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急性脑梗塞患者溶栓后END发生风险预测模型的构建及急救优化方案的预防效果

Construction of risk prediction model of END after thrombolysis in patients with acute cerebral infarction prevention effect of first aid optimization scheme
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摘要 目的探讨急性脑梗塞(ACI)患者溶栓后早期神经功能恶化(END)发生风险预测模型的构建,以此优化急救方案并分析其预防效果。方法回顾性分析2019年1月~2021年12月期间,于发病4.5 h内在我院接受静脉溶栓的120例ACI患者的临床资料,根据其是否发生END分为END组(n=21)和无END组(n=99),通过单因素分析和多因素分析筛选影响ACI患者溶栓后发生END的风险因素,并根据OR值拟合出END预测评分量表,采用受试者工作特征曲线(ROC)分析该预测模型的预测效能,并基于此预测模型制定急救优化方案。另纳入2022年1月~2022年12月期间收治的60例ACI患者为观察对象,比较患者接受急救优化方案的END发生率、神经功能缺损症状及日常生活能力差异。结果本研究中ACI患者的END发生率为17.5%。年龄≥60岁、HMCAS阳性、合并房颤、基线NIHSS评分偏高及溶栓前血糖、白细胞计数、D-二聚体水平偏高均是影响ACI患者溶栓后发生END的独立危险因素(P<0.05)。ROC曲线分析结果显示,END预测评分预测ACI患者溶栓后24 h内发生END的AUC为0.905(0.832~0.977),灵敏度和特异度分别为87.54%和82.48%,截断值为5分,约登指数为0.700。经急救优化方案干预后,ACI患者溶栓后END发生率、NDS评分均明显降低,ALD评分显著升高(P<0.05)。结论(1)影响ACI患者溶栓后发生END的影响因素较多,其中年纪较大、合并房颤、溶栓前血糖、白细胞计数、D-二聚体水平偏高的ACI患者更应警惕END的发生。(2)根据预测评分量表优化后的急救方案可明显降低END发生率,并明显改善ACI患者神经缺损症状,提升其日常生活能力。 Objective To explore the construction of risk prediction model of early neurological deterioration(END)in patients with acute cerebral infarction(ACI)after thrombolysis,and to analyze the prevention effect of first aid optimization scheme.Methods A retrospective analysis was performed on the clinical data of 120patients with ACI who received intravenous thrombolysis in our hospital within 4.5hfrom January 2019to December 2021.They were divided into END group(n=21)and no END group(n=99)according to whether they had END.The risk factors influencing the occurrence of END in patients with ACI after thrombolysis were screened through single factor analysis and multifactor analysis,and the END prediction score scale was fitted according to OR value.receiver operating characteristic curve(ROC)was used to analyze the prediction efficiency of the prediction model,and the emergency optimization plan was formulated based on the prediction model.In addition,60patients with ACI who were admitted to our hospital from January 2022to December 2022were included as the observation subjects,and the incidence of END,neurological deficit symptoms and differences in daily living ability of patients receiving the optimal emergency plan were compared.Results The incidence of END in patients with ACI in this study was 17.5%.Age≥60years old,HMCAS positive,concomitant atrial fibrillation,high baseline NIHSS score,and high blood glucose,white blood cell count,and D-dimer level before thrombolysis were all the independent risk factors for END in patients with ACI after thrombolysis(P<0.05).ROC curve analysis results show that the AUC of END predicted by the END prediction score in ACI patients within 24hafter thrombolysis is 0.905(0.832-0.977),the sensitivity and specificity are 87.54%and 82.48%,respectively.The cutoff value is 5points,and the Jordan Index is 0.700.After the intervention with the emergency optimization protocol,the incidence of END and NDS score of ACI patients after thrombolysis were significantly reduced,while ALD score was significantly increased(P<0.05).Conclusion①There are many influencing factors for the occurrence of END in patients with ACI after thrombolysis,among which,older patients with ACI combined with atrial fibrillation,pre-thrombolytic high blood glucose,white blood cell count and D-dimer level should be more alert to the occurrence of END.②The emergency plan optimized according to the prediction score scale can significantly reduce the incidence of END and significantly improve the neurological deficit symptoms and the ability of daily living of patients with ACI.
作者 边丽 张秀梅 李妍 杨国防 BIAN Li;ZHANG Xiu-mei;LI Yan;无(Emergency Department,the First Affiliated Hospital of Henan University of Traditional Chinese Medicine,Zhengzhou450000,China;Brain Disease Diagnosis and Treatment Center,the First Affiliated Hospital of Henan University of Traditional Chinese Medicine,Zhengzhou450000,China)
出处 《中国实验诊断学》 2023年第6期631-637,共7页 Chinese Journal of Laboratory Diagnosis
基金 国家中医药管理局段海辰全国名老中医药专家传承工作室项目(国中医药人教函【2018】134号)。
关键词 急性脑梗塞 静脉溶栓 早期神经功能恶化 风险预测模型 急救优化方案 acute cerebral infarction Intravenous thrombolysis early neurological deterioration Risk prediction model Emergency optimization plan
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