進(jìn)展性缺血性腦卒中危險(xiǎn)因素篩選及其預(yù)測(cè)評(píng)分系統(tǒng)的構(gòu)建
發(fā)布時(shí)間：2018-08-27 08:01
【摘要】：目的篩選進(jìn)展性缺血性腦卒中(Progressive ischemic stroke,PIS)的相關(guān)危險(xiǎn)因素,基于這些危險(xiǎn)因素構(gòu)建PIS的預(yù)測(cè)評(píng)分系統(tǒng),以便臨床應(yīng)用,為其預(yù)防和治療提供依據(jù)。方法收集2015年12月到2016年12月于唐山工人醫(yī)院神經(jīng)內(nèi)科住院的急性缺血性腦卒中患者186例。在患者入院時(shí)和病情變化時(shí)采用美國國立研究院卒中量表(National Institutes of Health Stroke Scale,NIHSS)進(jìn)行神經(jīng)功能評(píng)分,根據(jù)發(fā)病6小時(shí)至發(fā)病7天內(nèi)NIHSS評(píng)分是否增加2分或2分以上,分為進(jìn)展性缺血性腦卒中(PIS)組和非進(jìn)展缺血性腦卒中(Non progressive ischemic stroke,NPIS)組,其中PIS組86例,NPIS組100例。收集可能影響患者病情的相關(guān)指標(biāo),包括性別、年齡、既往病史、吸煙、飲酒史、發(fā)熱、感染、肥胖、體重指數(shù)、血白細(xì)胞計(jì)數(shù)、空腹血糖、纖維蛋白原、血脂、同型半胱氨酸、超敏C反應(yīng)蛋白、尿酸、入院時(shí)血壓及血壓變化、貧血、頸動(dòng)脈斑塊、責(zé)任血管狹窄程度、梗死部位、入院NIHSS評(píng)分、病情變化時(shí)NIHSS評(píng)分等。對(duì)上述資料進(jìn)行單因素分析及多因素Logistic回歸分析,篩選PIS的危險(xiǎn)因素,建立Logistic回歸方程;基于各因素的b系數(shù),采用“計(jì)分法”構(gòu)建PIS的預(yù)測(cè)評(píng)分系統(tǒng)。通過繪制ROC曲線及配對(duì)卡方檢驗(yàn)和一致性檢驗(yàn)等對(duì)PIS預(yù)測(cè)評(píng)分系統(tǒng)的辨別力和準(zhǔn)確度進(jìn)行評(píng)估。結(jié)果1單因素分析:飲酒史、發(fā)熱、感染、體重指數(shù)、血白細(xì)胞計(jì)數(shù)、甘油三酯、超敏C反應(yīng)蛋白、尿酸、入院收縮壓、腦梗死后血壓下降、責(zé)任血管中-重度狹窄、內(nèi)囊后肢梗死、分水嶺梗死在PIS組和NPIS組比較,差異有統(tǒng)計(jì)學(xué)意義(P0.05);性別、年齡、高血壓病史、冠心病史、糖尿病史、房顫病史、腦卒中/TIA病史、吸煙史、肥胖、空腹血糖、纖維蛋白原、總膽固醇、低密度脂蛋白膽固醇、同型半胱氨酸、入院舒張壓、貧血、頸動(dòng)脈斑塊、頸動(dòng)脈不穩(wěn)定斑塊、OCSP各型、橋腦梗死、入院時(shí)NIHSS評(píng)分在兩組間比較,差異無統(tǒng)計(jì)學(xué)意義(P0.05)。2多因素Logistic回歸分析:體重指數(shù)、飲酒史、梗死后血壓下降、責(zé)任血管中-重度狹窄、內(nèi)囊后肢梗死、分水嶺梗死、發(fā)熱,7個(gè)因素納入Logistic回歸模型,是PIS的危險(xiǎn)因素,各因素的優(yōu)勢(shì)(Odds ratio,OR)比分別為1.300、4.027、15.852、4.702、4.322、4.185、11.999。3構(gòu)建預(yù)測(cè)PIS的Logistic回歸模型:Logit P=-10.035+0.262X1+1.393X2+2.763X3+1.548X4+1.464X5+1.432X6+2.458X7(注:X1表示BMI;X2表示飲酒史;X3表示梗死后血壓下降;X4表示責(zé)任血管中-重度狹窄;X5表示內(nèi)囊后肢梗死;X6表示分水嶺區(qū)梗死;X7表示發(fā)熱)。4簡(jiǎn)化Logistic回歸模型,構(gòu)建了總分為10分、預(yù)測(cè)界值為4分的PIS預(yù)測(cè)評(píng)分系統(tǒng),該評(píng)分系統(tǒng)ROC曲線下面積為0.911,評(píng)價(jià)效果如下:靈敏度0.860,特異度0.920,總符合率0.892,陽性似然比10.750,陰性似然比0.152,Kappa值0.783。結(jié)論1體重指數(shù)、飲酒史、腦梗死后血壓下降、責(zé)任血管中-重度狹窄、內(nèi)囊后肢梗死、分水嶺梗死、發(fā)熱是PIS的重要危險(xiǎn)因素。2構(gòu)建的PIS預(yù)測(cè)評(píng)分系統(tǒng),評(píng)估效果較好,應(yīng)用方便簡(jiǎn)捷,具有一定的臨床實(shí)用性。
[Abstract]:Objective to screen the risk factors associated with progressive ischemic stroke (Progressive ischemic stroke,PIS) and to construct a predictive scoring system for PIS based on these risk factors so as to provide evidence for its prevention and treatment. Methods 186 patients with acute ischemic stroke were collected from December 2015 to December 2016 in Department of Neurology, Tangshan Workers Hospital. The neurological function was assessed by the National Institutes of America Stroke scale (National Institutes of Health Stroke Scale,NIHSS) on admission and at the time of disease change. According to whether the NIHSS score increased by 2 points or more within 6 hours to 7 days after the onset of the disease, the neurological function of the patients was assessed by the National Institute of Stroke scale (National Institutes of Health Stroke Scale,NIHSS). The patients were divided into progressive ischemic stroke (PIS) group and non progressive ischemic stroke (Non progressive ischemic stroke,NPIS) group. There were 86 cases in PIS group and 100 cases in PIS group. Collect relevant indicators that may affect the patient's condition, including sex, age, past medical history, smoking, alcohol