Signals Of Disproportionate Reporting Research Articles

Descriptive analysis on disproportionate medication errors and associated patient characteristics in the Food and Drug Administration's Adverse Event Reporting System.

Medication errors (MEs) are a major public health concern which can cause harm and financial burden within the healthcare system. Characterizing MEs is crucial to develop strategies to mitigate MEs in the future. To characterize ME-associated reports, and investigate signals of disproportionate reporting (SDRs) on MEs in the Food and Drug Administration's Adverse Event Reporting System (FAERS). FAERS data from 2004 to 2020 was used. ME reports were identified with the narrow Standardised Medical Dictionary for Regulatory Activities® (MedDRA®) Query (SMQ) for MEs. Drug names were converted to the Anatomical Therapeutic Chemical (ATC) classification. SDRs were investigated using the reporting odds ratio (ROR). In total 488 470 ME reports were identified, mostly (59%) submitted by consumers and mainly (55%) associated with females. Median age at time of ME was 57 years (interquartile range: 37-70 years). Approximately 1 out of 3 reports stated a serious health outcome. The most prevalent reported drug class was "antineoplastic and immunomodulating agents" (25%). The most common ME type was "incorrect dose administered" (9%). Of the 1659 SDRs obtained, adalimumab was the most common drug associated with MEs, noting a ROR of 1.22 (95% confidence interval: 1.21-1.24). This study offers a first of its kind characterization of MEs as reported to FAERS. Reported MEs are frequent and may be associated with serious health outcomes. This FAERS data provides insights on ME prevention and offers possibilities for additional in-depth analyses.

Abstract 18525: Sex Differences in Reporting of Statin-Associated Diabetes Mellitus - A Pharmacovigilance Analysis of Us Drug Safety Data

Introduction: Limited observational and clinical trial data have suggested a higher likelihood of statin-associated diabetes mellitus (DM) in women compared with men. However, real-world evidence of sex differences in statin-associated DM generally and with specific statins are lacking. Spontaneous adverse reporting pharmacovigilance systems provide an opportunity to assess these potential sex differences. Hypothesis: Statin-associated DM is reported to the FDA disproportionately more in women than in men. Methods: : Adverse event reports from the FDA Adverse Event Reporting System between 1960 and 2021 were obtained. Cases that included lovastatin, fluvastatin, pravastatin, simvastatin, atorvastatin, and rosuvastatin were analyzed together and individually with respect to reporting of DM vs. all other reactions starting at FDA approval of each. Signals of disproportionate reporting (SDR) were identified using the proportional reporting ratio (PRR) with a threshold of 2, and X2 of 4 with > 3 cases of reaction if interest. Differences between SDR in men vs. women were identified using a lower bound of the 95% confidence interval of the Reporting Odds Ratio (ROR) > 1 in > 3 events. Results: In both men and women, DM was associated with a significantly elevated PRR for all statins combined and individually for atorvastatin, lovastatin, rosuvastatin, fluvastatin, simvastatin, and nominally for pitavastatin. Pravastatin was disproportionately reported in women only (M=1.80, W=2.32). Using ROR, statin-induced DM was reported significantly more frequently for atorvastatin, simvastatin, pravastatin, and rosuvastatin but not for pitavastatin, fluvastatin, or lovastatin. Conclusions: Statin-associated DM is disproportionately reported to the FDA in both men and women for most agents compared with other adverse events. Importantly, statin-associated DM is reported significantly more in women than men for the statins most commonly used in current practice.

Impact of the COVID-19 pandemic on the spontaneous reporting and signal detection of adverse drug events

AbstractExternal factors severely affecting in a short period of time the spontaneous reporting of adverse events (AEs) can significantly impact drug safety signal detection. Coronavirus disease 2019 (COVID-19) represented an enormous challenge for health systems, with over 767 million cases and massive vaccination campaigns involving over 70% of the worldwide population. This study investigates the potential masking effect on certain AEs caused by the substantial increase in reports solely related to COVID-19 vaccines within various spontaneous reporting systems (SRSs). Three SRSs were used to monitor AEs reporting before and during the pandemic, namely, the World Health Organisation (WHO) global individual case safety reports database (VigiBase®), the United States Food and Drug Administration Adverse Event Reporting System (FAERS) and the Japanese Adverse Drug Event Report database (JADER). Findings revealed a sudden over-reporting of 35 AEs (≥ 200%) during the pandemic, with an increment of the RRF value in 2021 of at least double the RRF reported in 2020. This translates into a substantial reduction in signals of disproportionate reporting (SDR) due to the massive inclusion of COVID-19 vaccine reports. To mitigate the masking effect of COVID-19 vaccines in post-marketing SRS analyses, we recommend utilizing COVID-19-corrected versions for a more accurate assessment.

