Detection algorithms and attentive points of safety signal using spontaneous reporting systems as a clinical data source.

Continuous evaluation of drug safety is needed following approval to determine adverse events (AEs) in patient populations with diverse backgrounds. Spontaneous reporting systems are an important source of information for the detection of AEs not identified in clinical trials and for safety assessments that reflect the real-world use of drugs in specific populations and clinical settings. The use of spontaneous reporting systems is expected to detect drug-related AEs early after the launch of a new drug. Spontaneous reporting systems do not contain data on the total number of patients that use a drug; therefore, signal detection by disproportionality analysis, focusing on differences in the ratio of AE reports, is frequently used. In recent years, new analyses have been devised, including signal detection methods focused on the difference in the time to onset of an AE, methods that consider the patient background and those that identify drug-drug interactions. However, unlike commonly used statistics, the results of these analyses are open to misinterpretation if the method and the characteristics of the spontaneous reporting system cannot be evaluated properly. Therefore, this review describes signal detection using data mining, considering traditional methods and the latest knowledge, and their limitations.

Comparison of Signal Detection Algorithms Based on Frequency Statistical Model for Drug-Drug Interaction Using Spontaneous Reporting Systems.

PURPOSE: Adverse events (AEs) caused by polypharmacy have recently become a clinical problem, and it is important to monitor the safety profile of drug-drug interactions (DDIs). Mining signals using the spontaneous reporting systems is a very effective method for single drug-induced AE monitoring as well as early detection of DDIs. The objective of this study was to compare signal detection algorithms for DDIs based on frequency statistical models. METHODS: Five frequency statistical models: the Ω shrinkage measure, additive (risk difference), multiplicative (risk ratio), combination risk ratio, and chi-square statistics models were compared using the Japanese Adverse Drug Event Report (JADER) database as the spontaneous reporting system in Japan. The drugs targeted for the survey are all registered and classified as "suspect drugs" in JADER, and the AEs targeted for this study were the same as those in a previous study on Stevens-Johnson syndrome (SJS). RESULTS: Of 3924 pairs that reported SJS, the number of signals detected by the Ω shrinkage measure, additive, multiplicative, combination risk ratio, and chi-square statistics models was 712, 3298, 2252, 739, and 1289 pairs, respectively. Among the five models, the Ω shrinkage measure model showed the most conservative signal detection tendency. CONCLUSION: Specifically, caution should be exercised when the number of reports is low because results differ depending on the statistical models. This study will contribute to the selection of appropriate statistical models to detect signals of potential DDIs.

Risk of stress cardiomyopathy associated with selective serotonin reuptake inhibitors and serotonin and norepinephrine reuptake inhibitors: a real-world pharmacovigilance analysis.

Selective serotonin reuptake inhibitors (SSRIs) and serotonin and norepinephrine reuptake inhibitors (SNRIs) are reported to cause stress cardiomyopathy (SC). This study evaluated the association between SSRI/SNRI use and the occurrence of cardiomyopathy in the publicly available U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database. Disproportionate analysis and likelihood ratio tests were used to identify risk associated with SSRIs or SNRIs and the incidence of SC, using data from between from 2012 to 2022 acquired from the FAERS database. The study identified 132 individual case safety reports (ICSRs) of SC associated with SSRIs or SNRIs. Venlafaxine (48%) and fluoxetine (27%) were the most common antidepressants of the ICSRs. Approximately 80% of SC cases were reported in females, with individuals aged 45-65 years identified as a high-risk population. Both venlafaxine (ratio-scale information component [RSIC] 2.54, 95% CI 2.06-3.04) and fluoxetine (RSIC 3.20, 95% CI 2.31-4.47) were associated with SC, with likelihood ratio estimates of 3.55 (p = 0.02) for venlafaxine and 4.82 (p = 0.008) for fluoxetine. The median time to cardiomyopathy onset was 20 days, with hospitalization reported in 48.33% of patients. Venlafaxine and fluoxetine were associated with SC risk, particularly in middle-aged women. Caution should be exercised when using SSRIs or SNRIs combined with other serotonergic medications.

