Novel classification system for SJS/TEN aims to improve patient care

Introduction

First identified by Stevens and Johnson in 1922, SJS and TEN are now recognized as disorders with a continuum of severity, from milder forms (SJS) to the most severe (TEN). SJS/TEN is associated with multiple etiological factors, most notably drug-induced liver injury (DILI), making the identification of the responsible agent crucial for patient management. However, previous studies have lacked uniformity in diagnostic approaches, limiting the ability to draw clear conclusions about causality.

Epidemiology

The incidence of SJS/TEN varies across regions, with notable differences between studies. For instance, in Europe and the USA, the incidence of TEN is reported at about 1–2 cases per million people per year. However, studies from Asia are less comprehensive, with significant gaps in data. Importantly, epidemiological studies often fail to apply the diagnostic algorithms needed to confirm drug causality, such as the Roussel Uclaf Causality Assessment Method (RUCAM) or the Algorithm of Drug Causality for Epidermal Necrolysis (ALDEN). These gaps lead to inconsistencies and unreliable conclusions about the true incidence of drug-induced liver injury in SJS/TEN cases.

Methods for diagnosis and causality assessment

To accurately diagnose SJS/TEN and its associated liver injury, causality assessment tools like RUCAM and ALDEN are essential. RUCAM, which has been widely used for drug-induced liver injury since 1993, provides a structured framework for evaluating causality based on specific criteria like the timing of drug administration and the progression of liver enzyme levels. The ALDEN algorithm, developed for SJS/TEN, assesses the role of drugs in causing epidermal necrolysis by scoring key elements such as the timing of drug exposure, rechallenge, and dechallenge.

Despite their utility, these algorithms are underutilized in clinical settings, leading to incomplete or inaccurate diagnoses in many cases. The review highlights the importance of applying both RUCAM and ALDEN to improve the reliability of diagnoses in SJS/TEN patients with suspected DILI.

Classification of SJS/TEN types

This review introduces a novel classification system for SJS/TEN, dividing it into five types based on the causative factors and the diagnostic algorithms used:

1. Type 1: SJS/TEN caused by drugs, diagnosed using both RUCAM and ALDEN.
2. Type 2: SJS/TEN caused by drugs, diagnosed using ALDEN only.
3. Type 3: SJS/TEN caused by drugs, diagnosed by non-ALDEN tools.
4. Type 4: SJS/TEN caused by non-drug factors, assessed using various diagnostic tools.
5. Type 5: SJS/TEN with unidentified causative factors, often due to idiopathic cases.

This typology aims to bring clarity to the diagnosis and treatment of SJS/TEN by providing more consistent criteria for identifying the underlying causes and improving patient management.

Discussion

The review underscores the critical need for standardized diagnostic tools in the study and treatment of SJS/TEN. By applying rigorous causality assessments, clinicians can better differentiate between drug-induced and non-drug-induced cases, leading to more targeted and effective treatment strategies. Additionally, the introduction of a standardized classification system allows for a more systematic approach to research, enabling better comparisons across different studies and regions.

Future research should focus on enhancing the application of these diagnostic algorithms, especially in areas where SJS/TEN is underreported or misdiagnosed. By ensuring greater homogeneity in cohort studies, it will be possible to improve the understanding of SJS/TEN and its relationship to liver injury, ultimately leading to better clinical outcomes for patients.

Conclusion

Liver injury in SJS/TEN remains a complex and challenging aspect of managing these severe skin disorders. The introduction of a new typology based on diagnostic algorithms represents a step forward in providing clarity and consistency in diagnosing and treating SJS/TEN. By embracing validated causality tools like RUCAM and ALDEN, clinicians can improve the accuracy of diagnoses, optimize treatment strategies, and ultimately enhance patient care.