Cardiac Arrhythmia Related to Remdesivir in COVID-19

Guerra, Miguel Andres; Mendoza, Christian Julius P.; Kandhi, Sameer; Sun, Haozhe; Saad, Muhammad; Vittorio, Timothy J.

doi:10.29024/ijsm.64

Cited by 3 publications

(4 citation statements)

References 21 publications

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“…As a result of the observed better prognosis, the authors suggest that RDV should be continued, but with increased measures of patient surveillance. This observation of a more favorable outcome among those with bradycardia was also documented in the descriptive retrospective study by Rodriguez-Guerra et al, which found that patients with bradycardia had a shorter hospitalization (15.5 vs. 20.5 days) [25].…”

Section: Discussionsupporting

confidence: 67%

“…Several cohort studies using real-world data have investigated the relationship of RDV and bradyarrhythmia in COVID-19 patients but did not meet the inclusion criteria for the present study. A retrospective cohort study from BronxCare Hospital Center of 37 COVID-19 patients who received RDV therapy found that 43% (n = 16) developed bradycardia [ 25 ]. The investigators found a higher incidence of bradycardia in patients who received infusion for three or more days, which is consistent with other reports.…”

Section: Resultsmentioning

confidence: 99%

“…However, in a later retrospective study by the same authors, the frequency of bradycardia after RDV was found to be 16.8% in a cohort of 473 patients hospitalized with COV-ID-19 [27]. Characteristics of these observational studies and case series can be found in Table 3 [22][23][24][25][26][27].…”

Section: Narrative Review Of Literaturementioning

confidence: 99%

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Bradyarrhythmia After Remdesivir Administration in SARS-CoV-2: A Review of Literature and Meta-Analysis of Observational Studies in Epidemiology

et al. 2022

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Background The coronavirus disease 2019 (COVID-19) pandemic has required timely and informed decisions about treatment recommendations for clinical practice. One such drug used for the treatment of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is remdesivir (RDV), and several cardiac side effects have been reported including bradyarrhythmia (e.g., transient sinus bradycardia, symptomatic sinus bradycardia, complete atrioventricular (AV) block). The current study aimed to explore the association between RDV treatment for SARS-CoV-2 infection and the risk of bradyarrhythmia by presenting a review and meta-analysis of available published studies. Methods We presented a review of published literature and meta-analysis of observational studies (MOOSE). A narrative summary of RDV and bradyarrhythmia in COVID-19 infection and pooled analysis of observational studies that meet inclusion criteria was included. Studies included were published between January 2020 and December 2021 (identified through PubMed and ScienceDirect) and examined the association between treatment with RDV in SARS-CoV-2 infection and the risk of bradyarrhythmia. Results Three studies (two retrospective cohort studies and one prospective cohort study) met inclusion criteria for pooled meta-analysis of bradyarrhythmia and RDV therapy in COVID-19 patients. Treatment with RDV was associated with increased risk of sinus bradycardia when compared to controls (odds ratio 3.27 (95% confidence interval 1.90 - 5.63)). In the pooled analysis, the incidence of bradycardia in those that received RDV was 34.07% vs. 18.13% among controls. Thirteen case reports, three case series, and three disproportionality analyses were identified in review of the literature. Conclusion Data from real-world observational studies suggest that treating COVID-19 patients with RDV may predispose the development of bradyarrhythmia. The importance of this observation is of uncertain clinical significance as some observational studies have reported more favorable outcomes among patients who experience bradycardia after RDV therapy. The current study is limited by the small number of studies that could be meaningfully pooled and more well-designed cohort studies are needed to explore this association.

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Section: Discussionsupporting

confidence: 67%

Section: Resultsmentioning

confidence: 99%

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Bradyarrhythmia After Remdesivir Administration in SARS-CoV-2: A Review of Literature and Meta-Analysis of Observational Studies in Epidemiology

et al. 2022

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“…3c) and ivermectin increased over time since September 2021, and the turning point for remdesivir came at almost the same time of emerging studies showing that the drug is ineffective [31] and has severe side effects. [32][33][34] For ivermectin, public sentiment was associated with announcements of health authorities and celebrity effects. For example, the FDA denouncing the use of ivermectin for COVID-19 on August 29 th , 2021 had simultaneously increasing negative discussions.…”

Section: Changes In Sentiment For Five Most Frequent Mentioned Drugsmentioning

confidence: 99%

Characterizing Public Sentiments and Drug Interactions during COVID-19: A Pretrained Language Model and Network Analysis of Social Media Discourse

