How can the Quality of Medical Data in Pharmacovigilance, Pharmacoepidemiology and Clinical Studies be Guaranteed?

Alla, François; Rosilio, Myriam; Funck‐Brentano, Christian; Barthélémy, Philippe; Brisset, Sophie; Cellier, Dominique; Chassany, Olivier; Demarez, Jean-Paul; Diebolt, Vincent; Francillon, A; Gambotti, Laëtitia; Hannachi, Hakima; Lechat, Philippe; Lemaire, François; Lièvre, Michel; Misse, Christophe; Nguon, Marina; Pariente, Alexandre; Rosenheim, M.; Weisslinger-Darmon, Nadine

doi:10.2515/therapie/2013040

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…The pitfalls of the secondary use of observational data to support research are well documented. [8][9][10][11][12][13] Typically these data are collected either for billing or diagnostic purposes and not with research endpoints in mind. Von Lucadou, et al notes that information in the electronic health record may not be as granular as data captured during the course of a clinical trial and time stamps of clinical events should be examined prior to inferring temporal relationships.…”

Section: Introductionmentioning

confidence: 99%

Increasing Trust in Real-World Evidence Through Evaluation of Observational Data Quality

Blacketer

DeFalco

Ryan

2021

Preprint

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Advances in standardization of observational healthcare data have enabled methodological breakthroughs, rapid global collaboration, and generation of real-world evidence to improve patient outcomes. Standardizations in data structure, such as use of Common Data Models (CDM), need to be coupled with standardized approaches for data quality assessment. To ensure confidence in real-world evidence generated from the analysis of real-world data, one must first have confidence in the data itself. The Data Quality Dashboard is an open-source R package that reports potential quality issues in an OMOP CDM instance through the systematic execution and summarization of over 3,300 configurable data quality checks. We describe the implementation of check types across a data quality framework of conformance, completeness, plausibility, with both verification and validation. We illustrate how data quality checks, paired with decision thresholds, can be configured to customize data quality reporting across a range of observational health data sources. We discuss how data quality reporting can become part of the overall real-world evidence generation and dissemination process to promote transparency and build confidence in the resulting output. Transparently communicating how well CDM standardized databases adhere to a set of quality measures adds a crucial piece that is currently missing from observational research. Assessing and improving the quality of our data will inherently improve the quality of the evidence we generate.

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Section: Introductionmentioning

confidence: 99%

Increasing Trust in Real-World Evidence Through Evaluation of Observational Data Quality

Blacketer

DeFalco

Ryan

2021

Preprint

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“…causality assessment. In order to assess the possibility of a causal relationship between exposure to a medicinal product during pregnancy and an adverse pregnancy outcome, it is important that the underlying data (raw variables) and information (processing and interpretation of raw data), in individual case reports and case series, are both present and of high quality [ 7 ]. Therefore, in order to establish reliably whether there is a causal relationship between a medicinal product and a reported event, relevant information for the causality assessment needs to be present .…”

Section: Introductionmentioning

confidence: 99%

Validation of a Novel Method to Assess the Clinical Quality of Information in Pregnancy-Related Pharmacovigilance Case Reports: A ConcePTION Project

van Rijt-Weetink,

Egberts,

van Hunsel

et al. 2024

Drug Saf

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Background To assess the causal relationship between a medicinal product and a reported event, relevant information needs to be present. Information elements for assessing cases of exposure to medicinal products during pregnancy were predefined and used in a new tool to assess the quality of information. However, the extent in which the presence or absence of these predefined information elements is associated with the overall clinical quality of these cases, as evaluated by pharmacovigilance experts, remains uncertain. Objective We aimed to validate a novel method to assess the clinical quality of information in real-world pregnancy pharmacovigilance case reports. Methods The clinical quality of case reports regarding medicinal product exposure and pregnancy-related outcomes was appraised from spontaneous reports, literature, Teratology Information Services (UK and Switzerland), The Dutch Pregnancy Drug Register, the Gilenya pregnancy registry and the Enhanced PV programme of Novartis. Assessment was done by means of the novel standardised tool based on the presence and relevance of information, and by expert judgement. The novel tool was validated compared to the expert assessment as the gold standard expressed as the area under the receiver operating characteristic curves, after which the sensitivity and specificity were calculated using cross-tabulations. Inter-rater variability was determined by means of weighted Cohen’s kappa. Results One hundred and eighty-six case reports were included. The clinical quality score as assessed by the novel method was divided into three categories with cut-off values of 45% (poor to intermediate) and 65% (intermediate to excellent). Sensitivity was 0.93 and 0.96 for poor to intermediate and intermediate to excellent, respectively. Specificity was respectively 0.52 and 0.73. Inter-rater variability was 0.65 (95% confidence interval 0.53–0.78) for the newly developed approach, and 0.40 (95% confidence interval 0.28–0.52) for the gold standard assessment. Conclusions The tool described in this study using the presence and relevance of elements of information is the first designed, validated and standardised method for the assessment of the quality of information of case reports in pregnancy pharmacovigilance data. This method confers less inter-rater variability compared with a quality assessment by experts of pregnancy-related pharmacovigilance data. Supplementary Information The online version contains supplementary material available at 10.1007/s40264-023-01389-y.

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“…After deficiencies in the essential documents, inadequate/inaccurate case histories and source documentation issues rank second in the most common findings in both US Food and Drug Agency (FDA) and European Medicines Agency inspections of trial sites, partly being classified as critical findings. [2][3][4] The use of computerized systems has had an impact on the conduct of clinical trials in recent years. The focus of regulatory guidelines and efforts of the industry have been on the second step of the data flow in clinical trials: the transcription of the source data via CRFs into the trial database.…”

Section: Introductionmentioning

confidence: 99%

Tools in a clinical information system supporting clinical trials at a Swiss University Hospital

2014

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How can the Quality of Medical Data in Pharmacovigilance, Pharmacoepidemiology and Clinical Studies be Guaranteed?

Cited by 3 publications

References 13 publications

Increasing Trust in Real-World Evidence Through Evaluation of Observational Data Quality

Increasing Trust in Real-World Evidence Through Evaluation of Observational Data Quality

Validation of a Novel Method to Assess the Clinical Quality of Information in Pregnancy-Related Pharmacovigilance Case Reports: A ConcePTION Project

Tools in a clinical information system supporting clinical trials at a Swiss University Hospital

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