Methods for drug safety signal detection using routinely collected observational electronic health care data: a systematic review
https://doi.org/10.37489/2782-3784-myrwd-35
EDN: BNYASS
Abstract
Signal detection is a crucial step in the discovery of post-marketing adverse drug reactions. There is a growing interest in using routinely collected data to complement established spontaneous report analyses.
The aim. This work aims to systematically review the methods for drug safety signal detection using routinely collected healthcare data and their performance, both in general and for specific types of drugs and outcomes.
Metodology. We conducted a systematic review following the PRISMA guidelines, and registered a protocol in PROSPERO.
Results. The review included 101 articles, among which there were 39 methodological works, 25 performance assessment papers, and 24 observational studies. Methods included adaptations from those used with spontaneous reports, traditional epidemiological designs, methods specific to signal detection with real-world data. More recently, implementations of machine learning have been studied in the literature. Twenty-five studies evaluated method performances, 16 of them using the area under the curve (AUC) for a range of positive and negative controls as their main measure. Despite the likelihood that performance measurement could vary by drug-event pair, only 10 studies reported performance stratified by drugs and outcomes, in a heterogeneous manner. The replicability of the performance assessment results was limited due to lack of transparency in reporting and the lack of a gold standard reference set.
About the Authors
A. Sh. Motrinchuk
Federal State Budgetary Educational Institution of Higher Education "First St. Petersburg State Medical University named after Academician I.P. Pavlov" of the Ministry of Health of the Russian Federation
Russian Federation
Competing Interests:
Russian Federation
Motrinchuk Aiten S. — resident of the department of Clinical Pharmacology and Evidence-Based Medicine
St. Petersburg
Competing Interests:
The authors declare no conflict of interest.
O. A. Loginovskaya
Federal State Budgetary Educational Institution of Higher Education "First St. Petersburg State Medical University named after Academician I.P. Pavlov" of the Ministry of Health of the Russian Federation; Flex Databases LLC
Russian Federation
Competing Interests:
Russian Federation
Loginovskaya Olga A. — Director of Quality and Corporate Development Flex Databases; Assistant of the Department Of Clinical Pharmacology and Evidence-Based Medicine Pavlov First Saint Petersburg State Medical University
St. Petersburg
Competing Interests:
The authors declare no conflict of interest.
V. P. Kolbatov
Flex Databases LLC
Russian Federation
Competing Interests:
Russian Federation
Kolbatov Vladimir P. — The best signal specialist on Flex Databases
St. Petersburg
Competing Interests:
The authors declare no conflict of interest.
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Motrinchuk A.Sh., Loginovskaya O.A., Kolbatov V.P. Methods for drug safety signal detection using routinely collected observational electronic health care data: a systematic review. Real-World Data & Evidence. 2023;3(2):42-55. (In Russ.) https://doi.org/10.37489/2782-3784-myrwd-35. EDN: BNYASS
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