Pharmacovigilance study for SGLT 2 inhibitors- Safety review of real-world data & randomized clinical trials.

PURPOSE: Despite effectiveness of sodium-glucose cotransporter 2 (SGLT 2) inhibitors, concerns have been raised about the potential side effects of these drugs. Thus, a pharmaco-vigilance study was designed that aims to identify any discrepancies between the reported adverse events & assess the safety profile of SGLT2 inhibitors. METHODS: We studied diabetic ketoacidosis (DKA), euglycemic DKA, amputation, urinary tract infection (UTI), mycotic genital infection & hypotension associated with empagliflozin, dapagliflozin, canagliflozin & ertugliflozin in RCTs and reporting databases. WHO's VigiBase, FAERS, EMA's EudraVigilance & DAEN were thoroughly studied to obtain spontaneously reported real-world adverse events. RESULTS: Different SGLT2 inhibitors exhibit varied side effect profiles. Additionally, the findings suggest that adverse events may be more likely to occur in a broader population in the real world than in a highly inclusive clinical trial subset CONCLUSION: Our study provides comparison of the real world reported adverse events to adverse events reported in the clinical trials studying the efficacy of SGLT 2 inhibitors.

A Gender Hypothesis of sex disparities in adverse drug events.

Pharmacovigilance databases contain larger numbers of adverse drug events (ADEs) that occurred in women compared to men. The cause of this disparity is frequently attributed to sex-linked biological factors. We offer an alternative Gender Hypothesis, positing that gendered social factors are central to the production of aggregate sex disparities in ADE reports. We describe four pathways through which gender may influence observed sex disparities in pharmacovigilance databases: healthcare utilization; bias and discrimination in the clinic; experience of a drug event as adverse; and pre-existing social and structural determinants of health. We then use data from the U.S. FDA Adverse Event Reporting System (FAERS) to explore how the Gender Hypothesis might generate novel predictions and explanations of sex disparities in ADEs in existing widely referenced datasets. Analyzing more than 3 million records of ADEs between 2014 and 2022, we find that patient-reported ADEs show a larger female skew than healthcare provider-reported ADEs and that the sex disparity is markedly smaller for outcomes involving death or hospitalization. We also find that the sex disparity varies greatly across types of ADEs, for example, cosmetically salient ADEs are skewed heavily female and sexual dysfunction ADEs are skewed male. Together, we interpret these findings as providing evidence of the promise of the Gender Hypothesis for identifying intervenable mechanisms and pathways contributing to sex disparities in ADEs. Rigorous application of the Gender Hypothesis to additional datasets and in future research studies could yield new insights into the causes of sex disparities in ADEs.

Uncovering Knowledge Gaps in the Safety Profile of Antiangiogenic Drugs in Cancer Patients: Insights from Spontaneous Reporting Systems Studies.

Global repositories of postmarketing safety reports improve understanding of real-life drug toxicities, often not observed in clinical trials. The aim of this scoping review was to map the evidence from spontaneous reporting systems studies (SRSs) of antiangiogenic drugs (AADs) in cancer patients and highlight if the found disproportionality signals of adverse events (AEs) were validated and thus mentioned in the respective Summary of product Characteristics (SmPC). This scoping review was conducted according to PRISMA guidelines for scoping reviews. A knowledge gap on the safety of AADs was found: firstly, several cardiovascular AEs were not mentioned in the SmPCs and no pharmacovigilance studies were conducted despite the well-known safety concerns about these drugs on the cardiovascular system. Second, a disproportionality signal (not validated through causality assessment) of pericardial disease was found in the literature for axitinib with no mention in SmPC of the drug. Despite the exclusion of pharmacoepidemiological studies, we believe that this scoping review, which focuses on an entire class of drugs, could be considered as a novel approach to highlight possible safety concerns of drugs and as a guide for the conduction of a target postmarketing surveillance on AADs.

