Li Shaojie, Li Hongjian, Wu Baofang, Pan Rujun, Liu Yuqi, Wang Jiayin, Wei De, Gao Hongzhi
Department of Neurosurgery, the Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, People's Republic of China.
School of Medical Imaging, North Sichuan Medical College, Nanchong, 634700, People's Republic of China.
J Multidiscip Healthc. 2025 Jan 16;18:157-170. doi: 10.2147/JMDH.S498420. eCollection 2025.
Post-traumatic cerebral infarction (PTCI) is a severe complication resulting from traumatic brain injury (TBI), which can lead to permanent neurological damage or death. The investigation of the factors associated with PTCI and the establishment of predictive models are crucial for clinical practice.
We made a retrospective analysis of clinical data from 1484 TBI patients admitted to the Neurosurgery Department of a provincial hospital from January 2018 to December 2023. Predictive factors were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) and multivariable logistic regression analysis. Several machine learning (ML) classification models were developed and compared. The interpretations of the ML models' predictions were provided by SHAP values.
Key predictors included age, bilateral brain contusions, platelet count, uric acid, glucose, traumatic subarachnoid hemorrhage, and surgical treatment. The logistic regression (LR) model outperformed other ML algorithms, demonstrating superior performance in the test set with an AUC of 0.821, accuracy of 0.845, Matthews correlation coefficient (MCC) of 0.264, area under the receiver operating characteristic curve (AUROC) of 0.711, precision of 0.56, and specificity of 0.971. It had stable performance in the ten-fold cross-validation.
ML algorithms, integrating demographic and clinical factors, accurately predicted the risk of PTCI occurrence. Interpretations using the SHAP method offer guidance for personalized treatment of different patients, filling gaps between complex clinical data and actionable insights.
创伤后脑梗死（PTCI）是创伤性脑损伤（TBI）导致的一种严重并发症，可导致永久性神经损伤或死亡。研究与PTCI相关的因素并建立预测模型对临床实践至关重要。
我们对2018年1月至2023年12月在某省级医院神经外科住院的1484例TBI患者的临床资料进行了回顾性分析。使用最小绝对收缩和选择算子（LASSO）及多变量逻辑回归分析确定预测因素。开发并比较了几种机器学习（ML）分类模型。通过SHAP值对ML模型的预测进行解释。
关键预测因素包括年龄、双侧脑挫裂伤、血小板计数、尿酸、血糖、创伤性蛛网膜下腔出血和手术治疗。逻辑回归（LR）模型优于其他ML算法，在测试集中表现优异，曲线下面积（AUC）为0.821，准确率为0.845，马修斯相关系数（MCC）为0.264，受试者工作特征曲线下面积（AUROC）为0.711，精确率为0.56，特异性为0.971。在十折交叉验证中具有稳定的性能。
整合人口统计学和临床因素的ML算法准确预测了PTCI发生的风险。使用SHAP方法的解释为不同患者的个性化治疗提供了指导，填补了复杂临床数据与可操作见解之间的空白。
