What Is the Plural of a 'Yellow' Anecdote?

Drug-induced immune hemolytic anemia: detection of new signals and risk assessment in a nationwide cohort study

ABSTRACT

More than 130 drugs have been suspected to induce immune hemolytic anemia. Comparative studies measuring the risk of drug-induced immune hemolytic anemia (DIIHA) are lacking. We aimed (1) to detect new signals of DIIHA, excluding vaccines, and (2) to assess the association between all suspected drugs and the occurrence of immune hemolytic anemia in a nationwide comparative study. The new signals were identified using a disproportionality study (case/noncase design) in the World Pharmacovigilance Database, Vigibase, among the cases of adverse drug reactions reported up to February 2020 (>20 million). We then conducted a comparative study in the French National health database that links sociodemographic, out-of-hospital, and hospital data for the entire population (67 million individuals). Associations between exposure to drugs (those already reported as DIIHA, plus new signals identified in Vigibase) and incident cases of immune hemolytic anemia (D59.0 and D59.1 diagnosis codes of the International Classification of Diseases, version 10) from 2012 to 2018 were assessed with case-control and case-crossover designs. In Vigibase, 3371 cases of DIIHA were recorded. Fifty-nine new signals were identified resulting in a final list of 112 drugs marketed in France and measurable in the nationwide cohort (n = 4746 patients with incident immune hemolytic anemia included in the case-control analysis matched with 22 447 controls from the general population). We identified an association between immune hemolytic anemia occurrence and some antibiotics, antifungal drugs, ibuprofen, acetaminophen, furosemide, azathioprine, and iomeprol.

Adverse Drug Reaction Risk Measures: A Comparison of Estimates from Drug Surveillance and Randomised Trials

BACKGROUND

Most drug regulatory agencies and pharmaceutical companies hold databases of spontaneous reports of suspected adverse drug reactions (ADRs). Detection systems for ADR signals have been created by specialists to analyse such reports, based on the concept of disproportionality, in order to support safety decision making. However, these measures are often misinterpreted by non-specialists in pharmacovigilance.

OBJECTIVES

Our aim was to assess agreement between estimates of risk from spontaneous reports of suspected ADRs and estimates of risks of ADRs from randomised controlled trials (RCTs).

METHODS

From 150 drugs randomly selected from the US Food and Drug Administration's Adverse Event Reporting System (FAERS), we identified drugs where FAERS provided reporting odds ratios (RORs) and corresponding systematic reviews from the Cochrane database gave (pooled) odds ratios (ORs) for the same drugs and adverse reactions. We assessed agreement between (ln) RORs and (ln) ORs using the Pearson correlation coefficient and the Bland-Altman agreement method, and performed sensitivity analyses.

RESULTS

We identified 6 drugs and 125 ADRs. Overall, there was a weak correlation (r = 0.20) between RORs (FAERS) and ORs (RCTs). However, we observed a stronger correlation (r = 0.78) between RORs and ORs for one drug (roflumilast) that received market approval relatively recently (2011).

CONCLUSIONS

Spontaneous reporting of suspected ADRs is an important tool for regulatory agencies and pharmaceutical companies in making decisions and detecting drug safety signals. Although there was moderate-to-strong agreement between ADR risk estimates from drug surveillance and RCTs for one drug, this study illustrates the current recommendations not to use disproportionality measures as valid proxies for risk estimates.

Good Signal Detection Practices: Evidence from IMI PROTECT

Over a period of 5 years, the Innovative Medicines Initiative PROTECT (Pharmacoepidemiological Research on Outcomes of Therapeutics by a European ConsorTium) project has addressed key research questions relevant to the science of safety signal detection. The results of studies conducted into quantitative signal detection in spontaneous reporting, clinical trial and electronic health records databases are summarised and 39 recommendations have been formulated, many based on comparative analyses across a range of databases (e.g. regulatory, pharmaceutical company). The recommendations point to pragmatic steps that those working in the pharmacovigilance community can take to improve signal detection practices, whether in a national or international agency or in a pharmaceutical company setting. PROTECT has also pointed to areas of potentially fruitful future research and some areas where further effort is likely to yield less.

Revisiting the reported signal of acute pancreatitis with rasburicase: an object lesson in pharmacovigilance

INTRODUCTION

There is an interest in methodologies to expeditiously detect credible signals of drug-induced pancreatitis. An example is the reported signal of pancreatitis with rasburicase emerging from a study [the 'index publication' (IP)] combining quantitative signal detection findings from a spontaneous reporting system (SRS) and electronic health records (EHRs). The signal was reportedly supported by a clinical review with a case series manuscript in progress. The reported signal is noteworthy, being initially classified as a false-positive finding for the chosen reference standard, but reclassified as a 'clinically supported' signal.

OBJECTIVE

This paper has dual objectives: to revisit the signal of rasburicase and acute pancreatitis and extend the original analysis via reexamination of its findings, in light of more contemporary data; and to motivate discussions on key issues in signal detection and evaluation, including recent findings from a major international pharmacovigilance research initiative.

METHODOLOGY

We used the same methodology as the IP, including the same disproportionality analysis software/dataset for calculating observed to expected reporting frequencies (O/Es), Medical Dictionary for Regulatory Activities Preferred Term, and O/E metric/threshold combination defining a signal of disproportionate reporting. Baseline analysis results prompted supplementary analyses using alternative analytical choices. We performed a comprehensive literature search to identify additional published case reports of rasburicase and pancreatitis.

RESULTS

We could not replicate positive findings (e.g. a signal or statistic of disproportionate reporting) from the SRS data using the same algorithm, software, dataset and vendor specified in the IP. The reporting association was statistically highlighted in default and supplemental analysis when more sensitive forms of disproportionality analysis were used. Two of three reports in the FAERS database were assessed as likely duplicate reports. We did not identify any additional reports in the FAERS corresponding to the three cases identified in the IP using EHRs. We did not identify additional published reports of pancreatitis associated with rasburicase.

DISCUSSION

Our exercise stimulated interesting discussions of key points in signal detection and evaluation, including causality assessment, signal detection algorithm performance, pharmacovigilance terminology, duplicate reporting, mechanisms for communicating signals, the structure of the FAERs database, and recent results from a major international pharmacovigilance research initiative.