Faculty Bibliography

INTRODUCTION/BACKGROUND:Signal detection remains a cornerstone activity of pharmacovigilance. Routine quantitative signal detection primarily focuses on screening of spontaneous reports. In striving to enhance quantitative signal detection capability further, other data streams are being considered for their potential contribution as sources of emerging signals, one of which is longitudinal observational databases, including electronic medical record (EMR) and transactional insurance claims databases. Quantitative signal detection on such databases is a nascent field-with published methods being primarily based either on individual metrics, which may not effectively represent the complexity of the longitudinal records and their necessary variation for analysis for drug-outcome pairs, or on visualization discovery approaches leveraging multiple aspects of the records, which are not particularly tractable to high-throughput hypothesis-free signal detection. One extensively tested example of the latter is chronographs. METHODS:We apply a disturbance detection algorithm to chronographs using UK EMR The Health Improvement Network (THIN) data. The algorithm utilizes autoregressive integrated moving average (ARIMA)-based time series methodology designed to find disturbances that occur outside the normal pattern trends of the ARIMA model for the chronograph. Chronographs currently highlight drug-event pairs as potentially worthy of further clinical assessment, via filter-based increases in disproportionality scores from before to after the index drug exposure, tested across a range of case and control windows. RESULTS:We replicate the findings on six exemplar chronographs from a previous publication, and show how disturbances can be effectively detected across this set of pairs. Further, 692 disturbances were detected in analysis of all 384 individual READ code outcomes ever recorded 50 or more times for patients prescribed sibutramine. The disturbances are algorithmically further broken into subsets of clinical interest. CONCLUSION/CONCLUSIONS:Overall, the disturbance algorithm approach shows promising capacity for detecting outliers, and shows tractability of the algorithmic approach for large-scale screening. The method offers an array of pattern types for detection and clinical review.

A visual aid for teaching the formula for the Mann–Whitney U statistic is proposed, inspired by ‘proof without words’, a didactic exercise from the mathematics literature that uses a visual approach to justify mathematical statements, including formulas and theorems

INTRODUCTION: Proton pump inhibitors (PPIs) have been implicated in the occurrence of moderate to severe myopathies in several case reports. AIM: This study was performed to assess the reporting risk of muscular adverse drug reactions (ADRs) associated with PPIs in the Italian National Network of Pharmacovigilance database. METHODS: A disproportionality analysis (case/non-case) was performed using spontaneous reports collected in the database between July 1983 and May 2016. Reporting odds ratio (ROR) and 95% confidence intervals (CIs) were calculated as a measure of disproportionality. In a secondary and tertiary analysis, we explored the association of PPIs with muscular ADRs after taking into account the masking effect of statins. Moreover, the possibility of an interaction between PPIs and statins, leading to the occurrence of muscular ADRs, was also tested. RESULTS: The study was carried out on 274,108 reports. The ROR of muscular ADRs for PPIs, adjusted for age and gender, was 1.484 (95% CI 1.204-1.829; p < 0.001), whereas the ROR for rhabdomyolysis was 0.621 (95% CI 0.258-1.499). Similar results were obtained in the secondary analysis. The tertiary analysis, where PPIs were considered regardless of whether their role was suspected or concomitant, showed a potential disproportionate reporting for the combination PPIs-rhabdomyolysis (ROR 1.667, 95% CI 1.173-2.369; p < 0.01). The PPIs-statins combination was not associated with an enhanced ROR of muscular ADRs/rhabdomyolysis compared with statins alone. CONCLUSIONS: This explorative study suggests that the class of PPIs could be involved in reports of muscular ADRs, rather than any other ADR, more frequently than any non-statin drug. Our results must be corroborated by further studies.

