决策树模型与logistic回归模型在脑出血预后分析中的应用

Risk stratification for prognosis in intracerebral hemorrhage: A decision tree model and logistic regression

目的通过应用决策树分类和回归树模型与logistic回归模型分析影响脑出血患者预后的风险因素,为临床治疗脑出血提供借鉴。方法根据临床常见影响脑出血患者预后的风险因素,建立决策树模型和logistic回归模型,比较两种方法分析结果的差异。结果 Logistic回归分析结果显示血肿体积(OR=0.953)、首次GCS评分(OR=1.210)、肺部感染(OR=0.295)、基底节区出血(OR=0.336)是脑出血预后不良的风险因素。决策树模型分析结果显示,血肿体积和首次格拉斯哥昏迷GCS评分是影响脑出血预后最主要的因素。两种模型对脑出血预后的评价作用近似(Z=0.402,P=0.688)。结论决策树模型判断脑出血预后的价值与logistic模型近似,同时还具有可对风险因素进行交互分析、更为直观的特点。

Objective To analyze the risk factors for prognosis in intracerebral hemorrhage using decision tree （classification and regression tree, CART） model and logistic regression model. Methods CART model and logistic regression model were established according to the risk factors for prognosis of patients with cerebral hemorrhage. The differences in the results were compared between the two methods. Results Logistic regression analyses showed that hematoma volume （OR-value 0.953）, initial Glasgow Coma Scale （GCS） score （OR-value 1.210）, pulmonary infection （OR-value 0.295）, and basal ganglia hemorrhage （OR-value 0.336） were the risk factors for the prognosis of cerebral hemorrhage. The results of CART analysis showed that volume of hematoma and initial GCS score were the main factors affecting the prognosis of cerebral hemorrhage. The effects of two models on the prognosis of cerebral hemorrhage were similar （Z-value 0.402, P=0.688）. Conclusions CART model has a similar value to that of logistic model in judging the prognosis of cerebral hemorrhage, and it is characterized by using transactional analysis between the risk factors, and it is more intuitive.