Tasaki Osamu, Shiozaki Tadahiko, Hamasaki Toshimitsu, Kajino Kentaro, Nakae Haruhiko, Tanaka Hiroshi, Shimazu Takeshi, Sugimoto Hisashi
Department of Traumatology and Acute Critical Medicine, Osaka University Graduate School of Medicine, Osaka, Japan.
J Trauma. 2009 Feb;66(2):304-8. doi: 10.1097/TA.0b013e31815d9d3f.
Although some predictive models for patient outcomes after severe traumatic brain injury have been proposed, a mathematical model with high predictive value has not been established. The purpose of the present study was to analyze the most important indicators of prognosis and to develop the best outcome prediction model.
One hundred eleven consecutive patients with a Glasgow Coma Scale score of <9 were examined and 14 factors were evaluated. Intracranial pressure and cerebral perfusion pressure were recorded at admission to the intensive care unit. The absence of the basal cisterns, presence of extensive subarachnoid hemorrhage, and degree of midline shift were evaluated by means of computed tomography within 24 hours after injury. Multivariate logistic regression analysis was used to identify independent risk factors for a poor prognosis and to develop the best prediction model.
The best model included the following variables: age (p < 0.01), light reflex (p = 0.01), extensive subarachnoid hemorrhage (p = 0.01), intracranial pressure (p = 0.04), and midline shift (p = 0.12). Positive predictive value of the model was 97.3%, negative predictive value was 87.1%, and overall predictive value was 94.2%. The area under the receiver operating characteristic curve was 0.977, and the p value for the Hosmer-Lemeshow goodness-of-fit was 0.866.
Our predictive model based on age, absence of light reflex, presence of extensive subarachnoid hemorrhage, intracranial pressure, and midline shift was shown to have high predictive value and will be useful for decision making, review of treatment, and family counseling in case of traumatic brain injury.
尽管已经提出了一些用于预测重型颅脑损伤患者预后的模型，但尚未建立具有高预测价值的数学模型。本研究的目的是分析预后的最重要指标，并开发最佳的预后预测模型。
对111例连续的格拉斯哥昏迷量表评分<9分的患者进行检查，并评估14个因素。在重症监护病房入院时记录颅内压和脑灌注压。在受伤后24小时内通过计算机断层扫描评估基底池的有无、广泛蛛网膜下腔出血的存在以及中线移位的程度。采用多因素logistic回归分析确定预后不良的独立危险因素，并开发最佳预测模型。
最佳模型包括以下变量：年龄（p<0.01）、光反射（p=0.01）、广泛蛛网膜下腔出血（p=0.01）、颅内压（p=0.04）和中线移位（p=0.12）。该模型的阳性预测值为97.3%，阴性预测值为87.1%，总体预测值为94.2%。受试者工作特征曲线下面积为0.977，Hosmer-Lemeshow拟合优度检验的p值为0.866。
我们基于年龄、无光反射、广泛蛛网膜下腔出血的存在、颅内压和中线移位的预测模型显示具有高预测价值，将有助于颅脑损伤患者的决策制定、治疗评估和家属咨询。
