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

Hauben, Manfred; Hung, Eric; Chen, Yan
PURPOSE/OBJECTIVE:) in a large spontaneous reporting database. METHODS:, we repeated the analyses with an additional year of data to gauge temporal stability of our findings. FINDINGS/RESULTS:persisting after updating the analysis with an additional year of data. IMPLICATIONS/CONCLUSIONS: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.
PMID: 37919188
ISSN: 1879-114x
CID: 5625832

More Extreme Duplication in FAERS Detected by Literature Reference Normalization and Fuzzy String Matching

Hung, Eric; Hauben, Manfred; Essex, Henry; Zou, Chen; Bright, Steve
PURPOSE/OBJECTIVE:Literature reports of Adverse Drug Events can replicate across multiple companies, resulting in extreme duplication (defined as a majority of reports being duplicates) and can escape legacy duplicate detection algorithms routinely deployed on the FAERS database. Literature Reference field, added to in 2014, could potentially be utilized to identify replicated reports. FAERS does not enforce adherence to the Vancouver referencing convention, thus the same article may be referenced differently leading to duplication. The objective of this analysis is to determine if variations of the same literature references observed in FAERS can be resolved with text normalization and fuzzy string matching. METHODS:We normalized the literature references recorded in the FAERS database through the first quarter of 2021 with a rule-based algorithm so that they better conform to the Vancouver Convention. Levenshtein distance is then utilized to merge sufficiently similar normalized literature references together. RESULTS:Normalization of literature references increases the percentage that can be parsed into Author, Title, and Journal from 61.74% to 93.93%. We observe that about 98% of pairs within groups do have a Levenshtein similarity of the title above the threshold. The extreme duplication ranged from 66% to 87% with a median of 72% of reports being duplicates and often involved addictovigilance scenarios. CONCLUSIONS:We have shown that these normalized references can be merged via fuzzy string matching to improve enumeration of all the ICSRs that refer to the same article. Inclusion of the PubMed ID and adherence to the Vancouver Convention could facilitate identification of duplicates in the FAERS dataset. Awareness of this phenomenon may improve disproportionality analysis, especially in areas such as addictovigilance.
PMID: 36369928
ISSN: 1099-1557
CID: 5357692

Correction to: Individual Case Safety Report Replication: An Analysis of Case Reporting Transmission Networks

van Stekelenborg, John; Kara, Vijay; Haack, Roman; Vogel, Ulrich; Garg, Anju; Krupp, Markus; Gofman, Kate; Dreyfus, Brian; Hauben, Manfred; Bate, Andrew
PMID: 36689132
ISSN: 1179-1942
CID: 5419482

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

Hauben, Manfred; Hung, Eric; Chen, Yan
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.
SCOPUS:85175432790
ISSN: 0149-2918
CID: 5616542

Individual Case Safety Report Replication: An Analysis of Case Reporting Transmission Networks

