Chen Jingshu, Li Jinze, Xu Zhihua, Zhang Luojin, Qi Shouliang, Yang Benqiang, Chen Zimeng, Wang Xinrui, Duan Yang
Center for Neuroimaging, Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China.
Center for Neuroimaging, Northern Theater Command Postgraduate Training Base of Jinzhou Medical University General Hospital, Shenyang, China.
Front Neurol. 2022 Aug 18;13:903730. doi: 10.3389/fneur.2022.903730. eCollection 2022.
The purpose of this study is to establish and evaluate an early biomarker prediction model of massive cerebral infarction caused by anterior circulation occlusion.
One hundred thirty-four patients with acute cerebral infarction from January 2018 to October 2020 were selected to establish the development cohort for the internal test of the nomogram. Ninety-one patients with acute cerebral infarction hospitalized in our hospital from December 2020 to December 2021 were constituted the validation cohort for the external validation. All patients underwent baseline computed tomography (CT) scans within 12 h of onset and early imaging signs (hyperdense middle cerebral artery sign, obscuration of the lentiform nucleus, insular ribbon sign) of acute cerebral infarction were identified on CT by two neurologists. Based on follow-up CT images, patients were then divided into a massive cerebral infarction group and a non-massive cerebral infarction group. The nomogram model was constructed based on logistic regression analysis with R language. The nomogram was subsequently validated in an independent external validation cohort. Accuracy and discrimination of the prediction model were evaluated by a calibration chart, receiver operating characteristic (ROC) curve, and decision curve.
The indicators, including insular ribbon sign, reperfusion therapy, National Institutes of Health Stroke Scale (NHISS) score, previous cerebral infarction, and atrial fibrillation, were entered into the prediction model through binary logistic regression analysis. The prediction model showed good predictive ability. The area under the ROC curve of the prediction model was 0.848. The specificity, sensitivity, and Youden index were 0.864, 0.733, and 0.597, respectively. This nomogram to the validation cohort also showed good discrimination (AUC = 0.940, 95% CI 0.894-0.985) and calibration.
Demonstrating favorable predictive efficacy and reproducibility, this study successfully established a prediction model of CT imaging signs and clinical data as early biomarkers of massive cerebral infarction caused by anterior circulation occlusion.
本研究旨在建立并评估一种由前循环闭塞引起的大面积脑梗死的早期生物标志物预测模型。
选取2018年1月至2020年10月期间的134例急性脑梗死患者作为开发队列，用于列线图的内部验证测试。选取2020年12月至2021年12月在我院住院的91例急性脑梗死患者作为验证队列，用于外部验证。所有患者在发病12小时内均接受了基线计算机断层扫描（CT），两名神经科医生在CT上识别出急性脑梗死的早期影像征象（大脑中动脉高密度征、豆状核模糊、岛带征）。根据后续的CT图像，将患者分为大面积脑梗死组和非大面积脑梗死组。使用R语言基于逻辑回归分析构建列线图模型。随后在独立的外部验证队列中对列线图进行验证。通过校准图、受试者操作特征（ROC）曲线和决策曲线评估预测模型的准确性和辨别力。
通过二元逻辑回归分析将岛带征、再灌注治疗、美国国立卫生研究院卒中量表（NHISS）评分、既往脑梗死和心房颤动等指标纳入预测模型。该预测模型显示出良好的预测能力。预测模型的ROC曲线下面积为0.848。特异性、敏感性和尤登指数分别为0.864、0.733和0.597。该列线图在验证队列中也显示出良好的辨别力（AUC = 0.940，95%CI 0.894 - 0.985）和校准度。
本研究成功建立了一个以CT影像征象和临床数据作为前循环闭塞所致大面积脑梗死早期生物标志物的预测模型，显示出良好的预测效能和可重复性。
