脑梗死分型与进展性缺血性脑卒中相关性研究
发布时间：2019-06-28 19:02
【摘要】：目的探讨5种脑梗死分型亚型与进展性缺血性脑卒中的相关性；将与进展性缺血性脑卒中相关的脑梗死分型亚型进行联合，构建不同组合并相互比较，得到最准确预测脑梗死进展的最优因素组合，为进展性缺血性脑卒中的预防及治疗提供理论依据。 方法选取经颅脑核磁证实的急性缺血性脑卒中患者407例，，分成进展性缺血性脑卒中组（进展组）106例和非进展性缺血性脑卒中组（非进展组）301例，两组患者均于入院时行TOAST分型、OCSP分型、CT分型、CISS分型、ASCO分型，同时收集患者的性别、年龄、吸烟史、饮酒史、高血压病病史、糖尿病病史、冠心病病史、卒中病史等资料。对上述因素进行单因素分析，挑选进展性缺血性脑卒中的危险因素，进行多因素非条件Logistic回归分析（Forward LR法）并将有统计学意义的因素组建不同组合。从诊断试验的真实性、预测效果及与原样本吻合度3个角度对组合进行检验；最后检验最优组合的准确性。 结果1经χ2分析：高血压病史，糖尿病病史，TOAST分型中LAA亚型、SAO亚型，OCSP分型中的TACI亚型、PACI亚型、POCI亚型、LACI亚型，CT分型中的大梗死亚型、中梗死亚型、腔隙性梗死亚型，CISS分型中的LAA亚型、PAD亚型、UE亚型，ASCO分型中的A亚型、S亚型在进展组与非进展组间存在统计学差异（P0.05）；性别、吸烟史、饮酒史、冠心病史、卒中病史及脑梗死分型中的其他亚型在进展组与非进展组间不存在统计学差异（P0.05）。经多因素非条件Logistic回归分析：糖尿病病史，OCSP分型中的TACI亚型、PACI亚型，CT分型中的大梗死亚型、腔隙性梗死亚型，CISS分型中UE亚型进展组与非进展组(组间)比较具有统计学意义（P0.05）。卒中进展的风险（OR值）分别为：4.097倍、7.552倍、10.428倍、2.969倍、0.296倍、0.225倍；2对糖尿病病史，OCSP分型中的TACI亚型、PACI亚型，CT分型中的大梗死亚型、腔隙性梗死亚型，CISS分型中的UE亚型采用多因素非条件Logistic回归分析（Forward LR法），按OR值大小依次引入，构建不同的因素组合。通过比较组合的真实性、预测效果及与原样本吻合度，结果显示因素组合⑤的灵敏度(0.717)、特异度(0.841)、约登指数(0.558)、Kappa值(0.528)、阳性预测值(0.613)、阴性预测值(0.894)较其他组合优秀；381例患者对因素组合⑤进行检验的灵敏度=0.810，特异度=0.833，总的判对率=1－误判率=0.827，与原样本的吻合度Kappa=0.588。 结论1高血压病史、糖尿病病史，TOAST分型中LAA亚型、SAO亚型，OCSP分型中的TACI亚型、PACI亚型、POCI亚型、LACI亚型，CT分型中的大梗死亚型、中梗死亚型、腔隙性梗死亚型，CISS分型中的LAA亚型、PAD亚型、UE亚型，ASCO分型中的A亚型、S亚型与进展性缺血性脑卒中相关；2PACI亚型、TACI亚型、糖尿病病史、CISS分型中的UE亚型、CT分型中的腔隙性脑梗死亚型组合是预测进展性缺血性脑卒中的最优组合，能够提高预测的准确度。
[Abstract]:Objective to explore the correlation between five subtypes of cerebral infarction and progressive ischemic stroke, to combine the subtypes of cerebral infarction associated with progressive ischemic stroke, to construct different combinations and compare with each other, and to obtain the optimal factor combination to accurately predict the progress of cerebral infarction, so as to provide theoretical basis for the prevention and treatment of progressive ischemic stroke. Methods 407 patients with acute ischemic stroke confirmed by craniocerebral MRI were divided into progressive ischemic stroke group (n = 106) and non-progressive ischemic stroke group (n = 301). TOAST classification, OCSP classification, CT classification, CISS classification and ASCO classification were performed on admission. At the same time, the sex, age, smoking history, drinking history, hypertension history and diabetes history of the patients were collected. History of coronary heart disease, history of stroke, etc. The above factors were analyzed by univariate analysis, the risk factors of progressive ischemic stroke were selected, and the multivariate unconditional Logistic regression analysis (Forward LR method was used to form different combinations of statistically significant factors. The combination was tested from three angles: the authenticity of the diagnostic test, the prediction effect and the degree of coincidence with the original sample. Finally, the accuracy of the optimal combination was tested. Results 1 there were significant differences in hypertension history, diabetes history, LAA subtypes, SAO subtypes, TACI subtypes, PACI subtypes, POCI subtypes, LACI subtypes, large infarction subtypes, middle infarction subtypes, lacunar infarction subtypes, LAA subtypes, PAD subtypes, UE subtypes, A subtypes in ASCO classification between progressive group and non-progressive group (P 0.05). There was no significant difference in sex, smoking history, drinking history, coronary heart disease history, stroke history and other subtypes in cerebral infarction classification between progressive group and non-progressive group (P 0.05). Multivariate unconditional Logistic regression analysis showed that the history of diabetes mellitus, TACI subtypes in OCSP classification, PACI subtypes, large infarction subtypes in CT classification, lacunar infarction subtypes, UE subtypes in CISS classification were significantly higher than those in non-progressive groups (P 0.05). The risk of stroke progress (OR value) was 4.097 times, 7.552 times, 10.428 times, 2.969 times, 0.296 times and 0.225 times, respectively. 2 for the history of diabetes mellitus, TACI subtypes, PACI subtypes in OCSP classification, large infarction subtypes in CT classification, lacunar infarction subtypes, UE subtypes in CISS classification were introduced by multivariate unconditional Logistic regression analysis (Forward LR method), and different factor combinations were constructed according to the OR value. By comparing the authenticity, prediction effect and coincidence with the original sample, the results showed that the sensitivity (0.717), specificity (0.841), Jordan index (0.558), Kappa value (0.528), positive predictive value (0.613) and negative predictive value (0.894) of factor combination 5 were better than those of other combinations. The sensitivity of factor combination 5 was 0.810, the specificity was 0.833, the total judgment rate was 1-misjudgment rate = 0.827, and the coincidence degree Kappa=0.588. with the original sample was 0.810, 0.833, 0.827, 0.827, 0.827, 0.827, 0.827, 0.827, 0.827, 0.827, 0.827, 0.827 and 0.827, respectively. Conclusion (1) the history of hypertension, diabetes mellitus, LAA subtypes, SAO subtypes, TACI subtypes, PACI subtypes, POCI subtypes, LACI subtypes, large infarction subtypes, middle infarction subtypes, lacunar infarction subtypes, LAA subtypes, PAD subtypes, UE subtypes, A subtypes and S subtypes in CISS classification are related to progressive ischemic stroke. The combination of 2PACI subtypes, TACI subtypes, diabetes history, UE subtypes in CISS classification and lacunar cerebral infarction subtypes in CT classification is the best combination to predict progressive ischemic stroke, which can improve the accuracy of prediction.