Characteristics of drug safety alerts issued by the Spanish Medicines Agency.

Objectives: To describe the characteristics of safety alerts issued by the Spanish Medicines Agency (AEMPS) and the Spanish Pharmacovigilance System over a 7-year period and the regulatory actions they generated. Methods: A retrospective analysis was carried out of drug safety alerts published on the AEMPS website from 1 January 2013 to 31 December 2019. Alerts that were not drug-related or were addressed to patients rather than healthcare professionals were excluded. Results: During the study period, 126 safety alerts were issued, 12 of which were excluded because they were not related to drugs or were addressed to patients and 22 others were excluded as they were duplications of previous alerts. The remaining 92 alerts reported 147 adverse drug reactions (ADRs) involving 84 drugs. The most frequent source of information triggering a safety alert was spontaneous reporting (32.6%). Four alerts (4.3%) specifically addressed health issues related to children. ADRs were considered serious in 85.9% of the alerts. The most frequent ADRs were hepatitis (seven alerts) and congenital malformations (five alerts), and the most frequent drug classes were antineoplastic and immunomodulating agents (23%). Regarding the drugs involved, 22 (26.2%) were "under additional monitoring." Regulatory actions induced changes in the Summary of Product Characteristics in 44.6% of alerts, and in eight cases (8.7%), the alert led to withdrawal from the market of medicines with an unfavorable benefit/risk ratio. Conclusion: This study provides an overview of drug safety alerts issued by the Spanish Medicines Agency over a 7-year period and highlights the contribution of spontaneous reporting of ADRs and the need to assess safety throughout the lifecycle of medicines.

Verification of the "Upward Variation in the Reporting Odds Ratio Scores" to Detect the Signals of Drug-Drug Interactions.

The reporting odds ratio (ROR) is easy to calculate, and there have been several examples of its use because of its potential to speed up the detection of drug-drug interaction signals by using the "upward variation of ROR score". However, since the validity of the detection method is unknown, this study followed previous studies to investigate the detection trend. The statistics models (the Ω shrinkage measure and the "upward variation of ROR score") were compared using the verification dataset created from the Japanese Adverse Drug Event Report database (JADER). The drugs registered as "suspect drugs" in the verification dataset were considered as the drugs to be investigated, and the target adverse event in this study was Stevens-Johnson syndrome (SJS), as in previous studies. Of 3924 pairs that reported SJS, the number of positive signals detected by the Ω shrinkage measure and the "upward variation of ROR score" (Model 1, the Susuta Model, and Model 2) was 712, 2112, 1758, and 637, respectively. Furthermore, 1239 positive signals were detected when the Haldane-Anscombe 1/2 correction was applied to Model 2, the statistical model that showed the most conservative detection trend. This result indicated the instability of the positive signal detected in Model 2. The ROR scores based on the frequency-based statistics are easily inflated; thus, the use of the "upward variation of ROR scores" to search for drug-drug interaction signals increases the likelihood of false-positive signal detection. Consequently, the active use of the "upward variation of ROR scores" is not recommended, despite the existence of the Ω shrinkage measure, which shows a conservative detection trend.

Identifying Herbal Adverse Events From Spontaneous Reporting Systems Using Taxonomic Name Resolution Approach.

