Zhu Min, Blears Elizabeth E, Cummins Claire B, Wolf Jordan, Nunez Lopez Omar A, Bohanon Fredrick J, Kramer George C, Radhakrishnan Ravi S
Department of Surgery, University of Texas Medical Branch, Galveston, USA.
Surgery, Allegheny Health Network, Pittsburgh, USA.
Cureus. 2022 Jul 12;14(7):e26783. doi: 10.7759/cureus.26783. eCollection 2022 Jul.
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.
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.
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.
Heart rate variability represents a promising diagnostic tool to aid in the diagnosis of traumatic brain injury within 1 hour of injury.
在多器官系统创伤患者中，由于出血性休克造成的损伤会混淆创伤性脑损伤的临床和影像学表现，因此同时存在的创伤性脑损伤的诊断可能会很困难。在本研究中，在猪模型中开发了一种使用心率变异性的新技术，以在出血性休克情况下早期检测创伤性脑损伤，而无需进行影像学检查或临床检查。
使用猪出血性休克模型，一组猪仅接受出血性休克，另一组接受出血性休克合并创伤性脑损伤。基于心率监测仪传输的电压波形，在不同时间间隔收集高分辨率心率频率。分析波形以评估出血性休克合并创伤性脑损伤者与仅出血性休克者心率变异性参数之间的统计学显著差异。使用随机分析评估结果的有效性，并通过机器学习创建一个模型，以更好地评估创伤性脑损伤的存在。
两组之间在几个心率变异性参数上发现了显著差异。此外，在创伤性脑损伤猪与无创伤性脑损伤猪中，在出血诱导后1小时内，心率变异性参数也存在显著差异。这些结果通过随机分析得到证实，并使用机器学习生成一个模型，该模型在出血性休克情况下确定创伤性脑损伤存在的准确率为91.6%。
心率变异性是一种有前景的诊断工具，有助于在损伤后1小时内诊断创伤性脑损伤。
