Can Natural Language Processing Improve the Efficiency of Vaccine Adverse Event Report Review?

2016 ◽  
Vol 55 (02) ◽  
pp. 144-150 ◽  
Author(s):  
M. Nguyen ◽  
E. J. Woo ◽  
S. Winiecki ◽  
J. Scott ◽  
D. Martin ◽  
...  
Keyword(s):  
Adverse Event ◽  
Information Extraction ◽  
Full Text ◽  
Alternative Explanation ◽  
Adverse Event Reporting System ◽  
Time Interval ◽  
Adverse Event Report ◽  
Event Report ◽  
Review Time ◽  
Time To Onset

SummaryBackground: Individual case review of spontaneous adverse event (AE) reports remains a cornerstone of medical product safety surveil-lance for industry and regulators. Previously we developed the Vaccine Adverse Event Text Miner (VaeTM) to offer automated information extraction and potentially accelerate the evaluation of large volumes of unstructured data and facilitate signal detection.Objective: To assess how the information extraction performed by VaeTM impacts the accuracy of a medical expert’s review of the vaccine adverse event report.Methods: The “outcome of interest” (diagnosis, cause of death, second level diagnosis), “onset time,” and “alternative explanations” (drug, medical and family history) for the adverse event were extracted from 1000 reports from the Vaccine Adverse Event Reporting System (VAERS) using the VaeTM system. We compared the human interpretation, by medical experts, of the VaeTM extracted data with their interpretation of the traditional full text reports for these three variables. Two experienced clinicians alternately reviewed text miner output and full text. A third clinician scored the match rate using a predefined algorithm; the proportion of matches and 95% confidence intervals (CI) were calculated. Review time per report was analyzed.Results: Proportion of matches between the interpretation of the VaeTM extracted data, compared to the interpretation of the full text: 93% for outcome of interest (95% CI: 91– 94%) and 78% for alternative explanation (95% CI: 75 – 81%). Extracted data on the time to onset was used in 14% of cases and was a match in 54% (95% CI: 46 – 63%) of those cases. When supported by structured time data from reports, the match for time to onset was 79% (95% CI: 76 – 81%). The extracted text averaged 136 (74%) fewer words, resulting in a mean reduction in review time of 50 (58%) seconds per report.Conclusion: Despite a 74% reduction in words, the clinical conclusion from VaeTM extracted data agreed with the full text in 93% and 78% of reports for the outcome of interest and alternative explanation, respec -tively. The limited amount of extracted time interval data indicates the need for further development of this feature. VaeTM may improve review efficiency, but further study is needed to determine if this level of agreement is sufficient for routine use.

scholarly journals Hepatotoxicity-Related Adverse Effects of Proton Pump Inhibitors: A Cross-Sectional Study of Signal Mining and Analysis of the FDA Adverse Event Report System Database

2021 ◽  
Vol 8 ◽  
Author(s):  
Yifan Zeng ◽  
Ying Dai ◽  
Ziye Zhou ◽  
Xuben Yu ◽  
Dawei Shi
Keyword(s):  
Adverse Event ◽  
Adverse Effects ◽  
Proton Pump Inhibitors ◽  
Proton Pump ◽  
Case Reports ◽  
Adverse Event Reporting System ◽  
Adverse Event Report ◽  
Event Report ◽  
Hepatocellular Injury ◽  
Suspect Drug

Background and Objectives: Mounting evidence demonstrates that proton pump inhibitors (PPIs) are associated with a number of adverse effects. However, the literatures about hepatotoxicity-related adverse effects (HRAEs) of PPIs are mostly case reports and a few clinical studies.Methods: We evaluated the association between PPIs and HAREs using the reporting odd ratio (ROR) for mining the adverse event report signals in the FDA Adverse Event Reporting System (FAERS) database.Results: There were 23,825 reports of PPIs as primary suspect drug or second suspect drug, of which 3,253 reports were HRAEs. The top five HRAE signals caused by PPIs were hepatitis cholestatic, cholestasis, fulminant hepatitis, subacute hepatic failure, and acute hepatitis. We also summarized the signals of the HRAEs caused by each PPI. The simultaneous signals were cholestasis and hepatitis cholestatic. For the cholestasis signal, esomeprazole showed an ROR of 21.556 (95% CI 17.592–26.413); pantoprazole showed the highest ROR of 22.611 (95% CI 17.794–28.733) in the hepatic cholestatic signal; lansoprazole was the only PPI with expression in the coma hepatic signal, with an ROR of 10.424 (95% CI 3.340–32.532). By analyzing the reports of pantoprazole-induced hepatic encephalopathy, we found that patients aged over 65 years and males reported the highest rate. And from the combination of drugs and indications of drugs, no significant results were obtained.Conclusions: The RORs of signals of “cholestasis” were generally higher than those of “hepatocellular injury.” And the signals about “cholestasis” in HRAE caused by PPIs are more reported.

