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Introduction: Qualitative approaches based on drug causality assessment estimate the causal link between a drug and the occurrence of an adverse event from individual case safety reports. Quantitative approaches based on disproportionality analyses were developed subsequently to allow automated statistical signal detection from pharmacovigilance databases. This study assessed the potential value of causality assessment for automated safety signal detection., Methods: All drug-serious adverse event pairs with a positive rechallenge and a semiology suggestive of drug causality were identified in the French pharmacovigilance database (BNPV) from 2011 to 2017. The results were compared with those obtained from automated disproportionality analyses of the BNPV/World Health Organization (WHO) VigiBase ® , complemented by the list of signals validated by the WHO-UMC (Uppsala Monitoring Centre). Summary of Product Characteristics (SmPCs), Martindale ® , Meyler's ® and MedLINE ® were used as other sources of information for the purpose of comparison., Results: Of the 155 pairs of interest, 115 (74.2%) were also identified by another source of information. Since the individual case reporting in the BNPV, 23 (14.8%) of the adverse events (AEs) have been added to the SmPC, seven of which were not identified by disproportionality. Finally, 40 pairs were not identified by any other source of information, 13 of which were considered as potential new safety signals after analysis of case reports by pharmacovigilance experts. The signals identified by causality assessment involved antineoplastic and immunomodulatory drugs especially, in comparison with signals identified by WHO-UMC or by disproportionality within the BNPV., Conclusion: The approach therefore appears useful as an additional tool for safety signal detection, especially for antineoplastic and immunomodulating agents.

Objective: The objective of this study was to evaluate the performance of Standardised MedDRA® Queries (SMQs) in adverse drug reaction (ADR) identification., Methods: ADR cases included in the last complete year of the French Pharmacovigilance database for research were used to test four SMQs (narrow and broad): agranulocytosis, demyelination, osteonecrosis and psychosis. Reference cases were identified by free-text search and validated by two authors. Sensitivity (Se), specificity (Sp), positive predictive value (PPV) and negative predictive value (NPV) of narrow and broad searches of each SMQ were calculated and reported as proportions with 95% exact confidence interval (CI)., Results: Among 20,830 cases reported in 2009, 337 validated cases of agranulocytosis, 17 of demyelination, 52 of osteonecrosis and 230 of psychosis were included in the reference sets. Estimations of SMQ narrow search performance were as follows: Se 62.9% (95% CI 57.5-68.1) and PPV 46.8% (95% CI 42.1-51.5) for agranulocytosis; Se 88.2% (95% CI 63.6-98.5) and PPV 34.1% (95% CI 20.5-49.9) for demyelination; Se 94.2% (95% CI 84.1-98.8) and PPV 74.2% (95% CI 62.0-84.2) for osteonecrosis; and Se 75.1% (95% CI 69.0-80.6) and PPV 87.8% (95% CI 82.3-92.0) for psychosis. Results obtained using the broad search were similar except for PPV concerning osteonecrosis (52.7% [95% CI 42.1-63.1]) and psychosis (61.4% [95% CI 55.7-66.8]). For all selected SMQs, Sp and NPV were greater than 98% for both narrow and broad searches., Conclusion: Heterogeneous performance of SMQs for case retrieval was found and seems to be related to the characteristics of the condition of interest. It could therefore be expected that for other SMQs, performance may be affected in the same manner.

Purpose: To test an automated method to decrease the number of false-positive (FP) signals of disproportionate reportings (SDRs) generated by co-prescription., Methods: Automated backward stepwise removal of reports concerning the drug associated with the highest ranked SDR for an event was tested for gastric and oesophageal haemorrhages (GOH), central nervous system haemorrhages and cerebrovascular accidents (CNSH), ischaemic coronary artery disorders and muscle pains (MP) using the reporting odds ratio in the French spontaneous reporting research database. After ranking SDRs detected in the complete dataset on the lower limit of the reporting odds ratio 95% confidence interval, reports concerning the drug with the highest ranked SDR were removed. In the dataset thus generated, SDRs were again identified, ranked and reports related to the drug involved in the newly highest ranked SDR removed. The process was repeated until no signal was detected. Initially detected SDRs eliminated using this technique were assessed regarding the summary of products characteristics and the literature to determine their FP nature., Results: Seventeen SDRs were successively eliminated for GOH, 37 for CNSH, 15 for ischaemic coronary artery disorders, and 36 for MP. Four were FP for GOH, 29 for CNSH, 7 for ACI and none were FP for MP. The positive predictive value of the backward stepwise removal procedure in identifying FP SDRs ranged from 0% (MP) to 78.4% (CNSH)., Conclusions: Although further adjustment is needed to improve the method presented herein, our results suggest that numerous FP signals because of co-prescription bias could be eliminated using an automated method., (Copyright © 2013 John Wiley & Sons, Ltd.)