van Stekelenborg, John; Kara, Vijay; Haack, Roman; Vogel, Ulrich; Garg, Anju; Krupp, Markus; Gofman, Kate; Dreyfus, Brian; Hauben, Manfred; Bate, Andrew
INTRODUCTION:The basis of pharmacovigilance is provided by the exchange of Individual Case Safety Reports (ICSRs) between the recipient of the original report and other interested parties, which include Marketing Authorization Holders (MAHs) and Health Authorities (HAs). Different regulators have different reporting requirements for report transmission. This results in replication of each ICSR that will exist in multiple locations. Adding in the fact that each case will go through multiple versions, different recipients may receive different case versions at different times, potentially influencing patient safety decisions and potentially amplifying or obscuring safety signals inappropriately. OBJECTIVE:The present study aimed to investigate the magnitude of replication, the variability among recipients, and the subsequent divergence across recipients of ICSRs. METHODS:Seven participating TransCelerate Member Companies (MCs) queried their respective safety databases covering a 3-year period and provided aggregate ICSR submission statistics for expedited safety reports to an independent project manager. As measured in the US Food and Drug Administration (FDA)'s Adverse Event Reporting System (FAERS), ICSR volume for these seven MCs makes up approximately 20% of the total case volume. Aggregate metrics were calculated from the company data, specifically: (i) number of ICSR transmissions, (ii) average number of recipients (ANR) per case version transmitted, (iii) a submission selectivity metric, which measures the percentage of recipients not having received all sequential case version numbers, and (iv) percent of common ISCRs residing in two or more MAH databases. RESULTS:The analysis reflects 2,539,802 case versions, distributed through 7,602,678 submissions. The overall mean replication rate is 3.0 submissions per case version. The distribution of the ANR replication measure was observed to be very long-tailed, with a significant fraction of case versions (~ 12.4% of all transmissions) being sent to ten or more HA recipients. Replication is higher than average for serious, unlisted, and literature cases, ranging from 3.5 to 6.1 submissions per version. Within the subset of ICSR versions sent to three recipients, a significant degree of variability in the actual recipients (i.e., HAs) was observed, indicating that there is not one single combination of the same three HAs predominantly receiving an ICSR. Submission selectivity increases with the case version. For case version 6, the range of the submission selectivity for the MAHs ranges from ~ 10% to over 50%, with a median of 30.2%. Within the participating MAHs, the percentage of cases that reside within at least two safety databases is approximately 2% across five databases. Further analysis of the data from three MAHs showed percentages of 13.4%, 15.6%, and 27.9% of ICSRs originating from HAs and any other partners such as other MAHs and other institutions. CONCLUSION:Replication of ICSRs and the variation of available safety information in recipient databases were quantified and shown to be substantial. Our work shows that multiple processors and medical reviewers will likely handle the same original ICSR as a result of replication. Aside from the obvious duplicate work, this phenomenon could conceivably lead to differing clinical assessments and decisions. If replication could be reduced or even eliminated, this would enable more focus on activities with a benefit for patient safety.
PMID: 36565374
ISSN: 1179-1942
CID: 5409452

Perspectives

Hauben, Manfred
PMCID:9835918
PMID: 36631244
ISSN: 1936-959x
CID: 5410462

Artificial intelligence in pharmacovigilance: Do we need explainability?

Hauben, Manfred
PMID: 35747938
ISSN: 1099-1557
CID: 5268802

Perspectives

Hauben, Manfred
PMID: 36574321
ISSN: 1936-959x
CID: 5409572

Perspectives

Hauben, Manfred
PMID: 36344221
ISSN: 1936-959x
CID: 5357092

A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation

Stafford, Imogen S; Gosink, Mark M; Mossotto, Enrico; Ennis, Sarah; Hauben, Manfred
BACKGROUND:Inflammatory bowel disease (IBD) is a gastrointestinal chronic disease with an unpredictable disease course. Computational methods such as machine learning (ML) have the potential to stratify IBD patients for the provision of individualized care. The use of ML methods for IBD was surveyed, with an additional focus on how the field has changed over time. METHODS:On May 6, 2021, a systematic review was conducted through a search of MEDLINE and Embase databases, with the search structure ("machine learning" OR "artificial intelligence") AND ("Crohn* Disease" OR "Ulcerative Colitis" OR "Inflammatory Bowel Disease"). Exclusion criteria included studies not written in English, no human patient data, publication before 2001, studies that were not peer reviewed, nonautoimmune disease comorbidity research, and record types that were not primary research. RESULTS:Seventy-eight (of 409) records met the inclusion criteria. Random forest methods were most prevalent, and there was an increase in neural networks, mainly applied to imaging data sets. The main applications of ML to clinical tasks were diagnosis (18 of 78), disease course (22 of 78), and disease severity (16 of 78). The median sample size was 263. Clinical and microbiome-related data sets were most popular. Five percent of studies used an external data set after training and testing for additional model validation. DISCUSSION/CONCLUSIONS:Availability of longitudinal and deep phenotyping data could lead to better modeling. Machine learning pipelines that consider imbalanced data and that feature selection only on training data will generate more generalizable models. Machine learning models are increasingly being applied to more complex clinical tasks for specific phenotypes, indicating progress towards personalized medicine for IBD.
PMID: 35699597
ISSN: 1536-4844
CID: 5282582