Renal Safety Profile of BCR-ABL Tyrosine Kinase Inhibitors in a Real-Life Setting: A Study Based on Vigibase®, the WHO Pharmacovigilance Database.

Alongside their BCR-ABL specificity, TKIs used in chronic myeloid leukemia also target other tyrosine kinases expressed in the kidney such as PDGFR, c-KIT, SRC, and VEGFR, which may result in specific renal adverse drug reaction (ADR). To evaluate the renal safety profile in real-life conditions, a case/non-case study was performed on VigiBase®, the WHO global safety database. From 7 November 2001 to 2 June 2021, all cases in which the involvement of imatinib, dasatinib, nilotinib, bosutinib, and ponatinib was suspected in the occurrence of renal ADR were extracted from VigiBase®. Disproportionality analyses were assessed using the reporting odds ratio. A total of 1409 cases were included. Imatinib accounts for half of the reported cases. A signal of disproportionate reporting (SDR) of renal failure and fluid retention was found for the five TKIs. Only dasatinib and nilotinib were related to an SDR for nephrotic syndrome. Nilotinib and ponatinib were related to an SDR for renal artery stenosis, while dasatinib was related to an SDR for thrombotic microangiopathy. No SDR for tubulointerstitial nephritis was observed. This study identified a new safety signal, nephrotic syndrome, for nilotinib and highlights the importance of post-marketing safety surveillance.

Supervised Machine Learning-Based Decision Support for Signal Validation Classification.

IntroductionSignal validation in pharmacovigilance is the process of evaluating data to decide whether evidence is sufficient to justify further assessment of a detected signal. During the signal validation process, safety experts in our organization are required to review signals of disproportionate reporting (SDRs) and classify them into one of six predefined categories.ObjectiveThis experiment explored the extent to which predictive machine learning (ML) models can support the decision making of safety experts by accurately identifying the most appropriate predefined signal validation category.MethodsWe extracted cumulative data for six medicinal products, consisting of historic SDR validations and Individual Case Safety Reports, from the company’s safety database for training and testing of the ML model. We implemented a decision tree-based supervised multiclass classifier model termed Gradient Boosted Trees followed by a SHapley Additive exPlanations (SHAP) analysis to mitigate the “black box” effect of the ensemble model by identifying the key predicting features in the model. Following a retrospective analysis, a prospective experiment was conducted to test the model accuracy and user acceptance in a real-life setting.ResultsThe prediction accuracy of our ML model ranged from 83 to 86% over 3 months for the six medicinal products. The applicability of the model was confirmed by the company’s safety experts. Additionally, the systematic predictions provided valuable information to the safety experts and assisted them in reviewing the SDRs efficiently and consistently.ConclusionsThis experiment demonstrated that it is possible to train a multiclass classification model to accurately predict signal validation categories for SDRs. More importantly, the transparency of the predictions provided by the SHAP analysis led to high acceptance by the safety experts.

Renal Safety Profile of EGFR Targeted Therapies: A Study from VigiBase® the WHO Global Database of Individual Case Safety Reports.

