# Clinical utility of jugular venous blood gas biomarkers for assessing cranial brain injury severity and predicting ICU stay duration: A biochemical and predictive modeling approach

## TL;DR

This study shows that jugular venous blood gas markers can assess brain injury severity and predict ICU stay length, improving clinical decision-making.

## Contribution

The study introduces a predictive model using JVBG biomarkers and machine learning to forecast ICU duration in cranial brain injury patients.

## Key findings

- Severe CBI patients had significantly lower SjvO2 and PjO2 and higher AVDO2 and AVDL.
- JVBG markers strongly correlated with clinical severity scores like the Glasgow Coma Scale.
- A machine learning model achieved high accuracy in predicting prolonged ICU stays using JVBG and clinical variables.

## Abstract

Biochemical monitoring of cerebral oxygen metabolism is essential in the management of cranial brain injury (CBI). Jugular venous blood gas (JVBG) analysis provides real-time data reflecting cerebral biochemical states, offering a valuable window into brain oxygenation and metabolic demands. This study aimed to evaluate JVBG biochemical markers as indicators of CBI severity and as predictors of prolonged intensive care unit (ICU) stay, emphasizing their integration into machine learning models for clinical utility.

A retrospective analysis was conducted on 100 ICU-admitted CBI patients. Serial measurements of JVBG parameters-jugular venous oxygen saturation (SjvO2), partial pressure of oxygen (PjO2), arteriovenous oxygen difference (AVDO2), and oxygen content difference (AVDL)-were performed over five days. Spearman correlation and random forest algorithms were employed to assess relationships between JVBG biomarkers, clinical severity (via Glasgow Coma Scale), and ICU duration.

Patients with more severe CBI exhibited significantly reduced SjvO2 and PjO2, and elevated AVDO and AVDL (p < 0.001). Strong correlations were found between JVBG markers and clinical severity scores. A multivariable prediction model incorporating age, SaO2, and PaCO2 at one day post-admission yielded excellent predictive performance for prolonged ICU stay (AUC = 0.974; sensitivity = 100%; specificity = 94.8%).

JVBG biomarkers serve as clinically informative biochemical indicators for assessing CBI severity and forecasting ICU duration. Their integration into predictive algorithms may enhance precision in neurocritical care and support biochemical decision-making in intensive care settings.