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Predicting mortality in intensive care unit patients with CAUTI using an interpretable machine learning model: a
Longcha Liu1, Xueshu Yu1, Zhi Chen1
1Department of Intensive Care Unit, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Frontiers in Medicine
|September 25, 2025
Summary
This study developed a reliable model to predict mortality in intensive care unit (ICU) patients with catheter-associated urinary tract infection (CAUTI). The Logistic Regression model demonstrated strong predictive performance, identifying key risk factors for improved patient care.
Area of Science:
- Critical Care Medicine
- Medical Informatics
- Infectious Diseases
Background:
- Catheter-associated urinary tract infections (CAUTI) are a significant cause of morbidity and mortality in intensive care units (ICUs).
- Accurate prediction of mortality risk in CAUTI patients is crucial for timely intervention and resource allocation.
Purpose of the Study:
- To develop and validate a reliable machine learning model for predicting mortality in ICU patients diagnosed with CAUTI.
- To identify key predictors of mortality in this patient population.
Main Methods:
- Utilized the MIMIC-IV database for model development and validation.
- Trained and tested four machine learning models: XGBoost, DecisionTree (DT), Logistic Regression (LR), and Random Forest (RF).
- Employed the SHAP method for model interpretability and identification of important predictors.
Main Results:
- A total of 545 CAUTI patients were analyzed, with an ICU mortality rate of 7.89%.
- The Logistic Regression model achieved the highest prediction performance with an Area Under the Curve (AUC) of 0.871.
- Key predictors of mortality included vasoactive drug use, shock index, APSIII score, and concomitant malignancy.
Conclusions:
- An interpretable prediction model can significantly aid healthcare professionals in assessing mortality risk for ICU patients with CAUTI.
- The developed model can support clinical decision-making and potentially improve patient outcomes.
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