Simple SummaryDrugs targeting the epithelial growth factor receptor (EGFR) are used in pulmonary and digestive cancers and represent major medical progress. In addition to its localization in cancer cells, EGFR can also be found in the kidney. This observation raises the question of the renal toxicity of these drugs. This issue has been addressed in the present study conducted on safety data from the largest international pharmacovigilance database, VigiBase®. This study showed that the renal toxicity of these drugs is mainly represented by renal failure in the context of digestive toxicity. A new adverse effect called haemolytic and uremic syndrome or thrombotic microangiopathy has been found for erlotinib, which is the first anti-EGFR drug to obtain market authorisation. This signal has to be confirmed. No other renal toxicity has been found related to anti-EGFR drugs, in particular, neither glomerular nor tubular toxicity.Kidney EGFR expression together with reported cases of glomerular diseases in the context of anti-EGFR drug administration raise concerns about the renal safety profile of these drugs. This issue is addressed in a case/non-case study carried out on VigiBase®, the WHO global database of individual case safety reports (ICRS). Disproportionality analysis of renal adverse effects related to the selected anti-EGFR drugs, erlotinib, gefitinib, afatinib, osimertinib, cetuximab and panitumumab, was assessed using the reporting odds ratio (ROR). Nine hundred and eighty-nine ICRSs were included. A signal of disproportionate reporting (SDR) was found for afatinib (ROR = 2.70; 95% CI [2.22–3.29]) and erlotinib (ROR = 1.73; 95% CI [1.46–2.04]) with acute kidney injury, and for afatinib (ROR = 2.41; 95% CI [1.78–3.27]), cetuximab (ROR = 1.42; 95% CI [1.14–1.78]) and erlotinib (ROR = 2.23; 95% CI [1.80–2.77]) with renal failure. The preferred term “diarrhoea” was frequently reported in the included cases. An SDR was found for erlotinib with haemolytic and uremic syndrome (ROR = 4.01; 95% CI [1.80–8.94]) and thrombotic microangiopathy (ROR = 4.94; 95% CI [2.80–8.72]). No SDR was seen for glomerular or tubule-interstitial diseases. This study showed that the anti-EGFR drug renal toxicity is mainly related to renal failure in the context of digestive toxicity.

Safety Profile of Ibrutinib: An Analysis of the WHO Pharmacovigilance Database.

As ibrutinib has become a standard of care in B-cell malignancies in monotherapy or in combination with other agents, definition of its safety profile appears essential. The aim of this study was to further characterize the safety profile of ibrutinib through the identification of potential safety signals in a large-scale pharmacovigilance database. All serious individual case safety reports (ICSRs) in patients aged ≥18 years involving ibrutinib suspected in the occurrence of serious adverse drug reactions or drug interacting from November 13th, 2013 to December 31st, 2020 were extracted from VigiBase, the World Health Organization global safety database. Disproportionality reporting was assessed using the information component (IC) and the proportional reporting ratio (PRR), with all other anticancer drugs used as the reference group. To mitigate the confounding of age, two subgroups were considered: patients aged<75 years and ≥75 years. A signal of disproportionate reporting (SDR) was defined if both IC and PRR were significant. A total of 16,196 ICSRs were included. The median age of patients was 72.9 years, 42.6% of ICSRs concerned patients aged ≥75 years, and 64.2% male patients. More than half (56.2%) of ICSRs resulted in hospitalization or prolonged hospitalization. Among 713 SDRs, 36 potential safety signals emerged in ibrutinib-treated patients, mainly ischemic heart diseases, pericarditis, uveitis, retinal disorders and fractures. All potential safety signals having arisen in this analysis may support patient care and monitoring of ongoing clinical trials. However, owing to the mandatory limitations of this study, our results need further confirmation using population-based studies.

Leveraging the Variability of Pharmacovigilance Disproportionality Analyses to Improve Signal Detection Performances.

Background: A plethora of methods and models of disproportionality analyses for safety surveillance have been developed to date without consensus nor a gold standard, leading to methodological heterogeneity and substantial variability in results. We hypothesized that this variability is inversely correlated to the robustness of a signal of disproportionate reporting (SDR) and could be used to improve signal detection performances. Methods: We used a validated reference set containing 399 true and false drug-event pairs and performed, with a frequentist and a Bayesian disproportionality method, seven types of analyses (model) for which the results were very unlikely to be related to actual differences in absolute risks of ADR. We calculated sensitivity, specificity and plotted ROC curves for each model. We then evaluated the predictive capacities of all models and assessed the impact of combining such models with the number of positive SDR for a given drug-event pair through binomial regression models. Results: We found considerable variability in disproportionality analysis results, both positive and negative SDR could be generated for 60% of all drug-event pairs depending on the model used whatever their truthfulness. Furthermore, using the number of positive SDR for a given drug-event pair largely improved the signal detection performances of all models. Conclusion: We therefore advocate for the pre-registration of protocols and the presentation of a set of secondary and sensitivity analyses instead of a unique result to avoid selective outcome reporting and because variability in the results may reflect the likelihood of a signal being a true adverse drug reaction.

