Evaluation of Postmarketing Reports from Industry-Sponsored Programs in Drug Safety Surveillance

Characterization of Pediatric Reports in the US Food and Drug Administration Adverse Event Reporting System from 2010-2020: A Cross-Sectional Study

INTRODUCTION

The Food and Drug Administration Adverse Event Reporting System (FAERS) is a database of adverse event (AE) and medication error reports for drugs and therapeutic biologics. Examining trends of reported individual case safety reports (ICSRs) provides context for evaluating safety concerns.

OBJECTIVE

Characterize pediatric FAERS ICSRs and compare trends (1) to adult reports; (2) within pediatric subgroups.

METHODS

This cross-sectional study examined FAERS ICSRs received between January 1, 2010, through December 31, 2020. Stratified age bands were neonates, infants, younger children, older children, adolescents, and adults. We characterized groups by patient demographic information, suspect products, AEs, and reporter type.

RESULTS

From 2010 to 2020, there were 11,258,995 FAERS ICSRs; 3.1% described pediatric patients. Compared to adults, pediatric ICSRs had higher proportions of all serious outcomes except death. Within pediatric subgroups, neonates had the highest proportions of serious outcomes (96.2%) compared to infants, younger children, older children, and adolescents (79.8%, 67.9%, 59.5%, and 52.7%, respectively). Younger pediatric age groups were more likely to have weight information than older age groups but were less likely to include gender information. The most frequently reported AE was off label use for pediatrics and drug ineffective for adults. Products and AEs reported also differed among pediatric subgroups. Neonates, infants, and adolescents had entirely distinct sets of top five product-event combinations.

CONCLUSION

Pediatric ICSRs represent a minority of FAERS reports but have distinctly different attributes relative to adult ICSRs. Reporting trends also vary within pediatric subgroups, which highlights the need for unique considerations for pediatric safety surveillance.

Performance of subgrouped proportional reporting ratios in the US Food and Drug Administration (FDA) adverse event reporting system

BACKGROUND

Many signal detection algorithms give the same weight to information from all products and patients, which may result in signals being masked or false positives being flagged as potential signals. Subgrouped analysis can be used to help correct for this.

RESEARCH DESIGN AND METHODS

The publicly available US Food and Drug Administration Adverse Event Reporting System quarterly data extract files from 1 January 2015 through 30 September 2017 were utilized. A proportional reporting ratio (PRR) analysis subgrouped by either age, sex, ADE report type, seriousness of ADE, or reporter was compared to the crude PRR analysis using sensitivity, specificity, precision, and c-statistic.

RESULTS

Subgrouping by age (n = 78, 34.5% increase), sex (n = 67, 15.5% increase), and reporter (n = 64, 10.3% increase) identified more signals than the crude analysis. Subgrouping by either age or sex increased both the sensitivity and precision. Subgrouping by report type or seriousness resulted in fewer signals (n = 50, -13.8% for both). Subgrouped analyses had higher c-statistic values, with age having the highest (0.468).

CONCLUSIONS

Subgrouping by either age or sex produced more signals with higher sensitivity and precision than the crude PRR analysis. Subgrouping by these variables can unmask potentially important associations.

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.

Leveraging Case Narratives to Enhance Patient Age Ascertainment from Adverse Event Reports

INTRODUCTION

Missing age presents a significant challenge when evaluating individual case safety reports (ICSRs) in the FDA Adverse Event Reporting System (FAERS). When age is missing in an ICSR's structured field, it may be in the report's free-text narrative.

OBJECTIVES

This study aimed to evaluate the performance and assess the potential impact of a rule-based natural language processing (NLP) tool that utilizes a text string search to identify patients' numerical age from unstructured narratives.

METHODS

Using FAERS ICSRs from 2002 to 2018, we evaluated the annual proportion of ICSRs with age missing in the structured field before and after NLP application. Reviewers manually identified patients' age from ICSR narratives (gold standard) from a random sample of 1500 ICSRs. The gold standard was compared to the NLP-identified age.

RESULTS

During the study period, the percentage of ICSRs missing age in the structured field increased from 21.9 to 43.8%. The NLP tool performed well among the random sample: sensitivity 98.5%, specificity 92.9%, positive predictive value (PPV) 94.9%, and F-measure 96.7%. It also performed well for the subset of ICSRs missing age in the structured field; when applied to these cases, NLP identified age for an additional one million ICSRs (10% of the total number of ICSRs from 2002 to 2018) and decreased the percentage of ICSRs missing age to 27% overall.

