Methods for Identifying Culprit Drugs in Cutaneous Drug Eruptions: A Scoping Review

Abstract

Background Cutaneous drug eruptions are a significant source of morbidity, mortality, and cost to the healthcare system. Identifying the culprit drug is essential; however, despite numerous methods being published, there are no consensus guidelines. Objectives Conduct a scoping review to identify all published methods of culprit drug identification for cutaneous drug eruptions, compare the methods, and generate hypotheses for future causality assessment studies. Eligibility criteria Peer-reviewed publications involving culprit drug identification methods. Sources of evidence Medline, Embase, and Cochrane Central Register of Controlled Trials. Charting methods Registered PRISMA-ScR format protocol on Open Science Forum. Results In total, 109 studies and 26 reviews were included comprising 656,635 adverse drug events, most of which were cutaneous. There were 54 methods of culprit drug identification published, categorized as algorithms, probabilistic approaches, and expert judgment. Algorithms had higher sensitivity and positive predictive value, but lower specificity and negative predictive value. Probabilistic approaches had lower sensitivity and positive predictive value, but higher specificity and negative predictive value. Expert judgment was subjective, less reproducible, but the most frequently used to validate other methods. Studies suggest that greater accuracy may be achieved by specifically assessing cutaneous drug eruptions and using combinations of causality assessment categories. Conclusions Culprit drug identification for adverse drug reactions remains a challenge. Many methods have been published, but there are no consensus guidelines. Using causality assessment methods specifically for cutaneous drug eruptions and combining aspects of the different causality assessment categories may improve efficacy. Further studies are needed to validate this hypothesis.

Estimating Culprit Drugs for Adverse Drug Reactions Based on Bayesian Inference

“…However, identifying a culprit drug from several candidate drugs remains challenging to this date due to multiple candidate drugs, time‐constraints, and sometimes diagnostic urgency 6–8 . A previous study demonstrated that 31% of healthcare practitioners reported, “It is often too difficult to identify the causative drug.” 7 Although several conventional methods (e.g., Naranjo, Jones, and Karch‐Lasagna algorithm) have been used for the identification of the culprit drugs in ADRs, 9–11 these methods did not show acceptable accuracy in medical practice (accuracy 45%) 12 .…”