Exploring the utility of a spontaneous adverse drug reaction reporting system in identifying drug–drug interactions between antiretrovirals, antitubercular drugs, and cotrimoxazole: a case/non-case analysis

Drug–drug interactions (DDIs) cause significant morbidity and mortality, especially in patients with HIV with opportunistic infections such as tuberculosis. However, the literature on quantitative signal detection analyses for DDIs within the national spontaneous reporting systems (SRSs) of countries with high HIV/tuberculosis burdens is lacking. Our objective was to explore the utility of using post-marketing SRSs in quantitative signal detection analyses of DDIs. A case/non-case analysis using the Zimbabwean adverse drug reaction (ADR) database obtained from VigiBase® was utilized for quantitative signal detection using 2 × 2 contingency table calculations. Cases were defined as individual case safety reports (ICSRs) with the ADR of interest, and non-cases included the rest of the ICSRs. The exposure of interest was the use of a drug of interest. Signals of disproportionate reporting (SDRs) were observed for hepatotoxicity for the combined use of highly active antiretroviral therapy (HAART) and antitubercular treatments (ATT) [ADR reporting odds ratio (ROR) 43.78; 95% CI 5.24–366.08], HAART and isoniazid (ROR 44.84; 95% CI 5.36–374.99), and isoniazid and nevirapine-based HAART (ROR 35.60; 95% CI 9.39–134.89). SDRs were also observed for the combined use of nevirapine-based HAART and co-trimoxazole for Stevens–Johnson syndrome (ROR 28.91; 95% CI 14.00–59.70), severe cutaneous ADRs (ROR 16.10; 95% CI 9.40–27.57), and rash (OR 2.18; 95% CI 1.69–2.81). It is feasible to conduct signal detection analyses for DDIs within relatively small SRS databases. However, the observed potential SDR for the respective DDI should be investigated further as the method is only a hypothesis-generation analysis.

Serious Adverse Drug Reactions and Safety Signals in Children: A Nationwide Database Study.

Children are more exposed to inappropriate medicine use and its consequent harms. Spontaneous reporting of suspected Serious Adverse Drug Reactions (SADR) increases knowledge and prevention of pharmacotherapy risk. Disproportionality measures are useful to quantify unexpected safety issues associated with a given drug-event pair (signals of disproportionality). This cross-sectional study aimed to assess SADR reporting and safety signals for Brazilian children from 0-12 years old, notified between January 2008 and December 2013 from the Brazilian Surveillance Agency (Notivisa). Information from serious reports (gender and age of the patient, event description, suspected drug) was included. Disproportionality analysis based on Reporting Odds Ratios with a confidence interval of 95% was conducted to identify possible signals of disproportionate reporting (SDR). Almost 30% of 1,977 suspected SADR was related to babies (0-1-year-old). 69% of reports happened with intravenous dosage forms, and 35% of suspected SADR involved off label use according to age. Laronidase, miglustat, imipenem/cilastatin, and clofarabine were involved in six or more suspected deaths among 75 deaths reported. There were 107 SDRs, of which 16 events (15%) were not described in the product labels. There was a relatively higher number of SADRs in Brazilian children compared with studies from other countries. SDRs found, (especially drug-event pairs ‘imipenen/cilastatin–pneumonia’ and ‘laronidase–respiratory insufficiency’) should be investigated more. The reports of SADR with IV dosage forms and OL drug use suggest the need for drug research and the use of better dosage forms for children in Brazil.

Cardiovascular Toxicity of Tyrosine Kinase Inhibitors Used in Chronic Myeloid Leukemia: An Analysis of the FDA Adverse Event Reporting System Database (FAERS).