scholarly journals Safety of SGLT2 Inhibitors: A Pharmacovigilance Study From 2015 to 2020 Based on FDA Adverse Event Report System Database

2021 ◽  
Author(s):  
Xiang Zhou ◽  
Xiaofei Ye ◽  
Yinghong Zhai ◽  
Fangyuan Hu ◽  
Yongqing Gao ◽  
...  
Keyword(s):  
Adverse Event ◽  
Adverse Events ◽  
Diabetic Ketoacidosis ◽  
Adverse Event Reporting System ◽  
Kidney Injury ◽  
Adverse Event Reporting ◽  
Adverse Event Report ◽  
System Database ◽  
Report System ◽  
Euglycemic Diabetic Ketoacidosis

Aim: With the widespread use of SGLT2i, various adverse events (AEs) have been reported. This study aimed to describe the distribution of SGLT2i-related AEs in different systems, quantify the association of important medical events (IMEs) and SGLT2i regimens, and build a signal profile of SGLT2i- induced IMEs. Methods: Data from 2015 Q1 to 2020 Q4 in the FDA Adverse Event Reporting System database (FAERS) were selected to conduct disproportionality analysis. Two signal indicators, the reported odds ratio (ROR) and information component (IC), were used to evaluate the correlation between SGLT2i and IMEs. The lower end of the 95% confidence interval of IC (IC025) exceeding zero was deemed a signal. For ROR, it was defined a signal if ROR025 over one, with at least 3 cases. Results: A total of 45,771,436 records were involved, including 111,564 records related to SGLT2i, with 38,366 records of SGLT2i-induced IMEs. Overall, SGLT2i was significantly associated with IMEs (IC=0.36, 95% CI: 0.35-0.38; ROR=1.44, 95% CI: 1.42-1.46). Most SGLT2i-related adverse events occurred in monotherapy (92.93%). Diabetic ketoacidosis was the most IMEs. Specifically, acute osteomyelitis has the strongest signal of all SGLT2i (IC025=7.83), and it was unique to canagliflozin. Diabetic ketoacidosis, acute kidney injury, ketoacidosis, Fournier’s gangrene, and euglycemic diabetic ketoacidosis were common to the four FDA-approved SGLT2i. Conclusion: Our study demonstrated that different SGLT2i regimens lead to different important adverse events, but there are overlapping events. Early identification and management of SGLT2i-associated IMEs are essential for clinical practice.

scholarly journals Drug target prediction using adverse event report systems: a pharmacogenomic approach

2012 ◽  
Vol 28 (18) ◽  
pp. i611-i618 ◽  
Author(s):  
M. Takarabe ◽  
M. Kotera ◽  
Y. Nishimura ◽  
S. Goto ◽  
Y. Yamanishi
Keyword(s):  
Adverse Event ◽  
Drug Target ◽  
Target Prediction ◽  
Adverse Event Report ◽  
Event Report ◽  
Drug Target Prediction

scholarly journals Shedding the Light on Post-Vaccine Myocarditis and Pericarditis in COVID-19 and Non-COVID-19 Vaccine Recipients

Vaccines ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1186
Author(s):  
Rima Hajjo ◽  
Dima A. Sabbah ◽  
Sanaa K. Bardaweel ◽  
Alexander Tropsha
Keyword(s):  
Adverse Event ◽  
Systems Biology ◽  
Adverse Events ◽  
Preventive Measures ◽  
Adverse Event Reporting System ◽  
Vaccine Dose ◽  
Adverse Event Reporting ◽  
Time Interval ◽  
Vaccine Type ◽  
Cardiac Adverse Events

Myocarditis and pericarditis have been linked recently to COVID-19 vaccines without exploring the underlying mechanisms, or compared to cardiac adverse events post-non-COVID-19 vaccines. We introduce an informatics approach to study post-vaccine adverse events on the systems biology level to aid the prioritization of effective preventive measures and mechanism-based pharmacotherapy by integrating the analysis of adverse event reports from the Vaccine Adverse Event Reporting System (VAERS) with systems biology methods. Our results indicated that post-vaccine myocarditis and pericarditis were associated most frequently with mRNA COVID-19 vaccines followed by live or live-attenuated non-COVID-19 vaccines such as smallpox and anthrax vaccines. The frequencies of cardiac adverse events were affected by vaccine, vaccine type, vaccine dose, sex, and age of the vaccinated individuals. Systems biology results suggested a central role of interferon-gamma (INF-gamma) in the biological processes leading to cardiac adverse events, by impacting MAPK and JAK-STAT signaling pathways. We suggest that increasing the time interval between vaccine doses minimizes the risks of developing inflammatory adverse reactions. We also propose glucocorticoids as preferred treatments based on system biology evidence. Our informatics workflow provides an invaluable tool to study post-vaccine adverse events on the systems biology level to suggest effective mechanism-based pharmacotherapy and/or suitable preventive measures.

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