Developing a signal triage algorithm for Thai national adverse drug reaction database
Abstract
There was increasing adverse drug reaction (ADR) reports submitted to the Health Product Vigilance Center under the Thai FDA. In order to find some signals, the Thai Signal Detection Program was developed to identify and filter the potential signals, called signals of disproportionate reporting (SDRs). A large number of SDRs cannot be in-depth assessed by the Signal Detection Advisory Working Group (SDAWG) in time. The prioritized SDRs with concentrated in-depth assessment might help find some true signals. This preliminary study aimed at developing a signal triage algorithm that can prioritize SDRs to assign an in-depth assessment.
A multi-criteria decision analysis (MCDA) was chosen for proposing a triage algorithm by generating scores for priority rankings on clinical importance of SDRs. This study had three main steps. Key attributes for a triage decision was first identified and followed by the development of a signal triage algorithm. After that, the triage algorithm was tested by comparing the triage results of the proposed algorithm with triaging by experts.
Six factors were selected as key attributes, i.e. fatal outcome, serious ADRs, positive rechallenge, new drug, change in reporting and sources of reports. Four attributes used in the Thai Signal Detection Program were excluded, i.e. the drug-ADR associations, WHO-ART critical term, disproportionality and volume of reports. Six experts gave the weight for the six key attributes using their experiences and score criteria were set.
To test the proposed algorithm, systemic antibiotics with 86 SDRs in total were triaged by SDAWG and eight SDRs were further assessed. Six of them were consistent with the result of the proposed signal triage algorithm (75% agreement) and were was the top of the priority ranking. The other two SDRs were selected by SDAWG because of the highly-concerned, serious ADR and unfamiliar case. These could be because of the drug or ADRs for the current interest, level of being key attributes, comorbidity and concurrent medication use, and characteristics of experts' opinions.
The signal triage algorithm can enhance the efficiency of the triage method by experts, as it is systematic, transparent, timely, repeatable and also scientifically based. More research is necessary to evaluate and/or improve this triage algorithm.
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Hauben M, Bate A. (2009). Decision support methods for the detection of adverse events in post-marketing data. Drug Discov Today. 14(7-8):343-57. Epub 2009 Jan 31.
Heeley E, Waller P, Moseley J.Testing and implementing signal impact analysis in a regulatory setting: results of a pilot study. Drug Safety. 2005;28(10):901-6.
Levitan B, Yee CL, Russo L, Bayney R, Thomas AP, Klincewicz SL. A model for decision support in signal triage. Drug Safety.2008;31(9):727-35.
Food and Drug Administration, Thailand. Thai vigibase [Internet]. [cited 2012, December 17] Available from: http://www.fda.moph.go.th/vigilance.
Brian L. Strom (Editor) and Stephen E. Kimmel (Editor).Textbook of Pharmacoepidemiology. England: John Wiley & Sons, Ltd; 2005.
van Puijenbroek EP, van Grootheest K, Diemont WL, Leufkens HG, Egberts AC. Determinants of signal selection in a spontaneous reporting system for adverse drug reactions. Br J Clin Pharmacol.2001;52(5):579-86.
Stahl , M. , M. Lindquist , I. R. Edwards&E. G. Brown : Introducing triage logic as a new strategy for the detection of signals in the WHO Drug Monitoring Database. Pharmacoepidemiol. Drug Saf.2004;13, 355–363.
Waller P, Heeley E, Moseley J.Impact analysis of signals detected from spontaneous adverse drug reaction reporting data. Drug Safety. 2005;28(10):843-50.
Food and Drug Administration, Thailand. (2012). Minute of 2/2012 Working Group Meeting of Signal Detection Advisory Working Group, on September 21, 2012.
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