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.

Pooling Different Safety Data Sources: Impact of Combining Solicited and Spontaneous Reports on Signal Detection In Pharmacovigilance.

INTRODUCTION: The volume of adverse events (AEs) collected, analysed, and reported has been increasing at a rapid rate for over the past 10 years, largely due to the growth of solicited programmes. The proportion of various forms of solicited case data has evolved over time, with the main relative volume increase coming from Patient Support Programmes. In this study, we sought to examine the impact of the pooling of AE report data from solicited sources with data from spontaneous sources to safety signal detection using disproportionality analysis methods. METHODS: Two conditions were explored in which disproportionality scores from hypothetical drugs were evaluated in a simulated safety database. The first condition held occurrence of events constant and varied solicited case volume, while the second condition varied both proportion of occurrence of events and solicited case volume. RESULTS: In the first setting, where all AE terms have the same probability to occur with any drug, increasing volumes of solicited cases while keeping occurrence of events constant leads to reduced variability in disproportionality scores, consequently reducing or eliminating identified signals of disproportionate reporting. In the second setting, varying both case volume and reporting rates can mask true safety signals and falsely identify signals where there are none. CONCLUSIONS: This analysis of simulated data suggests that pooling AE data from solicited sources with spontaneous case data may impact the results of disproportionality analyses, masking true safety signals and identifying false positives. Therefore, increased volumes of safety data do not necessarily correlate with improved safety signal detection.

An Alternative to Disproportionality: A Frequency-Based Method for Pharmacovigilance Data Mining.

BACKGROUND: Safety surveillance relies on mining of large pharmacovigilance (PV) databases to generate insights regarding the safe use of pharmaceutical products. The predominant approach to PV data mining involves computation of disproportionality scores for drug-adverse event (drug-AE) pairs. However, this approach requires a database to be sufficiently large, sufficiently diverse for the analysis to be reliably sensitive and specific, and fails to consider the particular safety profile of a product. OBJECTIVE: The present study proposes and tests a novel, frequency-based approach to PV data mining that (1) leverages product knowledge and historical drug-AE trends and (2) imposes no requirement for the size and diversity of the database to which it is applied. METHOD: A focus group of physicians and scientists was convened to identify quantitative characteristics of data trends that they consider informative when reviewing counts of adverse events for products under surveillance. Feedback was transferred into a series of decision rules that, when applied to adverse event counts, identifies adverse event trends that are classified as Continuing Trend, Emerging Trend, or No Trend. Regression analyses are completed to verify the presence of a linear trend; and categorical measures of association completed to compare this frequency-based approach to disproportionality scores in a simulated database. RESULTS: A significant, positive linear trend is present for the Continuing Trend and Emerging Trend categories ( P < .0001). There is a significant association between trend categorizations and disproportionality scores ( P < .0001). CONCLUSION: The proposed alternative frequency-based method for PV data mining would be useful where disproportionalities scores are not appropriate. Additionally, this method may be useful in conjunction with disproportionality scores, where appropriate, highlighting adverse events that are both reported disproportionately and have increasing trends.

Timing of administration of dolasetron affects dose necessary to prevent postoperative nausea and vomiting.

STUDY OBJECTIVE: To determine if the timing of administration affects the dose of dolasetron necessary to prevent postoperative nausea and vomiting (PONV). DESIGN: Pooled data from 8 randomized, multicenter, double-blind, placebo-controlled studies with common endpoints. SETTING: University hospital. PATIENTS: A total of 4,587 ASA physical status I, II, and III patients, including 4,124 females undergoing primarily gynecologic procedures and 463 males undergoing various procedures (i.e., thyroidectomy or orthopedic, ophthalmologic, urologic, ENT, or laparoscopic surgery). INTERVENTIONS: Balanced general anesthesia was used during all procedures. Patients received a dose of dolasetron either for prevention of PONV (25 or 50 mg IV at induction; 25, 50, 100, or 200 mg orally 1 to 2 hours pre-induction; or 12.5, 25, 50, or 100 mg IV at end of anesthesia) or for treatment of PONV (12.5, 25, 50, or 100 mg IV). One PONV prevention study had an ondansetron (comparator) group. MEASUREMENTS: Outcome measures over a 24-hour study period included complete response (defined as no vomiting/retching and no need for rescue medication), percentage of patients without nausea [defined as nausea visual analog scale (VAS) score < 5 mm], and maximum nausea according to VAS score. MAIN RESULTS: A 12.5-mg IV dose of dolasetron resulted in a complete response rate that was statistically significantly higher than placebo and comparable to higher dolasetron doses (25 mg to 100 mg IV) when administered either near the end of anesthesia for prevention of PONV or at the onset of symptoms for treatment of PONV. In contrast, when administered at induction of anesthesia, a statistically significant treatment response was observed with dolasetron 50 mg IV, but not at a lower dose. CONCLUSIONS: When dosed near the end of anesthesia, a 12.5 mg IV dose of dolasetron was comparable to higher doses administered at or before induction of anesthesia.