The efficacy and safety of herbal supplements suffer from challenges due to non-uniform representation of ingredient terms within biomedical and observational health data sources. The nature of how supplement data are reported within Spontaneous Reporting Systems (SRS) can limit analyses of supplement-associated adverse events due to the use of incorrect nomenclature or failing to identify herbs. This study aimed to extract, standardize, and summarize supplement-relevant reports from two SRSs: (1) Food and Drug Administration Adverse Event Reporting System (FAERS) and (2) Canada Vigilance Adverse Reaction (CVAR) database. A thesaurus of plant names was developed and integrated with a mapping and normalization approach that accommodated misspellings and variants. The reports gathered from FAERS between the years 2004 and 2016 show 185,915 herbal and 7,235,330 non-herbal accounting for 2.51%. The data from CVAR found 36,940 reports of herbal and 503,580 non-herbal reports between the years 1965 and 2017 for a total of 6.83%. Although not all cases were actual adverse events due to numerous variables and incomplete reporting, it is interesting to note that the herbs most frequently reported and significantly associated with adverse events were as follows: Avena sativa (Oats), Cannabis sativa (marijuana), Digitalis purpurea (foxglove), Humulus lupulus (hops), Hypericum perforatum (St John's Wort), Paullinia cupana (guarana), Phleum pretense (timothy-grass), Silybum marianum (milk thistle), Taraxacum officinale (Dandelion), and Valeriana officinalis (valerian). Using a scalable approach for mapping and resolution of herb names allowed data-driven exploration of potential adverse events from sources that have remained isolated in this specific area of research. The results from this study highlight several herb-associated safety issues providing motivation for subsequent in-depth analyses, including those that focus on the scope and severity of potential safety issues with supplement use.

Subset Analysis for Screening Drug-Drug Interaction Signal Using Pharmacovigilance Database.

Many patients require multi-drug combinations, and adverse event profiles reflect not only the effects of individual drugs but also drug-drug interactions. Although there are several algorithms for detecting drug-drug interaction signals, a simple analysis model is required for early detection of adverse events. Recently, there have been reports of detecting signals of drug-drug interactions using subset analysis, but appropriate detection criterion may not have been used. In this study, we presented and verified an appropriate criterion. The data source used was the Japanese Adverse Drug Event Report (JADER) database; "hypothetical" true data were generated through a combination of signals detected by three detection algorithms. The accuracy of the signal detection of the analytic model under investigation was verified using indicators used in machine learning. The newly proposed subset analysis confirmed that the signal detection was improved, compared with signal detection in the previous subset analysis, on the basis of the indicators of Accuracy (0.584 to 0.809), Precision (= Positive predictive value; PPV) (0.302 to 0.596), Specificity (0.583 to 0.878), Youden's index (0.170 to 0.465), F-measure (0.399 to 0.592), and Negative predictive value (NPV) (0.821 to 0.874). The previous subset analysis detected many false drug-drug interaction signals. Although the newly proposed subset analysis provides slightly lower detection accuracy for drug-drug interaction signals compared to signals compared to the Ω shrinkage measure model, the criteria used in the newly subset analysis significantly reduced the amount of falsely detected signals found in the previous subset analysis.

Improved Detection Criteria for Detecting Drug-Drug Interaction Signals Using the Proportional Reporting Ratio.

There is a current demand for "safety signal" screening, not only for single drugs but also for drug-drug interactions. The detection of drug-drug interaction signals using the proportional reporting ratio (PRR) has been reported, such as through using the combination risk ratio (CRR). However, the CRR does not consider the overlap between the lower limit of the 95% confidence interval of the PRR of concomitant-use drugs and the upper limit of the 95% confidence interval of the PRR of single drugs. In this study, we proposed the concomitant signal score (CSS), with the improved detection criteria, to overcome the issues associated with the CRR. "Hypothetical" true data were generated through a combination of signals detected using three detection algorithms. The signal detection accuracy of the analytical model under investigation was verified using machine learning indicators. The CSS presented improved signal detection when the number of reports was ≥3, with respect to the following metrics: accuracy (CRR: 0.752 → CSS: 0.817), Youden's index (CRR: 0.555 → CSS: 0.661), and F-measure (CRR: 0.780 → CSS: 0.820). The proposed model significantly improved the accuracy of signal detection for drug-drug interactions using the PRR.

Review of Statistical Methodologies for Detecting Drug-Drug Interactions Using Spontaneous Reporting Systems.

