scholarly journals Combining a Pharmacological Network Model with a Bayesian Signal Detection Algorithm to Improve the Detection of Adverse Drug Events

2022 ◽  
Vol 12 ◽  
Author(s):  
Xiangmin Ji ◽  
Guimei Cui ◽  
Chengzhen Xu ◽  
Jie Hou ◽  
Yunfei Zhang ◽  
...  
Keyword(s):  
Signal Detection ◽  
Network Model ◽  
Drug Safety ◽  
Adverse Drug Events ◽  
High Efficiency ◽  
Adverse Event Reporting System ◽  
Detection Algorithm ◽  
Drug Event ◽  
Statistical Approaches ◽  
Youden’S Index

Introduction: Improving adverse drug event (ADE) detection is important for post-marketing drug safety surveillance. Existing statistical approaches can be further optimized owing to their high efficiency and low cost.Objective: The objective of this study was to evaluate the proposed approach for use in pharmacovigilance, the early detection of potential ADEs, and the improvement of drug safety.Methods: We developed a novel integrated approach, the Bayesian signal detection algorithm, based on the pharmacological network model (ICPNM) using the FDA Adverse Event Reporting System (FAERS) data published from 2004 to 2009 and from 2014 to 2019Q2, PubChem, and DrugBank database. First, we used a pharmacological network model to generate the probabilities for drug-ADE associations, which comprised the proper prior information component (IC). We then defined the probability of the propensity score adjustment based on a logistic regression model to control for the confounding bias. Finally, we chose the Side Effect Resource (SIDER) and the Observational Medical Outcomes Partnership (OMOP) data to evaluate the detection performance and robustness of the ICPNM compared with the statistical approaches [disproportionality analysis (DPA)] by using the area under the receiver operator characteristics curve (AUC) and Youden’s index.Results: Of the statistical approaches implemented, the ICPNM showed the best performance (AUC, 0.8291; Youden’s index, 0.5836). Meanwhile, the AUCs of the IC, EBGM, ROR, and PRR were 0.7343, 0.7231, 0.6828, and 0.6721, respectively.Conclusion: The proposed ICPNM combined the strengths of the pharmacological network model and the Bayesian signal detection algorithm and performed better in detecting true drug-ADE associations. It also detected newer ADE signals than a DPA and may be complementary to the existing statistical approaches.

Adverse Drug Events and the Freedom of Information Act: An Apple in Eden

1992 ◽  
Vol 26 (2) ◽  
pp. 238-243 ◽  
Author(s):  
Paul E. Stang ◽  
Janet L. Fox ◽  
Abraham G. Hartzema ◽  
Miquel S. Porta ◽  
Hugh H. Tilson
Keyword(s):  
Drug Safety ◽  
Adverse Drug Events ◽  
Reporting System ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Public Access ◽  
Drug Event ◽  
Freedom Of Information ◽  
Freedom Of Information Act ◽  
Spontaneous Adverse Drug Reaction

OBJECTIVE: To review some of the abuses and proper uses of the Food and Drug Administration's (FDA's) spontaneous adverse-reaction reporting system, as a way of educating the reader to its strengths and limitations. DATA SOURCE: Published literature and reports based on information obtained from the FDA's database of spontaneous adverse drug-event reports. DATA SYNTHESIS: The Freedom of Information Act has increased public access to the FDA's database of spontaneous adverse drug reaction reports. As these reports are voluntarily received and reported to the FDA, their use for comparisons of drug safety is severely limited. Despite these limitations and the FDA's caveats for use of these data, consumer advocacy groups, researchers, and various pharmaceutical marketing groups have used this source to project the incidence of adverse drug reactions. CONCLUSIONS: The FDA's spontaneous adverse-event reporting system is designed to generate signals of unexpected adverse drug events. Use of the data gathered by this system to make drug safety comparisons is beyond their credible scope because many factors influence the reporting of adverse events. Researchers and peer reviewers should place these data in the proper perspective and support sound research into questions of drug safety.

