Fournier's gangrene (FG) is a rare, rapidly progressing necrotizing fasciitis of the external genitalia and perineum, with mortality rates ranging from 20 % to 50 %. Early identification of high-risk patients is essential for timely intervention. The quick Sequential Organ Failure Assessment (qSOFA) and the Fournier Gangrene Severity Index (FGSI) are commonly used prognostic tools, but their comparative performance in FG remains unclear. This study evaluates their predictive accuracy in a large FG cohort and explores their complementary roles in clinical decision-making.

A retrospective cohort study was conducted on 153 FG patients admitted to Hasan Sadikin General Hospital, Indonesia, from January 2013 to December 2023. Clinical and laboratory data, including qSOFA and FGSI scores, were analyzed to assess in-hospital mortality. The predictive performance of both scoring systems was evaluated using receiver operating characteristic (ROC) curve analysis, with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Multivariate logistic regression estimated adjusted odds ratios (ORs) for mortality while accounting for age and comorbidities.

The overall mortality rate was 30 %. Non-survivors were significantly older and had higher rates of comorbidities, including acute kidney injury and cardiovascular disease. Both qSOFA and FGSI demonstrated strong predictive capabilities (AUC = 0.818). qSOFA had a specificity of 94.6 % but lower sensitivity (62.2 %), making it effective for identifying low-risk patients. FGSI demonstrated higher sensitivity (70.3 %) and specificity (85.9 %), making it more suitable for high-risk patient identification. Combining qSOFA's rapid bedside utility with FGSI's comprehensive risk assessment offers a powerful strategy for timely intervention and resource allocation.

This study is among the first to compare qSOFA and FGSI in a large FG cohort, highlighting their complementary roles in clinical decision-making. A combined approach can enhance early risk stratification, optimize critical care resource allocation, and improve patient outcomes. Future research should explore integrating biomarkers such as lactate and procalcitonin to refine predictive accuracy, particularly in resource-limited settings.