Concomitant use of multiple drugs for therapeutic purposes is known as "polypharmacy situations," which has been recognized as an important social problem recently. In polypharmacy situations, each drug not only induces adverse events (AEs) but also increases the risk of AEs due to drug-drug interactions (DDIs). The proportion of AEs caused by DDIs is estimated to be around 30% of unexpected AEs. The randomized clinical trials in pre-marketing typically focus emphasis on the verification of single drug safety and efficacy rather than the surveys of DDI, and therefore, patients on multiple drugs are usually excluded. However, unlike pre-marketing randomized clinical trials, in clinical practice (= post marketing), many patients use multiple drugs. The spontaneous reporting system is one of the significant sources drug safety surveillance in post-marketing. Commonly, signals of potential drug-induced AEs detected from this source are validated in real-world settings. Recently, not only methodological studies on signal detection of "single" drug, but also on several methodological studies on signal detection of DDIs have been conducted. On the other hand, there are few articles that systematically summarize the statistical methodology for signal detection of DDIs. Therefore, this article reviews the studies on the latest statistical methodologies from classical methodologies for signal detection of DDIs using spontaneous reporting system. This article describes how to calculate for each detection method and the major findings from the published literatures about DDIs. Finally, this article presented several limitations related to the currently used methodologies for signal detection of DDIs and suggestions for further studies.

New safety signals assessed by the Pharmacovigilance Risk Assessment Committee at EU level in 2014-2017.

BACKGROUND: Safety monitoring of all drugs throughout their entire life cycle is mandatory in order to protect the public health. Our objective was to describe all new safety signals assessed at EU level by the Pharmacovigilance Risk Assessment Committee (PRAC). METHODS: Publicly available data on signals assessment from PRAC meeting minutes for the period January 2014-November 2017 were analyzed and classified. RESULTS: A total of 239 new signals for 194 drugs/drug combinations/therapeutic classes were evaluated by PRAC. A total of 154 signals were triggered by spontaneous reporting, 31 by literature case reports, and 26 by observational studies. In 188 signals, the drugs involved were authorized for more than 5 years. The drug classes for which most signals were detected were antineoplastic/immunomodulators (n = 75), anti-infectives (n = 34), and drugs acting on the nervous system (n = 27). Signals were triggered for drug interactions (n = 15), in utero exposure (n = 7), medication errors (n = 6), and for different disorders, among which the skin/subcutaneous tissue disorders were more common. PRAC recommendations consisted in label updates (n = 86), in Direct Healthcare Professional Communications (n = 17), and in eight recommendations for a more complex evaluation through referral procedures. CONCLUSIONS: Most new signals assessed were triggered by spontaneous reporting and led to routine risk minimization measures, such as updating the product information.

A New Search Method Using Association Rule Mining for Drug-Drug Interaction Based on Spontaneous Report System.

Background: Adverse events (AEs) can be caused not only by one drug but also by the interaction between two or more drugs. Therefore, clarifying whether an AE is due to a specific suspect drug or drug-drug interaction (DDI) is useful information for proper use of drugs. Whereas previous reports on the search for drug-induced AEs with signal detection using spontaneous reporting systems (SRSs) are numerous, reports on drug interactions are limited. This is because in methods that use "a safety signal indicator" (signal), which is frequently used in pharmacovigilance, a huge number of combinations must be prepared when signal detection is performed, and each risk index must be calculated, which makes interaction search appear unrealistic. Objective: In this paper, we propose association rule mining (AR) using large dataset analysis as an alternative to the conventional methods (additive interaction model (AI) and multiplicative interaction model (MI)). Methods: The data source used was the Japanese Adverse Drug Event Report database. The combination of drugs for which the risk index is detected by the "combination risk ratio (CR)" as the target was assumed to be true data, and the accuracy of signal detection using the AR methods was evaluated in terms of sensitivity, specificity, Youden's index, F-score. Results: Our experimental results targeting Stevens-Johnson syndrome indicate that AR has a sensitivity of 99.05%, specificity of 92.60%, Youden's index of 0.917, F-score of 0.876, AI has a sensitivity of 95.62%, specificity of 96.92%, Youden's index of 0.925, and F-score of 0.924, and MI has a sensitivity of 65.46%, specificity of 98.78%, Youden's index of 0.642, and F-score of 0.771. This result was about the same level as or higher than the conventional method. Conclusions: If you use similar calculation methods to create combinations from the database, not only for SJS, but for all AEs, the number of combinations would be so enormous that it would be difficult to perform the calculations. However, in the AR method, the "Apriori algorithm" is used to reduce the number of calculations. Thus, the proposed method has the same detection power as the conventional methods, with the significant advantage that its calculation process is simple.