consumption, fever, infection, obesity, body mass index, white blood cell count, fasting blood glucose, fibrinogen, blood lipids, Homocysteine, hypersensitive C-reactive protein, uric acid, blood pressure and blood pressure changes at admission, anemia, carotid plaque, degree of responsible vascular stenosis, infarct location, admission NIHSS score, NIHSS score at the time of disease change, etc. Univariate analysis and multivariate Logistic regression analysis were carried out to screen the risk factors of PIS and establish the Logistic regression equation. Based on the b coefficients of each factor, the prediction scoring system of PIS was constructed by "scoring method". The discriminative power and accuracy of PIS prediction scoring system are evaluated by drawing ROC curve, paired chi-square test and consistency test. Results 1 single factor analysis: alcohol history, fever, infection, body mass index, white blood cell count, triglyceride, hypersensitive C-reactive protein, uric acid, systolic blood pressure, blood pressure after cerebral infarction, moderate to severe stenosis of responsible blood vessel. There were significant differences between PIS group and NPIS group (P0.05); gender, age, history of hypertension, coronary heart disease, diabetes mellitus, atrial fibrillation, stroke / TIA history, smoking history, obesity, fasting blood glucose, Fibrinogen, total cholesterol, low density lipoprotein cholesterol, homocysteine, diastolic blood pressure, anemia, carotid plaque, carotid artery unstable plaque, pontine infarction, NIHSS scores at admission were compared between the two groups. There was no significant difference (P0.05) in multivariate Logistic regression analysis: body mass index (BMI), alcohol consumption history, blood pressure after infarction, moderate to severe stenosis of responsible vessels, internal capsule hind limb infarction, watershed infarction, fever, and 7 factors were included in Logistic regression model. Is a risk factor for PIS, The odds ratio of each factor (Odds ratio,OR) was 1.300 / 4.02715.852n 4.702n 4.3224.1854.1855 / 11.999.3 to construct a Logistic regression model for predicting PIS: logit Pn-10.035 0.262X1 1.393X2 2.763X3 1.548X4 1.464X5 1.432X6 2.458X7 (note: BMI;X2 indicates drinking history; X3 means blood pressure after infarction; X4 indicates moderate to severe stenosis of the responsible vessel; X4 means the internal of the responsible vessels with moderate to severe stenoses; X4 indicates that the blood pressure after infarction is decreased; X4 indicates that the responsible vessels have moderate to severe stenosis. X6 for watershed area infarction X7 for fever) .4 simplified Logistic regression model. A PIS prediction scoring system with a total score of 10 points and a prediction threshold of 4 points was constructed. The area under the ROC curve was 0.911. The evaluation results were as follows: sensitivity 0.860, specificity 0.920, total coincidence rate 0.889 2, positive likelihood ratio 10.750, negative likelihood ratio 0.152% and Kappa value 0.783. Conclusion 1 body mass index (BMI), alcohol consumption history, blood pressure decrease after cerebral infarction, moderate to severe stenosis of responsible vessels, posterior limb infarction, watershed infarction and fever are the important risk factors of PIS. 2. The application is simple and convenient, and has certain clinical practicability.
