Decision making in drug safety—a literature review of criteria used to prioritize newly detected safety issues

Păcurariu, Alexandra; Coloma, Preciosa M.; Gross-Martirosyan, Liana; Sturkenboom, Miriam; Straus, Sabine M. J. M.

doi:10.1002/pds.4128

Cited by 7 publications

(7 citation statements)

References 23 publications

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“…The features we identified are generally in keeping with previous research; they may reflect the ‘strength of evidence’ of reports of ADRs [ 14 ]. We have shown how ‘experimental evidence’ was among one of the most frequently considered features.…”

Section: Discussionsupporting

confidence: 75%

“…There was a difference of 1 year in median TTC for the periods 2000–2012 and 2013–2019. However, prior documentation of ADRs may affect the TTC [ 36 ], as may the time on market of medicinal products [ 37 ] or the public health impact/intensity of ADRs in signal prioritization [ 4 , 14 ]. The number of reports of suspected ADRs has grown with time, and VigiBase holds over 30 million reports as of 2022.…”

Section: Discussionmentioning

confidence: 99%

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Signals of Adverse Drug Reactions Communicated by Pharmacovigilance Stakeholders: A Scoping Review of the Global Literature

et al. 2022

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Introduction and Objective Signals of adverse drug reactions (ADRs) can be supported by reports of ADRs and by interventional and non-interventional studies. The evidence base and features of ADR reports that are used to support signals remain to be comprehensively described. To this end, we have undertaken a scoping review. Methods We searched the following databases: PubMed, EMBASE, PsycINFO, Web of Science, and Google Scholar, without language or time restrictions. We also hand searched the bibliographies of relevant studies. We included studies of any design if the results were described as signals . We assessed the levels of evidence using the Oxford Centre for Evidence-Based Medicine (OCEBM) criteria and coded features of reports of ADRs using the Bradford Hill guidelines. Results Overall, 1974 publications reported 2421 studies of signals; 1683/2421 were clinical assessments of anecdotal reports of ADRs, but only 225 (13%) of these included explicit judgments on which features of the ADR reports were supportive of a signal. These 225 studies yielded 228 signals; these were supported by features, which were: ‘experimental evidence’ (i.e., positive dechallenge or rechallenge, 154 instances [68%]), ‘temporality’ (i.e., time to onset, 130 [57%]), ‘exclusion of competing causes’ (49 [21%]), and others (40 [17%]). Positive dechallenge/rechallenge often co-occurred with temporality (77/228). OCEBM 4 (i.e., case series and case-control studies) was the most frequent level of evidence (2078 studies). Between 2013 and 2019, there was a three-fold increase in clinical assessments of reports of ADRs compared with a less than two-fold increase in studies supported by higher levels of evidence (i.e., OCEBM 1–3). We identified an increased rate between 2013 and 2019 in disproportionality analyses (about 15 studies per year), mostly from academia. Conclusions Most signals were supported by temporality and dechallenge/rechallenge, but clear reporting of judgments on causality remains infrequent. The number of studies supported only by anecdotal reports of ADRs increased from year to year. The impact of a growing number of signals of disproportionate reporting communicated without an accompanying clinical assessment should be evaluated.

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Section: Discussionsupporting

confidence: 75%

Section: Discussionmentioning

confidence: 99%

Signals of Adverse Drug Reactions Communicated by Pharmacovigilance Stakeholders: A Scoping Review of the Global Literature

et al. 2022

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“…There have been a few attempts to combine criteria for signal prioritization in algorithms or frameworks to support the drug signal selection and prioritization process by different organizations responsible in managing drug safety issues . Enhancement of such decision support frameworks with valid predictive criteria may increase their accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…Several signal characteristics have been postulated to help in signal assessment, including strength of evidence, public health impact, and the novelty of drugs and/or safety issues . However, information on the predictive validity of these criteria, ie, whether they can predict if the safety signal reviewed requires a PI update is lacking.…”

Section: Introductionmentioning

confidence: 99%

Characteristics of drugs safety signals that predict safety related product information update

