

Compliance with Ethical Standards

Funding

The work reported here has been supported by the US Food and Drug Administration’s Safe Use Initiative, HHSF223201400189C, and a Grant from the Bert W. Martin Foundation.

Conflict of interest

The authors, Raymond L. Woosley, Klaus Romero, Craig W. Heise, Tyler Gallo, Jared Tate, Raymond David Woosley, and Sophie Ward have no conflicts of interest that are directly relevant to the content of this study.

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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.AZCERTOro ValleyUSA
  2. 2.Critical Path InstituteTucsonUSA
  3. 3.University of Arizona, College of Medicine-PhoenixPhoenixUSA
  4. 4.University of Arizona, College of PharmacyTucsonUSA
  5. 5.Mel and Enid Zuckerman College of Public HeathUniversity of ArizonaTucsonUSA
  6. 6.University of Arizona, College of NursingTucsonUSA

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