The 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). Signals 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). SDRs 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. The 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. Organizations 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.

BACKGROUND The clinical trial safety database for tiotropium has been augmented with a 4-year trial in patients with COPD, which provides an opportunity to better evaluate the cardiovascular (CV) profile of tiotropium. METHODS Trials with the following criteria were considered: > or = 4 weeks, randomized, double-blind, parallel-group, placebo-controlled. Inclusion/exclusion criteria were similar, including spirometry-confirmed COPD, > or = 10 pack-year smoking, and age > or = 40 years. Adverse events were collected throughout each trial using standardized case report forms. Incidence rates (IRs) were determined from the total number of patients with an event divided by total time at risk. Rate ratios (RRs) and 95% CI for tiotropium/placebo were calculated. IRs were determined for all-cause mortality and selected CV events, including a composite CV end point encompassing CV deaths, nonfatal myocardial infarction (MI), nonfatal stroke, and the terms sudden death, sudden cardiac death, and cardiac death. RESULTS There were 19,545 patients randomized: 10,846 (tiotropium) and 8,699 (placebo) from 30 trials. Mean FEV(1) = 1.15 +/- 0.46 L (41 +/- 14% predicted), 76% men, mean age = 65 +/- 9 years. Cumulative exposure to study drug was 13,146 (tiotropium) and 11,095 (placebo) patient-years. For all-cause mortality, the IR was 3.44 (tiotropium) and 4.10 (placebo) per 100 patient-years (RR [95% CI] = 0.88 [0.77-0.999]). IR for the CV end point was 2.15 (tiotropium) and 2.67 (placebo) per 100 patient-years (RR [95% CI] = 0.83 (0.71-0.98]). The IR for the CV mortality excluding nonfatal MI and stroke was 0.91 (tiotropium) and 1.24 (placebo) per 100 patient-years (RR [95% CI] = 0.77 [0.60-0.98]). For total MI, cardiac failure, and stroke the RRs (95% CI) were 0.78 (0.59-1.02), 0.82 (0.69-0.98), and 1.03 (0.79-1.35), respectively. CONCLUSION Tiotropium was associated with a reduction in the risk of all-cause mortality, CV mortality, and CV events.

Data mining algorithms are increasingly being used to support the process of signal detection and evaluation in pharmacovigilance. Published data mining exercises formulated within a screening paradigm typically calculate classical performance indicators such as sensitivity, specificity, predictive value and receiver operator characteristic curves. Extrapolating signal detection performance from these isolated data mining exercises to performance in real-world pharmacovigilance scenarios is complicated by numerous factors and some published exercises may promote an inappropriate and exclusive focus on only one aspect of performance. In this article, we discuss a variation on positive predictive value that we call the ‘number needed to detect’ that provides a simple and intuitive screening metric that might usefully supplement the usual presentations of data mining performance. We use a series of figures to demonstrate the nature and application of this metric, and selected adaptive variations. Even with simple and intuitive metrics, precisely quantifying the performance of contemporary data mining algorithms in pharmacovigilance is complicated by the complexity of the phenomena under surveillance and the manner in which the data are recorded in spontaneous reporting systems.

OBJECTIVE:Heart rate has been shown to predict cardiovascular morbidity and mortality in a very reliable and easily accessible manner. The exact definition of the conditions under which heart rate is measured appears to be as crucial as the determinations of blood pressure or plasma catecholamines. It was investigated how accurately heart rate measurements were performed, and conditions were described in 56 studies identified from prominent journals within the Medline database: (a) search phrases were "heart rate" and "rest" or "resting"; (b) publication date was from 1996 to 2001; and (c) publication type was "clinical trial".METHODS:Five conditions were considered as most influential: (a) resting period before measurement; (b) posture of the patient; (c) environmental conditions such as temperature or visual and acoustic stimuli; (d) method used to record heart rate; and (e) data analysis, i.e., derivation from raw data. An average of only 1.7 of those 5 criteria for the determination of heart rate were met in the studies included. Information on conditions of, for example, resting period, or environmental conditions is almost completely lacking.RESULTS AND CONCLUSION:The data show that a very important risk predictor and treatment target heart rate-is not reported in a scientifically sufficient manner, even in large trials. Valuable information is lost despite the fact that the investment of adequately defining, controlling, and performing this determination is modest in comparison to the potential gain. It is recommended to standardize heart rate measurements in analogy to that of blood pressure determinations.