Useful Interplay Between Spontaneous ADR Reports and Electronic Healthcare Records in Signal Detection

Background and Objective: Spontaneous reporting systems (SRSs) remain the cornerstone of post-marketing drug safety surveillance despite their well-known limitations. Judicious use of other available data source

Early Detection of Adverse Drug Reaction Signals by Association Rule Mining Using Large-Scale Administrative Claims Data

INTRODUCTION: Adverse drug reactions (ADRs) are a leading cause of mortality worldwide and should be detected promptly to reduce health risks to patients. A data-mining approach using large-scale medical records might be a useful method for the early detection of ADRs. Many studies have analyzed medical records to detect ADRs; however, most of them have focused on a narrow range of ADRs, limiting their usefulness. OBJECTIVE: This study aimed to identify methods for the early detection of a wide range of ADR signals. METHODS: First, to evaluate the performance in signal detection of ADRs by data-mining, we attempted to create a gold standard based on clinical evidence. Second, association rule mining (ARM) was applied to patient symptoms and medications registered in claims data, followed by evaluating ADR signal detection performance. RESULTS: We created a new gold standard consisting of 92 positive and 88 negative controls. In the assessment of ARM using claims data, the areas under the receiver-operating characteristic curve and the precision-recall curve were 0.80 and 0.83, respectively. If the detection criteria were defined as lift > 1, conviction > 1, and p-value < 0.05, ARM could identify 156 signals, of which 90 were true positive controls (sensitivity: 0.98, specificity: 0.25). Evaluation of the capability of ARM with short periods of data revealed that ARM could detect a greater number of positive controls than the conventional analysis method. CONCLUSIONS: ARM of claims data may be effective in the early detection of a wide range of ADR signals

Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records

Unknown adverse reactions to drugs available on the market present a significant health risk and limit accurate judgement of the cost/benefit trade-off for medications. Machine learning has the potential to predict unknown adverse reactions from current knowledge. We constructed a knowledge graph containing four types of node: drugs, protein targets, indications and adverse reactions. Using this graph, we developed a machine learning algorithm based on a simple enrichment test and first demonstrated this method performs extremely well at classifying known causes of adverse reactions (AUC 0.92). A cross validation scheme in which 10% of drug-adverse reaction edges were systematically deleted per fold showed that the method correctly predicts 68% of the deleted edges on average. Next, a subset of adverse reactions that could be reliably detected in anonymised electronic health records from South London and Maudsley NHS Foundation Trust were used to validate predictions from the model that are not currently known in public databases. High-confidence predictions were validated in electronic records significantly more frequently than random models, and outperformed standard methods (logistic regression, decision trees and support vector machines). This approach has the potential to improve patient safety by predicting adverse reactions that were not observed during randomised trials

Nephrotoxicity with VCM and Nephrotoxins

There is a growing concern about the relationship between vancomycin-associated nephrotoxicity (VAN) and concomitant use of nephrotoxins. We examined this relationship by combined retrospective analyses of two real-world databases. Initially, the FDA Adverse Event Reporting System (FAERS) was analyzed for the effects of concomitant use of one or more nephrotoxins on VAN and the types of combinations of nephrotoxins that exacerbate VAN. Next, electronic medical records (EMRs) of patients who received vancomycin (VCM) at Tokushima University Hospital between January 2006 and March 2019 were examined to confirm the FAERS analysis. An elevated reporting odds ratio (ROR) was observed with increases in the number of nephrotoxins administered (VCM + one nephrotoxin, adjusted ROR (95% confidence interval [CI]) 1.67 [1.51–1.85]; VCM + ≥ 2 nephrotoxins, adjusted ROR [95% CI] 1.54 [1.37–1.73]) in FAERS. EMRs analysis showed that the number of nephrotoxins was associated with higher incidences of VAN [odds ratio: 1.99; 95% CI: 1.42–2.78]. Overall, concomitant use of nephrotoxins was associated with an increased incidence of VAN, especially when at least one of those nephrotoxins was a renal hypoperfusion medication (furosemide, non-steroidal anti-inflammatory drugs, and vasopressors). The concomitant use of multiple nephrotoxins, especially including renal hypoperfusion medication, should be avoided to prevent VAN

