Early diagnosis and prognostic prediction of secondary bloodstream infections caused by

Background: Secondary bloodstream infections (sBSI) caused by Methods: The multicenter, retrospective study enrolled 4,267 ICU patients with Results: The AB-sBSI risk diagnosis model, constructed with 11 features, identified red cell distribution width as the most significant predictor. The AdaBoost model outperformed both comparative models (Linear Discriminant Analysis, Logistic Regression, LinearSVC) and the conventional biomarker C-reactive protein (AUC = 0.66), with AUCs of 0.937 in training and 0.786 in validation. For 30-day mortality prediction, another model based on 11 features selected lymphocyte count as the most influential variable. The AdaBoost model showed prominent efficacy, surpassing other model (Multilayer Perceptron, BernoulliNB, SGD) and achieving AUC values of 0.986 in training and 0.821 in validation. Conclusion: We developed two ML based models for predicting AB-sBSI risk and 30-day mortality. As a preliminary exploration, both models have been converted into accessible web tools. These tools are designed to assist clinicians in making informed decisions and promptly adjusting treatment strategies for critically ill patients.