Differential completeness of spontaneous adverse event reports among hospitals/clinics, pharmacies, consumers, and pharmaceutical companies in South Korea

A Comparison of Signals of Designated Medical Events and Non-designated Medical Events: Results from a Scoping Review

INTRODUCTION AND OBJECTIVE

The European Medicines Agency (EMA) maintains a list of designated medical events (DMEs), events that are inherently serious and are prioritized for signal detection, irrespective of statistical criteria. We have analysed the results of our previously published scoping review to determine whether DME signals differ from those of other adverse events in terms of time to communication and characteristics of supporting reports of suspected adverse drug reactions.

METHODS

For all signals, we obtained the launch year of medicinal products from textbooks or regulatory agencies, extracted the year of the first report in VigiBase and calculated the interval between the first report and communication (time to communication, TTC). We further retrieved the average completeness (via vigiGrade) of the reports in each case series in the years before the communication. We categorised as DME signals those concerning an event in the EMA's list. We described the two groups of signals using medians and interquartile ranges (IQR) and compared them using the Brunner-Munzel test, calculating 95% confidence intervals (95% CI) and P values.

RESULTS

Of 4520 signals, 919 concerned DMEs and 3601 concerned non-DMEs. Signals of DMEs were supported by a median of 15 reports (IQR 6-38 reports) with a completeness score of 0.52 (IQR 0.43-0.62) and signals of non-DMEs by 20 reports (IQR 6-84 reports) with a completeness score of 0.46 (IQR 0.38-0.56). The probability that a random DME signal was supported by fewer reports than non-DME signals was 0.56 (95% CI 0.54-0.58, P < 0.001) and that of one having lower average completeness was 0.39 (95% CI 0.36-0.41, P < 0.001). The median TTCs of DME and non-DME signals did not differ (10 years), but the TTC was as low as 2 years when signals (irrespective of classification) were supported by reports whose average completeness was > 0.80.

CONCLUSIONS

Signals of designated medical events were supported by fewer reports and higher completeness scores than signals of other adverse events. Although statistically significant, the differences in effect sizes between the two groups were small. This suggests that listing certain adverse events as DMEs is not having the expected effect of encouraging a focus on reports of the types of suspected adverse reactions that deserve special attention. Further enhancing the completeness of the reports of suspected adverse drug reactions supporting signals of designated medical events might shorten their time to communication and reduce the number of reports required to support them.

Trends in Adverse Event Reporting Before and After the Introduction of the Med Safety App in Nigeria

INTRODUCTION

Spontaneous reporting of adverse events (AEs) is a mainstay of pharmacovigilance, and an ongoing challenge is how to ensure that more high-quality reports are collected for comprehensive information provision. The Med Safety App, a smartphone-based application, was launched in Nigeria in November 2020 to provide an electronic platform for users to seamlessly report AEs. There has been a paucity of evidence on the use of this application or other mobile applications for reporting adverse drug reactions/AEs following immunization in the Nigerian environment.

OBJECTIVE

The aim of this study was to evaluate the trends in adverse event reporting before and after the introduction of the Med Safety App in Nigeria.

METHODS

This was a retrospective, observational study using data from the VigiFlow database to compare adverse event reporting in Nigeria before and after the deployment of the Med Safety App. The baseline period was 1st April 2019 to 30th October 2020 and the comparison period was 1st November 2020 to 31st May 2022. We used Vigilance Hub, the back-end system for the Med Safety App, to extract data on App downloads and de-identified user statistics. Data were summarized using descriptive statistics, frequencies and proportions. Quality was assessed by assigning a completeness score to each individual case safety report. The Kruskal-Wallis test was used to test for differences in medians between groups.

