Preliminary Results of a Novel Algorithmic Method Aiming to Support Initial Causality Assessment of Routine Pharmacovigilance Case Reports for Medication-Induced Liver Injury: The PV-RUCAM

Erik Scalfaro (), Henk Johan Streefkerk, Michael Merz, Christoph Meier and David Lewis
Additional contact information
Erik Scalfaro: Novartis Pharma AG
Henk Johan Streefkerk: Novartis Pharma AG
Michael Merz: Novartis Institutes for BioMedical Research
Christoph Meier: University of Basel
David Lewis: Novartis Pharma AG

Drug Safety, 2017, vol. 40, issue 8, No 7, 715-727

Abstract: Abstract Introduction Data incompleteness in pharmacovigilance (PV) health records limits the use of current causality assessment methods for drug-induced liver injury (DILI). In addition to the inherent complexity of this adverse event, identifying cases of high causal probability is difficult. Objective The aim was to evaluate the performance of an improved, algorithmic and standardised method called the Pharmacovigilance-Roussel Uclaf Causality Assessment Method (PV-RUCAM), to support assessment of suspected DILI. Performance was compared in different settings with regard to applicability and differentiation capacity. Methods A PV-RUCAM score was developed based on the seven sections contained in the original RUCAM. The score provides cut-off values for or against DILI causality, and was applied on two datasets of bona fide individual case safety reports (ICSRs) extracted randomly from clinical trial reports and a third dataset of electronic health records from a global PV database. The performance of PV-RUCAM adjudication was compared against two standards: a validated causality assessment method (original RUCAM) and global introspection. Results The findings showed moderate agreement against standards. The overall error margin of no false negatives was satisfactory, with 100% sensitivity, 91% specificity, a 25% positive predictive value and a 100% negative predictive value. The Spearman’s rank correlation coefficient illustrated a statistically significant monotonic association between expert adjudication and PV-RUCAM outputs (R = 0.93). Finally, there was high inter-rater agreement (K w = 0.79) between two PV-RUCAM assessors. Conclusion Within the PV setting of a pharmaceutical company, the PV-RUCAM has the potential to facilitate and improve the assessment done by non-expert PV professionals compared with other methods when incomplete reports must be evaluated for suspected DILI. Prospective validation of the algorithmic tool is necessary prior to implementation for routine use.

Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s40264-017-0541-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:drugsa:v:40:y:2017:i:8:d:10.1007_s40264-017-0541-2

Ordering information: This journal article can be ordered from
http://www.springer.com/adis/journal/40264

DOI: 10.1007/s40264-017-0541-2

Access Statistics for this article

Drug Safety is currently edited by Nitin Joshi

More articles in Drug Safety from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().