Li,

Hua,

Zhou

et al. 2024

Preprint

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ObjectiveHarnessing drug-related data posted on social media in real time can offer insights into how the pandemic impacts drug use and monitor misinformation. This study developed a natural language processing (NLP) pipeline tailored for the analysis of social media discourse on COVID-19 related drugs.MethodsThis study constructed a full pipeline for COVID-19 related drug tweet analysis, utilizing pre-trained language model-based NLP techniques as the backbone. This pipeline is architecturally composed of four core modules: named entity recognition (NER) and normalization to identify medical entities from relevant tweets and standardize them to uniform medication names, target sentiment analysis (TSA) to reveal sentiment polarities associated with the entities, topic modeling to understand underlying themes discussed by the population, and drug network analysis to potential adverse drug reactions (ADR) and drug-drug interactions (DDI). The pipeline was deployed to analyze tweets related to COVID-19 and drug therapies between February 1, 2020, and April 30, 2022.ResultsFrom a dataset comprising 2,124,757 relevant tweets sourced from 1,800,372 unique users, our NER model identified the top five most-discussed drugs: Ivermectin, Hydroxychloroquine, Remdesivir, Zinc, and Vitamin D. Sentiment and topic analysis revealed that public perception was predominantly shaped by celebrity endorsements, media hotspots, and governmental directives rather than empirical evidence of drug efficacy. Co-occurrence matrices and complex network analysis further identified emerging patterns of DDI and ADR that could be critical for public health surveillance like better safeguarding public safety in medicines use.ConclusionThis study evidences that an NLP-based pipeline can be a robust tool for large-scale public health monitoring and can offer valuable supplementary data for traditional epidemiological studies concerning DDI and ADR. The framework presented here aspires to serve as a cornerstone for future social media-based public health analytics.

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Characterizing Public Sentiments and Drug Interactions during COVID-19: A Pretrained Language Model and Network Analysis of Social Media Discourse (Preprint)

Li,

Hua,

Zhou

et al. 2024

Preprint

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BACKGROUND While COVID-19 pandemic has induced massive discussion of available medications on social media, traditional studies focused only on limited aspects such as public opinions and suffer from reporting biases, inefficiency and long collection times. OBJECTIVE Harnessing drug-related data posted on social media in real time can offer insights into how the pandemic impacts drug use and monitor misinformation. This study developed a natural language processing (NLP) pipeline tailored for the analysis of social media discourse on COVID-19 related drugs. METHODS This study constructed a full pipeline for COVID-19 related drug tweet analysis, utilizing pre-trained language model-based NLP techniques as the backbone. This pipeline is architecturally composed of four core modules: named entity recognition (NER) and normalization to identify medical entities from relevant tweets and standardize them to uniform medication names, target sentiment analysis (TSA) to reveal sentiment polarities associated with the entities, topic modeling to understand underlying themes discussed by the population, and drug network analysis to potential adverse drug reactions (ADR) and drug-drug interactions (DDI). The pipeline was deployed to analyze tweets related to COVID-19 and drug therapies between February 1, 2020, and April 30, 2022. RESULTS From a dataset comprising 2,124,757 relevant tweets sourced from 1,800,372 unique users, our NER model identified the top five most-discussed drugs: Ivermectin, Hydroxychloroquine, Remdesivir, Zinc, and Vitamin D. Sentiment and topic analysis revealed that public perception was predominantly shaped by celebrity endorsements, media hotspots, and governmental directives rather than empirical evidence of drug efficacy. Co-occurrence matrices and complex network analysis further identified emerging patterns of DDI and ADR that could be critical for public health surveillance like better safeguarding public safety in medicines use. CONCLUSIONS This study evidences that an NLP-based pipeline can be a robust tool for large-scale public health monitoring and can offer valuable supplementary data for traditional epidemiological studies concerning DDI and ADR. The framework presented here aspires to serve as a cornerstone for future social media-based public health analytics.

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Cardiac Arrhythmia Related to Remdesivir in COVID-19

Cited by 3 publications

References 21 publications

Bradyarrhythmia After Remdesivir Administration in SARS-CoV-2: A Review of Literature and Meta-Analysis of Observational Studies in Epidemiology

Bradyarrhythmia After Remdesivir Administration in SARS-CoV-2: A Review of Literature and Meta-Analysis of Observational Studies in Epidemiology

Characterizing Public Sentiments and Drug Interactions during COVID-19: A Pretrained Language Model and Network Analysis of Social Media Discourse

Characterizing Public Sentiments and Drug Interactions during COVID-19: A Pretrained Language Model and Network Analysis of Social Media Discourse (Preprint)

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