Signal detection of human papillomavirus vaccines using the Korea Adverse Events Reporting System database, between 2005 and 2016

Ran, Jung Young; Yang, Ji-Young; Lee, Ji-Ho; Kim, Hyejun; Choi, Junyeong; Shin, Ju‐Young

doi:10.1007/s11096-019-00881-9

Cited by 7 publications

(1 citation statement)

References 25 publications

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“…The ROR is the odds of a specific AE occurring in a patient exposed to a drug of interest divided by the odds of an AE specific to another drug. The IC is a logarithmic metric of the value, which is calculated by dividing the likelihood of drug use and a specific AE by the product of the probability of drug use and the probability of a particular AE occurring when drug use and specific AE occurring are independent (15). The equations and criteria for the two algorithms (14,16) are shown in Supplementary Table 1.…”

Section: Data Mining Algorithmmentioning

confidence: 99%

Lung Cancer Adverse Events Reports for Angiotensin-Converting Enzyme Inhibitors: Data Mining of the FDA Adverse Event Reporting System Database

et al. 2021

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Because of contradictory evidence from clinical trials, the association between angiotensin-converting enzyme inhibitors (ACEIs) and lung cancer needs further evaluation. As such, the current study is to assess disproportionate reporting of primary malignant lung cancer among reports for ACEIs submitted to the FDA adverse event reporting system utilizing a pharmacovigilance approach. We conducted a disproportionality analysis of primary malignant lung cancer adverse events associated with 10 ACEIs by calculating the reported odds ratios (ROR) and information component (IC) with 95% confidence intervals (CI). ROR was adjusted for sex, age, and reporting year by logistic regression analyses. From January 2004 to March 2020, a total of 622 cases of lung cancer adverse event reports were identified for ACEIs users. Significant disproportionate association was found for ACEIs as a drug class (ROR: 1.22, 95% CI: 1.13–1.32; IC: 0.28, 95% CI: 0.17–0.39. adjusted ROR: 1.23, 95% CI: 1.02–1.49). After stratification based on gender, a subset analysis suggested that female patients exhibited a significant disproportionate association, while male patients did not. Sensitivity analyses that limited the data by reporting region, comorbidity, and reporting year also showed similar trends. Statistical significant lung cancer signals were detected among patients who received ACEI, especially female patients. The disproportionality analysis of the FAERS database suggests mildly increased reporting of lung cancer among ACEI users. Further robust epidemiological studies are necessary to confirm this relationship.

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Section: Data Mining Algorithmmentioning

confidence: 99%

Lung Cancer Adverse Events Reports for Angiotensin-Converting Enzyme Inhibitors: Data Mining of the FDA Adverse Event Reporting System Database

et al. 2021

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Detecting early safety signals of infliximab using machine learning algorithms in the Korea adverse event reporting system

Lee

Kim

Bae

et al. 2022

Sci Rep

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There has been a growing attention on using machine learning (ML) in pharmacovigilance. This study aimed to investigate the utility of supervised ML algorithms on timely detection of safety signals in the Korea Adverse Event Reporting System (KAERS), using infliximab as a case drug, between 2009 and 2018. Input data set for ML training was constructed based on the drug label information and spontaneous reports in the KAERS. Gold standard dataset containing known AEs was randomly divided into the training and test sets. Two supervised ML algorithms (gradient boosting machine [GBM], random forest [RF]) were fitted with hyperparameters tuned on the training set by using a fivefold validation. Then, we stratified the KAERS data by calendar year to create 10 cumulative yearly datasets, in which ML algorithms were applied to detect five pre-specified AEs of infliximab identified during post-marketing surveillance. Four AEs were detected by both GBM and RF in the first year they appeared in the KAERS and earlier than they were updated in the drug label of infliximab. We further applied our models to data retrieved from the US Food and Drug Administration Adverse Event Reporting System repository and found that they outperformed existing disproportionality methods. Both GBM and RF demonstrated reliable performance in detecting early safety signals and showed promise for applying such approaches to pharmacovigilance.

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Application of tree-based machine learning classification methods to detect signals of fluoroquinolones using the Korea Adverse Event Reporting System (KAERS) database

Jang

Cha

Kim

et al. 2023

Expert Opinion on Drug Safety

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Signal detection of human papillomavirus vaccines using the Korea Adverse Events Reporting System database, between 2005 and 2016

Cited by 7 publications

References 25 publications

Lung Cancer Adverse Events Reports for Angiotensin-Converting Enzyme Inhibitors: Data Mining of the FDA Adverse Event Reporting System Database

Lung Cancer Adverse Events Reports for Angiotensin-Converting Enzyme Inhibitors: Data Mining of the FDA Adverse Event Reporting System Database

Detecting early safety signals of infliximab using machine learning algorithms in the Korea adverse event reporting system

Application of tree-based machine learning classification methods to detect signals of fluoroquinolones using the Korea Adverse Event Reporting System (KAERS) database

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