Tsimitrea Eleni, Anagnostopoulou Dimitra, Chatzi Maria, Fradelos Evangelos C, Tsimitrea Garyfallia, Lykas George, Flouris Andreas D
University General Hospital of Larissa, Larissa, Greece.
Laboratory of Clinical Nursing, Department of Nursing, University of Thessaly Larissa, Larissa, Greece.
Adv Exp Med Biol. 2023;1424:255-263. doi: 10.1007/978-3-031-31982-2_29.
The brain's temperature measurements (T) in patients with severe brain damage are important, in order to offer the optimal treatment. The purpose of this research is the creation of mathematical models for the T's prediction, based on the temperatures in the bladder (T), femoral artery (T), ear canal (T), and axilla (T), without the need for placement of intracranial catheter, contributing significantly to the research of the human thermoregulatory system.The research involved 18 patients (13 men and 5 women), who were hospitalized in the adult intensive care units (ICU) of Larissa's two hospitals, with severe brain injury. An intracranial catheter with a thermistor was used to continuously measure T and other parameters. The T's measurements, and simultaneously one or more of T, T, T, and T, were recorded every 1 h.To create T predicting models, the data of each measurement was separated into (a) model sample (measurements' 80%) and (b) validation sample (measurements' 20%). Multivariate linear regression analysis demonstrated that it is possible to predict brain's temperature (PrT), using independent variables (R was T = 0.73, T = 0.80, T = 0.27, and T = 0.17, p < 0.05). Significant linear associations were found, statistically, and no difference in means between T and PrT of each prediction model. Also, the 95% limits of agreement and the percent coefficient of variation showed sufficient agreement between the T and PrT in each prediction model.In conclusion, brain's temperature prediction models based on T, T, T, and T were successful. Its determination contributes to the improvement of clinical decision-making.
对于重症脑损伤患者，测量脑部温度（T）对于提供最佳治疗至关重要。本研究的目的是基于膀胱温度（T）、股动脉温度（T）、耳道温度（T）和腋窝温度（T）创建用于预测脑部温度的数学模型，无需放置颅内导管，这对人体体温调节系统的研究有显著贡献。该研究涉及18名患者（13名男性和5名女性），他们因严重脑损伤入住拉里萨两家医院的成人重症监护病房（ICU）。使用带有热敏电阻的颅内导管连续测量脑部温度和其他参数。每小时记录一次脑部温度测量值，同时记录T、T、T和T中的一个或多个。为了创建脑部温度预测模型，将每次测量的数据分为（a）模型样本（测量值的80%）和（b）验证样本（测量值的20%）。多元线性回归分析表明，使用自变量（R分别为T = 0.73、T = 0.80、T = 0.27和T = 0.17，p < 0.05）可以预测脑部温度（PrT）。统计发现了显著的线性关联，每个预测模型的T和PrT之间均值无差异。此外，每个预测模型中T和PrT之间的95%一致性界限和变异系数百分比显示出足够的一致性。总之，基于T、T、T和T的脑部温度预测模型是成功的。其测定有助于改善临床决策。
