A Real-Time Dynamic Warning Method for MODS in Trauma Sepsis Patients Based on a Pre-Trained Transfer Learning Algorithm

<b>Objectives:</b> Multiple organ dysfunction syndrome (MODS) is a serious, prognostically poor complication in trauma sepsis. We developed an interpretable, multicenter-validated prediction model to enable early, individualized risk assessment and guide timely care. <b>Methods:</b> Using MIMIC-IV and eICU data, we built a pre-trained transfer-learning model with a separation processing strategy and assessed interpretability with SHAP. <b>Results:</b> Internal validation included 700 MIMIC-IV patients; external validation included 110 eICU patients. Across 6-, 12-, and 24-h prediction windows, the best pre-trained model achieved an average AUC of 0.906. Notably, fine-tuning on only 100 trauma sepsis cases (3.6% of the training set) still yielded an AUC of 0.846, surpassing the non-pre-trained model by 0.165. SHAP analysis further revealed that platelet count was one of the most important variables contributing to MODS prediction. <b>Conclusions:</b> Overall, the pre-trained MODS model demonstrated robust discrimination, generalizability, and clear interpretability in both internal and external validations, highlighting its portability and clinical potential for early identification of high-risk trauma sepsis patients.