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Triage algorithms for early discovery of adverse drug interactions
Linköping University, Department of Medical and Health Sciences, Clinical Pharmacology. Linköping University, Faculty of Health Sciences.
Uppsala Monitoring Centre, WHO Collaborating Centre for International Drug Monitoring, Uppsala, Sweden.
Uppsala Monitoring Centre, WHO Collaborating Centre for International Drug Monitoring, Uppsala, Sweden.
Uppsala Monitoring Centre, WHO Collaborating Centre for International Drug Monitoring, Uppsala, Sweden.
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

Background: Most methodological research for broad surveillance of drug interactions in large collections of suspected ADR reports has focused on measures of disproportionality. However, recent results indicate that reported clinical information and pharmacological characteristics may be at least as valuable to detect adverse drug interactions early.

Objective: To develop triage algorithms for adverse drug interaction surveillance, and to evaluate the algorithms prospectively relative to expert clinical assessment.

Methods: A previously developed reference set based on Stockley’s Drug Interactions was used to train the algorithms. Logistic regression was used to set the relative weights of the different indicators (information potentially suggestive adverse drug interactions such as pharmacological properties including cytochrome P450 (CYP) activity; explicit suspicions of drug interactions as noted by the reporter in different forms; clinical details such as dose and treatment overlap; and a measure of disproportionality based on the total number of reports on two drugs and one ADR together) of each algorithm. Three triage algorithms were designed. All are logistic regression models producing an estimated probability that a given case series constitutes an adverse drug interaction signal. Two of them are data driven: one which used a very broad set of indicators (full data-driven) and one which used a more narrow set (lean data-driven). The third was manually derived (lean clinical) as a simplified version of the full data-driven algorithm. An independent evaluation set was constructed that consisted of 100 randomly selected case series in the WHO Global Individual Case Safety Report (ICSR) Database, VigiBase, from January 1990 to February 2011. Each algorithm’s ranking of case series was evaluated against an evaluation set. In a complementary analysis the algorithm were compared to a pure disproportionality analysis.

Results: The two lean algorithms were comparable in performance. However both outperformed the full data-driven algorithm on the independent evaluation set. The areas under the curve (AUC) for the receiver operating characteristics (ROC) curves were as follows: 71% (lean clinical) and 69% (lean data-driven). For a false positive rate (FPR) of up to 0.04 the lean algorithms classifies about 14,000 case series as potential interaction signals. Thresholds corresponding to greater FPRs are unlikely to be feasible in practice. The algorithms clearly outperform disproportionality analysis alone.

Conclusions: The value of incorporating clinical and pharmacological information in first-pass screening for adverse drug interactions is clear. Two triage algorithms have been proposed that each effectively identify adverse drug interaction signals and clearly outperforming pure disproportionality analysis in this respect.

Keyword [en]
Individual Case Safety Reports, Adverse Drug Interactions, VigiBase, Triage algorithms
National Category
Medical and Health Sciences
URN: urn:nbn:se:liu:diva-70848OAI: diva2:442067
Available from: 2011-09-20 Created: 2011-09-20 Last updated: 2011-09-20Bibliographically approved
In thesis
1. Drug interaction surveillance using individual case safety reports
Open this publication in new window or tab >>Drug interaction surveillance using individual case safety reports
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Background: Drug interactions resulting in adverse drug reactions (ADRs) represent a major health problem both for individuals and society in general. Post-marketing pharmacovigilance reporting databases with compiled individual case safety reports (ICSRs) have been shown to be particularly useful in the detection of novel drug - ADR combinations, though these reports have not been fully used to detect adverse drug interactions.

Aim: To explore the potential to identify drug interactions using ICSRs and to develop a method to facilitate the detection of adverse drug interaction signals in the WHO Global ICSR Database, VigiBase.

Methods: All six studies included in this thesis are based on ICSRs available in VigiBase. Two studies aimed to characterise drug interactions reported in VigiBase. In the first study we examined if contraindicated drug combinations (given in a reference source of drug interactions) were reported on the individual reports in the database, and in the second study we examined the scientific literature for interaction mechanisms for drug combinations most frequently co-reported as interacting in VigiBase. Two studies were case series analyses where the individual reports were manually reviewed. The two remaining studies aimed to develop a method to facilitate detection of novel adverse drug interactions in VigiBase. One examined what information (referred to as indicators) was reported on ICSRs in VigiBase before the interactions became listed in the literature. In the second methodological study, logistic regression was used to set the relative weights of the indicators to form triage algorithms. Three algorithms (one completely data driven, one semi-automated and one based on clinical knowledge) based on pharmacological and reported clinical information and the relative reporting rate of an ADR with a drug combination were developed. The algorithms were then evaluated against a set of 100 randomly selected case series with potential adverse drug interactions. The algorithm’s performances were then evaluated among DDAs with high coefficients.

Results: Drug interactions classified as contraindicated are reported on the individual reports in VigiBase, although they are not necessarily recognised as interactions when reported. The majority (113/123) of drug combinations suspected for being responsible for an ADR were established drug interactions in the literature. Of the 113 drug interactions 46 (41%) were identified as purely pharmacodynamic; 28 (25%) as pharmacokinetic; 18 (16%) were a mix of both types and for 21 (19%) the mechanism have not yet been identified. Suspicions of a drug interaction explicitly noted by the reporter are much more common for known adverse drug interactions than for drugs not known to interact. The clinical evaluation of the triage algorithms showed that 20 were already known in the literature, 30 were classified as signals and 50 as not signals. The performance of the semi-automated and the clinical algorithm were comparable. In the end the clinical algorithm was chosen. At a relevant level, 38% were of the adverse drug interactions were already known in the literature and of the remaining 80% were classified as signals for this algorithm.

Conclusions: This thesis demonstrated that drug interactions can be identified in large post-marketing pharmacovigilance reporting databases. As both pharmacokinetic and pharmacodynamic interactions were reported on ICSRs the surveillance system should aim to detect both. The proposed triage algorithm had a high performance in comparison to the disproportionality measure alone.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2011. 45 p.
Linköping University Medical Dissertations, ISSN 0345-0082 ; 1252
Adverse drug reactions, adverse drug interaction surveillance, drug interactions, individual case safety reports, postmarketing pharmacovigilance, signal detection
National Category
Medical and Health Sciences
urn:nbn:se:liu:diva-70424 (URN)978-91-7393-106-9 (ISBN)
Public defence
2011-10-06, Nils Holger, Campus US, Linköpings universitet, Linköping, 13:00 (English)
Available from: 2011-09-07 Created: 2011-09-07 Last updated: 2011-09-20Bibliographically approved

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