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10.3389/fphar.2021.740707 http://scihub22266oqcxt.onion/10.3389/fphar.2021.740707 34955821!8694570!34955821     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=34955821&cmd=llinks): Failed to open stream: HTTP request failed! 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Development of a Network-Based Signal Detection Tool: The COVID-19 Adversome in the FDA Adverse Event Reporting System |pdf=|usr=}}{{34955821}} gab.com Text Development of a Network-Based Signal Detection Tool: The COVID-19 Adversome in the FDA Adverse Event Reporting System JO: Front Pharmacol http://www.kidney.de/pget.php?&tw=1&pmid=34955821 #FOAvip Introduction: The analysis of pharmacovigilance databases is crucial for the safety profiling of new and repurposed drugs, especially in the COVID-19 era. Traditional pharmacovigilance analyses-based on disproportionality approaches-cannot usually account for the complexity of spontaneous reports often with multiple concomitant drugs and events. We propose a network-based approach on co-reported events to help assessing disproportionalities and to effectively and timely identify disease-, comorbidity- and drug-related syndromes, especially in a rapidly changing low-resources environment such as that of COVID-19. Materials and Methods: Reports on medications administered for COVID-19 were extracted from the FDA Adverse Event Reporting System quarterly data (January-September 2020) and queried for disproportionalities (Reporting Odds Ratio corrected for multiple comparisons). A network (the Adversome) was estimated considering events as nodes and conditional co-reporting as links. Communities of significantly co-reported events were identified. All data and scripts employed are available in a public repository. Results: Among the 7,082 COVID-19 reports extracted, the seven most frequently suspected drugs (remdesivir, hydroxychloroquine, azithromycin, tocilizumab, lopinavir/ritonavir, sarilumab, and ethanol) have shown disproportionalities with 54 events. Of interest, myasthenia gravis with hydroxychloroquine, and cerebrovascular vein thrombosis with azithromycin. Automatic clustering identified 13 communities, including a methanol-related neurotoxicity associated with alcohol-based hand-sanitizers and a long QT/hepatotoxicity cluster associated with azithromycin, hydroxychloroquine and lopinavir-ritonavir interactions. Conclusion: Findings from the Adversome detect plausible new signals and iatrogenic syndromes. Our network approach complements traditional pharmacovigilance analyses, and may represent a more effective signal detection technique to guide clinical recommendations by regulators and specific follow-up confirmatory studies. Twit Text FOAVip Development of a Network-Based Signal Detection Tool: The COVID-19 Adversome in the FDA Adverse Event Reporting System ????Front Pharmacol http://www.kidney.de/pget.php?&tw=1&pmid=34955821 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34955821 #FOAvip English Wikipedia <ref>{{Cite pmid|34955821}}</ref> Development of a Network-Based Signal Detection Tool: The COVID-19 Adversome in the FDA Adverse Event Reporting System #MMPMID34955821 Fusaroli M; Raschi E; Gatti M; De Ponti F; Poluzzi E Front Pharmacol 2021[]; 12 (ä): 740707 PMID34955821show ga Introduction: The analysis of pharmacovigilance databases is crucial for the safety profiling of new and repurposed drugs, especially in the COVID-19 era. Traditional pharmacovigilance analyses-based on disproportionality approaches-cannot usually account for the complexity of spontaneous reports often with multiple concomitant drugs and events. We propose a network-based approach on co-reported events to help assessing disproportionalities and to effectively and timely identify disease-, comorbidity- and drug-related syndromes, especially in a rapidly changing low-resources environment such as that of COVID-19. Materials and Methods: Reports on medications administered for COVID-19 were extracted from the FDA Adverse Event Reporting System quarterly data (January-September 2020) and queried for disproportionalities (Reporting Odds Ratio corrected for multiple comparisons). A network (the Adversome) was estimated considering events as nodes and conditional co-reporting as links. Communities of significantly co-reported events were identified. All data and scripts employed are available in a public repository. Results: Among the 7,082 COVID-19 reports extracted, the seven most frequently suspected drugs (remdesivir, hydroxychloroquine, azithromycin, tocilizumab, lopinavir/ritonavir, sarilumab, and ethanol) have shown disproportionalities with 54 events. Of interest, myasthenia gravis with hydroxychloroquine, and cerebrovascular vein thrombosis with azithromycin. Automatic clustering identified 13 communities, including a methanol-related neurotoxicity associated with alcohol-based hand-sanitizers and a long QT/hepatotoxicity cluster associated with azithromycin, hydroxychloroquine and lopinavir-ritonavir interactions. Conclusion: Findings from the Adversome detect plausible new signals and iatrogenic syndromes. Our network approach complements traditional pharmacovigilance analyses, and may represent a more effective signal detection technique to guide clinical recommendations by regulators and specific follow-up confirmatory studies. äDevelopment of a Network-Based Signal Detection Tool: The COVID-19 Adv(epmc).pdf DeepDyve Pubget Overpricing