Lu Hsueh-Yi, Li Tzu-Chi, Tu Yong-Kwang, Tsai Jui-Chang, Lai Hong-Shiee, Kuo Lu-Ting
Department of Industrial Engineering and Management, National Yunlin University of Science and Technology, Douliou City, Yun-Lin county, 640, Taiwan,
J Med Syst. 2015 Feb;39(2):14. doi: 10.1007/s10916-014-0187-x. Epub 2015 Jan 31.
Previous studies have identified some clinical parameters for predicting long-term functional recovery and mortality after traumatic brain injury (TBI). Here, data mining methods were combined with serial Glasgow Coma Scale (GCS) scores and clinical and laboratory parameters to predict 6-month functional outcome and mortality in patients with TBI. Data of consecutive adult patients presenting at a trauma center with moderate-to-severe head injury were retrospectively analyzed. Clinical parameters including serial GCS measurements at emergency department, 7th day, and 14th day and laboratory data were included for analysis (n = 115). We employed artificial neural network (ANN), naïve Bayes (NB), decision tree, and logistic regression to predict mortality and functional outcomes at 6 months after TBI. Favorable functional outcome was achieved by 34.8% of the patients, and overall 6-month mortality was 25.2%. For 6-month functional outcome prediction, ANN was the best model, with an area under the receiver operating characteristic curve (AUC) of 96.13%, sensitivity of 83.50%, and specificity of 89.73%. The best predictive model for mortality was NB with AUC of 91.14%, sensitivity of 81.17%, and specificity of 90.65%. Sensitivity analysis demonstrated GCS measurements on the 7th and 14th day and difference between emergency room and 14th day GCS score as the most influential attributes both in mortality and functional outcome prediction models. Analysis of serial GCS measurements using data mining methods provided additional predictive information in relation to 6-month mortality and functional outcome in patients with moderate-to-severe TBI.
先前的研究已经确定了一些用于预测创伤性脑损伤（TBI）后长期功能恢复和死亡率的临床参数。在此，数据挖掘方法与连续的格拉斯哥昏迷量表（GCS）评分以及临床和实验室参数相结合，以预测TBI患者6个月时的功能结局和死亡率。对在一家创伤中心就诊的连续成年中重度颅脑损伤患者的数据进行了回顾性分析。纳入分析的临床参数包括急诊科、第7天和第14天的连续GCS测量值以及实验室数据（n = 115）。我们采用人工神经网络（ANN）、朴素贝叶斯（NB）、决策树和逻辑回归来预测TBI后6个月的死亡率和功能结局。34.8%的患者获得了良好的功能结局，6个月时的总体死亡率为25.2%。对于6个月功能结局预测，ANN是最佳模型，其受试者操作特征曲线（AUC）下面积为96.13%，灵敏度为83.50%，特异性为89.73%。死亡率的最佳预测模型是NB，AUC为91.14%，灵敏度为81.17%，特异性为90.65%。敏感性分析表明，第7天和第14天的GCS测量值以及急诊室和第14天GCS评分之间差异是死亡率和功能结局预测模型中最具影响力的属性。使用数据挖掘方法对连续GCS测量值进行分析，为中重度TBI患者6个月时的死亡率和功能结局提供了额外的预测信息。
