Baseline characteristics.

Background
Infective endocarditis (IE) carries high in-hospital mortality, particularly among intensive care unit (ICU) patients. The predictive role of blood culture positivity in these patients remains unclear.
Methods
We analyzed 484 adult IE patients from the Medical Information Mart for Intensive Care III (MIMIC-III) database, divided into training (n = 339) and testing (n = 145) cohorts. A suite of demographic, clinical, laboratory, and blood culture variables was used to develop tree-based machine learning models. Random Forest (RF) and Extreme Gradient Boosting (XGB) emerged as top performers and were combined into an ensemble model. SHapley Additive exPlanations (SHAP) quantified variable importance, while the Generative Adversarial Nets for Inference of Individualized Treatment Effects (GANITE) model assessed the average treatment effect (ATE) and conditional treatment effects (CATE) of blood culture positivity on in-hospital mortality across various clinical subgroups.
Results
The ensemble model demonstrated robust performance with an area under the receiver operating characteristic curve (AUROC) of 0.826 and an accuracy of 0.821 on the test set. Blood culture positivity consistently ranked among the top predictors of mortality. SHAP analysis revealed that the presence of bacteremia increased the predicted probability of in-hospital mortality. Specifically, the GANITE model estimated that blood culture positivity raised mortality by 0.9% (95% confidence interval [CI]: −0.9% to 2.6%) in the training set, 7.4% (95% CI: 4.3% to 10.4%) in the test set, and 2.8% (95% CI: 1.2% to 4.4%) overall. Furthermore, CATE analysis highlighted that the adverse impact of blood culture positivity was significantly more pronounced in patients aged 60 years and older, those with systolic blood pressure below 100 mmHg, and in certain endocarditis subtypes.
Conclusions
Blood culture positivity at ICU admission is associated with a modest yet clinically significant increase in in-hospital mortality among IE patients. The application of advanced machine learning and causal inference models enhances risk stratification and may inform more targeted clinical interventions in this high-risk group.