Background: Automated disproportionality analysis of spontaneous reporting is increasingly used routinely. It can theoretically be influenced by a competition bias for signal detection owing to the presence of reports related to well-established drug-event associations., Objective: The aim of the study was to explore the effects of competition bias on safety signals generated from a large spontaneous reporting research database., Methods: Using the case/non-case approach in the French spontaneous reporting research database, which includes data of reporting in France from January 1986 to December 2001, the effects of the competition bias were explored by generating safety signals associated with six events of interest (gastric and oesophageal haemorrhages, central nervous system haemorrhage and cerebrovascular accidents, ischaemic coronary disorders, migraine headaches, muscle pains, and hepatic enzymes and function abnormalities) before and after removing from the database reports relating to drugs known to be strongly associated with these events, whether they constituted cases or non-cases. As this study was performed on a closed database (last data entered 31 December 2001), potential signals unmasked by removal were considered as real signals if no or only incomplete knowledge about the association was available from the literature before 1 January 2002., Results: For gastric and oesophageal haemorrhages, after removing reports involving antithrombotic agents or NSAIDs, three potential signals were unmasked (prednisone, rivastigmine and isotretinoin). For central nervous system haemorrhage and cerebrovascular accidents, after removing reports involving antithrombotic agents, three potential signals were unmasked (ethinylestradiol, interferon-α-2B and methylprednisolone). For ischaemic coronary disorders, after removing reports involving anthracyclines, bleomycine, anti-HIV drugs or triptans, one potential signal was unmasked (ondansetron). For migraine headaches, after removing reports involving nitrates, calcium channel blockers, opioid analgesics or intravenous immunoglobulins, six potential signals were unmasked (ammonium chloride, leflunomide, milnacipran, montelukast, proguanil and pyridostigmine). For muscle pains, after removing reports involving statins or fibrates, seven potential signals were unmasked (hydroxychloroquine, lactulose, levodopa in combination with dopadecarboxylase inhibitor, nevirapine, nomegestrol, ritonavir and stavudine). Finally, for hepatic enzymes and function abnormalities, after removing reports involving NSAIDs, anilides, antituberculosis drugs, antiepileptics, ketoconazole, tacrine, or amineptine, two potential signals were unmasked (caffeine, metformin). Of all these unmasked potential signals, ten appeared non/incompletely documented as at 1 January 2002 and were considered as real signals, with three of these later being confirmed by the literature and finally considered as true positives (isotretinoin, methylprednisolone and milnacipran)., Conclusion: This study confirms that a competition bias can occur when performing safety signal generation in spontaneous reporting databases. The minimization of this bias could lead to previously masked signals being revealed.

Background: Improving the detection of drug safety signals has led several pharmacovigilance regulatory agencies to incorporate automated quantitative methods into their spontaneous reporting management systems. The three largest worldwide pharmacovigilance databases are routinely screened by the lower bound of the 95% confidence interval of proportional reporting ratio (PRR₀₂.₅), the 2.5% quantile of the Information Component (IC₀₂.₅) or the 5% quantile of the Gamma Poisson Shrinker (GPS₀₅). More recently, Bayesian and non-Bayesian False Discovery Rate (FDR)-based methods were proposed that address the arbitrariness of thresholds and allow for a built-in estimate of the FDR. These methods were also shown through simulation studies to be interesting alternatives to the currently used methods., Objective: The objective of this work was twofold. Based on an extensive retrospective study, we compared PRR₀₂.₅, GPS₀₅ and IC₀₂.₅ with two FDR-based methods derived from the Fisher's exact test and the GPS model (GPS(pH0) [posterior probability of the null hypothesis H₀ calculated from the Gamma Poisson Shrinker model]). Secondly, restricting the analysis to GPS(pH0), we aimed to evaluate the added value of using automated signal detection tools compared with 'traditional' methods, i.e. non-automated surveillance operated by pharmacovigilance experts., Methods: The analysis was performed sequentially, i.e. every month, and retrospectively on the whole French pharmacovigilance database over the period 1 January 1996-1 July 2002. Evaluation was based on a list of 243 reference signals (RSs) corresponding to investigations launched by the French Pharmacovigilance Technical Committee (PhVTC) during the same period. The comparison of detection methods was made on the basis of the number of RSs detected as well as the time to detection., Results: Results comparing the five automated quantitative methods were in favour of GPS(pH0) in terms of both number of detections of true signals and time to detection. Additionally, based on an FDR threshold of 5%, GPS(pH0) detected 87% of the RSs associated with more than three reports, anticipating the date of investigation by the PhVTC by 15.8 months on average., Conclusions: Our results show that as soon as there is reasonable support for the data, automated signal detection tools are powerful tools to explore large spontaneous reporting system databases and detect relevant signals quickly compared with traditional pharmacovigilance methods.

Purpose: To study whether reports related to known drug-event associations could hinder the detection of new signals by increasing the detection thresholds when using disporportionality analyses in spontaneous reporting (SR) databases., Methods: The French SR database (2005-2006 data) was used to test this hypothesis for the following events: bleeding, headache, hepatitis, myalgia, myocardial infarction, stroke, and toxic epidermal necrolysis (TEN). For each of these, using the Proportional Reporting Ratio (PRR) and the Reporting Odds Ratio (ROR), the number of cases needed to trigger a signal out of 50, 100, and 200 reports for a hypothetical newly introduced drug were computed before and after removing from the database reports involving drugs known to be associated with the event., Results: For bleeding and stroke, removing potentially competitive data resulted in a decrease of the number of cases needed to trigger a signal for a newly introduced drug for both PRR and ROR (e.g., from 9 to 4, and 5 to 3 cases out of 50 reports for bleeding and stroke, respectively using the PRR). They were not or only slightly modified for the other studied events., Conclusions: Removing reports related to known drug-event associations could increase the sensitivity of signal detection in SR databases. This should be considered when using SR databases for signal detection as it could result in earlier identification of new drug-event associations., (Copyright © 2010 John Wiley & Sons, Ltd.)