A Machine Learning Model Based on CT Imaging Metrics and Clinical Features to Predict the Risk of Hospital-Acquired Pneumonia After Traumatic Brain Injury

Abstract

Objective: To develop a validated machine learning (ML) algorithm for predicting the risk of hospital-acquired pneumonia (HAP) in patients with traumatic brain injury (TBI).
Materials and Methods: We employed the Least Absolute Shrinkage and Selection Operator (LASSO) to identify critical features related to pneumonia. Five ML models—Logistic Regression (LR), Extreme Gradient Boosting (XGB), Random Forest (RF), Naive Bayes Classifier (NB), and Support Vector Machine (SVC)—were developed and assessed using the training and validation datasets. The optimal model was selected based on its performance metrics and used to create a dynamic web-based nomogram.
Results: In a cohort of 858 TBI patients, the HAP incidence was 41.02%. LR was determined to be the optimal model with superior performance metrics including AUC, accuracy, and F1-score. Key predictive factors included Age, Glasgow Coma Score, Rotterdam Score, D-dimer, and the Systemic Immune Response to Inflammation Index (SIRI). The nomogram developed based on these predictors demonstrated high predictive accuracy, with AUCs of 0.818 and 0.819 for the training and validation datasets, respectively. Decision curve analysis (DCA) and calibration curves validated the model’s clinical utility and accuracy.
Conclusion: We successfully developed and validated a high-performance ML algorithm to assess the risk of HAP in TBI patients. The dynamic nomogram provides a practical tool for real-time risk assessment, potentially improving clinical outcomes by aiding in early intervention and personalized patient management.

Keywords: traumatic brain injury, machine learning, hospital-acquired pneumonia, dynamic nomogram, imaging metrics