【學(xué)位授予單位】：華北理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R743.3
本文編號(hào)：2206622
[Abstract]:Objective to screen the risk factors associated with progressive ischemic stroke (Progressive ischemic stroke,PIS) and to construct a predictive scoring system for PIS based on these risk factors so as to provide evidence for its prevention and treatment. Methods 186 patients with acute ischemic stroke were collected from December 2015 to December 2016 in Department of Neurology, Tangshan Workers Hospital. The neurological function was assessed by the National Institutes of America Stroke scale (National Institutes of Health Stroke Scale,NIHSS) on admission and at the time of disease change. According to whether the NIHSS score increased by 2 points or more within 6 hours to 7 days after the onset of the disease, the neurological function of the patients was assessed by the National Institute of Stroke scale (National Institutes of Health Stroke Scale,NIHSS). The patients were divided into progressive ischemic stroke (PIS) group and non progressive ischemic stroke (Non progressive ischemic stroke,NPIS) group. There were 86 cases in PIS group and 100 cases in PIS group. Collect relevant indicators that may affect the patient's condition, including sex, age, past medical history, smoking, alcohol consumption, fever, infection, obesity, body mass index, white blood cell count, fasting blood glucose, fibrinogen, blood lipids, Homocysteine, hypersensitive C-reactive protein, uric acid, blood pressure and blood pressure changes at admission, anemia, carotid plaque, degree of responsible vascular stenosis, infarct location, admission NIHSS score, NIHSS score at the time of disease change, etc. Univariate analysis and multivariate Logistic regression analysis were carried out to screen the risk factors of PIS and establish the Logistic regression equation. Based on the b coefficients of each factor, the prediction scoring system of PIS was constructed by "scoring method". The discriminative power and accuracy of PIS prediction scoring system are evaluated by drawing ROC curve, paired chi-square test and consistency test. Results 1 single factor analysis: alcohol history, fever, infection, body mass index, white blood cell count, triglyceride, hypersensitive C-reactive protein, uric acid, systolic blood pressure, blood pressure after cerebral infarction, moderate to severe stenosis of responsible blood vessel. There were significant differences between PIS group and NPIS group (P0.05); gender, age, history of hypertension, coronary heart disease, diabetes mellitus, atrial fibrillation, stroke / TIA history, smoking history, obesity, fasting blood glucose, Fibrinogen, total cholesterol, low density lipoprotein cholesterol, homocysteine, diastolic blood pressure, anemia, carotid plaque, carotid artery unstable plaque, pontine infarction, NIHSS scores at admission were compared between the two groups. There was no significant difference (P0.05) in multivariate Logistic regression analysis: body mass index (BMI), alcohol consumption history, blood pressure after infarction, moderate to severe stenosis of responsible vessels, internal capsule hind limb infarction, watershed infarction, fever, and 7 factors were included in Logistic regression model. Is a risk factor for PIS, The odds ratio of each factor (Odds ratio,OR) was 1.300 / 4.02715.852n 4.702n 4.3224.1854.1855 / 11.999.3 to construct a Logistic regression model for predicting PIS: logit Pn-10.035 0.262X1 1.393X2 2.763X3 1.548X4 1.464X5 1.432X6 2.458X7 (note: BMI;X2 indicates drinking history; X3 means blood pressure after infarction; X4 indicates moderate to severe stenosis of the responsible vessel; X4 means the internal of the responsible vessels with moderate to severe stenoses; X4 indicates that the blood pressure after infarction is decreased; X4 indicates that the responsible vessels have moderate to severe stenosis. X6 for watershed area infarction X7 for fever) .4 simplified Logistic regression model. A PIS prediction scoring system with a total score of 10 points and a prediction threshold of 4 points was constructed. The area under the ROC curve was 0.911. The evaluation results were as follows: sensitivity 0.860, specificity 0.920, total coincidence rate 0.889 2, positive likelihood ratio 10.750, negative likelihood ratio 0.152% and Kappa value 0.783. Conclusion 1 body mass index (BMI), alcohol consumption history, blood pressure decrease after cerebral infarction, moderate to severe stenosis of responsible vessels, posterior limb infarction, watershed infarction and fever are the important risk factors of PIS. 2. The application is simple and convenient, and has certain clinical practicability.
【學(xué)位授予單位】：華北理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R743.3
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