# Impact of blood culture positivity at intensive care unit admission on mortality in infective endocarditis: Machine learning and deep learning-based causal inference models

## TL;DR

Blood culture positivity at ICU admission is linked to higher mortality in infective endocarditis patients, as shown using machine learning and causal inference models.

## Contribution

Novel use of machine learning and causal inference models to assess the impact of blood culture positivity on mortality in ICU infective endocarditis patients.

## Key findings

- Blood culture positivity was a top predictor of mortality in ICU infective endocarditis patients.
- Machine learning models showed blood culture positivity increased mortality by up to 7.4% in test data.
- The effect was strongest in older patients and those with low blood pressure.

## Abstract

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.

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.

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.

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.