ALDRESS: A Retrospective Pilot Study to Develop a Pharmacological Causality Algorithm for Drug Reaction with Eosinophilia and Systemic Symptoms (DRESS)

Abstract

Background: The drug reaction with eosinophilia and systemic symptoms (DRESS) syndrome represents a severe form of drug hypersensitivity reaction characterized by significant morbidity, mortality, and long-term sequelae, coupled with limited therapeutic avenues. Accurate identification of the causative drug(s) is paramount for acute management, exploration of safe therapeutic alternatives, and prevention of future occurrences. However, the absence of a standardized diagnostic test and a specific causality algorithm tailored to DRESS poses a significant challenge in its clinical management. Methods: We conducted a retrospective case–control study involving 37 DRESS patients to validate a novel causality algorithm, the ALDRESS, designed explicitly for this syndrome, comparing it against the current standard algorithm, SEFV. Results: The ALDRESS algorithm showcased superior performance, exhibiting an 85.7% sensitivity and 93% specificity with comparable negative predictive values (80.6% vs. 97%). Notably, the ALDRESS algorithm yielded a substantially higher positive predictive value (75%) compared to SEFV (51.40%), achieving an overall accuracy rate of 92%. Conclusions: Our findings underscore the efficacy of the ALDRESS algorithm in accurately attributing causality to drugs implicated in DRESS syndrome. However, further validation studies involving larger, diverse cohorts are warranted to consolidate its clinical utility and broaden its applicability. This study lays the groundwork for a refined causality assessment tool, promising advancements in the diagnosis and management of DRESS syndrome.