A Machine Learning Model Integrating Systemic Immune-Inflammation Index for Predicting 28-Day Mortality in Geriatric Sepsis Secondary to Community-Acquired Bacterial Pneumonia
Abstract
Purpose Sepsis is a life-threatening clinical syndrome characterized by a dysregulated host response to infection. The systemic immune-inflammation index (SII) is a novel prognostic biomarker, However, its predictive value for early prognosis in elderly individuals suffering from sepsis secondary to community-acquired bacterial pneumonia (CABP) remains unclear. This study intends to apply machine learning techniques to develop an interpretable model for predicting prognosis. Patients and Methods This medical records review included elderly patients with sepsis secondary to CABP admitted to ICUs at Beijing Shijitan Hospital. Clinical outcomes based on 28-day survival status served as the basis for dividing participants into survivor and non-survivor groups. For prognostic prediction, five machine learning algorithms were developed, including Gradient Boosting Machine (GBM), Logistic Regression, Random Forest (RF), Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (XGBoost). Model efficacy was quantified using the area under the receiver operating characteristic curve (AUROC) metric and assessed clinically through decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) visualization provided insights into the models’ decision-making processes. Results In this investigation, clinical information from 364 geriatric participants suffering from sepsis secondary to CABP was examined. For developing prediction models, twelve predictors were…
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