A Brief Primer on Automated Signal Detection

2003 ◽  
Vol 37 (7-8) ◽  
pp. 1117-1123 ◽  
Author(s):  
Manfred Hauben
Keyword(s):  
Signal Detection ◽  
Adverse Drug Events ◽  
Data Extraction ◽  
Performance Characteristics ◽  
Data Sources ◽  
Drug Event ◽  
Computer Assisted ◽  
Statistical Independence ◽  
Medline Search ◽  
Advantages And Disadvantages

BACKGROUND: Statistical techniques have traditionally been underused in spontaneous reporting systems used for postmarketing surveillance of adverse drug events. Regulatory agencies, pharmaceutical companies, and drug monitoring centers have recently devoted considerable efforts to develop and implement computer-assisted automated signal detection methodologies that employ statistical theory to enhance screening efforts of expert clinical reviewers. OBJECTIVE: To provide a concise state-of-the-art review of the most commonly used automated signal detection procedures, including the underlying statistical concepts, performance characteristics, and outstanding limitations, and issues to be resolved. DATA SOURCES: Primary articles were identified by MEDLINE search (1965–December 2002) and through secondary sources. STUDY SELECTION AND DATA EXTRACTION: All of the articles identified from the data sources were evaluated and all information deemed relevant was included in this review. DATA SYNTHESIS: Commonly used methods of automated signal detection are self-contained and involve screening large databases of spontaneous adverse event reports in search of interestingly large disproportionalities or dependencies between significant variables, usually single drug–event pairs, based on an underlying model of statistical independence. The models vary according to the underlying model of statistical independence and whether additional mathematical modeling using Bayesian analysis is applied to the crude measures of disproportionality. There are many potential advantages and disadvantages of these methods, as well as significant unresolved issues related to the application of these techniques, including lack of comprehensive head-to-head comparisons in a single large transnational database, lack of prospective evaluations, and the lack of gold standard of signal detection. CONCLUSIONS: Current methods of automated signal detection are nonclinical and only highlight deviations from independence without explaining whether these deviations are due to a causal linkage or numerous potential confounders. They therefore cannot replace expert clinical reviewers, but can help them to focus attention when confronted with the difficult task of screening huge numbers of drug–event combinations for potential signals. Important questions remain to be answered about the performance characteristics of these methods. Pharmacovigilance professionals should take the time to learn the underlying mathematical concepts in order to critically evaluate accumulating experience pertaining to the relative performance characteristics of these methods that are incompletely defined.

Adverse Drug Events in the Prevention and Treatment of COVID-19: A Data Mining Study on FDA Adverse Event Reporting System (FAERS) (Preprint)

2021 ◽  
Author(s):  
Qiang Guo ◽  
Shaojun Duan ◽  
Yaxi Liu ◽  
Yinxia Yuan
Keyword(s):  
Data Mining ◽  
Adverse Event ◽  
Adverse Drug Events ◽  
Reporting System ◽  
Proportional Reporting Ratio ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Drug Event ◽  
Event Reporting ◽  
Prevention And Treatment

BACKGROUND In the emergency situation of COVID-19, off-label therapies and newly developed vaccines may bring the patients adverse drug event (ADE) risks. Data mining based on spontaneous reporting systems (SRSs) is a promising and efficient way to detect potential ADEs so as to help health professionals and patients get rid of these risks. OBJECTIVE This pharmacovigilance study aimed to investigate the ADEs of “Hot Drugs” in COVID-19 prevention and treatment based on the data of the US Food and Drug Administration (FDA) adverse event reporting system (FAERS). METHODS FAERS ADE reports associated with COVID-19 from the 2nd quarter of 2020 to the 2nd quarter of 2021 were retrieved with “Hot Drugs” and frequent ADEs recognized. A combination of support, proportional reporting ratio (PRR) and Chi-square (2) test was applied to detect significant “Hot Drug” & ADE signals by Python programming language on Jupyter notebook. RESULTS 13,178 COVID-19 cases were retrieved with 18 “Hot Drugs” and 312 frequent ADEs on “Preferred Term” (PT) level. 18  312 = 5,616 “Drug & ADE” candidates were formed for further data mining. The algorithm finally produced 219 significant ADE signals associated with 17 “Hot Drugs”and 124 ADEs.Some unexpected ADE signals were observed for chloroquine, ritonavir, tocilizumab, Oxford/AstraZeneca COVID-19 Vaccine and Moderna COVID-19 Vaccine. CONCLUSIONS Data mining is a promising and efficient way to assist pharmacovigilance work and the result of this paper could help timely recognize ADEs in the prevention and treatment of COVID-19.