【学位授予单位】：河北联合大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：R743.3
本文编号：2507532
[Abstract]:Objective to explore the correlation between five subtypes of cerebral infarction and progressive ischemic stroke, to combine the subtypes of cerebral infarction associated with progressive ischemic stroke, to construct different combinations and compare with each other, and to obtain the optimal factor combination to accurately predict the progress of cerebral infarction, so as to provide theoretical basis for the prevention and treatment of progressive ischemic stroke. Methods 407 patients with acute ischemic stroke confirmed by craniocerebral MRI were divided into progressive ischemic stroke group (n = 106) and non-progressive ischemic stroke group (n = 301). TOAST classification, OCSP classification, CT classification, CISS classification and ASCO classification were performed on admission. At the same time, the sex, age, smoking history, drinking history, hypertension history and diabetes history of the patients were collected. History of coronary heart disease, history of stroke, etc. The above factors were analyzed by univariate analysis, the risk factors of progressive ischemic stroke were selected, and the multivariate unconditional Logistic regression analysis (Forward LR method was used to form different combinations of statistically significant factors. The combination was tested from three angles: the authenticity of the diagnostic test, the prediction effect and the degree of coincidence with the original sample. Finally, the accuracy of the optimal combination was tested. Results 1 there were significant differences in hypertension history, diabetes history, LAA subtypes, SAO subtypes, TACI subtypes, PACI subtypes, POCI subtypes, LACI subtypes, large infarction subtypes, middle infarction subtypes, lacunar infarction subtypes, LAA subtypes, PAD subtypes, UE subtypes, A subtypes in ASCO classification between progressive group and non-progressive group (P 0.05). There was no significant difference in sex, smoking history, drinking history, coronary heart disease history, stroke history and other subtypes in cerebral infarction classification between progressive group and non-progressive group (P 0.05). Multivariate unconditional Logistic regression analysis showed that the history of diabetes mellitus, TACI subtypes in OCSP classification, PACI subtypes, large infarction subtypes in CT classification, lacunar infarction subtypes, UE subtypes in CISS classification were significantly higher than those in non-progressive groups (P 0.05). The risk of stroke progress (OR value) was 4.097 times, 7.552 times, 10.428 times, 2.969 times, 0.296 times and 0.225 times, respectively. 2 for the history of diabetes mellitus, TACI subtypes, PACI subtypes in OCSP classification, large infarction subtypes in CT classification, lacunar infarction subtypes, UE subtypes in CISS classification were introduced by multivariate unconditional Logistic regression analysis (Forward LR method), and different factor combinations were constructed according to the OR value. By comparing the authenticity, prediction effect and coincidence with the original sample, the results showed that the sensitivity (0.717), specificity (0.841), Jordan index (0.558), Kappa value (0.528), positive predictive value (0.613) and negative predictive value (0.894) of factor combination 5 were better than those of other combinations. The sensitivity of factor combination 5 was 0.810, the specificity was 0.833, the total judgment rate was 1-misjudgment rate = 0.827, and the coincidence degree Kappa=0.588. with the original sample was 0.810, 0.833, 0.827, 0.827, 0.827, 0.827, 0.827, 0.827, 0.827, 0.827, 0.827, 0.827 and 0.827, respectively. Conclusion (1) the history of hypertension, diabetes mellitus, LAA subtypes, SAO subtypes, TACI subtypes, PACI subtypes, POCI subtypes, LACI subtypes, large infarction subtypes, middle infarction subtypes, lacunar infarction subtypes, LAA subtypes, PAD subtypes, UE subtypes, A subtypes and S subtypes in CISS classification are related to progressive ischemic stroke. The combination of 2PACI subtypes, TACI subtypes, diabetes history, UE subtypes in CISS classification and lacunar cerebral infarction subtypes in CT classification is the best combination to predict progressive ischemic stroke, which can improve the accuracy of prediction.
【学位授予单位】：河北联合大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：R743.3
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相关期刊论文 前1条
1 吕扬勋;王圣槐;赵红霞;;脑梗死患者血清钙镁含量变化及临床意义[J];中国实用神经疾病杂志;2006年03期
本文编号：2507532
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