Significance of data mining in routine signal detection: Analysis based on the safety signals identified by the FDA.

PURPOSE: Data mining has been introduced as one of the most useful methods for signal detection by spontaneous reports, but data mining is not always effective in detecting all safety issues. To investigate appropriate situations in which data mining is effective in routine signal detection activities, we analyzed the characteristics of signals that the US Food and Drug Administration (FDA) identified from the FDA Adverse Event Reporting System (FAERS). METHODS: Among the signals that the FDA identified from the FAERS between 2008 1Q and 2014 4Q, we selected 233 signals to evaluate in this study. We conducted a disproportionality analysis and classified these signals into two groups according to the presence or absence of statistical significance in the reporting odds ratio (ROR). Then, we compared the two groups based on the characteristics of the suspected drugs and adverse events (AEs). RESULTS: Safety signals were most frequently identified for new drugs that had been on the market for less than 5 years, but some signals were still identified for old drugs (≥20 years), and most of them were statistically significant. The proportion of the signals for "serious" events was significantly higher in the group of nonsignals by ROR (Fisher's exact test, P = 0.032). CONCLUSIONS: Data mining was shown to be effective in the following situations: (1) early detection of safety issues for newly marketed drugs, (2) continuous monitoring of safety issues for old drugs, and (3) signal detection of nonserious AEs, to which little attention is usually given.

Association between infection and severe drug adverse reactions: an analysis using data from the Japanese Adverse Drug Event Report database.

PURPOSE: It has been reported recently that immune reactions are involved in the pathogenesis of certain types of adverse drug reactions (ADRs). We aimed to determine the associations between infections and drug-induced interstitial lung disease (DILD), rhabdomyolysis, Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN), or drug-induced liver injury (DILI) using a spontaneous adverse drug event reporting database in Japan. METHODS: The reported cases were classified into three categories (anti-infectious drug group, concomitant infection group, and non-infection group) based on the presence of anti-infectious drugs (either as primary suspected drug or concomitant drug) and infectious disease. We assessed the association between four severe ADRs and the presence and seriousness of infection using logistic regression analysis. RESULTS: We identified 177,649 cases reported in the study period (2009-2013). Logistic regression analysis showed significant positive associations between infection status and onset of SJS/TEN or DILI (SJS/TEN: anti-infectious drug group: odds ratio (OR) 2.04, 95% CI [1.85-2.24], concomitant infection group: OR 2.44, 95% CI [2.21-2.69], DILI: anti-infectious drug group: OR 1.27, 95% CI [1.09-1.49], concomitant infection group: OR 1.25, 95% CI [1.04-1.49]), compared to the non-infection group. By contrast, there were negative or no associations between infection and DILD or rhabdomyolysis. A significantly positive association between infection and SJS/TEN seriousness (OR 1.48, 95% CI [1.10-1.98]) was observed. CONCLUSIONS: This study suggested that infection plays an important role in the development of SJS/TEN and DILI. For the patients with infection and/ or anti-infectious drugs, careful monitoring for severe ADRs, especially SJS/TEN, might be needed.

Factors Influencing Regulatory Decision-Making in Signal Management: Analysis Based on the Signals Identified from the FAERS.

PURPOSE: This study aimed to identify factors that influence the decision to take safety regulatory actions in routine signal management based on spontaneous reports. For this purpose, we analyzed the safety signals identified from the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) and related information. METHOD: From the signals that the FDA identified in the FAERS between 2008 1Q and 2014 4Q, we selected 216 signals for which regulatory action was or was not taken. Characteristics of the signals were extracted from the FAERS quarterly reports that give information about what signals were identified from the FAERS and what actions were taken for them, and the FAERS data released in the same quarter when the signal was published. Univariate and multivariable logistic regression analysis was used to assess the relationship between the characteristics of each of the signals and the decision on regulatory action. RESULT: As a result of the univariate logistic regression analysis, we selected 5 factors (positive rechallenge, number of cases accumulated in the last one-year period before the signal indication, previous awareness, serious outcome, risk for special populations) to include in the multivariable logistic regression model (p < 0.2). The multivariate logistic regression analysis showed that the number of cases accumulated in the last one-year period before the signal indication and previous awareness were associated with the regulatory action (p < 0.05). CONCLUSION: The present study showed that number of cases accumulated in the last one-year period before the signal indication and previous awareness potentially associated with the United States regulatory action. When assessing safety signals, we should be careful of the adverse events with a large number of cases accumulated rapidly in a short period. In addition, we should pay attention to new information on not only unknown risks but also previously identified and potential risks.

