Biomedical subjects

Marie Lindquist

Publications and source records attributed to Marie Lindquist.

6 recordsLinked to original sources

PubMed2006

Botanical nomenclature in pharmacovigilance and a recommendation for standardisation.

Nomenclature of plants in pharmacology can be presented by pharmaceutical names or scientific names in the form of Linnaean binomials. In this paper, positive and negative aspects of both systems are discussed in the context of the scientific nomenclatural framework and the systems' practical applicability. The Uppsala Monitoring Centre (UMC) runs the WHO Programme for International Drug Monitoring and is responsible for the WHO Adverse Drug Reaction (ADR) database that currently contains 3.6 million records. In order for the UMC to monitor pharmacovigilance through ADRs to herbal medicine products the following nomenclatural criteria are important: (i) the name should indicate only one species of plant; (ii) the source for this name must be authoritative; (iii) the name should indicate which part of the plant is used. Based on these criteria, the UMC investigated four options: (i) adopt main names used in recognised (inter-) national pharmacopoeias or authoritative publications; (ii) adopt option 1, but cite the publication for all names in abbreviated form; (iii) three-part pharmaceutical names consisting of Latinised part name plus Latinised genus name, plus Latinised specific epithet; (iv) scientific binomial names, optionally with author and plant part used. The UMC has chosen the latter option and will at its adoption utilise the scientific botanical nomenclature as defined by the International Code of Botanical Nomenclature. This decision satisfies all criteria set by the UMC and renders the necessity of creating a new system or upgrading an old inconsistent system obsolete. The UMC has also issued an extensive synonymy checklist of vernacular, pharmaceutical and scientific names for the herbals in the WHO ADR database. We strongly recommend the adoption of scientific names to denote plant ingredients in medicine.

Humans↗

PubMed2004

Data quality management in pharmacovigilance.

Pharmacovigilance relies on information gathered from the collection of individual case safety reports and other pharmacoepidemiological data. Even given the inherent limitations of spontaneous reports, the usefulness of this data source can be improved with good data quality management. Although under-reporting cannot be remedied this way, the negative impact of incomplete reports, which is another serious problem in pharmacovigilance, can be reduced. Quality management consists of quality planning, quality control, quality assurance and quality improvements. The pharmacovigilance data processing cycle starts with data collection and, in computerised systems, data entry; the next step is data storage and maintenance; followed by data selection, retrieval and manipulation. The resulting data output is analysed and assessed. Finally, conclusions are drawn and decisions made. The increased knowledge feeds back into the data processing cycle. Focussing on the first three steps of the data processing cycle, the different quality dimensions associated with these steps are described in this review, together with examples relevant to pharmacovigilance data. Functioning, well documented, and transparent quality management systems will benefit not only those involved in data collection, management and output production, but, ultimately, also the pharmacovigilance end users, the patients.

Adverse Drug Reaction Reporting Systems↗

PubMed2003

Assessing the impact of drug safety signals from the WHO database presented in 'SIGNAL': results from a questionnaire of National pharmacovigilance Centres.

INTRODUCTION: A major task for the Uppsala Monitoring Centre (UMC) is to detect early signals of suspected adverse drug reactions (ADRs) in the WHO Database. The database currently contains over 2.8 million spontaneously reported ADR case reports continuously collected by National Pharmacovigilance Centres in countries participating in the WHO Programme for International Drug Monitoring. The database is scanned every quarter and drug-ADR combinations are filtered out using different selection criteria intended to catch potential international drug safety signals at an early stage. Summary case data are reviewed by experts on the UMC's review panel and the signals are presented to the Programme members in the restricted circulation document entitled 'SIGNAL'. OBJECTIVE: The aim of the study was to investigate: (i). how the signals presented in 'SIGNAL' are used; (ii). if they reach the right target group; (iii). if they are of interest and relevance to the recipients; (iv). if they are timely and; (v). if they make any difference. We were also interested in knowing the view of member countries regarding the definition of what a signal is. METHODS: A questionnaire was sent out to 71 countries participating in the WHO Programme. The recipients were asked to state what actions were taken for 26 different signal headings included in three issues of 'SIGNAL' sent out during 2001 and to rate how useful they considered these topics to be. RESULTS: Responses were received from 45 countries (63%). The Centres' average ratings of relevance, importance and usefulness on a scale 1-10 of the selected 26 signals were all above the expected average rating 5.5. The content of 'SIGNAL' in general was seen as always or often useful in 63.5% of the respondents. In 2001, 17 countries took actions on at least one signal. Actions were rarely taken without considering the signal from the UMC. All responding centres agreed on the WHO definition of a signal, but there were differences in the interpretation of what constitutes a signal. CONCLUSION: The 'SIGNAL' publication is timely, plays an important role and has a direct impact on drug safety issues handled by members of the WHO Programme for International Drug Monitoring.

Adverse Drug Reaction Reporting Systems↗

PubMed2002

A data mining approach for signal detection and analysis.

The WHO database contains over 2.5 million case reports, analysis of this data set is performed with the intention of signal detection. This paper presents an overview of the quantitative method used to highlight dependencies in this data set. The method Bayesian confidence propagation neural network (BCPNN) is used to highlight dependencies in the data set. The method uses Bayesian statistics implemented in a neural network architecture to analyse all reported drug adverse reaction combinations. This method is now in routine use for drug adverse reaction signal detection. Also this approach has been extended to highlight drug group effects and look for higher order dependencies in the WHO data. Quantitatively unexpectedly strong relationships in the data are highlighted relative to general reporting of suspected adverse effects; these associations are then clinically assessed.

Adverse Drug Reaction Reporting Systems↗

PubMed2002

Signal selection and follow-up in pharmacovigilance.

The detection of unknown and unexpected connections between drug exposure and adverse events is one of the major challenges of pharmacovigilance. For the identification of possible connections in large databases, automated statistical systems have been introduced with promising results. From the large numbers of associations so produced, the human mind has to identify signals that are likely to be important, in need of further assessment and follow-up and that may require regulatory action. Such decisions are based on a variety of clinical, epidemiological, pharmacological and regulatory criteria. Likewise, there are a number of criteria that underlie the subsequent evaluation of such signals. A good understanding of the logic underlying these processes fosters rational pharmacovigilance and efficient drug regulation. In the future a combination of quantitative and qualitative criteria may be incorporated in automated signal detection.

Adverse Drug Reaction Reporting Systems↗

PubMedDate not supplied

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

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.

Adverse Drug Reaction Reporting Systems↗