Mining multi-item drug adverse effect associations in spontaneous reporting systems

<p>Abstract</p> <p>Background</p> <p>Multi-item adverse drug event (ADE) associations are associations relating multiple drugs to possibly multiple adverse events. The current standard in pharmacovigilance is bivariate association analysis, where each single drug-adverse effect combination is studied separately. The importance and difficulty in the detection of multi-item ADE associations was noted in several prominent pharmacovigilance studies. In this paper we examine the application of a well established data mining method known as association rule mining, which we tailored to the above problem, and demonstrate its value. The method was applied to the FDAs spontaneous adverse event reporting system (AERS) with minimal restrictions and expectations on its output, an experiment that has not been previously done on the scale and generality proposed in this work.</p> <p>Results</p> <p>Based on a set of 162,744 reports of suspected ADEs reported to AERS and published in the year 2008, our method identified 1167 multi-item ADE associations. A taxonomy that characterizes the associations was developed based on a representative sample. A significant number (67% of the total) of potential multi-item ADE associations identified were characterized and clinically validated by a domain expert as previously recognized ADE associations. Several potentially novel ADEs were also identified. A smaller proportion (4%) of associations were characterized and validated as known drug-drug interactions.</p> <p>Conclusions</p> <p>Our findings demonstrate that multi-item ADEs are present and can be extracted from the FDA’s adverse effect reporting system using our methodology, suggesting that our method is a valid approach for the initial identification of multi-item ADEs. The study also revealed several limitations and challenges that can be attributed to both the method and quality of data.</p

Learning signals of adverse drug-drug interactions from the unstructured text of electronic health records.

Drug-drug interactions (DDI) account for 30% of all adverse drug reactions, which are the fourth leading cause of death in the US. Current methods for post marketing surveillance primarily use spontaneous reporting systems for learning DDI signals and validate their signals using the structured portions of Electronic Health Records (EHRs). We demonstrate a fast, annotation-based approach, which uses standard odds ratios for identifying signals of DDIs from the textual portion of EHRs directly and which, to our knowledge, is the first effort of its kind. We developed a gold standard of 1,120 DDIs spanning 14 adverse events and 1,164 drugs. Our evaluations on this gold standard using millions of clinical notes from the Stanford Hospital confirm that identifying DDI signals from clinical text is feasible (AUROC=81.5%). We conclude that the text in EHRs contain valuable information for learning DDI signals and has enormous utility in drug surveillance and clinical decision support

Association between gastric acid suppressants and Clostridium difficile colitis and community-acquired pneumonia: analysis using pharmacovigilance tools

SummaryObjectiveRecent epidemiological studies identifying an association between some classes of gastric acid suppressants and Clostridium difficile colitis and community-acquired pneumonia prompted our analysis. Our objective was to retrospectively apply data mining algorithms (DMAs) to the Food and Drug Administration (FDA) drug safety database to see if they might have directed/redirected attention to the reported association of gastric acid suppressive drugs with C. difficile colitis and community-acquired pneumonia, prior to the published epidemiological findings that supported the association.DesignTwo statistical DMAs, proportional reporting ratios (PRRs) and multi-item gamma Poisson shrinker (MGPS), were applied to a spontaneous reporting system (SRS) database to identify signals of disproportionate reporting (SDRs).ResultsSDRs related to community-acquired pneumonia were observed for two proton pump inhibitors (lansoprazole and omeprazole), two H2 antagonists (famotidine and roxatidine), and one antacid (magnesium silicate hydroxide). For C. difficile colitis, an SDR was generated for one proton pump inhibitor (lansoprazole).ConclusionsAlthough our analysis suggests that there may be an association between the SDRs using SRS data and the epidemiological findings, these results may not have alerted public health professionals in advance of published studies to an association between proton pump inhibitors/gastric acid suppressants and C. difficile colitis or community-acquired pneumonia. However, the analysis reveals the potential utility of DMAs to direct attention to more subtle indirect drug adverse effects in SRS databases that as yet are often identified from epidemiological investigations

Data Mining Techniques in Pharmacovigilance: Analysis of the Publicly Accessible FDA Adverse Event Reporting System (AERS)