BACKGROUND:The lower bound of the 95% confidence interval of measures of disproportionality (Lower95CI) is widely used in signal detection. Masking is a statistical issue by which true signals of disproportionate reporting are hidden by the presence of other medicines. The primary objective of our study is to develop and validate a mathematical framework for assessing the masking effect of Lower95CI. METHODS:We have developed our new algorithm based on the masking ratio (MR) developed for the measures of disproportionality. A MR for the Lower95CI (MRCI) is proposed. A simulation study to validate this algorithm was also conducted. RESULTS:We have established the existence of a very close mathematical relation between MR and MRCI. For a given drug-event pair, the same product will be responsible for the highest masking effect with the measure of disproportionality and its Lower95CI. The extent of masking is likely to be very similar across the two methods. An important proportion of identical drug-event associations affected by the presence of an important masking effect is revealed by the unmasking exercise, whether the proportional reporting ratio (PRR) or its confidence interval are used. CONCLUSION/CONCLUSIONS:The detection of the masking effect of Lower95CI can be automated. The real benefits of this unmasking in terms of new true-positive signals (rate of true-positive/false-positive) or time gained by the revealing of signals using this method have not been fully assessed. These benefits should be demonstrated in the context of prospective studies.

BACKGROUND: The aim of this study was to investigate whether database restriction can improve oncology drug pharmacovigilance signal detection performance. METHODS: We used spontaneous adverse event (AE) reports in the United States (US) Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database. Positive control (PC) drug medical concept (DMC) pairs were selected from safety information not included in the product's first label but subsequently added as label changes. These medical concepts (MCs) were mapped to the Medical Dictionary for Regulatory Activities (MedDRA) preferred terms (PTs) used in FAERS to code AEs. Negative controls (NC) were MCs with circumscribed PTs not included in the corresponding US package insert (USPI). We calculated shrinkage-adjusted observed-to-expected (O/E) reporting frequencies for the aforementioned drug-PT pairs. We also formulated an adjudication framework to calculate performance at the MC level. Performance metrics [sensitivity, specificity, positive and negative predictive value (PPV, NPV), signal/noise (S/N), F and Matthews correlation coefficient (MCC)] were calculated for each analysis and compared. RESULTS: The PC reference set consisted of 11 drugs, 487 PTs, 27 MCs, 37 drug-MC combinations and 638 drug-event combinations (DECs). The NC reference set consisted of 11 drugs, 9 PTs, 5 MCs, 40 drug-MC combinations and 67 DECs. Most drug-event pairs were not highlighted by either analysis. A small percentage of signals of disproportionate reporting were lost, more noise than signal, with no gains. Specificity and PPV improved whereas sensitivity, NPV, F and MCC decreased, but all changes were small relative to the decrease in sensitivity. The overall S/N improved. CONCLUSION: This oncology drug restricted analysis improved the S/N ratio, removing proportionately more noise than signal, but with significant credible signal loss. Without broader experience and a calculus of costs and utilities of correct versus incorrect classifications in oncology pharmacovigilance such restricted analyses should be optional rather than a default analysis.

BACKGROUND:Severe cutaneous adverse reactions (SCARs) are prominent in pharmacovigilance (PhV). They have some commonalities such as nonimmediate nature and T-cell mediation and rare overlap syndromes have been documented, most commonly involving acute generalized exanthematous pustulosis (AGEP) and drug rash with eosinophilia and systemic symptoms (DRESS), and DRESS and toxic epidermal necrolysis (TEN). However, they display diverse clinical phenotypes and variations in specific T-cell immune response profiles, plus some specific genotype-phenotype associations. A question is whether causation of a given SCAR by a given drug supports causality of the same drug for other SCARs. If so, we might expect significant intercorrelations between SCARs with respect to overall drug-reporting patterns. SCARs with significant intercorrelations may reflect a unified underlying concept. METHODS:principle axis factoring (PAF). The number of factors was determined by scree plot/Kaiser's rule. We also examined solutions with an additional factor. We applied various oblique rotations. We assessed the strength of the solution by percentage of variance explained, minimum number of factors loading per major factor, the magnitude of the communalities, loadings and crossloadings, and reproduced- and residual correlations. RESULTS:The data were generally adequate for factor analysis but the amount of variance explained, shared variance, and communalities were low, suggesting caution in general against extrapolating causality between SCARs. SJS and TEN displayed most shared variance. AGEP and DRESS, the other SCAR pair most often observed in overlap syndromes, demonstrated modest shared variance, along with maculopapular rash (MPR). DRESS and TEN, another of the more commonly diagnosed pairs in overlap syndromes, did not. EM was uncorrelated with SJS and TEN. CONCLUSIONS:The notion that causality of a drug for one SCAR bolsters support for causality of the same drug with other SCARs was generally not supported.