Tyrosine kinase inhibitors (TKIs), the treatment of choice for chronic myeloid leukemia (CML), can be associated to cardiovascular (CV) adverse events (AEs). A case/non-case study was performed using AE reports registered in the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database to compare the risk of CV event reports related to TKIs indicated in the management of chronic myeloid leukemia (CML). Disproportionality of CV event-related TKIs was computed using the Reporting Odds Ratio (ROR) as a measure of potential risk increase. Nilotinib accounts for more than half of reported cases related to TKIs. Signal of Disproportionate Reporting (SDR) was found for cardiac failure, ischemic heart disease, cardiac arrhythmias, torsade de pointes/QT prolongation, hypertension, and pulmonary hypertension. Dasatinib and bosutinib were related to the highest disproportionality for cardiac failure. Nilotinib was associated with the highest SDR for ischemic heart disease, torsade de pointes/QT prolongation and cardiac arrhythmias. Only ponatinib was related to an SDR for hypertension, while dasatinib and imatinib were related to pulmonary hypertension. In the context of CML, TKIs have different safety profiles related to CV events, among which nilotinib seems particularly related to. These results claim for a revision of its CV safety profile mainly for the risk of torsade de pointes/QT prolongation.

Quantitative data mining in signal detection: the Singapore experience

ABSTRACTBackground: In Singapore, the Health Sciences Authority (HSA) reviews an average of 20,000 spontaneous adverse event (AE) reports yearly. Potential safety signals are identified manually and discussed on a weekly basis. In this study, we compared the use of four quantitative data mining (QDM) methods with weekly manual review to determine if signals of disproportionate reporting (SDRs) can improve the efficiency of manual reviews and thereby enhance drug safety signal detection.Methods: We formulated a QDM triage strategy to reduce the number of SDRs for weekly review and compared the results against those derived from manual reviews alone for the same 6-month period. We then incorporated QDM triage into the manual review workflow for the subsequent two 6-month periods and made further comparisons against QDM triage alone.Results: The incorporation of QDM triage into routine manual reviews resulted in a reduction of 20% to 30% in the number of drug–AE pairs identified for further evaluation. Sequential Probability Ratio Test (SPRT) detected more signals that mirror human manual signal detection than the other three methods.Conclusions: The adoption of QDM triage into our manual reviews is a more efficient way forward in signal detection, avoiding missing important drug safety signals.

Investigating Overlap in Signals from EVDAS, FAERS, and VigiBase®.

IntroductionThe Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) and VigiBase® are two established databases for safety monitoring of medicinal products, recently complemented with the EudraVigilance Data Analysis System (EVDAS).ObjectiveSignals of disproportionate reporting (SDRs) can characterize the reporting profile of a drug, accounting for the distribution of all drugs and all events in the database. This study aims to quantify the redundancy among the three databases when characterized by two disproportionality-based analyses (DPA).MethodsSDRs for 100 selected products were identified with two sets of thresholds (standard EudraVigilance SDR criteria for all vs Bayesian approach for FAERS and VigiBase®). Per product and database, the presence or absence of SDRs was determined and compared. Adverse events were considered at three levels: MedDRA® Preferred Term (PT), High Level Term (HLT), and HLT combined with Standardized MedDRA® Query (SMQ). Redundancy was measured in terms of recall (SDRs in EVDAS divided by SDRs from any database) and overlap (SDRs in EVDAS and at least one other database, divided by SDRs in EVDAS). Covariates with potential impact on results were explored with linear regression models.ResultsThe median overlap between EVDAS and FAERS or VigiBase® was 85.0% at the PT level, 94.5% at the HLT level, and 97.7% at the HLT or SMQ level. The corresponding median recall of signals in EVDAS as a percentage of all signals generated in all three databases was 59.4%, 74.1%, and 87.9% at the PT, HLT, and HLT or SMQ levels, respectively. The overlap difference is partially explained by the relative number of EU cases in EudraVigilance and the ratio of EVDAS cases and FAERS cases, presumably due to differences in marketing authorizations, or market penetration in different regions. Products with few cases in EVDAS (< 1500) also display limited recall of signals relative to FAERs/VigiBase®. Time-on-market does not predict signal redundancy between the three databases. The choice of the DPA has an expected but somewhat small effect on redundancy.ConclusionsOrganizations typically consider regulatory expectations, operating performance (e.g., positive predictive value), and procedural complexity when selecting databases for signal management. As SDRs can be seen as a proxy of general reporting characteristics identifiable in a systematic screening process, our results indicate that, for most products, these characteristics are largely similar in each of the databases.Electronic supplementary materialThe online version of this article (10.1007/s40264-019-00899-y) contains supplementary material, which is available to authorized users.