RESULTS

Following deployment of the App, the Nigerian National Pharmacovigilance Centre recorded an increase in the total number of adverse event reports received in VigiFlow, from 2051 in the baseline period to 18,995 following deployment of the App, with 81.7% of those reported via the Med Safety App. There was a reduction in the proportion of paper-based reporting from 98.4 to 15.7% post-deployment, and direct reporting by consumers increased from 2.7 to 17.6%. Of the 15,526 reports submitted via the App, 15,111 (97.3%) had a completeness score above 70% and 6993 (45%) had a completeness score of 100%. The median completeness score of adverse event reports on the Med Safety App was 6 out of 7. On bivariate analysis using the Kruskal-Wallis test, there was an association between means of reporting and completeness score, and this association was significant, with a p value of 0.0001, which may reflect the validation rules that are applied within the App.

CONCLUSION

Deployment of the Med Safety App increased both the number and quality of adverse event reports; however, more awareness and capacity building are needed to strengthen and sustain reporting on the tool by all categories of healthcare professionals and consumers/patients.

Data-Driven Identification of Factors That Influence the Quality of Adverse Event Reports: 15-Year Interpretable Machine Learning and Time-Series Analyses of VigiBase and QUEST

BACKGROUND

The completeness of adverse event (AE) reports, crucial for assessing putative causal relationships, is measured using the vigiGrade completeness score in VigiBase, the World Health Organization global database of reported potential AEs. Malaysian reports have surpassed the global average score (approximately 0.44), achieving a 5-year average of 0.79 (SD 0.23) as of 2019 and approaching the benchmark for well-documented reports (0.80). However, the contributing factors to this relatively high report completeness score remain unexplored.

OBJECTIVE

This study aims to explore the main drivers influencing the completeness of Malaysian AE reports in VigiBase over a 15-year period using vigiGrade. A secondary objective was to understand the strategic measures taken by the Malaysian authorities leading to enhanced report completeness across different time frames.

METHODS

We analyzed 132,738 Malaysian reports (2005-2019) recorded in VigiBase up to February 2021 split into historical International Drug Information System (INTDIS; n=63,943, 48.17% in 2005-2016) and newer E2B (n=68,795, 51.83% in 2015-2019) format subsets. For machine learning analyses, we performed a 2-stage feature selection followed by a random forest classifier to identify the top features predicting well-documented reports. We subsequently applied tree Shapley additive explanations to examine the magnitude, prevalence, and direction of feature effects. In addition, we conducted time-series analyses to evaluate chronological trends and potential influences of key interventions on reporting quality.

RESULTS

Among the analyzed reports, 42.84% (56,877/132,738) were well documented, with an increase of 65.37% (53,929/82,497) since 2015. Over two-thirds (46,186/68,795, 67.14%) of the Malaysian E2B reports were well documented compared to INTDIS reports at 16.72% (10,691/63,943). For INTDIS reports, higher pharmacovigilance center staffing was the primary feature positively associated with being well documented. In recent E2B reports, the top positive features included reaction abated upon drug dechallenge, reaction onset or drug use duration of <1 week, dosing interval of <1 day, reports from public specialist hospitals, reports by pharmacists, and reaction duration between 1 and 6 days. In contrast, reports from product registration holders and other health care professionals and reactions involving product substitution issues negatively affected the quality of E2B reports. Multifaceted strategies and interventions comprising policy changes, continuity of education, and human resource development laid the groundwork for AE reporting in Malaysia, whereas advancements in technological infrastructure, pharmacovigilance databases, and reporting tools concurred with increases in both the quantity and quality of AE reports.

CONCLUSIONS

Through interpretable machine learning and time-series analyses, this study identified key features that positively or negatively influence the completeness of Malaysian AE reports and unveiled how Malaysia has developed its pharmacovigilance capacity via multifaceted strategies and interventions. These findings will guide future work in enhancing pharmacovigilance and public health.

Automatic Extraction of Comprehensive Drug Safety Information from Adverse Drug Event Narratives in the Korea Adverse Event Reporting System Using Natural Language Processing Techniques

INTRODUCTION

Concerns have been raised over the quality of drug safety information, particularly data completeness, collected through spontaneous reporting systems (SRS), although regulatory agencies routinely use SRS data to guide their pharmacovigilance programs. We expected that collecting additional drug safety information from adverse event (ADE) narratives and incorporating it into the SRS database would improve data completeness.

