新预测模型AULTS评分对缺血性脑卒中的预测价值

王娅; 周厚援; 罗志强; 孟定强; 郑颖颖

doi:10.16766/j.cnki.issn.1674-4152.002137

新预测模型AULTS评分对缺血性脑卒中的预测价值

doi: 10.16766/j.cnki.issn.1674-4152.002137

1.
重庆铜梁区中医院神经内科，重庆 402560
2.
郑州大学第一附属医院心血管内科，河南郑州 450051

基金项目:

国家自然科学基金项目 81760043

详细信息

通讯作者:
周厚援，E-mail: 465491791@qq.com

中图分类号: R743.3 R544.1
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- 文章访问数: 192
- HTML全文浏览量: 103
- PDF下载量: 5
- 被引次数: 0
出版历程
- 收稿日期: 2020-11-01
- 网络出版日期: 2022-02-15

Predictive value of the new predictive model AULTS score for ischemic stroke

1.
Department of Neurology, Chongqing Tongliang District Hospital of Traditional, Chinese Medicine, Chongqing 402560, China

摘要

摘要: 目的本研究旨在建立新的预测模型，为脑卒中发病风险的评估提供新的方法。方法选择2012—2017年于重庆铜梁区中医院神经内科临床诊断为脑卒中和同时期住院或门诊体检的非脑卒中患者为研究对象。SPSS 22.0统计学软件和R语言用来进行数据分析和模型构建，根据ROC曲线下面积排序，筛选构建模型所用的变量，模型的构建采用R语言进行，并用列线图来呈现。结果单因素分析显示缺血性脑卒中组和对照组饮酒、尿酸、血脂和收缩压差异有统计学意义(均P < 0.05)。通过ROC曲线下面积排序进入最终模型的变量为年龄(AUC=0.737)、尿酸(AUC=0.567)、甘油三酯(AUC=0.537)、低密度脂蛋白胆固醇(AUC=0.541)、收缩压(AUC=0.615)，预测模型对脑卒中的预测价值的ROC曲线下面积为0.789(95% CI：0.765~0.814, P < 0.001)。根据模型预测积分构建新型列线图，按照模型预测可能性积分四分位，位于Q1发生脑卒中的概率为18.3%，Q2发生脑卒中的概率为40.3%，Q3发生脑卒中的概率为60.0%，Q4发生脑卒中的概率为82.7%。结论本研究显示新型风险预测模型对脑卒中有良好的预测价值，值得推广应用。
- 脑卒中 /
- 高血压 /
- 预测模型 /
- 列线图
Abstract: Objective To establish a new predictive model to provide a new method for the assessment of the risk of stroke. Methods Data were collected from hospitalised patients who were clinically diagnosed with stroke in the Department of Neurology, Chongqing Tongliang District Hospital of Traditional Chinese Medicine from 2012 to 2017. The controls were selected from subjects of outpatient physical examination during the same period. SPSS 22.0 software and R software were used for data analysis and model construction. We selected variables according to the area under the ROC curve. These variables were used to construct the model. The model construction was carried out in R software and presented with a nomogram. Results Univariate analysis showed significant differences in drinking, uric acid, blood lipids and systolic blood pressure between the ischemic stroke group and the control group (all P < 0.05). The variables that entered the final model included age (AUC=0.737), uric acid (AUC=0.567), triglycerides (AUC=0.537), low-density lipoprotein cholesterol (AUC=0.541) and systolic blood pressure (AUC=0.615). The area under the ROC curve of the predictive value of the predictive model for stroke was 0.789 (95% CI: 0.765-0.814, P < 0.001). We constructed a new nomogram based on the model prediction score. According to the model prediction probability score quartile, the probabilities of stroke in Q1, Q2, Q3 and Q4 were 18.3%, 40.3%, 60.0% and 82.7%, respectively. Conclusion This study shows that the new risk prediction model has good predictive value for stroke and is worthy of popularisation and application.
- Stroke /
- Hypertension /
- Prediction model /
- Nomogram

