進展性缺血性腦卒中危險因素篩選及其預(yù)測評分系統(tǒng)的構(gòu)建
發(fā)布時間：2018-08-27 08:01
【摘要】：目的篩選進展性缺血性腦卒中(Progressive ischemic stroke,PIS)的相關(guān)危險因素,基于這些危險因素構(gòu)建PIS的預(yù)測評分系統(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。在患者入院時和病情變化時采用美國國立研究院卒中量表(National Institutes of Health Stroke Scale,NIHSS)進行神經(jīng)功能評分,根據(jù)發(fā)病6小時至發(fā)病7天內(nèi)NIHSS評分是否增加2分或2分以上,分為進展性缺血性腦卒中(PIS)組和非進展缺血性腦卒中(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)、血白細胞計數(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、入院時血壓及血壓變化、貧血、頸動脈斑塊、責(zé)任血管狹窄程度、梗死部位、入院NIHSS評分、病情變化時NIHSS評分等。對上述資料進行單因素分析及多因素Logistic回歸分析,篩選PIS的危險因素,建立Logistic回歸方程;基于各因素的b系數(shù),采用“計分法”構(gòu)建PIS的預(yù)測評分系統(tǒng)。通過繪制ROC曲線及配對卡方檢驗和一致性檢驗等對PIS預(yù)測評分系統(tǒng)的辨別力和準(zhǔn)確度進行評估。結(jié)果1單因素分析:飲酒史、發(fā)熱、感染、體重指數(shù)、血白細胞計數(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、頸動脈斑塊、頸動脈不穩(wěn)定斑塊、OCSP各型、橋腦梗死、入院時NIHSS評分在兩組間比較,差異無統(tǒng)計學(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個因素納入Logistic回歸模型,是PIS的危險因素,各因素的優(yōu)勢(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簡化Logistic回歸模型,構(gòu)建了總分為10分、預(yù)測界值為4分的PIS預(yù)測評分系統(tǒng),該評分系統(tǒng)ROC曲線下面積為0.911,評價效果如下: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的重要危險因素。2構(gòu)建的PIS預(yù)測評分系統(tǒng),評估效果較好,應(yīng)用方便簡捷,具有一定的臨床實用性。
[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)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R743.3
本文編號：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)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R743.3
【參考文獻】
相關(guān)期刊論文 前10條
1 吉維忠;張亞清;張豪;吳芬香;李娜;;高海拔地區(qū)進展性腦卒中的相關(guān)因素分析[J];中國實用神經(jīng)疾病雜志;2016年17期
2 李利峰;路峰;杜遠生;;血清尿酸和進展性腦梗死關(guān)系探討[J];山西醫(yī)藥雜志;2016年16期
3 王占芬;張學(xué)艷;任秉麗;王莉;;進展性腦卒中危險因素分析及與H型高血壓的相關(guān)性研究[J];中國實用神經(jīng)疾病雜志;2016年13期
4 張慶軍;王清華;郭艷;李云霞;靳令經(jīng);聶志余;;ASTRAL評分預(yù)測急性缺血性卒中患者3個月預(yù)后的價值[J];中國卒中雜志;2016年06期
5 趙陽;鄭輯英;;進展性缺血性腦卒中危險因素的臨床研究[J];中西醫(yī)結(jié)合心腦血管病雜志;2016年08期
6 吳壽興;鄭賓;陳燕雄;;強化他汀降脂與傳統(tǒng)降脂治療進展性腦卒中的對比研究[J];中國實用神經(jīng)疾病雜志;2016年04期
7 方良;張超;孫中武;;分水嶺腦梗死與顱內(nèi)外血管狹窄的關(guān)系[J];安徽醫(yī)藥;2015年12期
8 郝勁松;;缺血性進展性腦卒中與三種風(fēng)險因子的相關(guān)性研究[J];醫(yī)學(xué)理論與實踐;2015年18期
9 周小丹;趙宏林;包成月;李方睿;楊麗榮;白文婷;馬龍;劉文波;呂利英;佟偉軍;;入院時體溫與急性缺血性腦卒中患者出院結(jié)局的關(guān)系[J];中國老年學(xué)雜志;2015年13期
10 丁俊麗;賀婕;李蘭;何曉丹;銀廣悅;肖麗萍;;進展性缺血性腦卒中的危險因素[J];中國老年學(xué)雜志;2015年11期
相關(guān)碩士學(xué)位論文 前7條
1 姚濤;缺血性腦卒中急性期超敏C-反應(yīng)蛋白水平變化與早期神經(jīng)功能惡化發(fā)生的相關(guān)性[D];安徽醫(yī)科大學(xué);2016年
2 賈革;進展性卒中相關(guān)危險因素分析[D];吉林大學(xué);2015年
3 呂紅霞;國內(nèi)進展性缺血性腦卒中危險因素的Meta分析[D];重慶醫(yī)科大學(xué);2014年
4 童寧;缺血性腦卒中早期神經(jīng)功能惡化相關(guān)危險因素分析[D];山東大學(xué);2013年
5 吳金輝;進展性缺血性腦卒中患者血清鐵蛋白水平及其臨床意義[D];河北聯(lián)合大學(xué);2011年
6 陸卉;進展性腦梗死與穩(wěn)定性腦梗死的臨床對比研究[D];天津醫(yī)科大學(xué);2008年
7 遲魯梅;進展性卒中的臨床及病因分析[D];吉林大學(xué);2007年
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