scholarly journals Pediatric Drug Safety Signal Detection: A New Drug–Event Reference Set for Performance Testing of Data-Mining Methods and Systems

Drug Safety ◽  
2015 ◽  
Vol 38 (2) ◽  
pp. 207-217 ◽  
Author(s):  
Osemeke U. Osokogu ◽  
Federica Fregonese ◽  
Carmen Ferrajolo ◽  
Katia Verhamme ◽  
Sandra de Bie ◽  
...  
Keyword(s):  
Data Mining ◽  
Signal Detection ◽  
Drug Safety ◽  
Performance Testing ◽  
Drug Event ◽  
Safety Signal ◽  
Pediatric Drug ◽  
New Drug ◽  
Reference Set ◽  
Mining Methods

scholarly journals The impact of database restriction on pharmacovigilance signal detection of selected cancer therapies

2017 ◽  
Vol 8 (5) ◽  
pp. 145-156 ◽  
Author(s):  
Manfred Hauben ◽  
Eric Hung ◽  
Jennifer Wood ◽  
Amit Soitkar ◽  
Daniel Reshef
Keyword(s):  
Adverse Event ◽  
Signal Detection ◽  
Safety Information ◽  
Adverse Event Reporting System ◽  
Package Insert ◽  
The United States ◽  
Drug Event ◽  
Reference Set ◽  
Oncology Drug ◽  
The Impact

Background: The aim of this study was to investigate whether database restriction can improve oncology drug pharmacovigilance signal detection performance. Methods: We used spontaneous adverse event (AE) reports in the United States (US) Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database. Positive control (PC) drug medical concept (DMC) pairs were selected from safety information not included in the product’s first label but subsequently added as label changes. These medical concepts (MCs) were mapped to the Medical Dictionary for Regulatory Activities (MedDRA) preferred terms (PTs) used in FAERS to code AEs. Negative controls (NC) were MCs with circumscribed PTs not included in the corresponding US package insert (USPI). We calculated shrinkage-adjusted observed-to-expected (O/E) reporting frequencies for the aforementioned drug–PT pairs. We also formulated an adjudication framework to calculate performance at the MC level. Performance metrics [sensitivity, specificity, positive and negative predictive value (PPV, NPV), signal/noise (S/N), F and Matthews correlation coefficient (MCC)] were calculated for each analysis and compared. Results: The PC reference set consisted of 11 drugs, 487 PTs, 27 MCs, 37 drug–MC combinations and 638 drug–event combinations (DECs). The NC reference set consisted of 11 drugs, 9 PTs, 5 MCs, 40 drug–MC combinations and 67 DECs. Most drug–event pairs were not highlighted by either analysis. A small percentage of signals of disproportionate reporting were lost, more noise than signal, with no gains. Specificity and PPV improved whereas sensitivity, NPV, F and MCC decreased, but all changes were small relative to the decrease in sensitivity. The overall S/N improved. Conclusion: This oncology drug restricted analysis improved the S/N ratio, removing proportionately more noise than signal, but with significant credible signal loss. Without broader experience and a calculus of costs and utilities of correct versus incorrect classifications in oncology pharmacovigilance such restricted analyses should be optional rather than a default analysis.

scholarly journals Gender Differences in Adverse Drug Events of Hydroxychloroquine: Analysis of Spontaneous Reports Submitted to FAERS

2021 ◽  
Author(s):  
Huan-huan JI ◽  
Lin SONG ◽  
Yun-tao JIA
Keyword(s):  
Gender Differences ◽  
Adverse Drug Events ◽  
Adverse Event Reporting System ◽  
Safety Data ◽  
Adverse Event Reporting ◽  
Drug Event ◽  
System Organ Class ◽  
Reporting Odds Ratio ◽  
Disproportionality Analysis ◽  
Spontaneous Reports

