Machine Learning-Based Mortality Risk Prediction Model in Patients with Sepsis.

Objective: The aim of our study was to establish and validate a machine learning-based predictive model for mortality risk in elderly patients with sepsis. By integrating traditional biomarkers, novel biomarkers, clinical data, and established scoring systems, the model seeks to enhance predictive accuracy and thereby improve clinical outcomes in high-risk patient population. Methods: Conducted at Beijing Chao-Yang Hospital from August 2021 to August 2023, our study included 180 emergency department patients meeting Sepsis 3.0 diagnostic criteria. Data collected included patient demographics, vital signs, laboratory parameters, disease-related scores, major comorbidities, and the 28-day mortality. Variables were analyzed using univariate analysis and LASSO regression, and the machine learning model was constructed using R statistical software and validated internally via bootstrap resampling and calibration curves. Results: The model identified seven significant variables: SOFA, APACHE II, MAP, ALB, PCT, LTB, and VEGF. These variables constituted our final prediction model, which achieved an AUC of 0.845 (95% CI: 0.786, 0.905), with a sensitivity of 75.9% and a specificity of 85.0%. Internal validation yielded a bootstrap-corrected AUC of 0.857 (95% CI: 0.799, 0.912), confirming the model's statistical robustness. The nomogram provided a visual tool for predicting 28-day mortality risk, and decision curve analysis demonstrated strong potential for clinical utility. Conclusion: The predictive model, which incorporates SOFA, APACHE II, MAP, ALB, PCT, LTB, and VEGF, shows significant potential in predicting the 28-day mortality risk for elderly sepsis patients. It provides a convenient and rapid tool for clinical use. Further research with larger sample sizes and external validation is warranted to confirm these findings and enhance the model's applicability.