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图 1 预测脑卒中风险的列线图

下载: 全尺寸图片幻灯片

图 2 新型模型预测脑卒中的准确性

下载: 全尺寸图片幻灯片

表 1 脑卒中组和健康对照组临床资料比较

组别	例数	年龄(x ±s，岁)	性别[例(%)]		吸烟[例(%)]	饮酒[例(%)]	尿素氮(x ±s，mmol/L)	肌酐(x ±s，μmol/L)	尿酸(x ±s，mmol/L)	葡萄糖(x ±s，mmol/L)
组别	例数	年龄(x ±s，岁)	男性	女性	吸烟[例(%)]	饮酒[例(%)]	尿素氮(x ±s，mmol/L)	肌酐(x ±s，μmol/L)	尿酸(x ±s，mmol/L)	葡萄糖(x ±s，mmol/L)
脑卒中组	644	49.76±14.50	340(52.80)	304(47.20)	190(29.50)	46(7.14)	4.79±1.90	72.16±21.46	290.37±78.72	5.32±2.03
对照组	662	50.47±17.00	350(52.87)	312(47.13)	158(23.87)	73(11.03)	4.94±1.54	70.87±19.38	276.68±81.84	5.24±1.73
统计量		-0.636^a	0.001^b		2.900^b	5.947^b	1.645^a	-1.089^a	-3.076^a	-0.710^a
P值		0.557	0.978		0.091	0.016	0.100	0.276	0.002	0.478

组别	例数	TC (x ±s，mmol/L)	TG (x ±s，mmol/L)		HDL-C (x ±s，mmol/L)	LDL-C (x ±s，mmol/L)		BMI (x ±s)	收缩压(x ±s，mm Hg)	舒张压(x ±s，mm Hg)
脑卒中组	644	4.50±1.18	1.74±1.67		1.19±0.37	2.93±0.97		25.98±4.29	145.36±24.84	89.05±18.86
对照组	662	4.68±1.15	1.52±1.11		1.27±0.43	2.73±0.85		26.41±4.20	136.10±23.05	87.10±20.21
统计量		2.828^a	-2.847^a		3.762^a	3.990^a		1.803^a	6.900^a	1.701^a
P值		0.005	0.004		< 0.001	< 0.001		0.072	< 0.001	0.089
注：^a为t值，^b为χ²值; 1 mm Hg=0.133 kPa。

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表 2 多变量logistic回归分析结果

变量	B	SE	Wald χ²	P值	OR(95% CI)
年龄	0.071	0.006	145.291	< 0.001	1.074(1.062~1.087)
性别	0.011	0.163	0.005	0.944	1.011(0.735~1.391)
肌酐	-0.001	0.003	0.217	0.642	0.999(0.992~1.005)
尿酸	0.002	0.001	6.740	0.009	1.002(1.001~1.004)
甘油三酯	0.134	0.055	5.868	0.015	1.143(1.026~1.275)
总胆固醇	0.075	0.112	0.447	0.504	1.077(0.866~1.341)
高密度脂蛋白	-0.009	0.189	0.002	0.964	0.991(0.685~1.435)
低密度脂蛋白	0.317	0.128	6.173	0.013	1.374(1.070~1.764)
吸烟	0.510	0.211	5.826	0.016	1.666(1.101~2.522)
饮酒	-0.033	0.288	0.013	0.908	0.967(0.550~1.701)
收缩压	0.032	0.005	40.909	< 0.001	1.033(1.022~1.043)
舒张压	-0.062	0.008	65.377	0.089	0.939(0.925~0.109)
常数项	4.304	0.586	54.001	< 0.001	73.982

下载: 导出CSV

表 3 入选变量的ROC曲线下面积

变量	AUC	SE	P值	95% CI
年龄	0.737	0.014	< 0.001	0.710~0.774
尿酸	0.567	0.016	< 0.001	0.535~0.598
甘油三酯	0.537	0.016	0.024	0.505~0.568
低密度脂蛋白	0.541	0.016	0.012	0.509~0.572
收缩压	0.615	0.016	< 0.001	0.585~0.646

下载: 导出CSV

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