Chen Ru, Deng Zelin, Song Zhi
Neurological Department, The Third Xiangya Hospital of Central South University, Hunan, China.
Department of software engineering, School of Computer and Communication Engineering, Changsha University of Science and Technology, Hunan, China.
J Stroke Cerebrovasc Dis. 2015 May;24(5):958-64. doi: 10.1016/j.jstrokecerebrovasdis.2014.12.016. Epub 2015 Mar 21.
Malignant middle cerebral artery infarction (MMI) is always associated with high mortality rates. Early decompressive craniectomy is crucial to its treatment. The purpose of this study was to establish a reliable model for an early prediction of MMI.
Using a retrospective survey, we have collected the data of 132 patients with middle cerebral artery infarction. According to a prognosis, the patients are divided into the MMI group (n = 36) and the non-MMI group (n = 96). All the patients are represented by their clinical, biochemical, and imaging features. Then a random forest (RF) prediction model is established on the clinical data. Meanwhile, 3 traditional prediction models, including univariate linear discriminant analysis (LDA) model, multivariate LDA model, and binary logistic regression analysis (BLRA), are built to compare with the RF model. The prediction performance of different models is assessed by the area under the receiver operating characteristic curves (AUCs).
Four parameters, Glasgow Coma Scale, midline shifting, area, and volume of focus, selected as predictors in all models. As independent predictors, their AUCs are .72-.80, and when the sensitivities are high (.91-.95), the specificities are low (.32-.53). The AUC of RF model is .96, 95% confidence interval (CI) is (.93-.99), sensitivity is 1, and specificity is .85. The AUC of the multivariate LDA model is .87 and 95% CI is (.80-.93). The AUC of the BLRA model is .86 and 95% CI is (.80-.93).
The RF performs very well in the given clinical data set, which indicates that the RF is applicable to the early prediction of the MMI.
恶性大脑中动脉梗死（MMI）总是与高死亡率相关。早期减压颅骨切除术对其治疗至关重要。本研究的目的是建立一个可靠的模型用于早期预测MMI。
采用回顾性调查，我们收集了132例大脑中动脉梗死患者的数据。根据预后情况，将患者分为MMI组（n = 36）和非MMI组（n = 96）。所有患者均以其临床、生化和影像学特征表示。然后基于临床数据建立随机森林（RF）预测模型。同时，构建3种传统预测模型，包括单变量线性判别分析（LDA）模型、多变量LDA模型和二元逻辑回归分析（BLRA），以与RF模型进行比较。不同模型的预测性能通过受试者操作特征曲线下面积（AUC）进行评估。
格拉斯哥昏迷量表、中线移位、病灶面积和体积这四个参数在所有模型中均被选为预测指标。作为独立预测指标，它们的AUC为0.72 - 0.80，当敏感度较高（0.91 - 0.95）时，特异度较低（0.32 - 0.53）。RF模型的AUC为0.96，95%置信区间（CI）为（0.93 - 0.99），敏感度为1，特异度为0.85。多变量LDA模型的AUC为0.87，95% CI为（0.80 - 0.93）。BLRA模型的AUC为0.86，95% CI为（0.80 - 0.93）。
RF在给定的临床数据集中表现非常出色，这表明RF适用于MMI的早期预测。