Effect of database profile variation on drug safety assessment: an analysis of spontaneous adverse event reports of Japanese cases.

BACKGROUND: The use of a statistical approach to analyze cumulative adverse event (AE) reports has been encouraged by regulatory authorities. However, data variations affect statistical analyses (eg, signal detection). Further, differences in regulations, social issues, and health care systems can cause variations in AE data. The present study examined similarities and differences between two publicly available databases, ie, the Japanese Adverse Drug Event Report (JADER) database and the US Food and Drug Administration Adverse Event Reporting System (FAERS), and how they affect signal detection. METHODS: Two AE data sources from 2010 were examined, ie, JADER cases (JP) and Japanese cases extracted from the FAERS (FAERS-JP). Three methods for signals of disproportionate reporting, ie, the reporting odds ratio, Bayesian confidence propagation neural network, and Gamma Poisson Shrinker (GPS), were used on drug-event combinations for three substances frequently recorded in both systems. RESULTS: The two databases showed similar elements of AE reports, but no option was provided for a shareable case identifier. The average number of AEs per case was 1.6±1.3 (maximum 37) in the JP and 3.3±3.5 (maximum 62) in the FAERS-JP. Between 5% and 57% of all AEs were signaled by three quantitative methods for etanercept, infliximab, and paroxetine. Signals identified by GPS for the JP and FAERS-JP, as referenced by Japanese labeling, showed higher positive sensitivity than was expected. CONCLUSION: The FAERS-JP was different from the JADER. Signals derived from both datasets identified different results, but shared certain signals. Discrepancies in type of AEs, drugs reported, and average number of AEs per case were potential contributing factors. This study will help those concerned with pharmacovigilance better understand the use and pitfalls of using spontaneous AE data.

Association between acute myelogenous leukemia and thrombopoietin receptor agonists in patients with immune thrombocytopenia.

OBJECTIVE: The development of myeloid malignancies is a concern when administering thrombopoietin receptor (or the myeloproliferative leukemia virus proto-oncogene product, MPL) agonists. Progression from myelodysplastic syndrome (MDS) to acute myelogenous leukemia [AML, 9 (6.12%) AML patients among 147 MDS subjects] was reported in a clinical trial. However, only one (0.15%) case of AML among 653 immune thrombocytopenic purpura (ITP) subjects was reported. Our objective was to determine whether there is currently a safety signal in the FDA files termed Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) for AML in ITP patients who receive MPL agonists. METHODS: We conducted a case-controlled study using the FAERS as a source of case and control data. We compared demographic characteristics, such as gender, age and exposure to MPL agonists between AML patients and others among ITP subjects registered between 2002 and 2011. RESULTS: Total of 4,821 ITP subjects were identified, including 62 AML patients. The number of patients treated with romiplostim and eltrombopag was 54 (1.74%) AML patients among 3,102 ITP subjects and nine (1.52%) AML patients among 594 ITP subjects, respectively. It should be noted that all AML patients were exposed to one or more MPL agonists. Another factor associated with AML was male gender. CONCLUSION: We herein report an association between AML and MPL agonist use in ITP subjects. Due to various biases and the incompleteness of the FAERS data, further studies are warranted to determine whether the detected signal is a real risk. Physicians should not alter their prescribing behaviors based on this single preliminary analysis.