引用本文:[点击复制]
[点击复制]
【打印本页】 【在线阅读全文】【下载PDF全文】 查看/发表评论下载PDF阅读器关闭

←前一篇|后一篇→

过刊浏览    高级检索

本文已被:浏览 166次   下载 321  
单中心脑小血管病影像学严重程度的预测 研究
吕金峰牟英峰马爽王丽王伟陈曦耿德勤
0
()
摘要:
目的 构建与脑小血管病(CSVD)MRI 总负担评分相关的预测模型,实现利用个体化指标 来预测影像学上 CSVD 的严重程度。方法 本研究为回顾性研究,共连续纳入 2018 年 7 月至 2019 年 3 月在徐州医科大学附属医院神经内科门诊及住院就诊的 429 例 CSVD 患者,收集所有患者的一般临床 资料(包括人口学特征如性别、年龄,血管危险因素如高血压病史、糖尿病史、冠心病史、卒中史、吸烟 及饮酒等)、实验室检查(包括血常规、肝肾功能、血脂、凝血功能、炎性指标、甲状腺功能等)及影像学资 料(如头部 MRI),根据评价影像学严重程度的总 CSVD 评分分为轻度组(总 CSVD 评分 0~1 分,198 例)、 中度组(总 CSVD 评分 2 分,93 例)和重度组(总 CSVD 评分 3~4 分,138 例)。单因素分析比较各组间各 资料的差异,以轻度组为参照,采用多因素 Logistic 回归分析影响中度或重度组 CSVD 发生的因素,并采 用受试者工作特征(ROC)曲线分析各因素预测中度或重度 CSVD 的价值。结果 以轻度 CSVD 作为参 照,多因素 Logistic 回归分析结果表明,年龄(OR=1.115,95%CI:1.077~1.156)、高血压病史(OR=2.549, 95%CI:1.393~4.662)、谷 草 转 氨 酶(OR=0.953, 95%CI:0.911~0.997)、胱 抑 素 C(OR=53.246, 95%CI: 2.774~1021.986)是中度 CSVD 的影响因素(均P< 0.05);年龄(OR=1.156,95%CI:1.113~1.200)、高血压 病史(OR=4.642,95%CI:2.425~8.883)、肌酐(OR=1.029,95%CI:1.007~1.052)、高密度脂蛋白(OR=0.312, 95%CI:0.102~0.952)、淋 巴 细 胞 计 数(OR=0.4406,95%CI:0.243~0.797)、活 化 部 分 凝 血 活 酶 时 间 (OR=1.158,95%CI:1.046~1.282)、同型半胱氨酸(OR=1.119,95%CI:1.025~1.220)是重度 CSVD 的影响 因素(均P<0.05)。ROC曲线分析结果显示,联合诊断中度CSVD的AUCROC为0.828(95%CI:0.777~0.878, P< 0.01),联合诊断重度 CSVD 的 AUCROC为 0.910(95%CI:0.880~0.940,P< 0.01),联合诊断中度或重 度 CSVD 的 AUCROC较单一因素的 AUCROC均大,且差异有统计学意义(均P< 0.05)。结论 了解 CSVD 患 者的人口学信息、既往史、实验室检验等信息构建 CSVD 严重程度的预测模型,相对于单一指标更有利 于中度或重度 CSVD 的预测。
关键词:  脑小血管病  磁共振成像  神经影像  预测
DOI:10.3969/j.issn.1009-6574.2019.12.003
基金项目:国家卫生计生委脑卒中防治工程“中国脑卒中高危人群干预适宜技术研究及推广项目” (GN-2016R0005)
Prediction of imaging severity in patients with unicentric cerebral small vessel disease
Lyu Jinfeng, Mu Yingfeng, Ma Shuang, Wang Li, Wang Wei, Chen Xi, Geng Deqin
()
Abstract:
Objectives To construct a prediction model related to MRI total burden score of cerebral small vessel disease (CSVD), so as to realize the use of individual indicators to predict the severity of CSVD in imaging. Methods This study is a retrospective study, including 429 CSVD patients in outpatient department and inpatient department of Neurology in Affiliated Hospital of Xuzhou Medical University from July 2018 to March 2019. General clinical data (including demographic characteristics such as gender, age, vascular risk factors such as hypertension history, diabetes history, coronary heart disease history, stroke history, smoking and drinking, etc.), laboratory examination (including blood routine, liver and kidney function, blood lipid, coagulation function, inflammatory index, thyroid function, etc.) and imaging data (such as brain MRI) of all patients were collected. According to the total CSVD score, the patients were divided into mild group (score 0 to 1, 198 cases), moderate group (score 2, 93 cases) and severe group (score 3 to 4, 138 cases).Single factor analysis was used to compare the data differences between the groups. With the mild group as a reference, multivariate Logistic regression was used to analyze the factors influencing the occurrence of CSVD in the moderate or the severe group, and ROC was used to analyze the value of each factor in predicting the moderate or severe CSVD. Results Compared with mild CSVD,multivariate Logistic regression showed that age (OR=1.115,95%CI:1.077-1.156),history of hypertension (OR=2.549,95%CI:1.393-4.662), AST (OR=0.953, 95%CI;0.911-0.997) and cystatin C (OR=53.246, 95%CI:2.774-1021.986) were predictors of moderate CSVD (P< 0.05), while age (OR=1.156,95%CI:1.113-1.200), history of hypertension (OR=4.642,95%CI:2.425- 8.883),creatinine (OR=1.029,95%CI:1.007-1.052), HDL (OR=0.312,95%CI:0.102-0.952), lymphocyte count (OR=0.4406,95%CI:0.243-0.797),APTT (OR=1.158,95%CI:1.046-1.282) and HCY (OR=1.119, 95%CI:1.025-1.220) were predictors of severe CSVD (P< 0.05). By plotting the ROC curve of the prediction probability obtained from the above model and the selected single variable,the AUCROC of joint diagnosis of moderate CSVD is 0.828(95%CI:0.777-0.878), and the AUCROC of combined diagnosis of severe CSVD is 0.910 (95%CI:0.880-0.940).The AUCROC of combined diagnosis of moderate/severe CSVD was larger than that of single factor AUCROC,and the difference was statistically significant (P< 0.05). Conclusions Understanding the demographic information, past history,laboratory tests and other information of CSVD patients to build a prediction model of CSVD severity is more conducive to the prediction of moderate/severe CSVD than a single index
Key words:  Cerebral small vessel disease  Magnetic resonance imaging  Neuroimaging  Prediction

用微信扫一扫

用微信扫一扫