OBJECTIVE

The aims of this study were to define the extraction of comprehensive drug safety information from ADE narratives reported through the Korea Adverse Event Reporting System (KAERS) as natural language processing (NLP) tasks and to provide baseline models for the defined tasks.

METHODS

This study used ADE narratives and structured drug safety information from individual case safety reports (ICSRs) reported through KAERS between 1 January 2015 and 31 December 2019. We developed the annotation guideline for the extraction of comprehensive drug safety information from ADE narratives based on the International Conference on Harmonisation (ICH) E2B(R3) guideline and manually annotated 3723 ADE narratives. Then, we developed a domain-specific Korean Bidirectional Encoder Representations from Transformers (KAERS-BERT) model using 1.2 million ADE narratives in KAERS and provided baseline models for the task we defined. In addition, we performed an ablation experiment to investigate whether named entity recognition (NER) models were improved when a training dataset contained more diverse ADE narratives.

RESULTS

We defined 21 types of word entities, six types of entity labels, and 49 types of relations to formulate the extraction of comprehensive drug safety information as NLP tasks. We obtained a total of 86,750 entities, 81,828 entity labels, and 45,107 relations from manually annotated ADE narratives. The KAERS-BERT model achieved F1-scores of 83.81 and 76.62% on the NER and sentence extraction tasks, respectively, while outperforming other baseline models on all the NLP tasks we defined except the sentence extraction task. Finally, utilizing the NER model for extracting drug safety information from ADE narratives resulted in an average increase of 3.24% in data completeness for KAERS structured data fields.

CONCLUSIONS

We formulated the extraction of comprehensive drug safety information from ADE narratives as NLP tasks and developed the annotated corpus and strong baseline models for the tasks. The annotated corpus and models for extracting comprehensive drug safety information can improve the data quality of an SRS database.

Completeness of pharmaceutical industry insulin adverse event reports from Africa and the Middle East

BACKGROUND

Growing numbers of patients with diabetes mellitus in Africa and the Middle East on antidiabetic therapies necessitate an understanding of adverse event reporting in these regions. We assessed pharmaceutical industry insulin individual case safety report (ICSR) completeness and completeness-associated variables.

RESEARCH DESIGN AND METHODS

The observational cross-sectional study, set in an insulin-manufacturing company's safety database, assessed vigiGrade® completeness scores of African and Middle Eastern post-marketing ICSRs during January to December 2018, and the effects of case variables on completeness. Low vigiGrade® scores indicated poorly documented ICSRs.

RESULTS

We analyzed 4854 ICSRs; 59.8% from the Middle East. The mean vigiGrade® score was 0.58. Middle Eastern ICSRs had higher mean scores than African ICSRs (0.65 vs. 0.46, p<0.001). Scores peaked at 0.32, 0.70, and 1.00, for Middle Eastern ICSRs compared to 0.35 and 0.50 for African ICSRs. Middle Eastern serious (0.77 vs. 0.47; p<0.001) and solicited (0.70 vs. 0.48; p<0.001) ICSRs had higher mean scores than African ICSRs. Mean scores were highest for Middle Eastern physicians (0.89) and other healthcare professionals (0.82) whereas, in Africa, scores were highest for consumer- (0.47) and pharmacist-reported ICSRs (0.47) (p<0.001).

CONCLUSIONS

Middle Eastern pharmaceutical industry insulin ICSRs were documented with greater detail than African ICSRs. Event seriousness, report source, and reporter type significantly impacted ICSR completeness.

Adverse Events Related to Off-Label Drugs Using Spontaneous Adverse Event Reporting Systems

Purpose

The purpose of this study was to investigate the adverse events (AEs) related to the use of off-label drugs.

