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A time-consuming step in signal detection of adverse reactions is the determination of whether an effect is already recorded in the product information. A database which can be searched for this information allows filtering or flagging reaction monitoring reports for signals related to unlisted reactions, thus improving considerably the efficiency of the signal detection process by restricting attention to those drugs and adverse event not already considered causally related. In research, it permits an evaluation of the effect of background restriction on the performance of statistical signal detection. An example of such database is the PROTECT Database of adverse drug reactions (EU SPC ADR database), a structured Excel database of all adverse drug reactions (ADRs) listed in Chapter 4.8 of the SmPC of medicinal products authorised in the European Union (EU) according to the centralised procedure, based exclusively on the Medical Dictionary for Regulatory Activities (MedDRA) terminology. Efforts to identify adverse drug reactions in regulatory documents using natural language processing are being explored and could help build and maintain such databases in the future. ADE Eval: An Evaluation of Text Processing Systems for Adverse Event Extraction from Drug Labels for Pharmacovigilance (Drug Saf. 2021;44(1):83-94) presents a systematic evaluation of different such approaches.
Other large observational databases such as claims and electronic health records databases are potentially useful as part of a larger signal detection and refinement strategy. Modern methods of pharmacovigilance: detecting adverse effects of drugs (Clin Med 2009;9(5):486-9) describes the strengths and weaknesses of different data sources for signal detection (spontaneous reports, electronic patient records and cohort-event monitoring). A number of studies have considered the use of observational data in electronic systems that complement existing methods of safety surveillance e.g. the PROTECT, OHDSI and Sentinel projects. Useful Interplay Between Spontaneous ADR Reports and Electronic Healthcare Records in Signal Detection (Drug Saf. 2015;38(12):1201-10) investigates the potential of using electronic health records alongside spontaneous reporting systems to improve signal detection, concluding that the former may have additional value for adverse events with a high background incidence. Toward multimodal signal detection of adverse drug reactions (J Biomed Inform. 2017;76:41-9) concludes that utilising and jointly analysing multiple data sources may lead to improved signal detection but development of this approach requires a deeper understanding the data sources used, additional benchmarks and further research on methods to generate and synthesise signals.
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