Development and validation of a comprehensive complication index for predicting outcomes in fournier's gangrene


GÖKALP F., ÖRDEK E., Sammala M., KURU D., Ucurmak F., GÖRÜR S.

IRISH JOURNAL OF MEDICAL SCIENCE, 2026 (SCI-Expanded, Scopus) identifier identifier

Özet

Backgrounds Fournier's gangrene is a life-threatening condition. To predict patient outcomes, it requires a thorough understanding of the variables influencing the severity of this illness and the emergence of comorbidities. Aims To evaluate the Comprehensive complication index score to predict postoperative mortality in Fournier gangrene. Methods Between 2013 and 2024, a retrospective analysis was used to identify patients who were diagnosed with Fournier's gangrene. The perioperative and clinical data were recorded. Postoperative complications were assessed using the Clavien-Dindo classification (CDC). The Comprehensive Complication index was calculated based on CDC and when multiple complications occurred, their values were combined using the CCI formula to generate a cumulative score for each patient. Upgrading was interpreted as a CCI cumulative score surpasses the baseline score. The data was divided into two groups based on patients' mortality outcome; group 1: survive group, group 2: mortality group Results The age and comorbidities were significantly higher in group 2, (p<0.001 and p=0.015). The frequency of sepsis was considerably higher in group 2 patients (p<0.001). The CCI score significantly higher in group 2 (p<0.001). ROC analysis showed that the CCI score greater than 36.60 could accurately predict mortality with high specificity and sensitivity (91.3% and 70.0%, respectively). Multivariate analysis identified both fage (OR: 1.436, p = 0.002) and CCI score (OR: 1.093, p = 0.008) as independent predictors of mortality. Conclusion The CCI is a useful instrument for evaluating cumulative postoperative morbidity and forecasting mortality in patients with Fournier's gangrene. Incorporating the CCI into clinical practice may enhance risk classification, optimize resource allocation, and inform treatment decision-making in FG management.