Abstract
BackgroundTemporal pattern discovery (TPD) is a method of signal detection using electronic healthcare databases, serving as an alternative to spontaneous reporting of adverse drug events. Here, we aimed to replicate and optimise a TPD approach previously used to assess temporal signals of statins with rhabdomyolysis (in The Health Improvement Network (THIN) database) by using the OHDSI tools designed for OMOP data sources.MethodsWe used data from the Truven MarketScan US Commercial Claims and the Commercial Claims and Encounters (CCAE). Using an extension of the OHDSI ICTemporalPatternDiscovery package, we ran positive and negative controls through four analytical settings and calculated sensitivity, specificity, bias and AUC to assess performance.ResultsSimilar to previous findings, we noted an increase in the Information Component (IC) for simvastatin and rhabdomyolysis following initial exposure and throughout the surveillance window. For example, the change in IC was 0.266 for the surveillance period of 1–30 days as compared to the control period of − 180 to − 1 days. Our modification of the existing OHDSI software allowed for faster queries and more efficient generation of chronographs.ConclusionOur OMOP replication matched the we can account forwe can account for of the original THIN study, only simvastatin had a signal. The TPD method is a useful signal detection tool that provides a single statistic on temporal association and a graphical depiction of the temporal pattern of the drug outcome combination. It remains unclear if the method works well for rare adverse events, but it has been shown to be a useful risk identification tool for longitudinal observational databases. Future work should compare the performance of TPD with other pharmacoepidemiology methods and mining techniques of signal detection. In addition, it would be worth investigating the relative TPD performance characteristics using a variety of observational data sources.
Highlights
Temporal pattern discovery (TPD) is a method of signal detection using electronic healthcare databases, serving as an alternative to spontaneous reporting of adverse drug events
We aimed to replicate the statins study conducted in the The Health Improvement Network (THIN) database using open-source software from Observational Health Data sciences and Informatics (OHDSI— ICTemporalPatternDiscovery [7]) on data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model [8]
Since we are unsure what the optimal design choices are for studying statins and rhabdomyolysis using TPD, we evaluated four different settings
Summary
Temporal pattern discovery (TPD) is a method of signal detection using electronic healthcare databases, serving as an alternative to spontaneous reporting of adverse drug events. Lavallee et al BMC Medical Informatics and Decision Making (2022) 22:31 reports-type analyses because the number of exposed patients is known, more information is available of potential risk factors and confounders, and it may even be possible to retrospectively assess missing information It allows the assessment of adverse event profiles by making comparisons with other drugs and across various time periods [2, 3]. Leveraging healthcare databases for drug safety signal detection requires evaluation of performance of existing methods to determine which is most appropriate for mining adverse drug reactions One such method is temporal pattern discovery (TPD), which was originally proposed by Noren et al in 2010 [4]. It is of interest to assess whether the TPD method can be used for detecting rare adverse drug events
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