Visualization of FDA Adverse Drug Reaction Reports: Development and Usability Study of the VisDrugs Web Server
Notice bibliographique
Résumé
Background: Adverse drug reactions (ADRs) are a major concern in drug safety, and the FDA Adverse Event Reporting System (FAERS) provides valuable ADR data. However, analyzing FAERS data is complex and requires bioinformatics expertise. Despite the vast amount of ADR data available, there is a lack of user-friendly tools that enable efficient visualization and comparison of ADRs for researchers and health care professionals. Objective: This study aimed to develop VisDrugs, a web-based platform that simplifies ADR visualization and comparison using FAERS data. The platform was designed to assist researchers and clinicians in assessing drug safety through interactive and interpretable graphical representations of ADR patterns. Methods: FAERS data were extracted in the American Standard Code for Information Interchange (ASCII) format, covering the period from Q3 (third quarter) 2014 to Q3 2024. About 2,700,000 reports from health care professionals, where only a single drug was implicated, were aggregated and processed using R for statistical analysis and visualization. The results are presented on a web-based platform for web-based analysis. The platform generates pie charts to visualize the most frequently reported ADRs, which are represented and analyzed using preferred terms based on the Medical Dictionary for Regulatory Activities (MedDRA) and forest plots illustrating reporting odds ratios (RORs) for these ADRs. Results: Using Paxlovid (COVID-19 treatment) and hydroxychloroquine (anti-malaria drug) as case studies, we benchmarked VisDrugs using reports for Paxlovid (n=16,708) and hydroxychloroquine (n=6150). Paxlovid was most frequently associated with "COVID-19" (ROR=47.26, 95% CI 45.22-49.40) and "dysgeusia" (ROR=59.65, 95% CI 55.56-64.03). Hydroxychloroquine showed strong associations with "retinal toxicity" (ROR=738.48, 95% CI 583.45-934.71), "retinopathy" (ROR=412.27, 95% CI 344.73-493.03), and "cardiotoxicity" (ROR=48.36, 95% CI 38.86-60.19). In subgroup analyses, female patients had significantly higher risks of retinopathy (3.24-fold) and cardiomyopathy (13.82-fold) compared to male patients, while patients aged >50 years had higher risks of retinopathy (4.20-fold) and cardiomyopathy (7.84-fold) compared to those ≤50 years. All differences were statistically significant (z test, P<.01). The majority of findings align with existing research, thereby validating the platform's utility. Clinical personnel have evaluated and refined the platform based on user feedback, confirming its efficacy in visualizing complex ADR data and identifying adverse effects across various drug subgroups. Conclusions: VisDrugs is a valuable tool for ADR analysis, offering an intuitive interface for exploring FAERS data. By visualizing and comparing ADRs, it helps researchers and health care providers assess drug safety efficiently. The platform's demographic analysis features add insights into ADR variations by age and gender, supporting drug safety research. In the future, the website will include more subgroup or condition filtering options, offering personalized ADR analysis and comparison features to meet the diverse research needs of users.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,022 | 0,045 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».