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Journal Article

Citation

Zhu M, Blears EE, Cummins CB, Wolf J, Nunez Lopez OA, Bohanon FJ, Kramer GC, Radhakrishnan RS. Cureus 2022; 14(7): e26783.

Copyright

(Copyright © 2022, Curēus)

DOI

10.7759/cureus.26783

PMID

35967157

PMCID

PMC9366034

Abstract

INTRODUCTION: In patients with multi-organ system trauma, the diagnosis of coinciding traumatic brain injury can be difficult due to injuries from the hemorrhagic shock that confound clinical and radiographic signs of traumatic brain injury. In this study, a novel technique using heart rate variability was developed in a porcine model to detect traumatic brain injury early in the setting of hemorrhagic shock without the need for radiographic imaging or clinical exam.

METHODS: A porcine model of hemorrhagic shock was used with an arm of swine receiving hemorrhagic shock alone and hemorrhagic shock with traumatic brain injury. High-resolution heart rate frequencies were collected at different time intervals using waveforms based on voltage delivered from the heart rate monitor. Waveforms were analyzed to assess statistically significant differences between heart rate variability parameters in those with hemorrhagic shock and traumatic brain injury versus those with only hemorrhagic shock. Stochastic analysis was used to assess the validity of results and create a model by machine learning to better assess the presence of traumatic brain injury.

RESULTS: Significant differences were found in several heart rate variability parameters between the two groups. Additionally, significant differences in heart rate variability parameters were found in swine within 1 hour of inducing hemorrhage in those with traumatic brain injury versus those without. These results were confirmed with stochastic analysis and machine learning was used to generate a model which determined the presence of traumatic brain injury in the setting of hemorrhage shock with 91.6% accuracy.

CONCLUSIONS:  Heart rate variability represents a promising diagnostic tool to aid in the diagnosis of traumatic brain injury within 1 hour of injury.


Language: en

Keywords

trauma; machine learning; traumatic brain injury; bovine model; heart rate variability; hemorrhagic shock

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