Simulating adverse event spontaneous reporting systems as preferential attachment networks

SummaryBackground: Spontaneous Reporting Systems [SRS] are critical tools in the post-licensure evaluation of medical product safety. Regulatory authorities use a variety of data mining techniques to detect potential safety signals in SRS databases. Assessing the performance of such signal detection procedures requires simulated SRS databases, but simulation strategies proposed to date each have limitations.Objective: We sought to develop a novel SRS simulation strategy based on plausible mechanisms for the growth of databases over time.Methods: We developed a simulation strategy based on the network principle of preferential attachment. We demonstrated how this strategy can be used to create simulations based on specific databases of interest, and provided an example of using such simulations to compare signal detection thresholds for a popular data mining algorithm.Results: The preferential attachment simulations were generally structurally similar to our targeted SRS database, although they had fewer nodes of very high degree. The approach was able to generate signal-free SRS simulations, as well as mimicking specific known true signals. Explorations of different reporting thresholds for the FDA Vaccine Adverse Event Reporting System suggested that using proportional reporting ratio [PRR] > 3.0 may yield better signal detection operating characteristics than the more commonly used PRR > 2.0 threshold.Discussion: The network analytic approach to SRS simulation based on the principle of preferential attachment provides an attractive framework for exploring the performance of safety signal detection algorithms. This approach is potentially more principled and versatile than existing simulation approaches.Conclusion: The utility of network-based SRS simulations needs to be further explored by evaluating other types of simulated signals with a broader range of data mining approaches, and comparing network-based simulations with other simulation strategies where applicable.Citation: Scott J, Botsis T, Ball R. Simulating adverse event spontaneous reporting systems as preferential attachment networks: Application to the Vaccine Adverse Event Reporting System. Appl Clin Inf 2014; 5: 206–218 http://dx.doi.org/10.4338/ACI-2013-11-RA-0097

Download Full-text

PRM87 - CRITICAL EVALUATION OF VARIOUS DATA MINING ALGORITHMS USED FOR SIGNAL DETECTION IN FDA ADVERSE EVENT REPORTING SYSTEM DATABASE

Value in Health ◽

10.1016/j.jval.2018.09.2208 ◽

2018 ◽

Vol 21 ◽

pp. S370

Author(s):

KS Koonisetty ◽

V Subeesh ◽

E Maheswari ◽

SS Minnikanti ◽

C Pudi

Keyword(s):

Data Mining ◽

Adverse Event ◽

Signal Detection ◽

Reporting System ◽

Adverse Event Reporting System ◽

Critical Evaluation ◽

Adverse Event Reporting ◽

Event Reporting ◽

Data Mining Algorithms ◽

Mining Algorithms

Download Full-text

Traceability of Biopharmaceuticals in Spontaneous Reporting Systems: A Cross-Sectional Study in the FDA Adverse Event Reporting System (FAERS) and EudraVigilance Databases

Drug Safety ◽

10.1007/s40264-013-0073-3 ◽

2013 ◽

Vol 36 (8) ◽

pp. 617-625 ◽

Cited By ~ 54

Author(s):

Niels S. Vermeer ◽

Sabine M. J. M. Straus ◽

Aukje K. Mantel-Teeuwisse ◽

Francois Domergue ◽

Toine C. G. Egberts ◽

...

Keyword(s):

Adverse Event ◽

Spontaneous Reporting ◽

Reporting System ◽

Adverse Event Reporting System ◽

Cross Sectional Study ◽

Adverse Event Reporting ◽

Sectional Study ◽

Cross Sectional ◽

Event Reporting ◽

Reporting Systems

Download Full-text

Novel Adverse Events of Iloperidone: A Disproportionality Analysis in US Food and Drug Administration Adverse Event Reporting System (FAERS) Database

Current Drug Safety ◽

10.2174/1574886313666181026100000 ◽

2019 ◽

Vol 14 (1) ◽

pp. 21-26 ◽

Cited By ~ 2

Author(s):

