Department of Neurosurgery, Huahsan Hospital, Fudan University, Shanghai, China.
Neurosurgical Institute of Fudan University, Shanghai, China.
CNS Neurosci Ther. 2021 Jan;27(1):92-100. doi: 10.1111/cns.13509. Epub 2020 Nov 28.
Coagulation abnormality is one of the primary concerns for patients with spontaneous intracerebral hemorrhage admitted to ER. Conventional laboratory indicators require hours for coagulopathy diagnosis, which brings difficulties for appropriate intervention within the optimal window. This study evaluates the possibility of building efficient coagulopathy prediction models using data mining and machine learning algorithms.
A retrospective cohort enrolled 1668 cases with acute spontaneous intracerebral hemorrhage from three medical centers, excluding those under antithrombotic therapies. Coagulopathy-related clinical parameters were initially screened by univariate analysis. Two machine learning algorithms, the random forest and the support vector machine, were deployed via an approach of four-fold cross-validation to screen out the most important parameters contributing to the occurrence of coagulopathy. Model discrimination was assessed using metrics, including accuracy, precision, recall, and F1 score.
Albumin/globulin ratio, neutrophil count, lymphocyte percentage, aspartate transaminase, alanine transaminase, hemoglobin, platelet count, white blood cell count, neutrophil percentage, systolic and diastolic pressure were identified as major predictors to the occurrence of acute coagulopathy. Compared to support vector machine, the model based on the random forest algorithm showed better accuracy (93.1%, 95% confidence interval [CI]: 0.913-0.950), precision (92.4%, 95% CI: 0.897-0.951), F1 score (91.5%, 95% CI: 0.889-0.964), and recall score (93.6%, 95% CI: 0.909-0.964), and yielded higher area under the receiver operating characteristic curve (AU-ROC) (0.962, 95% CI: 0.942-0.982).
The constructed models exhibit good prediction accuracy and efficiency. It might be used in clinical practice to facilitate target intervention for acute coagulopathy in patients with spontaneous intracerebral hemorrhage.
凝血异常是急诊自发性脑出血患者的主要关注点之一。常规实验室指标需要数小时才能诊断出凝血功能障碍，这给在最佳窗口期内进行适当干预带来了困难。本研究评估了使用数据挖掘和机器学习算法构建高效凝血功能障碍预测模型的可能性。
本回顾性队列研究纳入了来自三个医学中心的 1668 例急性自发性脑出血患者，排除了接受抗血栓治疗的患者。通过单因素分析初步筛选出与凝血功能障碍相关的临床参数。采用四折交叉验证的方法，利用随机森林和支持向量机两种机器学习算法筛选出导致凝血功能障碍发生的最重要参数。采用准确性、精确性、召回率和 F1 评分等指标评估模型的区分度。
白蛋白/球蛋白比值、中性粒细胞计数、淋巴细胞百分比、天门冬氨酸转氨酶、丙氨酸转氨酶、血红蛋白、血小板计数、白细胞计数、中性粒细胞百分比、收缩压和舒张压被确定为急性凝血功能障碍发生的主要预测因子。与支持向量机相比，基于随机森林算法的模型具有更好的准确性（93.1%，95%置信区间[CI]：0.913-0.950）、精确性（92.4%，95% CI：0.897-0.951）、F1 评分（91.5%，95% CI：0.889-0.964）和召回评分（93.6%，95% CI：0.909-0.964），且获得了更高的受试者工作特征曲线下面积（AU-ROC）（0.962，95% CI：0.942-0.982）。
所构建的模型具有良好的预测准确性和效率。它可用于临床实践，以促进自发性脑出血患者急性凝血功能障碍的目标干预。