Materials and Methods

A cross-sectional study was carried out using available data pertaining to off-label drug were sourced from U.S. FDA spontaneous adverse drug reaction reporting database (FAERS) and Korea Adverse Event Reporting System database (KIDS-KD) for the years 2014 to 2018. The number and frequencies of AE cases were calculated. Disproportionality was analyzed using the proportional reporting ratio (PRR), the reporting odds ratio (ROR), the information component (IC), and the empirical Bayes geometric mean (EBGM) methods.

Results

The reported AEs associated with off-label drug use were more common among older patients compared with younger patients. Gastric nonspecific symptoms and therapeutic procedure (4.16–4.57%) and haemorrage term (4.16–5.29%) were the most common AE symptoms and antithrombotic agents and immunosuppressants were the drugs most commonly reported to cause AEs in FAERS. Secondary term events (43.45–48.62%) including inappropriate schedule of drug administration and medication error were the most common AEs, and immunosuppressants and antipsychotics were the most common AE-related drugs from KIDS-KD. The numbers of reported AEs in new drug categories such as other antineoplastic agents trended to increase from 2014 to 2018 in both datasets.

Conclusion

The numbers of reported AEs with off-label drug increased annually. AEs associated with off-label drugs may have a significant impact on older patients. Healthcare experts should be concerned about prescriptions of off-label drugs, especially anticoagulants and newly developed drugs such as immunosuppressants and antineoplastic agents.

Comparative Safety Profiles of Sedatives Commonly Used in Clinical Practice: A 10-Year Nationwide Pharmacovigilance Study in Korea

This study aims to compare the prevalence and seriousness of adverse events (AEs) among sedatives used in critically ill patients or patients undergoing invasive procedures and to identify factors associated with serious AEs. Retrospective cross-sectional analysis of sedative-related AEs voluntarily reported to the Korea Adverse Event Reporting System from 2008 to 2017 was performed. All AEs were grouped using preferred terms and System Organ Classes per the World Health Organization-Adverse Reaction Terminology. Logistic regression was performed to identify factors associated with serious events. Among 95,188 AEs, including 3132 (3.3%) serious events, the most common etiologic sedative was fentanyl (58.8%), followed by pethidine (25.9%). Gastrointestinal disorders (54.2%) were the most frequent AEs. The most common serious AE was heart rate/rhythm disorders (33.1%). Serious AEs were significantly associated with male sex; pediatrics; etiologic sedative with etomidate at the highest risk, followed by dexmedetomidine, ketamine, and propofol; polypharmacy; combined sedative use; and concurrent use of corticosteroids, aspirin, neuromuscular blockers, and antihistamines (reporting odds ratio > 1, p < 0.001 for all). Sedative-induced AEs are most frequently reported with fentanyl, primarily manifesting as gastrointestinal disorders. Etomidate is associated with the highest risk of serious AEs, with the most common serious events being heart rate/rhythm disorders.

Prevalence and Seriousness of Analgesic-Induced Adverse Events in Korea: A 10-Year Nationwide Surveillance

OBJECTIVES

The objectives of this study were to describe the prevalence and seriousness of analgesic-induced adverse events (AEs) and to identify factors associated with serious analgesic-related AEs in Korea.

METHODS

Voluntarily reported analgesic-induced AEs to the Korea Adverse Event Reporting System from 2007 to 2016 were retrospectively reviewed. Analgesic medications were classified into nonopioids and opioids based on the Anatomical Therapeutic Chemical classification system. All AEs were grouped using System Organ Classes according to the World Health Organization-Adverse Reaction Terminology. Logistic regression was performed to identify factors associated with serious AEs.