Concomitant Use of Multiple Nephrotoxins including Renal Hypoperfusion Medications Causes Vancomycin-Associated Nephrotoxicity: Combined Retrospective Analyses of Two Real-World Databases

There is a growing concern about the relationship between vancomycin-associated nephrotoxicity (VAN) and concomitant use of nephrotoxins. We examined this relationship by combined retrospective analyses of two real-world databases. Initially, the FDA Adverse Event Reporting System (FAERS) was analyzed for the effects of concomitant use of one or more nephrotoxins on VAN and the types of combinations of nephrotoxins that exacerbate VAN. Next, electronic medical records (EMRs) of patients who received vancomycin (VCM) at Tokushima University Hospital between January 2006 and March 2019 were examined to confirm the FAERS analysis. An elevated reporting odds ratio (ROR) was observed with increases in the number of nephrotoxins administered (VCM + one nephrotoxin, adjusted ROR (95% confidence interval [CI]) 1.67 [1.51-1.85]; VCM + ≥2 nephrotoxins, adjusted ROR [95% CI] 1.54 [1.37-1.73]) in FAERS. EMRs analysis showed that the number of nephrotoxins was associated with higher incidences of VAN [odds ratio: 1.99; 95% CI: 1.42-2.78]. Overall, concomitant use of nephrotoxins was associated with an increased incidence of VAN, especially when at least one of those nephrotoxins was a renal hypoperfusion medication (furosemide, non-steroidal anti-inflammatory drugs, and vasopressors). The concomitant use of multiple nephrotoxins, especially including renal hypoperfusion medication, should be avoided to prevent VAN

Automatically Recognizing Medication and Adverse Event Information From Food and Drug Administration\u27s Adverse Event Reporting System Narratives

BACKGROUND: The Food and Drug Administration\u27s (FDA) Adverse Event Reporting System (FAERS) is a repository of spontaneously-reported adverse drug events (ADEs) for FDA-approved prescription drugs. FAERS reports include both structured reports and unstructured narratives. The narratives often include essential information for evaluation of the severity, causality, and description of ADEs that are not present in the structured data. The timely identification of unknown toxicities of prescription drugs is an important, unsolved problem. OBJECTIVE: The objective of this study was to develop an annotated corpus of FAERS narratives and biomedical named entity tagger to automatically identify ADE related information in the FAERS narratives. METHODS: We developed an annotation guideline and annotate medication information and adverse event related entities on 122 FAERS narratives comprising approximately 23,000 word tokens. A named entity tagger using supervised machine learning approaches was built for detecting medication information and adverse event entities using various categories of features. RESULTS: The annotated corpus had an agreement of over .9 Cohen\u27s kappa for medication and adverse event entities. The best performing tagger achieves an overall performance of 0.73 F1 score for detection of medication, adverse event and other named entities. C ONCLUSIONS: In this study, we developed an annotated corpus of FAERS narratives and machine learning based models for automatically extracting medication and adverse event information from the FAERS narratives. Our study is an important step towards enriching the FAERS data for postmarketing pharmacovigilance

Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records

Abstract Unknown adverse reactions to drugs available on the market present a significant health risk and limit accurate judgement of the cost/benefit trade-off for medications. Machine learning has the potential to predict unknown adverse reactions from current knowledge. We constructed a knowledge graph containing four types of node: drugs, protein targets, indications and adverse reactions. Using this graph, we developed a machine learning algorithm based on a simple enrichment test and first demonstrated this method performs extremely well at classifying known causes of adverse reactions (AUC 0.92). A cross validation scheme in which 10% of drug-adverse reaction edges were systematically deleted per fold showed that the method correctly predicts 68% of the deleted edges on average. Next, a subset of adverse reactions that could be reliably detected in anonymised electronic health records from South London and Maudsley NHS Foundation Trust were used to validate predictions from the model that are not currently known in public databases. High-confidence predictions were validated in electronic records significantly more frequently than random models, and outperformed standard methods (logistic regression, decision trees and support vector machines). This approach has the potential to improve patient safety by predicting adverse reactions that were not observed during randomised trials