Heart Rate Variability Can Detect Blunt Traumatic Brain Injury Within the First Hour

Area of Science:

• Physiology
• Biomedical Engineering
• Machine Learning

Background:

• Diagnosing traumatic brain injury (TBI) in multi-organ trauma patients is challenging due to confounding factors from hemorrhagic shock.
• Clinical and radiographic signs of TBI can be obscured by injuries associated with hemorrhagic shock.

Purpose of the Study:

• To develop a novel technique using heart rate variability (HRV) to detect TBI in a porcine model.
• To enable early TBI diagnosis in the context of hemorrhagic shock without relying on radiographic imaging or clinical examination.

Main Methods:

• A porcine model was used, with groups experiencing hemorrhagic shock alone or hemorrhagic shock with TBI.
• High-resolution heart rate frequencies were collected and analyzed for HRV parameter differences.
• Stochastic analysis and machine learning were employed to validate results and create a predictive model.

Main Results:

• Significant differences in HRV parameters were observed between the hemorrhagic shock and TBI groups.
• HRV parameter differences were detected within 1 hour of hemorrhage in swine with TBI compared to those without.
• A machine learning model achieved 91.6% accuracy in identifying TBI in the setting of hemorrhagic shock.

Conclusions:

• Heart rate variability (HRV) shows potential as a diagnostic tool for TBI.
• HRV can aid in the early diagnosis of TBI within one hour of injury, even in the presence of hemorrhagic shock.