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

Aug 2, 2026

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A validated machine-learning algorithm was developed to predict hospital-acquired pneumonia risk in inpatients with traumatic brain injury. Critical pneumonia-related features were selected using LASSO, then five models (logistic regression, XGB, random forest, naïve Bayes, and SVC) were trained and evaluated on training and validation datasets. Logistic regression produced the best metrics and enabled creation of a dynamic web-based nomogram. In 858 patients, the nomogram showed strong discrimination (AUC ~0.818/0.819) with decision curve and calibration results supporting clinical usefulness.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages15

Reading Time38 min

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Objective

Materials and Methods

Results

Conclusion

Introduction

Infection and Drug Resistance downloaded from [https://www.dovepress.com/](https://www.dovepress.com/) For personal use only. Infection and Drug Resistance Dovepress open access to scientific and medical research Open Access Full Text Article ORIGINAL RESEARCH A Machine Learning Model Based on CT Imaging Metrics and Clinical Features to Predict the Risk of Hospital-Acquired Pneumonia After Traumatic Brain Injury Shaojie Li 1 , *, Qiangqiang Feng 1 , *, Jiayin Wang 1 , Baofang Wu 1 , Weizhi Qiu 1 , Yiming Zhuang2 , Yong Wang 3 , Hongzhi Gao 1 1Department of Neurosurgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, 362000, People’s Republic of China; 2Internal Medicine, Quanzhou Quangang District Hillside Street Community Health Service Center, Quanzhou, Fujian, 362000, People’s Republic of China; 3Child and Adolescent Psychiatry, The Third Hospital of Quanzhou, Quanzhou, Fujian, 362000, People’s Republic of China *These authors contributed equally to this work Correspondence: Yong Wang; Hongzhi Gao, Email [120432246@qq.com](120432246@qq.com); [gaohongzhi@fjmu.edu.cn](gaohongzhi@fjmu.edu.cn) | Objective: To develop a validated machine learning (ML) algorithm for predicting the risk of hospital-acquired pneumonia (HAP) inpatients 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 | | --- | | Introduction
With the development of society and the rise of road traffic, the incidence of accidental injuries, especially traumatic brain injury (TBI), caused by frequent traffic accidents has been increasing year by year, thus substantially increasing the risk of death.1 According to the US authorities, 64 to 74 million people worldwide suffer from TBI every year, of which about 55.9 million people are affected by mild TBI, while severe TBI affects about 5.48 million people.2 For every 100,000 people, there are 262 cases of death or disability due to TBI-related causes. Especially in patients with severe TBI, further serious complications such as cerebral contusions, axonal injury, and delayed hemorrhage may develop, all of which can severely affect the cognitive and motor functions of patients.3 It has been demonstrated that there is a complex and fine-grained interaction between the brain and other body systems, such as the central nervous system and the respiratory system, in a manner known as the “lung-brain axis”.4 On the one hand, the brain sends signals through the | Receive

FAQs

What was the study objective?

To develop a validated machine-learning algorithm for predicting the risk of hospital-acquired pneumonia in patients with traumatic brain injury.

Which machine-learning method was selected as optimal?

Logistic regression, chosen based on superior performance metrics compared with other evaluated models.

What predictors and tools were used for risk estimation?

Key predictors included Age, Glasgow Coma Score, Rotterdam Score, D-dimer, and SIRI, and the model was implemented as a dynamic web-based nomogram for real-time assessment.

Aug 2, 2026

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