摘要
【目的】基于临床数据构建并验证急性缺血性脑卒中(AIS)患者静脉溶栓后症状性颅内出血(sICH)的列线图模型。【方法】采用分层抽样法将接受静脉溶栓治疗的213例AIS患者按7∶3分为训练组和验证组,应用多因素Logistic回归分析与sICH发生相关的独立预测因子,构建列线图模型并使用受试者工作特征(ROC)曲线、校准曲线及决策曲线分析(DCA)进行评估。【结果】训练集共纳入149例AIS患者,其中25例(16.78%)发生了sICH。经多因素Logistic回归分析,年龄、入院时美国国立卫生研究院卒中量表(NIHSS)评分、发病至溶栓时间(OTT)、中性粒细胞/淋巴细胞比值(NLR)、大脑中动脉高密度征(HMCAS)是发生sICH的独立预测因子(P<0.05)。ROC曲线分析显示,列线图模型在训练集和验证集的曲线下面积(AUC)分别为0.972和0.940,具有较好的区分能力。校准曲线表明,列线图预测的sICH风险与实际发生风险一致(P=0.196)。DCA显示,该列线图在临床实践中具有较好的应用价值。【结论】年龄、NIHSS评分、OTT、NLR、HMCAS为AIS患者静脉溶栓后发生sICH的独立预测因子,所构建的列线图模型能有效预测sICH发生风险。
【Objective】To construct and validate a nomogram model based on clinical data to predict symptomatic intracranial hemorrhage(sICH)following intravenous thrombolysis in patients with acute ischemic stroke(AIS).【Methods】A total of 213 AIS patients who received intravenous thrombolysis were randomly divided into a training set and a validation set in a 7∶3 ratio using stratified sampling.Multivariate logistic regression analysis was used to identify independent predictors of sICH,which were then used to build a nomogram model.The model was evaluated using receiver operating characteristic(ROC)curves,calibration curves,and decision curve analysis(DCA).【Results】The training set included 149 AIS patients,among whom^(2)5 cases(16.78%)developed sICH.Multivariate logistic regression analysis identified age,baseline National Institutes of Health Stroke Scale(NIHSS)score,onset-to-treatment time(OTT),neutrophil-to-lymphocyte ratio(NLR),and hyperdense middle cerebral artery sign(HMCAS)as independent predictors of sICH(P<0.05).ROC curve analysis showed that the area under the curve(AUC)of the nomogram model was 0.972 in the training set and 0.940 in the validation set,indicating good discrimination.The calibration curve demonstrated good agreement between predicted and observed sICH risk(P=0.196).DCA indicated that the nomogram had good clinical utility.【Conclusion】Age,NIHSS score,OTT,NLR,and HMCAS are independent predictors of sICH after intravenous thrombolysis in AIS patients.The developed nomogram model can effectively predict the risk of sICH.
作者
贾广杰
JIA Guangjie(Department of Neurology,Qixian People's Hospital,Qixian Henan 475200)
出处
《医学临床研究》
2025年第6期989-992,共4页
Journal of Clinical Research
关键词
脑缺血
卒中
急性病
血栓溶解疗法
颅内出血
Brain Ischemia
Stroke
Acute Disease
Thrombolytic Therapy
Intracranial Hemorrhages
