The nomogram and machine learning models provide enhanced predictive accuracy for multiple organ dysfunction syndrome (MODS) in trauma-induced sepsis patients compared to traditional scoring systems.

These tools, accessible via web-based applications, have the potential to improve early risk stratification and guide clinical decision-making, ultimately enhancing outcomes for trauma patients.

Further external validation is recommended to confirm their generalizability.

Among 1,295 trauma patients with sepsis, 349 (26.95%) developed MODS.

The 28-day mortality rates were 11.21% for non-MODS patients and 23.82% for MODS patients.

Key predictors of MODS included the simplified acute physiology score II score, use of mechanical ventilation, and vasopressor administration.

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