Uppsala Monitoring Centre

About: Uppsala Monitoring Centre is a other organization based out in Uppsala, Sweden. It is known for research contribution in the topics: Pharmacovigilance & Adverse drug reaction. The organization has 104 authors who have published 310 publications receiving 14245 citations.

A Bayesian neural network method for adverse drug reaction signal generation

Abstract: Objective: The database of adverse drug reactions (ADRs) held by the Uppsala Monitoring Centre on behalf of the 47 countries of the World Health Organization (WHO) Collaborating Programme for International Drug Monitoring contains nearly two million reports. It is the largest database of this sort in the world, and about 35 000 new reports are added quarterly. The task of trying to find new drug–ADR signals has been carried out by an expert panel, but with such a large volume of material the task is daunting. We have developed a flexible, automated procedure to find new signals with known probability difference from the background data. Method: Data mining, using various computational approaches, has been applied in a variety of disciplines. A Bayesian confidence propagation neural network (BCPNN) has been developed which can manage large data sets, is robust in handling incomplete data, and may be used with complex variables. Using information theory, such a tool is ideal for finding drug–ADR combinations with other variables, which are highly associated compared to the generality of the stored data, or a section of the stored data. The method is transparent for easy checking and flexible for different kinds of search. Results: Using the BCPNN, some time scan examples are given which show the power of the technique to find signals early (captopril–coughing) and to avoid false positives where a common drug and ADRs occur in the database (digoxin–acne; digoxin–rash). A routine application of the BCPNN to a quarterly update is also tested, showing that 1004 suspected drug–ADR combinations reached the 97.5% confidence level of difference from the generality. Of these, 307 were potentially serious ADRs, and of these 53 related to new drugs. Twelve of the latter were not recorded in the CD editions of The physician's Desk Reference or Martindale's Extra Pharmacopoea and did not appear in Reactions Weekly online. Conclusion: The results indicate that the BCPNN can be used in the detection of significant signals from the data set of the WHO Programme on International Drug Monitoring. The BCPNN will be an extremely useful adjunct to the expert assessment of very large numbers of spontaneously reported ADRs.

A comparison of measures of disproportionality for signal detection in spontaneous reporting systems for adverse drug reactions.

Abstract: SUMMARY Purpose A continuous systematic review of all combinations of drugs and suspected adverse reactions (ADRs) reported to a spontaneous reporting system, is necessary to optimize signal detection. To focus attention of human reviewers, quantitative procedures can be used to sift data in different ways. In various centres, different measures are used to quantify the extent to which an ADR is reported disproportionally to a certain drug compared to the generality of the database. The objective of this study is to examine the level of concordance of the various estimates to the measure used by the WHO Collaborating Centre for International ADR monitoring, the information component (IC), when applied to the dataset of the Netherlands Pharmacovigilance Foundation Lareb. Methods The Reporting Odds Ratio � 1.96 standard errors (SE), proportional reporting ratio � 1.96 SE, Yule’s Q � 1.96 SE, the Poisson probability and Chi-square test of all 17 330 combinations were compared with the IC minus 2 standard deviations. Additionally, the concordance of the various tests, in respect to the number of reports per combination, was examined. Results In general, sensitivity was high in respect to the reference measure when a combination of point- and precision estimate was used. The concordance increased dramatically when the number of reports per combination increased. Conclusion This study shows that the different measures used are broadly comparable when four or more cases per combination have been collected. Copyright # 2002 John Wiley & Sons, Ltd.

Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers

Abstract: The vision of creating accessible, reliable clinical evidence by accessing the clincial experience of hundreds of millions of patients across the globe is a reality. Observational Health Data Sciences and Informatics (OHDSI) has built on learnings from the Observational Medical Outcomes Partnership to turn methods research and insights into a suite of applications and exploration tools that move the field closer to the ultimate goal of generating evidence about all aspects of healthcare to serve the needs of patients, clinicians and all other decision-makers around the world.

Quantitative signal detection using spontaneous ADR reporting.

Abstract: Quantitative methods are increasingly used to analyse spontaneous reports. We describe the core concepts behind the most common methods, the proportional reporting ratio (PRR), reporting odds ratio (ROR), information component (IC) and empirical Bayes geometric mean (EBGM). We discuss the role of Bayesian shrinkage in screening spontaneous reports, the importance of changes over time in screening the properties of the measures. Additionally we discuss three major areas of controversy and ongoing research: stratification, method evaluation and implementation. Finally we give some suggestions as to where emerging research is likely to lead.