Skip to content
2000
Volume 15, Issue 2
  • ISSN: 1574-8863
  • E-ISSN: 2212-3911

Abstract

Background: Efficiency and accuracy for signal detection and evaluation activities are integral components of routine Pharmacovigilance (PV) practices. However, an Individual Case Safety Report (ICSR) may consist of a variety of confounders such as Concomitant Medications (CM), Past Medical History (PMH), and concurrent medical conditions that influence a safety officer’s evaluation of a potential Adverse Event (AE). Limited pharmacovigilance systems are currently available as a tool designed to enhance the efficiency and accuracy of signal detection and management. Objective: To introduce a systemic approach to make critical safety information readily available for users in order to discern possible interferences from CM and make informed decisions on the signal evaluation process – saving time while improving quality. Methods: Oracle Empirica Signal software was utilized to extract cases with CM that are Known Implicating Medications (KIM) for each AE according to public regulatory information from drug labels – FDA Structured Product Labeling (SPL) or EMA Summary of Product Characteristics (SPC). SAS Enterprise Guide was used to further process the data generated from Oracle Empirica Signal software. Results: For any target drug being evaluated for safety purposes, a KIM reference table can be generated, which summarizes all potential causality contributions from CMs. Conclusion: In addition to providing standalone KIM table as reference, adoption of this concept and automation may also be fully integrated into commercial signal detection and management software packages for easy use and accessibility and may even lead to reduced False Positive rate in signal detection within the PV space.

Loading

Article metrics loading...

/content/journals/cds/10.2174/1574886315666200224101011
2020-07-01
2025-09-03
Loading full text...

Full text loading...

/content/journals/cds/10.2174/1574886315666200224101011
Loading
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test