Dataset Information

Factors Affecting the Timing of Signal Detection of Adverse Drug Reactions.

ABSTRACT: We investigated factors affecting the timing of signal detection by comparing variations in reporting time of known and unknown ADRs after initial drug release in the USA. Data on adverse event reactions (AERs) submitted to U.S. FDA was used. Six ADRs associated with 6 drugs (rosuvastatin, aripiprazole, teriparatide, telithromycin, exenatide, varenicline) were investigated: Changes in the proportional reporting ratio, reporting odds ratio, and information component as indexes of signal detection were followed every 3 months after each drugs release, and the time for detection of signals was investigated. The time for the detection of signal to be detected after drug release in the USA was 2-10 months for known ADRs and 19-44 months for unknown ones. The median lag time for known and unknown ADRs was 99.0-122.5 days and 185.5-306.0 days, respectively. When the FDA released advisory information on rare but potentially serious health risks of an unknown ADR, the time lag to report from the onset of ADRs to the FDA was shorter. This study suggested that one factor affecting signal detection time is whether an ADR was known or unknown at release.

SUBMITTER: Hashiguchi M

PROVIDER: S-EPMC4671724 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Factors Affecting the Timing of Signal Detection of Adverse Drug Reactions.

Hashiguchi Masayuki M Imai Shungo S Uehara Keiko K Maruyama Junya J Shimizu Mikiko M Mochizuki Mayumi M

PloS one 20151207 12

We investigated factors affecting the timing of signal detection by comparing variations in reporting time of known and unknown ADRs after initial drug release in the USA. Data on adverse event reactions (AERs) submitted to U.S. FDA was used. Six ADRs associated with 6 drugs (rosuvastatin, aripiprazole, teriparatide, telithromycin, exenatide, varenicline) were investigated: Changes in the proportional reporting ratio, reporting odds ratio, and information component as indexes of signal detection ...[more]

PMID: 26641634

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Project description:BackgroundAdverse drug reactions (ADRs) occur in nearly all patients on chemotherapy, causing morbidity and therapy disruptions. Detection of such ADRs is limited in clinical trials, which are underpowered to detect rare events. Early recognition of ADRs in the postmarketing phase could substantially reduce morbidity and decrease societal costs. Internet community health forums provide a mechanism for individuals to discuss real-time health concerns and can enable computational detection of ADRs.ObjectiveThe goal of this study is to identify cutaneous ADR signals in social health networks and compare the frequency and timing of these ADRs to clinical reports in the literature.MethodsWe present a natural language processing-based, ADR signal-generation pipeline based on patient posts on Internet social health networks. We identified user posts from the Inspire health forums related to two chemotherapy classes: erlotinib, an epidermal growth factor receptor inhibitor, and nivolumab and pembrolizumab, immune checkpoint inhibitors. We extracted mentions of ADRs from unstructured content of patient posts. We then performed population-level association analyses and time-to-detection analyses.ResultsOur system detected cutaneous ADRs from patient reports with high precision (0.90) and at frequencies comparable to those documented in the literature but an average of 7 months ahead of their literature reporting. Known ADRs were associated with higher proportional reporting ratios compared to negative controls, demonstrating the robustness of our analyses. Our named entity recognition system achieved a 0.738 microaveraged F-measure in detecting ADR entities, not limited to cutaneous ADRs, in health forum posts. Additionally, we discovered the novel ADR of hypohidrosis reported by 23 patients in erlotinib-related posts; this ADR was absent from 15 years of literature on this medication and we recently reported the finding in a clinical oncology journal.ConclusionsSeveral hundred million patients report health concerns in social health networks, yet this information is markedly underutilized for pharmacosurveillance. We demonstrated the ability of a natural language processing-based signal-generation pipeline to accurately detect patient reports of ADRs months in advance of literature reporting and the robustness of statistical analyses to validate system detections. Our findings suggest the important contributions that social health network data can play in contributing to more comprehensive and timely pharmacovigilance.

Project description:IntroductionIn the treatment of the individual patient, a vision is to achieve the best possible balance between benefit and harm. Such tailored therapy relies upon the identification and characterisation of risk factors for adverse drug reactions. Information relevant to risk factor considerations can be captured in adverse event reports and could be utilised in statistical signal detection.ObjectiveThe aim of this study was to explore whether statistical screening of a broad range of risk factors within a global database of adverse event reports could uncover signals of risk groups for adverse drug reactions.MethodsSubgroup disproportionality analysis was applied to 15.4 million reports entered in VigiBase, the World Health Organization (WHO) global database of individual case safety reports, up to August 2017. Disproportionality analyses for drug-adverse event pairs were performed (1) in the full database and (2) across a range of subgroups defined by the following covariates: patient age, sex, body mass index, pregnancy, underlying condition, reporting country, and geographical region. Drug-adverse event pairs disproportionately over-reported in such subgroups, but not in the full database, and with a substantial difference between the two observed-to-expected ratios, were highlighted as statistical signals. These were further prioritised, through filtering and sorting, for clinical assessment, whereafter clinically relevant signals were communicated to the pharmacovigilance community and the public.ResultsAssessments were performed for 354 prioritised statistical signals, resulting in seven communicated signals describing previously unrecognised potential risk groups related to age (elderly), sex (male and female), body mass index (underweight and obese), and geographical region (Asia), all except one for already established adverse drug reactions. Important aspects considered in the assessments included an evaluation of the disproportionate over-reporting in the subgroup by reviewing alternative explanations and reporting patterns for similar drugs/adverse events/subgroups, and a search for plausible mechanisms to support the risk hypothesis.ConclusionsThis study reveals that it is possible to uncover signals of risk groups for adverse drug reactions through incorporation of broad risk factor screening into statistical signal detection in a global database of adverse event reports. Our findings suggest the potential to use such statistical methodologies for risk characterisation in subpopulations of concern.

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Factors Affecting the Timing of Signal Detection of Adverse Drug Reactions.

Publications

Factors Affecting the Timing of Signal Detection of Adverse Drug Reactions.

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