Insani

Păcurariu

Mantel‐Teeuwisse

et al. 2018

Pharmacoepidemiology and Drug

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PurposeInvestigation of drug safety signals is one of the major tasks in pharmacovigilance. Among many potential signals identified, only a few reflect adverse drug reactions requiring regulatory actions, such as product information (PI) update. Limited information is available regarding the signal characteristics that might predict PI update following signal evaluation. The objective of this study was to identify signal characteristics associated with PI updates following signal evaluation by the European Medicines Agency Pharmacovigilance Risk Assessment Committee during 2012 to 2016.MethodsA comparative study was performed based on data from 172 safety signals. Characteristics of signals were extracted from the European Pharmacovigilance Issues Tracking Tool database. Multivariable logistic regression analysis was used to assess the relationship between signal characteristics and the decision to update the PI.ResultsMultivariable logistic regression analysis showed that the presence of evidence in multiple types of data sources (adjusted odds ratio [OR] 7.8 95% CI [1.5, 40.1]); mechanistic plausibility of the drug‐event association (adjusted OR 3.9 95% CI [1.9, 8.0]); seriousness of the event (adjusted OR 4.2 95% CI [1.3, 13.9]); and age of drugs ≤5 years (adjusted OR 3.9 95% CI [1.2, 12.7]) were associated with the decision to change the PI (P < 0.05).ConclusionsThis study identified 4 characteristics of drug safety signals that have shown to be associated with PI changes as outcome of signal evaluation. These characteristics may be used as criteria for selection and prioritization of potential signals that are more likely to necessitate product information updates.

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“…The prioritization of a signal after its detection is also important; however, this is an entirely manual task. [ 27 ] The results may differ depending on the individual’s experience or knowledge; thus, prioritization should be based on a variety of evidence. However, no definitive guide has been provided for decision-making.…”

Section: Discussionmentioning

confidence: 99%

Machine learning model combining features from algorithms with different analytical methodologies to detect laboratory-event-related adverse drug reaction signals

et al. 2018

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BackgroundThe importance of identifying and evaluating adverse drug reactions (ADRs) has been widely recognized. Many studies have developed algorithms for ADR signal detection using electronic health record (EHR) data. In this study, we propose a machine learning (ML) model that enables accurate ADR signal detection by integrating features from existing algorithms based on inpatient EHR laboratory results.Materials and methodsTo construct an ADR reference dataset, we extracted known drug–laboratory event pairs represented by a laboratory test from the EU-SPC and SIDER databases. All possible drug–laboratory event pairs, except known ones, are considered unknown. To detect a known drug–laboratory event pair, three existing algorithms—CERT, CLEAR, and PACE—were applied to 21-year inpatient EHR data. We also constructed ML models (based on random forest, L1 regularized logistic regression, support vector machine, and a neural network) that use the intermediate products of the CERT, CLEAR, and PACE algorithms as inputs and determine whether a drug–laboratory event pair is associated. For performance comparison, we evaluated the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1-measure, and area under receiver operating characteristic (AUROC).ResultsAll measures of ML models outperformed those of existing algorithms with sensitivity of 0.593–0.793, specificity of 0.619–0.796, NPV of 0.645–0.727, PPV of 0.680–0.777, F1-measure of 0.629–0.709, and AUROC of 0.737–0.816. Features related to change or distribution of shape were considered important for detecting ADR signals.ConclusionsImproved performance of ML models indicated that applying our model to EHR data is feasible and promising for detecting more accurate and comprehensive ADR signals.

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Decision making in drug safety—a literature review of criteria used to prioritize newly detected safety issues

Cited by 7 publications

References 23 publications

Signals of Adverse Drug Reactions Communicated by Pharmacovigilance Stakeholders: A Scoping Review of the Global Literature

Signals of Adverse Drug Reactions Communicated by Pharmacovigilance Stakeholders: A Scoping Review of the Global Literature

Characteristics of drugs safety signals that predict safety related product information update

Machine learning model combining features from algorithms with different analytical methodologies to detect laboratory-event-related adverse drug reaction signals

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