Use of Spontaneous Reporting Systems to Detect Host-Medication Interactions: Sex Differences in Oral Anti-Diabetic Drug-Associated Myocardial Infarction.

Background Medical treatment should be tailored to an individual's characteristics to optimize treatment benefits. We examined whether case-only analyses from spontaneous reporting systems can detect host-medication interactions in oral antidiabetic drug-associated myocardial infarction. Methods and Results Interaction between sex and use of oral antidiabetic drugs was mined among patients with myocardial infarction in the US Food and Drug Administration Adverse Event Reporting System from 2004 to 2014, including 55 718 males and 42 428 females. The odds ratio ( OR ) of multiplicative interactions was used to estimate sex-drug interaction. Detected signs of these interactions were then validated by a nested case-control study utilizing a healthcare record database, Taiwan's National Health Insurance Research Database, from 2001 to 2014, including 31 585 cases and 126 340 controls. In the US Food and Drug Administration Adverse Event Reporting System, a higher proportion of male than female patients used metformin (10.32% in males versus 7.82% in females) and sulfonylureas (4.75% in males versus 3.43% in females); after adjusting for patients' pharmacy-based chronic disease score, males had a higher risk of metformin-associated ( OR =1.07; 99% confidence interval, 1.00-1.14) and sulfonylureas-associated ( OR =1.21; 99% confidence interval, 1.10-1.33) myocardial infarction than females. Detected signs of sex-drug interactions were validated in the National Health Insurance Research Database ( OR for metformin=1.14; 99% confidence interval, 1.03-1.26; OR for sulfonylureas=1.13; 99% confidence interval, 1.02-1.25). Conclusions Males have a higher risk of metformin- and sulfonylureas-associated myocardial infarction than females, which suggests that sex-drug interactions are a key issue in diabetes mellitus treatment plan development. This case-only approach using information from spontaneous reporting systems may be a potential tool for screening host-medication interactions that cause adverse events.

Exploiting heterogeneous publicly available data sources for drug safety surveillance: computational framework and case studies.

OBJECTIVE: Driven by the need of pharmacovigilance centres and companies to routinely collect and review all available data about adverse drug reactions (ADRs) and adverse events of interest, we introduce and validate a computational framework exploiting dominant as well as emerging publicly available data sources for drug safety surveillance. METHODS: Our approach relies on appropriate query formulation for data acquisition and subsequent filtering, transformation and joint visualization of the obtained data. We acquired data from the FDA Adverse Event Reporting System (FAERS), PubMed and Twitter. In order to assess the validity and the robustness of the approach, we elaborated on two important case studies, namely, clozapine-induced cardiomyopathy/myocarditis versus haloperidol-induced cardiomyopathy/myocarditis, and apixaban-induced cerebral hemorrhage. RESULTS: The analysis of the obtained data provided interesting insights (identification of potential patient and health-care professional experiences regarding ADRs in Twitter, information/arguments against an ADR existence across all sources), while illustrating the benefits (complementing data from multiple sources to strengthen/confirm evidence) and the underlying challenges (selecting search terms, data presentation) of exploiting heterogeneous information sources, thereby advocating the need for the proposed framework. CONCLUSIONS: This work contributes in establishing a continuous learning system for drug safety surveillance by exploiting heterogeneous publicly available data sources via appropriate support tools.

Adverse events associated with incretin-based drugs in Japanese spontaneous reports: a mixed effects logistic regression model.

