Epidemiological Characteristics and Prognostic Scoring in Toxic Epidermal Necrolysis and Stevens-Johnson Syndrome: Insights from a 17-Year Burn Center Experience.

: Toxic epidermal necrolysis (TEN) and Stevens-Johnson syndrome (SJS) are rare yet life-threatening dermatologic conditions characterized by severe skin and mucous membrane involvement. Accurate prognostic systems are crucial for clinical management to assess disease severity and predict outcomes. The primary objective of this study was to assess the epidemiological characteristics and clinical outcomes of patients with Stevens-Johnson syndrome (SJS), toxic epidermal necrolysis (TEN), and SJS/TEN overlap over a 17-year period at a specialized burn center. The secondary objectives were to evaluate the performance of existing prognostic scoring systems (SCORTEN, Re-SCORTEN, and ABCD-10) in predicting mortality and to propose a novel classification tree model to improve mortality prediction. : A 17-year retrospective study at a burn center included 68 patients with SJS, SJS/TEN overlap, or TEN. Demographic, clinical, laboratory data, and prognostic scores (SCORTEN, Re-SCORTEN, ABCD-10) were collected and analyzed for associations with mortality. A classification tree was created to detect unknown determinants of SJS/TEN mortality. : The drug most frequently associated with the occurrence of SJS/TEN was metamizole. The mortality rate was 51%. Affected body surface area, platelet count, and serum blood urea nitrogen differed significantly between survivors and non-survivors. Regarding the scoring systems, only the Re-SCORTEN showed reliable differentiation for these groups. A classification tree model achieved an accuracy of 89% in predicting the mortality risk. In the ROC curve analysis, the AUC values were 0.88 for the classification tree, 0.66 for Re-SCORTEN, 0.61 for SCORTEN, and 0.56 for ABCD-10. : This study explores mortality predictors in SJS/TEN via a classification tree model, highlighting potential factors for further investigation. While cautioning against immediate clinical application due to data constraints, the findings underscore the need for larger studies to validate and refine prediction models in this context.