Evaluation of quantitative signal detection in EudraVigilance for orphan drugs: possible risk of false negatives.

Different strategies have been studied to allow a better characterization of the safety profile of orphan drugs soon after their approval. At the end of the development phases only few data are available because of the small number of subjects exposed to an orphan medicine for the treatment of rare or ultra-rare conditions. As a consequence, the evaluation of the safety profile is limited at the time of the first approval. In the post-marketing period, all available sources should be combined for a better understanding of the safety of orphan drugs. These sources, include outputs from large databases such as the European Medicines Agency’s EudraVigilance database. Analyses of data from this source are required to be performed by marketing authorization holders (MAHs) as part of their signal management activities. In 2018, the Pharmacovigilance Risk Assessment Committee (PRAC) assessed 114 confirmed signals, 79% of which included data from EudraVigilance. MAHs have access to statistical calculations for drug–event combinations (DECs) from EudraVigilance, provided in the form of measures of disproportionality of ratios of the observed proportion of spontaneous cases for a DEC in relation to the proportion of cases that would be expected if no association existed between the drug and the event. However, such statistical summaries for orphan drugs could be misleading because of the very limited safety data available for orphan drugs (under-reporting together with low numbers of exposed patients). In addition, the applied statistical methodology in most instances is constrained by different confounding factors such as indications of specific medicines and the wide spectrum of medical conditions/diseases of patients from whom reporting of disproportionality ratios are derived (i.e. proportions of DECs for orphan drugs (ODECs) from a small patient population suffering the rare disease and the proportion of DECs in the rest of the population represented in the whole database who have been treated with other medicines for a wide range of indications, and prescribed to treat completely different medical conditions). As expected, these statistical calculations produced not only signals of disproportionate reporting (SDRs) that are false positives, but also not sensitive enough to detect certain SDRs, thus resulting in false negatives. In the context of rare/ultra-rare life-threatening diseases where new molecules have been made available on the market on the basis of their proven efficacy, but with only limited safety data at the time of approval, false negatives could be a special concern since unlikely converted in positives or becoming positives with notable delay. Subgroup analyses (using a limited dataset comprising ADRs within specific individual case safety reports (ICSRs), sorted by indication/disease relevant to the drug of interest could, at least in part, possibly reduce some of the weaknesses resulting from the abovementioned confounding factors. On the other hand it could also cause the loss of some identification of SDRs that would be captured if no database restrictions had been undertaken. Therefore, data subgroup analysis should not be selected as a preferred approach to quantitative signal detection for orphan drugs but rather evaluated as complementary possibly to confirm negatives or to further characterize detected SDRs. Some examples of false negatives originating from quantitative signal detection in EudraVigilance applied to orphan drugs are discussed in this article.

A characterization and disproportionality analysis of medication error related adverse events reported to the FAERS database

ABSTRACTObjectives: To characterize adverse reactions associated with medication errors (ME) reported in US Food and Drug Administration Adverse Event Reporting System (US-FAERS), and to identify the potential signals of disproportionate reporting (SDR) for different drugs.Methods: ME associated Individual Case Study Report (ICSRs) were identified. ICSRs were categorized by patient age groups, affected stages of medication process and Anatomical Therapeutic Chemical classification system. Disproportionality analyses were performed for different age groups.Results: 46,8677 ICSRs were retrieved. An increasing trend in reporting of cases of ME was observed during the studied period. Immunosuppressants and psycholeptic drugs were most frequently involved. Administration errors were reported most frequently, followed by prescribing and dispensing errors. In neonates, SDR following wrong drug administration, wrong dose, and accidental overdose were associated with methylergonovine, zidovudine, and acetaminophen. In elderlies, SDR were found for dose omission and underdose error associated with etanercept and evolocumab.Conclusion: While a detailed root-cause analysis for ME characteristic can rarely be performed on such a dataset, data mining for signals in spontaneous reporting database may assist in identifying potential ME in a more standardized and objective manner. Continued use of spontaneous reporting system for identifying MEs is encouraged to prevent unnecessary patient harm.