BACKGROUND: Spontaneous Reporting Systems (SRSs) are passive systems composed of reports of suspected Adverse Drug Events (ADEs), and are used for Pharmacovigilance (PhV), namely, drug safety surveillance. Exploration of analytical methodologies to enhance SRS-based discovery will contribute to more effective PhV. In this study, we proposed a statistical modeling approach for SRS data to address heterogeneity by a reporting time point. Furthermore, we applied this approach to analyze ADEs of incretin-based drugs such as DPP-4 inhibitors and GLP-1 receptor agonists, which are widely used to treat type 2 diabetes. METHODS: SRS data were obtained from the Japanese Adverse Drug Event Report (JADER) database. Reported adverse events were classified according to the MedDRA High Level Terms (HLTs). A mixed effects logistic regression model was used to analyze the occurrence of each HLT. The model treated DPP-4 inhibitors, GLP-1 receptor agonists, hypoglycemic drugs, concomitant suspected drugs, age, and sex as fixed effects, while the quarterly period of reporting was treated as a random effect. Before application of the model, Fisher's exact tests were performed for all drug-HLT combinations. Mixed effects logistic regressions were performed for the HLTs that were found to be associated with incretin-based drugs. Statistical significance was determined by a two-sided p-value <0.01 or a 99% two-sided confidence interval. Finally, the models with and without the random effect were compared based on Akaike's Information Criteria (AIC), in which a model with a smaller AIC was considered satisfactory. RESULTS: The analysis included 187,181 cases reported from January 2010 to March 2015. It showed that 33 HLTs, including pancreatic, gastrointestinal, and cholecystic events, were significantly associated with DPP-4 inhibitors or GLP-1 receptor agonists. In the AIC comparison, half of the HLTs reported with incretin-based drugs favored the random effect, whereas HLTs reported frequently tended to favor the mixed model. CONCLUSION: The model with the random effect was appropriate for analyzing frequently reported ADEs; however, further exploration is required to improve the model. The core concept of the model is to introduce a random effect of time. Modeling the random effect of time is widely applicable to various SRS data and will improve future SRS data analyses.

Using real-world healthcare data for pharmacovigilance signal detection - the experience of the EU-ADR project.

A prospective pharmacovigilance signal detection study, comparing the real-world healthcare data (EU-ADR) and two spontaneous reporting system (SRS) databases, US FDA's Adverse Event Reporting System and WHO's Vigibase is reported. The study compared drug safety signals found in the EU-ADR and SRS databases. The potential for signal detection in the EU-ADR system was found to be dependent on frequency of the event and utilization of drugs in the general population. The EU-ADR system may have a greater potential for detecting signals for events occurring at higher frequency in general population and those that are commonly not considered as potentially a drug-induced event. Factors influencing various differences between the datasets are discussed along with potential limitations and applications to pharmacovigilance practice.

Assessing liver injury associated with antimycotics: Concise literature review and clues from data mining of the FAERS database.

AIM: To inform clinicians on the level of hepatotoxic risk among antimycotics in the post-marketing setting, following the marketing suspension of oral ketoconazole for drug-induced liver injury (DILI). METHODS: The publicly available international FAERS database (2004-2011) was used to extract DILI cases (including acute liver failure events), where antimycotics with systemic use or potential systemic absorption were reported as suspect or interacting agents. The reporting pattern was analyzed by calculating the reporting odds ratio and corresponding 95%CI, a measure of disproportionality, with time-trend analysis where appropriate. RESULTS: From 1687284 reports submitted over the 8-year period, 68115 regarded liver injury. Of these, 2.9% are related to antimycotics (1964 cases, of which 112 of acute liver failure). Eleven systemic antimycotics (including ketoconazole and the newer triazole derivatives voriconazole and posaconazole) and terbinafine (used systemically to treat onychomicosis) generated a significant disproportionality, indicating a post-marketing signal of risk. CONCLUSION: Virtually all antimycotics with systemic action or absorption are commonly reported in clinically significant cases of DILI. Clinicians must be aware of this aspect and monitor patients in case switch is considered, especially in critical poly-treated patients under chronic treatment.