In order to ensure patient safety and comply with pharmacovigilance regulations, massive amounts of safety data must be collected and analyzed. seQure, a business unit of Arithmos, explains how automating pharmacovigilance systems can improve the identification of adverse events with real time results.
Updates on Pharmacovigilance Legislation
Regulatory authorities and governing bodies have been taking steps to improve patient safety. The European Union has put several modules into action under Good Vigilance Practices (GVP) in what it calls the new pharmacovigilance legislation. The changes are based on startling realizations, like the fact that over 200,000 patients die each year in the EU from adverse drug reactions (ADR) and a staggering number have died during post-market high tech medical device use. Most recently, the EMA made amendments to several modules in the new pharmacovigilance legislation including Module VIII which pertains to Post-Authorization Safety Studies. The changes are made to “clarify the link between the legislation on non-interventional post-authorisation safety studies (PASS)”. The guideline features a revised section on situations where certain adverse events are not collected in PASS, and requires companies to justify their approach to safety data collection. The motive behind these modifications is to better analyze and understand data from clinical studies—especially post-market studies and any risks to patients. One of the most significant modules is Module IX: Signal Management which includes information on the statistical analysis and systems used to detect signals. Safety signals may be detected using statistical analyses of databases taking into consideration the method used, that data should be assessed by a qualified person in a “timely” manner, the process should be documented and urgent action should be taken whenever a safety issue arises.
Signal Detection – what is the need?
Due to the increased regulation on safety data and the need to improve data quality, signal detection becomes very data intensive and difficult to manage. Clinical trial Sponsors are searching for a signal detection solution that can produce real time results with accurate signal identification at an affordable operational cost. Identifying adverse events requires laborious statistical analysis of aggregate data. Technology can facilitate the automation of signal detection to better manage the growing amount of safety data.
Automating Signal Detection: Combining Statistical Programming & Data Integration Technology
Combining the forces of statistical analysis, statistical programming and IT support with a commonly used platform like SAS is an ideal solution for automating data collection and analysis from multiple sources to implement an efficient signal detection prcoess. In order to automate the process, a system needs to be in place to pull and analyze data from ADR systems such as the Oracle Argus safety database or similar pharmacovigilance systems.
The statistician writing the Statistical Analysis Plan (SAP) must first identify, together with the pharmacovigilance unit, the algorithms to be implemented in the analysis considering that the method used should be appropriate to the dataset. Then, collaborating with IT and SAS programmers, the statistician must define the data mapping of the database fields. Consequently, the key aspects in this solution is data integration between safety databases, regulatory databases and patient data from external database sources as well as choosing the best statistical analysis method. This allows the statistical programming team to efficiently produce line listings and summary tabulations.
The identification of signal criteria and the implementation of standardized programs automates the signal detection process. The result of structured data means electronic regulatory submissions for Eudravigilance, required by the EU pharmacovigilance legislation, are produced quicker and with more accurate analysis.
This approach is applicable even to small companies or small amounts of data since automation can be done without a complex or expensive Business Intelligence platform.
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