Predicting Multidrug-Resistant Pneumonia: An Interpretable Machine Learning Model Validated in US and Chinese Patient...

Area of Science:

• Medical Informatics
• Machine Learning in Healthcare
• Infectious Disease Epidemiology

Background:

• Multidrug-resistant organisms (MDROs) pose a significant challenge in treating hospital-acquired and ventilator-associated pneumonia (HAP/VAP).
• Accurate prediction of MDRO risk is crucial for effective clinical decision support and infection control strategies.

Purpose of the Study:

• To develop and validate a machine learning (ML) model for predicting the risk of multidrug-resistant infections in pneumonia patients.
• To assess the clinical utility of the developed ML model for decision support in HAP/VAP management.

Main Methods:

• Developed and compared multiple ML models (Logistic Regression, Random Forest, XGBoost, LightGBM) using data from the MIMIC-IV database.
• Employed LASSO regression and chi-square tests for feature selection, followed by model tuning, calibration, and interpretability analysis using SHAP.
• Validated the best-performing model on an independent cohort from a Chinese tertiary hospital to assess generalizability.

Main Results:

• Logistic Regression demonstrated superior performance, achieving an AUC of 0.798 internally and 0.845 externally.
• Key predictors identified by SHAP analysis included the systemic immune-inflammation index (SII), albumin, C-reactive protein-to-albumin ratio (CAR), antibiotic classes, WBC, and LYM abs.
• The model's reliability was enhanced through feature selection and probability calibration, with robust generalizability confirmed via external validation.

Conclusions:

• Machine learning models, particularly Logistic Regression, can effectively predict MDRO risk in pneumonia patients.
• The validated model offers potential for clinical decision support in infection control for HAP/VAP.
• Further validation and integration of diverse clinical data are recommended for future development.