Web-Based Signal Detection Using Medical Forums Data in France: Comparative Analysis.

BackgroundWhile traditional signal detection methods in pharmacovigilance are based on spontaneous reports, the use of social media is emerging. The potential strength of Web-based data relies on their volume and real-time availability, allowing early detection of signals of disproportionate reporting (SDRs).ObjectiveThis study aimed (1) to assess the consistency of SDRs detected from patients’ medical forums in France compared with those detected from the traditional reporting systems and (2) to assess the ability of SDRs in identifying earlier than the traditional reporting systems.MethodsMessages posted on patients’ forums between 2005 and 2015 were used. We retained 8 disproportionality definitions. Comparison of SDRs from the forums with SDRs detected in VigiBase was done by describing the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, receiver operating characteristics curve, and the area under the curve (AUC). The time difference in months between the detection dates of SDRs from the forums and VigiBase was provided.ResultsThe comparison analysis showed that the sensitivity ranged from 29% to 50.6%, the specificity from 86.1% to 95.5%, the PPV from 51.2% to 75.4%, the NPV from 68.5% to 91.6%, and the accuracy from 68% to 87.7%. The AUC reached 0.85 when using the metric empirical Bayes geometric mean. Up to 38% (12/32) of the SDRs were detected earlier in the forums than that in VigiBase.ConclusionsThe specificity, PPV, and NPV were high. The overall performance was good, showing that data from medical forums may be a valuable source for signal detection. In total, up to 38% (12/32) of the SDRs could have been detected earlier, thus, ensuring the increased safety of patients. Further enhancements are needed to investigate the reliability and validation of patients’ medical forums worldwide, the extension of this analysis to all possible drugs or at least to a wider selection of drugs, as well as to further assess performance against established signals.

Safety of Biologics Approved for the Treatment of Rheumatoid Arthritis and Other Autoimmune Diseases: A Disproportionality Analysis from the FDA Adverse Event Reporting System (FAERS).

The molecular and pharmacological complexity of biologic disease-modifying antirheumatic drugs used for the management of rheumatoid arthritis (RA) favors the occurrence of adverse drug reactions (ADRs), which should be constantly monitored in post-marketing safety studies. The aim of this study was to identify signals of disproportionate reporting (SDR) of clinical relevance related to the use of biologic drugs approved for RA and other autoimmune diseases. All suspected ADRs registered in the FDA Adverse Event Reporting System between January 2003 and June 2016 were collected. The reporting odds ratio was used as a measure of disproportionality to identify possible SDRs related to biologics. Those involving important medical events and designated medical events (DME) were prioritized. In total, 2602 SDRs were prioritized. The most commonly reported were 'Infections and infestations' (32.2%) and 'Neoplasms benign, malignant, and unspecified' (20.4%), and were mainly related to use of infliximab (25.3%, p < 0.001, and 28.8%, p = 0.002, respectively). Sixty-three signals involving DMEs were identified, most of which were related to rituximab (n = 27), and were mainly due to 'blood disorders'. Amongst the DMEs detected for more than one biologic, 'intestinal perforation' and 'pulmonary fibrosis' were related to most of them. The results of this study highlight possible safety issues associated with biologics, whose relationship should be more thoroughly investigated. Our results contribute to future research on the identification of clinically relevant risks associated with these drugs, and may help contribute to their rational and safe use.

Post-marketing safety surveillance for inactivated and live-attenuated Japanese encephalitis vaccines in China, 2008–2013

