Personalized machine learning-based prognostic model for ICU-acquired bloodstream infections.

BACKGROUND

Intensive care unit-acquired bloodstream infections (ICU-BSIs) are among the most prevalent healthcare-associated infections and a major cause of mortality among ICU patients. We developed a machine learning (ML)-based model to predict the prognosis of ICU-BSIs.

METHODS

Adult patients with blood cultures drawn ≥48 hours after ICU admission were included: the Second Affiliated Hospital of Anhui Medical University (AMU, China) and the Medical Information Mart for Intensive Care IV (MIMIC-IV, USA). The AMU dataset was used for model training and internal validation, and the MIMIC-IV dataset served as the external validation set. The model incorporated routinely collected, easily obtainable clinical variables, including several representing the average rate of change in laboratory indicators. After comparing multiple algorithms, eXtreme Gradient Boosting (XGBoost) was selected and optimized using cross-validation and grid search.

RESULTS

A total of 1,903 patients from AMU and 3,496 from MIMIC-IV were included. In both cohorts, antibiotic duration, platelet count, serum creatinine, duration of invasive mechanical ventilation, and Charlson Comorbidity Index (CCI) were significantly associated with 28-day mortality (P < 0.001). The XGBoost model using 33 variables showed strong discrimination, with an AUROC of 0.92 (95% CI 0.90-0.94) for training and 0.85 (95% CI 0.80-0.90) for internal validation. Shapley Additive Explanations (SHAP) identified the 10 most important variables; a simplified model using these maintained good accuracy, with AUROC values of 0.81 (95% CI 0.76-0.85) and 0.71 (95% CI 0.70-0.73) for the internal and external validation sets, respectively. In pathogen subgroups, the internal AUROC was 0.91 (95% CI 0.87-0.94) and 0.90 (95% CI 0.86-0.93) for Gram-positive (Gram+) and Gram-negative (Gram-) infections, with external validation AUROCs of 0.72 (95% CI 0.66-0.77) and 0.72 (95% CI 0.62-0.82), respectively.

CONCLUSIONS

We developed and externally validated a personalized ML-based prognostic model for ICU-BSIs using multicenter time-series data. This model may facilitate early identification of high-risk patients, enabling timely intervention and optimized ICU resource allocation.