急性缺血性脑卒中静脉溶栓后出血预测模型的构建与验证

目的 探讨急性缺血性脑卒中患者接受静脉溶栓治疗后发生出血的危险因素,构建预测模型.方法 纳入2014年1月—2020年12月于深圳市某三级甲等医院进行静脉溶栓治疗的急性缺血性脑卒中患者462例,将其分为出血组(n=264)和未出血组(n=198).使用二分类Logistic回归模型分析相关危险因素,构建预测模型并进行验...

Construction and Verification of Predictive Model of Hemorrhage after Intravenous Thrombolysis in Acute Ischemic Stroke

Objective To explore the risk factors of bleeding in patients with acute ischemic stroke after receiving intravenous thrombolysis, and construct a predictive model. Methods Totally 462 patients with acute ischemic stroke who underwent intravenous thrombolysis in a tertiary grade-A hospital in Shenzhen from January 2014 to December 2020 were divided into bleeding group (n=264) and non-bleeding group (n=198). The binary logistic regression model was used to analyze the risk factors, and the predictive model was constructed and verified. Results Age, the score of National Institutes of Health Stroke Scale (NIHSS) before thrombolysis, time from onset to thrombolysis, history of hypertension, and leukoaraiosis were independent risk factors for hemorrhage after intravenous thrombolysis in patients with acute ischemic stroke. AUC of the predictive model was 0.786 and the specificity and sensitivity were 81.82% and 62.36% respectively. Decision curve analysis showed that implementing decision interventions in the bleeding risk range of 0.306 to 0.990 had net clinical benefit. And after verification, it had been shown that the model had good discrimination (AUC=0.743,95%CI:0.699~0.788) and Calibration degree (Hosmer-Lemeshow test:χ²=11.559,P=0.172). Conclusion The nomogram model constructed in this study has better predictive performance, clinical application value, and high repeatability.