A model for decision support in signal triage.

Spontaneous reporting of suspected adverse drug reactions (ADRs) has long been a cornerstone of pharmacovigilance. With the increasingly large volume of ADRs, regulatory agencies, scientific/academic organizations and marketing authorization holders have applied statistical tools to assist in signal detection by identifying disproportionate reporting relationships in spontaneous reporting databases. These tools have generated large numbers of signals defined as drug-ADR reporting associations that meet specified statistical criteria. The challenge is to identify which signals are most likely to be medically important and therefore warrant priority for further investigation. Decisions related to signal triage are often complex and are based on a combination of clinical, epidemiological, pharmacological and regulatory criteria. There are no specific regulations, guidelines or standards that provide an objective basis for these decisions. This paper describes preliminary work to identify and quantify the specific factors that contribute to a decision to prioritize a specific drug-ADR combination for further in-depth review. We applied a tool from the discipline of decision analysis to systematically assess the important attributes of spontaneously reported ADRs. A model was created that integrates these assessments and produces rankings for the signals generated from quantitative signalling methods. Although more research is necessary to evaluate the performance of this model fully, preliminary results suggest that the use of formal decision analysis approaches to support signal triage can provide potential benefit and will help meet an important need.

Perspectives on the use of data mining in pharmaco-vigilance.

In the last 5 years, regulatory agencies and drug monitoring centres have been developing computerised data-mining methods to better identify reporting relationships in spontaneous reporting databases that could signal possible adverse drug reactions. At present, there are no guidelines or standards for the use of these methods in routine pharmaco-vigilance. In 2003, a group of statisticians, pharmaco-epidemiologists and pharmaco-vigilance professionals from the pharmaceutical industry and the US FDA formed the Pharmaceutical Research and Manufacturers of America-FDA Collaborative Working Group on Safety Evaluation Tools to review best practices for the use of these methods.In this paper, we provide an overview of: (i) the statistical and operational attributes of several currently used methods and their strengths and limitations; (ii) information about the characteristics of various postmarketing safety databases with which these tools can be deployed; (iii) analytical considerations for using safety data-mining methods and interpreting the results; and (iv) points to consider in integration of safety data mining with traditional pharmaco-vigilance methods. Perspectives from both the FDA and the industry are provided. Data mining is a potentially useful adjunct to traditional pharmaco-vigilance methods. The results of data mining should be viewed as hypothesis generating and should be evaluated in the context of other relevant data. The availability of a publicly accessible global safety database, which is updated on a frequent basis, would further enhance detection and communication about safety issues.

Tendon or joint disorders in children after treatment with fluoroquinolones or azithromycin.

BACKGROUND: Fluoroquinolones (FQs) have been infrequently used in children, largely because of concern that these agents can cause lesions of the cartilage in juvenile animals. However, the relevance of this laboratory observation to children treated with FQs is unknown. A retrospective, observational study was conducted to assess the incidence and relative risk of tendon or joint disorders (TJDs) that occur after use of selected FQs compared with azithromycin (AZ), a drug with no known effect on cartilage or tendons in humans or animals. METHODS: An automated database was searched to identify patients younger than 19 years who had been prescribed ofloxacin (OFX), levofloxacin, ciprofloxacin (CPX), or AZ. Potential cases of TJD occurring within 60 days of a prescription of one of the study drugs were identified based on assignment of a claims diagnosis consistent with a TJD within this period. Verified cases were identified by a blinded review of abstracts of medical records from subjects identified as potential cases. RESULTS: The incidence of verified TJD was 0.82% for OFX (13 of 1593) and CPX (37 of 4531) and was 0.78% for AZ (118 of 15,073). The relative risk of TJD for OFX and CPX compared with AZ was 1.04 (95% confidence interval, 0.55 to 1.84) and 1.04 (95% confidence interval, 0.72 to 1.51), respectively. The distributions of claims diagnoses and time to onset of TJD were comparable for all groups. The most frequently reported category of TJD involved the joint followed by tendon, cartilage and gait disorder. CONCLUSIONS: In this observational study involving more than 6000 FQ-treated children, the incidence of TJD associated with selected FQ use in children was <1% and was comparable with that of the reference group, children treated with AZ.