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This research aims to identify factors affecting infection risk and discharge outcomes for patients with traumatic brain injury.

March 21, 2026Open Access

Predictive Traumatic Brain Injury Model for Determining Discharge Disposition and Infection Outcomes: A Machine Learning Approach Developed from the National Trauma Data Bank

Key Points

This research aims to identify factors affecting infection risk and discharge outcomes for patients with traumatic brain injury.
Analyzed data from the National Trauma Database using SQL queries.
Focused on adult patients diagnosed with traumatic brain injury.
Examined demographics, injury details, and vital signs to assess discharge and infection outcomes.
Developed machine learning models to predict discharge disposition and infection rates.
A total of 310,494 patient records were analyzed.
Predictive TBI-Disposition Model achieved 96% accuracy in discharge outcome predictions.
Infection and sepsis prediction models demonstrated accuracies of 93% and 94%, respectively.
Demographic and clinical factors were found to significantly influence outcomes.

Abstract

Background/Objectives: Traumatic brain injury (TBI) affects more than 50 million people annually worldwide. Challenges in managing moderate-to-severe TBI include high rates of hospital-acquired infections and substantial variability in discharge disposition, and these combined challenges contribute significantly to the cost and trajectory of health recovery. Although current strategies such as antibiotic-impregnated external ventricular drains (EVDs) offer some benefit in controlling infections, they remain limited by high cost and inconsistent implementation. A clearer understanding of clinical and demographic factors associated with infection risk and discharge disposition are essential for improving care pathways. This study aims to identify and quantify key determinants of infection and discharge outcomes in patients with TBI. Methods: The National Trauma Database (NTDB) was queried using structured query language (SQL) based on predefined inclusion criteria (adult patients with ICD-coded TBI), input variables (basic demographics, injury location and severity, and vital signs), and specified outcome variables (emergency department discharge disposition, infection, and sepsis) to identify and filter the eligible patient cohort. A set of machine learning models were trained for each outcome (e.g., Emergency Department (ED) discharge, types of infections, and sepsis). Results: Data from 310,494 patients were extracted. The prediction model we developed, the Predictive TBI-Disposition Model (PTDM), was able to predict the outcome of a patient’s discharge with 96% accuracy. The accuracy of the models for infection and sepsis was 93% and 94%, respectively. Conclusions: Demographic and clinical factors significantly influence the discharge disposition and infection risk among TBI patients. Machine learning models demonstrated strong predictive performance, suggesting their utility in early risk stratification and targeted clinical decision-making.

Predictive Traumatic Brain Injury Model for Determining Discharge Disposition and Infection Outcomes: A Machine Learning Approach Developed from the National Trauma Data Bank

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Abstract

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