IntroductionTwo types of Japanese encephalitis (JE) vaccines, inactivated JE vaccine (JE-I) and live-attenuated JE vaccine (JE-L), are available and used in China. In particular, one JE-L, produced by a domestic manufacturer in China, was prequalified by WHO in 2013. We assessed the safety of JE vaccines in China during 2008–2013 using the Chinese National Adverse Events Following Immunization Information System (CNAEFIS) data. MethodsWe retrieved AEFI reporting data about JE vaccines from CNAEFIS, 2008–2013, examined demographic characteristics of AEFI cases, and used administrative data on vaccine doses as denominator to calculate and compare crude reporting rates. We also used disproportionality reporting analysis between JE-I and JE-L to assess potential safety signals. ResultsA total of 34,879 AEFIs related with JE-I and JE-L were reported, with a ratio of male to female as 1.3:1; 361 (1.0%) cases were classified as serious. JE vaccines were administered concurrently with one or more other vaccines in 13,592 (39.0%) of cases. The overall AEFI reporting rates were 214.4 per million vaccination doses for JE-L and 176.9 for JE-I (rate ratio [RR]: 1.2, 95% confidence interval [CI]: 1.1–1.3) in 2010–2013. Febrile convulsions (FC) following JE-I was found as a signal of disproportionate reporting (SDR). However, there was no significant difference between the reporting rates of FC of JE-I and JE-L (0.3 per million vaccination doses for JE-L, 0.4 for JE-I, p=0.05). ConclusionsWhile our analysis did not find apparent safety concern of JE vaccines in China, further study should consider JE-I vaccines and febrile convulsion, and taking more sensitive methods to detect signals.

Detecting Signals of Disproportionate Reporting from Singapore's Spontaneous Adverse Event Reporting System: An Application of the Sequential Probability Ratio Test.

The ability to detect safety concerns from spontaneous adverse drug reaction reports in a timely and efficient manner remains important in public health. This paper explores the behaviour of the Sequential Probability Ratio Test (SPRT) and ability to detect signals of disproportionate reporting (SDRs) in the Singapore context. We used SPRT with a combination of two hypothesised relative risks (hRRs) of 2 and 4.1 to detect signals of both common and rare adverse events in our small database. We compared SPRT with other methods in terms of number of signals detected and whether labelled adverse drug reactions were detected or the reaction terms were considered serious. The other methods used were reporting odds ratio (ROR), Bayesian Confidence Propagation Neural Network (BCPNN) and Gamma Poisson Shrinker (GPS). The SPRT produced 2187 signals in common with all methods, 268 unique signals, and 70 signals in common with at least one other method, and did not produce signals in 178 cases where two other methods detected them, and there were 403 signals unique to one of the other methods. In terms of sensitivity, ROR performed better than other methods, but the SPRT method found more new signals. The performances of the methods were similar for negative predictive value and specificity. Using a combination of hRRs for SPRT could be a useful screening tool for regulatory agencies, and more detailed investigation of the medical utility of the system is merited.

Signal of Gastrointestinal Congenital Malformations with Antipsychotics After Minimising Competition Bias: A Disproportionality Analysis Using Data from Vigibase(®).

Investigations have highlighted the lack of evidence regarding the likelihood of congenital malformations following exposure to antipsychotic drugs during pregnancy. To gain further knowledge regarding their safety, we evaluated signals of congenital malformations with antipsychotics using VigiBase(®), the World Health Organization (WHO) Global Individual Case Safety Report (ICSR) database. A case/non-case study was conducted in VigiBase(®) between 1967 and 2014. Signals of disproportionate reporting (SDRs) were detected using the proportional reporting ratio (PRR), which defines SDRs as drug-report associations with a PRR ≥2, Chi square ≥4, and number of cases ≥3. SDR detection for antipsychotics was performed for congenital malformations after removing all reports related to drug competitors and reports of movement disorders from the database. After removing reports related to drug competitors (antiepileptics, antidepressants, antivirals) and movement disorders, three signals were revealed: 'palate disorders congenital' (PRR 2.1, 95% CI 1.6-2.9, Chi square=30; n=41), 'oesophageal disorders congenital' (PRR 2.5, 95% CI 1.3-4.7, Chi square=11; n=10) and 'anorectal disorders congenital' (PRR 3.0, 95% CI 1.6-5.6, Chi square=13; n=11). Among antipsychotics, phenothiazines with a piperazine side-chain, risperidone and aripiprazole appeared to be more suspect. Confirming a first signal from spontaneous reporting data, three SDRs for antipsychotics and gastrointestinal congenital abnormalities were unmasked in VigiBase(®). This signal should be further explored by ad hoc pharmacoepidemiologic studies in order to assess whether it is relevant for prescription and public health.