Abstract Background Several studies have investigated gender as a risk factor for the occurrence of adverse drug events (ADEs) and found that females are more likely to experience ADEs than male. Today, there is a poor knowledge about gender differences in safety profile of ADEs to hydroxychloroquine (HCQ). Identifying those gender differences in ADEs could reduce the experience of ADEs for patients with HCQ. Therefore, the aim of this explorative study was to investigate whether differences exist in reported ADEs of HCQ for male and female in the database of FDA Adverse Event Reporting System (FAERS). Methods We performed a descriptive gender-related analysis and disproportionality analysis of HCQ safety data, obtained from the FAERS. Reporting odds ratio (ROR) and 95% confidence interval (CI) were calculated to quantify the signals of gender differences for specific drug-event combinations at system organ class (SOC) and preferred term (PT) level. Results Disproportionality analysis indicated that 8 SOCs with 12 ADEs were statistically significantly more reported in female than male, including electrocardiogram Qt prolonged, retinal toxicity, musculoskeletal disorder, hypersensitivity, anaphylactic reaction, among others, and 5 SOCs with 11 ADEs were reported more in male than female, including cardiac failure, renal failure, completed suicidal, photosensitivity reaction. Common adverse events are similar between female and male. However, serious ADEs were more frequently reported in males. Conclusions Therefore, the recognition of gender differences in ADEs may be helpful in prescribing medications, e.g. greater caution should be taken when prescribing HCQ to female with conduction disorder.

scholarly journals Using ActionADE to create information continuity to reduce re-exposures to harmful medications: study protocol for a randomized controlled trial

Trials ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jeffrey P. Hau ◽  
Penelope M. A. Brasher ◽  
Amber Cragg ◽  
Serena Small ◽  
Maeve Wickham ◽  
...  
Keyword(s):  
Health Information ◽  
Adverse Drug Event ◽  
Primary Outcome ◽  
Adverse Drug Events ◽  
Primary Objective ◽  
Drug Event ◽  
Controlled Trials ◽  
Drug Reactions ◽  
Main Trial ◽  
Randomized Controlled

Abstract Background Repeat exposures to culprit medications are a common cause of preventable adverse drug events. Health information technologies have the potential to reduce repeat adverse drug events by improving information continuity. However, they rarely interoperate to ensure providers can view adverse drug events documented in other systems. We designed ActionADE to enable rapid documentation of adverse drug events and communication of standardized information across health sectors by integrating with legacy systems. We will leverage ActionADE’s implementation to conduct two parallel, randomized trials: patients with adverse drug reactions in the main trial and those diagnosed with non-adherence in a secondary trial. Primary objective of the main trial is to evaluate the effects of providing information continuity about adverse drug reactions on culprit medication re-dispensations over 12 months. Primary objective of the secondary trial is to evaluate the effect of providing information continuity on adherence over 12 months. Methods We will conduct two parallel group, triple-blind randomized controlled trials in participating hospitals in British Columbia, Canada. We will enroll adults presenting to hospital with an adverse drug event to prescribed outpatient medication. Clinicians will document the adverse drug event in ActionADE. The software will use an algorithm to determine patient eligibility and allocate eligible patients to experimental or control. In the experimental arm, ActionADE will transmit information to PharmaNet, where adverse drug event information will be displayed in community pharmacies when re-dispensations are attempted. In the control arm, ActionADE will retain information in the local record. We will enroll 3600 adults with an adverse drug reaction into the main trial. The main trial’s primary outcome is re-dispensation of a culprit or same-class medication within 12 months; the secondary trial’s primary outcome will be adherence to culprit medication. Secondary outcomes include health services utilization and mortality. Discussion These studies have the potential to guide policy decisions and investments needed to drive health information technology integrations to prevent repeat adverse drug events. We present an example of how a health information technology implementation can be leveraged to conduct pragmatic randomized controlled trials. Trial registration ClinicalTrials.gov NCT04568668, NCT04574648. Registered on 1 October 2020.

Drug-Drug Interaction Signal Detection from Drug Safety Reports

2018 ◽  
Author(s):  
Brian Zylich ◽  
Brian McCarthy ◽  
Andrew Schade ◽  
Huy Tran ◽  
Xiao Qin ◽  
...  
Keyword(s):  
Drug Interaction ◽  
Signal Detection ◽  
Drug Safety ◽  
Drug Drug Interaction
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