CONCLUSIONS

NLP has potential utility to extract patients' age from ICSR narratives. Use of this tool would enhance pharmacovigilance and research using FAERS data.

An Evaluation of Postmarketing Reports with an Outcome of Death in the US FDA Adverse Event Reporting System

INTRODUCTION

Adverse reactions with an outcome of death are inherently important for pharmacovigilance organizations to evaluate. Prior efforts to systematically evaluate individual case safety reports (ICSRs) with an outcome of death have been limited to high-level summaries.

OBJECTIVE

The aim of this study was to characterize ICSRs with an outcome of death contained in the US FDA Adverse Event Reporting System (FAERS) database.

METHODS

All ICSRs received through 31 December 2017 reporting an outcome of death were characterized by patient demographics, suspect product(s), adverse events, and reporter type. Using the ICSR's narrative and reporter information, we classified ICSRs by source to include those from industry-sponsored programs, poison control centers, specialty pharmacies, and litigation. Additionally, a random sample of ICSRs was evaluated for completeness of structured data fields and manually reviewed for the availability of key information in the narrative (i.e. cause of death, medical history, and causality assessment).

RESULTS

Overall, 1,053,716 ICSRs with a death outcome were received in the study period. Ten medications treating conditions for malignancies, pain, and kidney disease accounted for nearly 20% of all fatal ICSRs. ICSRs originating from industry-sponsored programs, poison control centers, litigation, and specialty pharmacies accounted for 14%, 6.5%, 5.0%, and 3.3% of all fatal ICSRs, respectively. ICSRs in which the only adverse event coded was 'death' were more likely to be missing structured data and less likely to include key information in the narrative.

CONCLUSION

Understanding the origins and characteristics of ICSRs with an outcome of death supports meaningful evaluations and interpretations of FAERS data. A wide variability in ICSR quality exists, even in those reports with the most serious outcome.

Exploring Completeness of Adverse Event Reports as a Tool for Signal Detection in Pharmacovigilance

BACKGROUND

Completeness of adverse event (AE) reports is an important component of quality for good pharmacovigilance practices. We aimed to evaluate the impact of incorporating a measure of completeness of AE reports on quantitative signal detection.

METHODS

An internal safety database from a global pharmaceutical company was used in the analysis. vigiGrade, an index score of completeness, was derived for each AE report. Data from various patient support programs (PSPs) were categorized based on average vigiGrade score per PSP. Performance of signal detection was compared between: (1) weighting and not weighting by vigiGrade score; and, (2) well documented and poorly documented PSPs using sensitivity, specificity, area under the receiver operating characteristics curve (AUC) and time-to-signal detection.

RESULTS

The ability to detect signals did not differ significantly when weighting by vigiGrade score [sensitivity (50% vs. 45%, p = 1), specificity (82.8% vs. 82.8%, p = 1), AUC (0.66 vs. 0.63, p = 0.051) or time-to-signal detection (HR 0.81, p = 0.63)] compared to not weighting. Well documented PSPs were better at detecting signals than poorly documented PSPs (AUC 0.66 vs. 0.52; p = 0.041) but time-to-signal detection did not differ significantly (HR 1.54, p = 0.42).

CONCLUSION

Completeness of AE reports did not significantly impact the ability to detect signals when weighting by vigiGrade score or restricting the database based on the level of completeness. While the vigiGrade helps provide quality assessments of AE reports and prioritize cases for review, our findings indicate the tool might not be useful for quantitative signal detection when used by itself.

Impact of adverse event reports from marketing authorization holder-sponsored patient support programs on the performance of signal detection in pharmacovigilance

OBJECTIVES

Marketing authorization holder (MAH)-sponsored patient support programs (PSPs) are a major source of adverse event (AE) reports. The impact of reports from PSPs on the ability to detect AE signals is unclear. We compared signal detection performance using data from PSPs vs. non-PSP sources, and between PSPs providing clinical services vs. PSPs not providing clinical services.