Pharmacovigilance is a clinically oriented discipline, which may guide appropriate drug use through a balanced assessment of drug safety. Although much has been done in recent years, efforts are needed to expand the border of pharmacovigilance. We have provided insight into the FDA_Adverse Events Reporting Systems (FDA_AERS), a worldwide publicly available pharmacovigilance archive, to exemplify how to address major methodological issues. We believe that fostering discussion among researchers will increase transparency and facilitate definition of the most reliable approaches. By virtue of its large population coverage and free availability, the FDA_AERS has the potential to pave the way to a new way of looking to signal detection in PhV. Our key messages are: (1) before applying statistical tools (i.e., Data Mining Approaches - DMAs) to pharmacovigilance database for signal detection, all aspects related to data quality should be considered (e.g., drug mapping, missing data and duplicates); (2) at present, the choice of a given DMA mostly relies on local habits, expertise and attitude and there is room for improvement in this area; (3) DMA performance may be highly situation dependent; (4) over-reliance on these methods may have deleterious consequences, especially with the so-called "designated medical events", for which a case-by-case analysis is mandatory and complements disproportionality; and (5) the most appropriate selection of pharmacovigilance tools needs to be tailored to each situation, being mindful of the numerous biases and confounders that may influence performance and incremental utility of DMAs

Analyzing Adverse Events from Publicly Available Web Sources

Data mining for drug-reaction associations is a major topic in the pharmaceutical industry. Historically the focus has been on using privately owned and maintained datasets consisting of information that has been transformed via the FDA Adverse Event Reporting System (FAERS) and privatized reporting systems that house the data from clinical trials. Our focus will be on building a pipeline that demonstrates an open source solution for building a drug’s safety profile from data collection through signal detection. In contrast this pipeline primarily uses the openFDA and social media data available through Reddit with all analysis being done in the R statistical programming language. The aim was to collect the information available in these public sources and apply popular data mining methodologies used to identify and predict the occurrence of adverse events. The results show the ability of the openFDA and social media sites to create real-time drug safety occurrence profiles by applying the same statistical methods applied in clinical trials. Social media will be shown to provide the best results when applied to prescribed daily use medications compared to common over-the-counter drugs or last line of defense medications. The information and results reported in this paper are not intended or implied to be a substitute for professional medical advice, diagnosis, or treatment. Do not delay seeking medical treatment or advice because of something you have read in this paper

Machine learning liver-injuring drug interactions with non-steroidal anti-inflammatory drugs (NSAIDs) from a retrospective electronic health record (EHR) cohort

Drug-drug interactions account for up to 30% of adverse drug reactions. Increasing prevalence of electronic health records (EHRs) offers a unique opportunity to build machine learning algorithms to identify drug-drug interactions that drive adverse events. In this study, we investigated hospitalizations\u27 data to study drug interactions with non-steroidal anti-inflammatory drugs (NSAIDS) that result in drug-induced liver injury (DILI). We propose a logistic regression based machine learning algorithm that unearths several known interactions from an EHR dataset of about 400,000 hospitalization. Our proposed modeling framework is successful in detecting 87.5% of the positive controls, which are defined by drugs known to interact with diclofenac causing an increased risk of DILI, and correctly ranks aggregate risk of DILI for eight commonly prescribed NSAIDs. We found that our modeling framework is particularly successful in inferring associations of drug-drug interactions from relatively small EHR datasets. Furthermore, we have identified a novel and potentially hepatotoxic interaction that might occur during concomitant use of meloxicam and esomeprazole, which are commonly prescribed together to allay NSAID-induced gastrointestinal (GI) bleeding. Empirically, we validate our approach against prior methods for signal detection on EHR datasets, in which our proposed approach outperforms all the compared methods across most metrics, such as area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC)

Data Mining and Applications for Pharmacovigilance

In order to ensure the safety and efficacy of post-market pharmaceutical products, the United States Food and Drug Administration relies on its pharmacovigilance efforts and input from the general public. The FDA receives submissions of adverse event reports from patients, health care practitioners and manufacturers. The FDA has started looking to the field of data mining to automate the search for safety signals. A training manual was created to introduce FDA employees to the concepts and applications of data mining techniques in pharmacovigilance

Adverse reactions to oncologic drugs: spontaneous reporting and signal detection

Oncology is one of the areas of medicine with the most active research being conducted on new drugs. New pharmacological entities frequently enter the clinical arena, and therefore, the safety profile of anticancer products deserves continuous monitoring. However, only very severe and (unusual) suspected adverse drug reactions (ADRs) are usually reported, since cancer patients develop ADRs very frequently and some practical selectivity must be used. Notably, a recent study was able to identify 76 serious ADRs reported in updated drug labels of oncologic drugs and 50% of them (n = 38) were potentially fatal. Of these, 49 and 58%, respectively, were not described in initial drug labels. The aims of this article are to provide an overview about spontaneous reporting of ADRs of oncologic drugs and to discuss the available methods to analyze the safety of anticancer drugs using databases of spontaneous ADR reporting