Dataset Information

Pattern of adverse events induced by aflibercept and ranibizumab: A nationwide spontaneous adverse event reporting database, 2007-2016.

ABSTRACT: Data regarding the safety of anti-vascular endothelial growth factor (anti-VEGF) treatment is limited.To compare the adverse events (AEs) induced by aflibercept and ranibizumab using a spontaneous reporting system and determine the signals.We used data from the Korea Institute of Drug Safety & Risk Management-Korea Adverse Event Reporting System Database (KIDS-KD), collected between 2007 and 2016. Differences in patient demographics, report type, reporter, causality, and serious-AEs between aflibercept and ranibizumab were compared. Metrics including proportional reporting ratio (PRR), reporting odds ratio (ROR), and information component (IC), were used to compare signals with the AEs on the drug labels in the United States of America and Korea. Logistic regression analysis was performed to identify AEs that are more likely to occur with drug use.A total of 32 aflibercept and 103 ranibizumab cases of AEs were identified. The proportion of AEs that were reported voluntarily was higher with aflibercept (50.5%) use than ranibizumab (4.9%), whereas the AEs reported by post-marketing surveillance were higher with ranibizumab (46.6%) use than aflibercept (31.3%). The percentage of AEs in patients >60 years old, reports by consumers, and the ratio of SAEs to AEs associated with aflibercept (84. %, 9.4%, and 75.0%, respectively) were higher than those of ranibizumab (77.7%, 1.9%, and 19.4%, respectively). The number of newly detected AEs after aflibercept and ranibizumab treatment was 3 and 8, respectively. Among these, conjunctivitis and medicine ineffective were not included on the aflibercept and ranibizumab labels, respectively. Endophthalmitis (OR 6.96, 95% CI 2.74-17.73) was more likely to be reported in patients with aflibercept than in patients without aflibercept, whereas medicine ineffective (OR 18.49, 95% CI 2.39-143.29) and retinal disorder (OR 7.03, 95% CI 1.60-30.96) were more likely to be reported in patients with ranibizumab than in patients without ranibizumab.New signals have been identified for aflibercept and ranibizumab. Further research is necessary to evaluate the causality of AEs that were detected as signals in this study.

SUBMITTER: Ha D

PROVIDER: S-EPMC6831246 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Publications

Pattern of adverse events induced by aflibercept and ranibizumab: A nationwide spontaneous adverse event reporting database, 2007-2016.

Ha Dongmun D Choi So-Ra SR Kwon Yongmin Y Park Han-Heui HH Shin Ju-Young JY

Medicine 20190801 33

Data regarding the safety of anti-vascular endothelial growth factor (anti-VEGF) treatment is limited.To compare the adverse events (AEs) induced by aflibercept and ranibizumab using a spontaneous reporting system and determine the signals.We used data from the Korea Institute of Drug Safety & Risk Management-Korea Adverse Event Reporting System Database (KIDS-KD), collected between 2007 and 2016. Differences in patient demographics, report type, reporter, causality, and serious-AEs between afli ...[more]

PMID: 31415382

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Project description:BackgroundSpontaneous reporting systems (SRSs) have been increasingly established to collect adverse drug events for fostering adverse drug reaction (ADR) detection and analysis research. SRS data contain personal information, and so their publication requires data anonymization to prevent the disclosure of individuals' privacy. We have previously proposed a privacy model called MS(k, θ*)-bounding and the associated MS-Anonymization algorithm to fulfill the anonymization of SRS data. In the real world, the SRS data usually are released periodically (eg, FDA Adverse Event Reporting System [FAERS]) to accommodate newly collected adverse drug events. Different anonymized releases of SRS data available to the attacker may thwart our single-release-focus method, that is, MS(k, θ*)-bounding.ObjectiveWe investigate the privacy threat caused by periodical releases of SRS data and propose anonymization methods to prevent the disclosure of personal privacy information while maintaining the utility of published data.MethodsWe identify potential attacks on periodical releases of SRS data, namely, BFL-attacks, mainly caused by follow-up cases. We present a new privacy model called PPMS(k, θ*)-bounding, and propose the associated PPMS-Anonymization algorithm and 2 improvements: PPMS+-Anonymization and PPMS++-Anonymization. Empirical evaluations were performed using 32 selected FAERS quarter data sets from 2004Q1 to 2011Q4. The performance of the proposed versions of PPMS-Anonymization was inspected against MS-Anonymization from some aspects, including data distortion, measured by normalized information loss; privacy risk of anonymized data, measured by dangerous identity ratio and dangerous sensitivity ratio; and data utility, measured by the bias of signal counting and strength (proportional reporting ratio).ResultsThe best version of PPMS-Anonymization, PPMS++-Anonymization, achieves nearly the same quality as MS-Anonymization in both privacy protection and data utility. Overall, PPMS++-Anonymization ensures zero privacy risk on record and attribute linkage, and exhibits 51%-78% and 59%-82% improvements on information loss over PPMS+-Anonymization and PPMS-Anonymization, respectively, and significantly reduces the bias of ADR signal.ConclusionsThe proposed PPMS(k, θ*)-bounding model and PPMS-Anonymization algorithm are effective in anonymizing SRS data sets in the periodical data publishing scenario, preventing the series of releases from disclosing personal sensitive information caused by BFL-attacks while maintaining the data utility for ADR signal detection.

Dataset Information

Pattern of adverse events induced by aflibercept and ranibizumab: A nationwide spontaneous adverse event reporting database, 2007-2016.

Publications

Pattern of adverse events induced by aflibercept and ranibizumab: A nationwide spontaneous adverse event reporting database, 2007-2016.

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