RESULTS

Overall, 194,566 AEs (32.2% for nonopioids, 67.8% for opioids) were included in this analysis. The most common causative nonopioid and opioid analgesics was ketorolac (n = 10,789) and tramadol (n = 53,727), respectively. The most frequent AEs were skin and appendage disorders for nonopioids (31.8%) and gastrointestinal disorders (59.5%) for opioids. Serious AEs occurred in 6102 (9.7%) and 3326 (2.5%) cases of the nonopioid and opioid groups, respectively. The most common serious AEs were skin and appendage disorders (33.2%) for nonopioids and neurologic disorders (19.3%) for opioids. Serious AEs were significantly associated with male (odds ratio [OR] = 1.423), advanced age (OR = 1.570), certain causality (OR = 2.304), nonopioid analgesics (OR = 4.182), and polypharmacy (OR = 1.009; P <0.001 for all).

CONCLUSIONS

In Korea, analgesic-induced AEs are prevalent with opioids more commonly implicated. Tramadol is the most common etiologic medication. Serious AEs are more frequently caused by nonopioids with skin and appendage disorders most common.

Signals and trends of Guillain-Barré syndrome after the introduction of live-attenuated vaccines for influenza in the US and South Korean adverse event reporting systems

BACKGROUND

With the advent of live-attenuated, quadrivalent, and cell-cultured vaccines for influenza, there have been discussions on the safety of these vaccines compared to conventional vaccines (such as inactivated, trivalent, and egg-cultured vaccines) because of the development of neurological adverse events (AEs). This study aimed to compare the trends and safety signals in the AE reporting systems of the US and South Korea and, more particularly, to evaluate the association between influenza vaccination and Guillain-Barré syndrome (GBS).

METHODS

In total, 400,535 AE reports from the US Vaccine Adverse Event Reporting System (VAERS) and 28,766 AE reports from the Korea Adverse Event Reporting System (KAERS) between 2005 and 2017 were assessed. Disproportionality analysis was performed to detect the safety signals and examine the potential risk of GBS with influenza vaccination using the case/non-case approach.

RESULTS

In both databases, GBS was the most frequently reported AE following influenza immunization. Using the case/non-case approach, the adjusted reporting odds ratio (ROR) of GBS was 3.57 (95% confidence interval [CI], 3.16-4.03) and 3.09 (95% CI, 0.83-11.45) in the VAERS and KAERS data, respectively. People vaccinated with live-attenuated vaccines reported 2.30 times (95% CI, 1.74-3.05) more cases of GBS than those vaccinated with other types of vaccines.

CONCLUSIONS

Our analysis of the VAERS and KAERS reports for AEs following immunization (AEFI) for influenza shows the need for cautious monitoring regarding the development of GBS after influenza vaccination, particularly, after live-attenuated vaccination. However, owing to potential reporting bias caused by limited AEFI reports after the introduction of new types of influenza vaccines, further prospective safety studies are needed.

Assessment of factors associated with completeness of spontaneous adverse event reporting in the United States: A comparison between consumer reports and healthcare professional reports

What is known and objective

The objectives of this study were to explore completeness of direct adverse event (AE) reports from consumers and healthcare professionals (HCPs), and to discuss the reasons completeness varied among reporters with different occupations.

Methods

We used a total of 5475 direct AE reports to the United States (US) Food and Drug Administration (FDA) from the first and second quarters of 2016 and assessed completeness of basic information (eg, patient sex, age, weight) and information relevant to AEs (eg, suspect and concomitant drugs). Logistic regression analysis was conducted to evaluate the associations between report completeness and reporting backgrounds.

Results and discussion

The completeness of AE reports from consumers was generally greater than that of reports from HCPs. Completeness of specific items varied among different occupations, which may reflect accessibility to, and/or availability of, relevant information for each type of reporter. There was a clear association between the proportion of ‘known’ ADRs in a report and completeness, suggesting that consumers and HCPs are likely to consult labelling information when reporting AEs.

What is new and conclusion

The quality of AE reports seemed to depend on information costs accrued to potential reporters. Researchers should consider the impact of database heterogeneity and possible sample selection bias when using spontaneous AE reports as a sample of events in the United States.