Viswam Subeesh ◽

Eswaran Maheswari ◽

Hemendra Singh ◽

Thomas Elsa Beulah ◽

Ann Mary Swaroop

Keyword(s):

Data Mining ◽

Adverse Event ◽

Adverse Events ◽

Reporting System ◽

Adverse Event Reporting System ◽

Adverse Event Reporting ◽

Disproportionality Analysis ◽

Positive Signal ◽

Data Mining Algorithms ◽

Mining Algorithms

Background: The signal is defined as “reported information on a possible causal relationship between an adverse event and a drug, of which the relationship is unknown or incompletely documented previously”. Objective: To detect novel adverse events of iloperidone by disproportionality analysis in FDA database of Adverse Event Reporting System (FAERS) using Data Mining Algorithms (DMAs). Methodology: The US FAERS database consists of 1028 iloperidone associated Drug Event Combinations (DECs) which were reported from 2010 Q1 to 2016 Q3. We consider DECs for disproportionality analysis only if a minimum of ten reports are present in database for the given adverse event and which were not detected earlier (in clinical trials). Two data mining algorithms, namely, Reporting Odds Ratio (ROR) and Information Component (IC) were applied retrospectively in the aforementioned time period. A value of ROR-1.96SE>1 and IC- 2SD>0 were considered as the threshold for positive signal. Results: The mean age of the patients of iloperidone associated events was found to be 44years [95% CI: 36-51], nevertheless age was not mentioned in twenty-one reports. The data mining algorithms exhibited positive signal for akathisia (ROR-1.96SE=43.15, IC-2SD=2.99), dyskinesia (21.24, 3.06), peripheral oedema (6.67,1.08), priapism (425.7,9.09) and sexual dysfunction (26.6-1.5) upon analysis as those were well above the pre-set threshold. Conclusion: Iloperidone associated five potential signals were generated by data mining in the FDA AERS database. The result requires an integration of further clinical surveillance for the quantification and validation of possible risks for the adverse events reported of iloperidone.

Download Full-text

Systematic analysis of safety profile for darunavir and its boosted agents using data mining in the FDA Adverse Event Reporting System database

Scientific Reports ◽

10.1038/s41598-021-91549-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Xiaojiang Tian ◽

Yao Yao ◽

Guanglin He ◽

Yuntao Jia ◽

Kejing Wang ◽

...

Keyword(s):

Data Mining ◽

Adverse Event ◽

Reporting System ◽

Proportional Reporting Ratio ◽

Adverse Event Reporting System ◽

Adverse Event Reporting ◽

Reporting Odds Ratio ◽

Systematic Analysis ◽

Event Reporting ◽

Exfoliative Dermatitis

AbstractThis current investigation was aimed to generate signals for adverse events (AEs) of darunavir-containing agents by data mining using the US Food and Drug Administration Adverse Event Reporting System (FAERS). All AE reports for darunavir, darunavir/ritonavir, or darunavir/cobicistat between July 2006 and December 2019 were identified. The reporting Odds Ratio (ROR), proportional reporting ratio (PRR), and Bayesian confidence propagation neural network (BCPNN) were used to detect the risk signals. A suspicious signal was generated only if the results of the three algorithms were all positive. A total of 10,756 reports were identified commonly observed in hepatobiliary, endocrine, cardiovascular, musculoskeletal, gastrointestinal, metabolic, and nutrition system. 40 suspicious signals were generated, and therein 20 signals were not included in the label. Severe high signals (i.e. progressive extraocular muscle paralysis, acute pancreatitis, exfoliative dermatitis, acquired lipodystrophy and mitochondrial toxicity) were identified. In pregnant women, umbilical cord abnormality, fetal growth restriction, low birth weight, stillbirth, premature rupture of membranes, premature birth and spontaneous abortion showed positive signals. Darunavir and its boosted agents induced AEs in various organs/tissues, and were shown to be possibly associated with multiple adverse pregnant conditions. This study highlighted some novel and severe AEs of darunavir which need to be monitored prospectively.

Download Full-text