METHODS

Data were obtained from an internal safety database for a global pharmaceutical company 2015-2017. We assessed whether signals were detected for the reference drug-AE pairs using data from PSPs vs. non-PSP sources, and among different PSP services. The performance was evaluated by four measures including area under the receiver operating characteristic curve (AUC) and time-to-signal detection.

RESULTS

While the majority of reports were from PSPs, non-PSP sources were better and faster at detecting signals (AUC 0.63 vs. 0.41, p = 0.035; HR 3.52, p = 0.014) compared to PSPs. Within PSPs, PSPs providing clinical services were marginally better at detecting signals (AUC 0.60 vs. 0.41, p = 0.053) but not faster compared to PSPs not providing clinical services.

CONCLUSION

Reports of AEs from PSPs had worse signal detection performance compared to non-PSP sources. Pharmacovigilance experts should be mindful when using databases that contain reports from PSPs for signal detection.

Adverse Drug Reaction Case Safety Practices in Large Biopharmaceutical Organizations from 2007 to 2017: An Industry Survey

INTRODUCTION

Drug safety remains a top global public health concern. An increase in the number of data sources available has increased the complexity of pharmacovigilance operations, so the US FDA has created draft guidance focusing on optimizing drug safety data for well-characterized medicines. However, to date, no data demonstrating changes in reports have been presented.

OBJECTIVES

This study provided data assessing changes in individual case safety reports (ICSRs) and aggregate reports (ARs) for large biopharmaceutical companies from 2007 to 2017. This study also evaluated current trends on the use of advanced machine and deep learning in order to process all data captured for ICSRs as well as opinions from industry thought leaders on creating a sustainable case-processing operation.

METHODOLOGY

Using data captured from Navitas Life Science's annual pvnet^® benchmark, we calculated workload indicators characterizing pharmacovigilance operations for large biopharmaceutical organizations. Workload indicators included the number of ICSRs by organization, the number of ARs, and the number and types of data sources used. We also conducted structured in-depth interviews with seven biopharmaceutical executives to discover the reasons for changes in workload indicators across time as well as current strategies for increasing efficiencies in drug safety reporting.

RESULTS

The median number of ICSRs increased from 84,960 cases in 2007 to over 200,000 cases in 2017; this increase was largely attributable to an increase in both nonserious cases and follow-up cases. Member companies reported using 12 ± 3 data sources for case identification. The number of ARs also increased from a median of 70 reports in 2007 to 258 reports in 2017. To address these increases, 61% of the biopharmaceutical organizations we surveyed planned to adopt machine learning for full ICSR processing; however, as of 2018, none of the organizations surveyed had mechanisms in place.

CONCLUSION

This study demonstrated that pharmacovigilance departments are currently burdened by ever-increasing case volumes. With increased guidance from regulatory agencies, as well as improvements in artificial intelligence and natural language processing, biopharmaceutical organizations must determine the most resource-efficient and sustainable methods to process the growing volume of cases.

Industry Assessment of the Contribution of Patient Support Programs, Market Research Programs, and Social Media to Patient Safety

BACKGROUND

Over the past decade, the volume of adverse events (AEs) reported to marketing authorization holders and regulators has been rapidly increasing each year, which has led to significant challenges in patient safety assessment. Three data sources that have largely contributed to the expansion in adverse event reports are patient support programs (PSPs), market research programs (MRPs), and social media. In this study, we sought to further understand the contribution of these safety data sources to the characterization of a product's safety profile.

METHODS

Three separate approaches were taken that, when combined, can be used to evaluate each data source. The first identified any core company data sheet changes or drug safety label changes. The second evaluated the similarity of information through proportions of AEs between each solicited data source and spontaneous sources. Lastly, the completeness of information reported was evaluated through vigiGrade and compared across each data source.

RESULTS

One drug safety label change was identified from a patient support program, which involved regular contact with health care providers. No label changes were identified from market research programs or social media. Patient support programs, market research programs, and social media report similar proportions for HLGT as spontaneous sources. Market research programs and social media display very low vigiGrade scores. When broken down by subtype, traditional PSPs display high vigiGrade scores, while patient assistance programs display lower vigiGrade scores that were program dependent.

CONCLUSIONS

This study did not demonstrate that certain data sources such as market research programs, social media, and patient assistance programs meaningfully contributed to the further understanding of the characterization of a product's safety profile.