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A pipeline to extract drug-adverse event pairs from multiple data sources.

ABSTRACT: Pharmacovigilance aims to uncover and understand harmful side-effects of drugs, termed adverse events (AEs). Although the current process of pharmacovigilance is very systematic, the increasing amount of information available in specialized health-related websites as well as the exponential growth in medical literature presents a unique opportunity to supplement traditional adverse event gathering mechanisms with new-age ones.We present a semi-automated pipeline to extract associations between drugs and side effects from traditional structured adverse event databases, enhanced by potential drug-adverse event pairs mined from user-comments from health-related websites and MEDLINE abstracts. The pipeline was tested using a set of 12 drugs representative of two previous studies of adverse event extraction from health-related websites and MEDLINE abstracts.Testing the pipeline shows that mining non-traditional sources helps substantiate the adverse event databases. The non-traditional sources not only contain the known AEs, but also suggest some unreported AEs for drugs which can then be analyzed further.A semi-automated pipeline to extract the AE pairs from adverse event databases as well as potential AE pairs from non-traditional sources such as text from MEDLINE abstracts and user-comments from health-related websites is presented.

SUBMITTER: Yeleswarapu S

PROVIDER: S-EPMC3936866 | biostudies-other | 2014 Feb

REPOSITORIES: biostudies-other

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A pipeline to extract drug-adverse event pairs from multiple data sources.

Yeleswarapu Srijyothsna S Rao Aditya A Joseph Thomas T Saipradeep Vangala Govindakrishnan VG Srinivasan Rajgopal R

BMC medical informatics and decision making 20140224

<h4>Background</h4>Pharmacovigilance aims to uncover and understand harmful side-effects of drugs, termed adverse events (AEs). Although the current process of pharmacovigilance is very systematic, the increasing amount of information available in specialized health-related websites as well as the exponential growth in medical literature presents a unique opportunity to supplement traditional adverse event gathering mechanisms with new-age ones.<h4>Method</h4>We present a semi-automated pipeline ...[more]

PMID: 24559132

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Project description:Hypertension is a recognized comorbidity for COVID-19. The association of antihypertensive medications with outcomes in patients with hypertension is not fully described. However, angiotensin-converting enzyme 2 (ACE2), responsible for host entry of the novel coronavirus (SARS-CoV-2) leading to COVID-19, is postulated to be upregulated in patients taking angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs). Here, we evaluated the occurrence of pulmonary adverse drug events (ADEs) in patients with hypertension receiving ACEIs/ARBs to determine if disparities exist between individual drugs within the respective classes using data from the FDA Spontaneous Reporting Systems. For this purpose, we proposed the proportional reporting ratio to provide a statistical summary for the commonality of an ADE for a specific drug as compared to the entire database for drugs in the same or other classes. In addition, a statistical procedure, multiple logistic regression analysis, was employed to correct hidden confounders when causative covariates are underreported or untrusted to correct analyses of drug-ADE combinations. To date, analyses have been focused on drug classes rather than individual drugs which may have different ADE profiles depending on the underlying diseases present. A retrospective analysis of thirteen pulmonary ADEs showed significant differences associated with quinapril and trandolapril, compared to other ACEIs and ARBs. Specifically, quinapril and trandolapril were found to have a statistically significantly higher incidence of pulmonary ADEs compared with other ACEIs as well as ARBs (P < 0.0001) for group comparison (i.e., ACEIs vs. ARBs vs. quinapril vs. trandolapril) and (P ≤ 0.0007) for pairwise comparison (i.e., ACEIs vs. quinapril, ACEIs vs. trandolapril, ARBs vs. quinapril, or ARBs vs. trandolapril). This study suggests that specific members of the ACEI antihypertensive class (quinapril and trandolapril) have a significantly higher cluster of pulmonary ADEs.

Project description:BackgroundAntipsychotics (APs) have been associated with risk of torsade de Pointes (TdP). This has important public health implications. Therefore, (a) we exploited the public FDA Adverse Event Reporting System (FAERS) to characterize their torsadogenic profile; (b) we collected drug utilization data from 12 European Countries to assess the population exposure over the 2005-2010 period.MethodsFAERS data (2004-2010) were analyzed based on the following criteria: (1) ? 4 cases of TdP/QT abnormalities; (2) Significant Reporting Odds Ratio, ROR [Lower Limit of the 95% confidence interval>1], for TdP/QT abnormalities, adjusted and stratified (Arizona CERT drugs as effect modifiers); (3) ? 4 cases of ventricular arrhythmia/sudden cardiac death (VA/SCD); (4) Significant ROR for VA/SCD; (5) Significant ROR, combined by aggregating TdP/QT abnormalities with VA and SCD. Torsadogenic signals were characterized in terms of signal strength: from Group A (very strong torsadogenic signal: all criteria fulfilled) to group E (unclear/uncertain signal: only 2/5 criteria). Consumption data were retrieved from 12 European Countries and expressed as defined daily doses per 1,000 inhabitants per day (DID).ResultsThirty-five antipsychotics met at least one criterium: 9 agents were classified in Group A (amisulpride, chlorpromazine, clozapine, cyamemazine, haloperidol, olanzapine, quetiapine, risperidone, ziprasidone). In 2010, the overall exposure to antipsychotics varied from 5.94 DID (Estonia) to 13.99 (France, 2009). Considerable increment of Group A agents was found in several Countries (+3.47 in France): the exposure to olanzapine increased across all Countries (+1.84 in France) and peaked 2.96 in Norway; cyamemazine was typically used only in France (2.81 in 2009). Among Group B drugs, levomepromazine peaked 3.78 (Serbia); fluphenazine 1.61 (Slovenia).ConclusionsThis parallel approach through spontaneous reporting and drug utilization analyses highlighted drug- and Country-specific scenarios requiring potential regulatory consideration: levomepromazine (Serbia), fluphenazine (Slovenia), olanzapine (across Europe), cyamemazine (France). This synergy should be encouraged to support future pharmacovigilance activities.

Dataset Information

A pipeline to extract drug-adverse event pairs from multiple data sources.

Publications

A pipeline to extract drug-adverse event pairs from multiple data sources.

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