ArticlePharmacoepidemiology and drug safety2018
A prediction model-based algorithm for computer-assisted database screening of adverse drug reactions in the Netherlands.
Article in Pharmacoepidemiology and drug safety, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Charting and Sidestepping the Pitfalls of Disproportionality Analysis.Drug safety · 2026Review
- Article
- Natural language processing for automated triage and prioritization of individual case safety reports for case-by-case assessment.Frontiers in drug safety and regulation · 2023Article
- Evaluation of four machine learning models for signal detection.Therapeutic advances in drug safety · 2023Article
- Adverse Drug Reactions of Intranasal Corticosteroids in the Netherlands: An Analysis from the Netherlands Pharmacovigilance Center.Drugs - real world outcomes · 2022Article
- Evaluating Drug Risk Using GAN and SMOTE Based on CFDA's Spontaneous Reporting Data.Journal of healthcare engineering · 2021Article
- Assessment of factors associated with completeness of spontaneous adverse event reporting in the United States: A comparison between consumer reports and healthcare professional reports.Journal of clinical pharmacy and therapeutics · 2020Article
- Towards Automating Adverse Event Review: A Prediction Model for Case Report Utility.Drug safety · 2020Article
- Future Directions of Pharmacovigilance Studies Using Electronic Medical Recording and Human Genetic Databases.Toxicological research · 2019Review
- Time to onset in statistical signal detection revisited: A follow-up study in long-term onset adverse drug reactions.Pharmacoepidemiology and drug safety · 2019Article
- A prediction model-based algorithm for computer-assisted database screening of adverse drug reactions in the Netherlands.Pharmacoepidemiology and drug safety · 2018Article
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4 authors.
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Abstract
purposeThe statistical screening of pharmacovigilance databases containing spontaneously reported adverse drug reactions (ADRs) is mainly based on disproportionality analysis. The aim of this study was to improve the efficiency of full database screening using a prediction model-based approach.
methodsA logistic regression-based prediction model containing 5 candidate predictors was developed and internally validated using the Summary of Product Characteristics as the gold standard for the outcome. All drug-ADR associations, with the exception of those related to vaccines, with a minimum of 3 reports formed the training data for the model. Performance was based on the area under the receiver operating characteristic curve (AUC). Results were compared with the current method of database screening based on the number of previously analyzed associations.
resultsA total of 25 026 unique drug-ADR associations formed the training data for the model. The final model contained all 5 candidate predictors (number of reports, disproportionality, reports from healthcare professionals, reports from marketing authorization holders, Naranjo score). The AUC for the full model was 0.740 (95% CI; 0.734-0.747). The internal validity was good based on the calibration curve and bootstrapping analysis (AUC after bootstrapping = 0.739). Compared with the old method, the AUC increased from 0.649 to 0.740, and the proportion of potential signals increased by approximately 50% (from 12.3% to 19.4%).
conclusionsA prediction model-based approach can be a useful tool to create priority-based listings for signal detection in databases consisting of spontaneous ADRs.
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