Potential Signals of COVID-19 as an Effect Modifier of Adverse Drug Reactions

Purpose: COVID-19 infection may interact with patients' medical conditions or medications. The objective of this study was to identify potential signals of effect modification of adverse drug reactions by statistical reporting interactions with COVID-19 infection (SRIsCOVID-19) in a large spontaneous reporting database. Methods: Data from the US Food and Drug Administration Adverse Event Reporting System through the second quarter of 2020 were used. Three-dimensional disproportionality analyses were conducted to identify drug-event-event (DEE) combinations, for which 1 of the events was COVID-19 infection, that were disproportionately reported. Effect size was quantified by an interaction signal score (INTSS) when COVID-19 was coreported as an adverse event or an indication (INTSSCOVID-19). An SRICOVID-19 exists when the calculated INTSSCOVID-19 is > 2. The analyses focused on pandemic-emergent SRIsCOVID-19. Screening for extreme duplication of cases was applied. To assess possible reporting artifacts during the early pandemic as an alternative explanation for pandemic-emergent SRICOVID-19, we repeated the analyses with an additional year of data to gauge temporal stability of our findings. Findings: When examining DEE interactions, 193 emergent SRIsCOVID-19 were identified, involving 44 drugs and 88 events, in addition to COVID-19 infection. Of the 44 drugs recorded, most were immunosuppressant or modulatory drugs, followed by antivirals. Seven drugs (eg, azithromycin) were identified in emergent SRIsCOVID-19 with preferred terms representing off-label use for prevention or treatment of COVID-19 infection. These drugs were in fact repurposed for COVID-19 treatment, supporting assay sensitivity of our procedure. Infections and infestations were the most frequently observed system organ class, followed by the general disorders and respiratory disorders. The psychiatric system organ class had only a few emergent SRIsCOVID-19 but contained the largest INTSSs. Less commonly reported manifestations of COVID-19 (e.g., skin events) were also identified. After excluding DEE combinations that were highly suggestive of extreme duplication, there remained a more robust set of emergent SRIsCOVID-19, which were supported by biological plausibility considerations. Our findings indicate a relative temporal stability, with > 90% of SRIsCOVID-19 persisting after updating the analysis with an additional year of data. Implications: The signals identified in the analyses could be critical in refining our understanding of the causality of spontaneously reported adverse drug events and thus informing the ongoing care of patients with COVID-19. Our findings also underscore the importance of undetected report duplication as a distorting influence on disproportionality analysis.