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Accuracy of an automated knowledge base for identifying drug adverse reactions.

ABSTRACT: INTRODUCTION:Drug safety researchers seek to know the degree of certainty with which a particular drug is associated with an adverse drug reaction. There are different sources of information used in pharmacovigilance to identify, evaluate, and disseminate medical product safety evidence including spontaneous reports, published peer-reviewed literature, and product labels. Automated data processing and classification using these evidence sources can greatly reduce the manual curation currently required to develop reference sets of positive and negative controls (i.e. drugs that cause adverse drug events and those that do not) to be used in drug safety research. METHODS:In this paper we explore a method for automatically aggregating disparate sources of information together into a single repository, developing a predictive model to classify drug-adverse event relationships, and applying those predictions to a real world problem of identifying negative controls for statistical method calibration. RESULTS:Our results showed high predictive accuracy for the models combining all available evidence, with an area under the receiver-operator curve of ?0.92 when tested on three manually generated lists of drugs and conditions that are known to either have or not have an association with an adverse drug event. CONCLUSIONS:Results from a pilot implementation of the method suggests that it is feasible to develop a scalable alternative to the time-and-resource-intensive, manual curation exercise previously applied to develop reference sets of positive and negative controls to be used in drug safety research.

SUBMITTER: Voss EA

PROVIDER: S-EPMC5316295 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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Accuracy of an automated knowledge base for identifying drug adverse reactions.

Voss E A EA Boyce R D RD Ryan P B PB van der Lei J J Rijnbeek P R PR Schuemie M J MJ

Journal of biomedical informatics 20161216

<h4>Introduction</h4>Drug safety researchers seek to know the degree of certainty with which a particular drug is associated with an adverse drug reaction. There are different sources of information used in pharmacovigilance to identify, evaluate, and disseminate medical product safety evidence including spontaneous reports, published peer-reviewed literature, and product labels. Automated data processing and classification using these evidence sources can greatly reduce the manual curation curr ...[more]

PMID: 27993747

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Project description:BackgroundNumerous observational studies suggest that preventable adverse drug reactions are a significant burden in healthcare, but no meta-analysis using a standardised definition for adverse drug reactions exists. The aim of the study was to estimate the percentage of patients with preventable adverse drug reactions and the preventability of adverse drug reactions in adult outpatients and inpatients.MethodsStudies were identified through searching Cochrane, CINAHL, EMBASE, IPA, Medline, PsycINFO and Web of Science in September 2010, and by hand searching the reference lists of identified papers. Original peer-reviewed research articles in English that defined adverse drug reactions according to WHO's or similar definition and assessed preventability were included. Disease or treatment specific studies were excluded. Meta-analysis on the percentage of patients with preventable adverse drug reactions and the preventability of adverse drug reactions was conducted.ResultsData were analysed from 16 original studies on outpatients with 48797 emergency visits or hospital admissions and from 8 studies involving 24128 inpatients. No studies in primary care were identified. Among adult outpatients, 2.0% (95% confidence interval (CI): 1.2-3.2%) had preventable adverse drug reactions and 52% (95% CI: 42-62%) of adverse drug reactions were preventable. Among inpatients, 1.6% (95% CI: 0.1-51%) had preventable adverse drug reactions and 45% (95% CI: 33-58%) of adverse drug reactions were preventable.ConclusionsThis meta-analysis corroborates that preventable adverse drug reactions are a significant burden to healthcare among adult outpatients. Among both outpatients and inpatients, approximately half of adverse drug reactions are preventable, demonstrating that further evidence on prevention strategies is required. The percentage of patients with preventable adverse drug reactions among inpatients and in primary care is largely unknown and should be investigated in future research.

Dataset Information

Accuracy of an automated knowledge base for identifying drug adverse reactions.

Publications

Accuracy of an automated knowledge base for identifying drug adverse reactions.

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