869: Infection-Aware Risk Ml Model for Early Prognosis and Treatment Contextualization in Sepsis

Introduction: To develop and validate an infection-aware Sepsis Risk Stratification (SRS) system for predicting in-hospital mortality and guiding early clinical decision-making. Methods: This was a multi-cohort prognostic study using a retrospective nationwide Chinese database (n = 116,852) for model development and internal validation. External validation was performed using the MIMIC-IV (n = 18,407), eICU Collaborative Research Database (n = 9,816), and a prospectively collected cohort. A machine learning–based model incorporating physiologic and infection-specific variables, including infection foci, was developed. The primary outcome was in-hospital mortality. Model performance was assessed by area under the receiver operating characteristic curve (AUROC) and calibration. Secondary outcomes included 28-day mortality, ICU length of stay, ventilator days, and hospitalization costs. Results: The study analyzed 116852, 18,407, and 9,816 sepsis patients in the internal, MIMIC-IV, eICU-CRD, and the prospective cohorts, respectively, with in-hospital mortality rates of 36.1%, 18.2%, 17.1%, and 27.6%. The risk model achieved AUROCs of 0.80 (95% CI: 0.79–0.80; internal), 0.68 (95% CI: 0.67–0.69; MIMIC-IV), 0.73 (95% CI: 0.71–0.74; eICU-CRD), and 0.75 (95% CI: 0.69–0.81; prospective cohort). External validation showed that the risk model significantly outperformed the SOFA (internal validation: 0.80 vs. 0.67, p < 0.001; MIMIC-IV: 0.68 vs. 0.67, p=0.01; eICU-CRD 0.73 vs. 0.70, p< 0.001, prospective cohort: 0.75 vs. 0.57, p=0.012) and APACHE II (internal validation: 0.80 vs. 0.66, p< 0.001; MIMIC-IV: 0.68 vs. 0.63, p=0.002, eICU-CRD: 0.73 vs. 0.70, p< 0.001). The risk scores were categorized into four sepsis risk levels with corresponding probability: low, medium, high, and very high. These risk categories predicted in-hospital mortality: low (6.4%), medium (21.7%), high (46.3%), and very high (69.5%) in the internal validation. Similar trends were observed for other metrics, such as 28-day mortality, length of ICU stay, ventilator day, and total cost. Conclusions: The SRS system offers a rapid, interpretable, and infection-integrated risk stratification tool, which improves upon conventional scores by incorporating infection context and supports individualized treatment strategies.