The association of imaging indicators on computed tomography angiography of the carotid arteries and blood lipid indicators with ischemic stroke

doi:10.3969/j.issn.1004-583X.2025.09.003

Abstract

Abstract:

Objective To explore the correlation of a combination of imaging indicators on computed tomography angiography(CTA) of the carotid arteries and blood lipid indicators with ischemic stroke, and the predictive value. Methods A total of 122 patients with cervical arteriosclerosis were selected and divided into the stroke group and non-stroke group based on whether ischemic stroke occurred. Their blood lipid indicators, including total cholesterol(TC), triglycerides(TG), high-density lipoprotein cholesterol(HDL-C), and low-density lipoprotein cholesterol(LDL-C) were measured. CTA of the carotid arteries was performed to analyze relevant imaging indicators, including the degree of vascular stenosis, plaque properties, and perivascular fat density. Logistic regression analysis was used to screen for independent risk factors for ischemic stroke. A joint prediction model was constructed. The predictive performance of the model was evaluated by drawing receiver operating characteristic(ROC) curves. According to the nature of plaques, they were divided into the non-plaque group, non-calcified plaque group, calcified plaque group, and mixed plaque group. Using unordered classification logistic regression analysis, the correlation of a combination of perivascular fat density and blood lipid indicators with vascular plaque properties was explored. According to the degree of vascular stenosis, patients were divided into non-stenosis group, mild stenosis group, moderate stenosis group, and severe stenosis group. Ordered classification logistic regression analysis was used to explore the correlation of a combination of perivascular fat density and blood lipid indicators with the degree of vascular stenosis. Results There were differences in the nature of internal carotid artery plaques, degree of internal carotid artery stenosis, average peripheral fat density, peripheral fat trough value, and LDL-C between the stroke group and the non-stroke group(P<0.05). Through logistic regression analysis, the degree of vascular stenosis, perivascular fat density, and LDL-C were identified as independent risk factors for ischemic stroke(P<0.001, 0.029, and <0.001, respectively), and a joint prediction model was constructed. The ROC curve results showed that the area under the curve(AUC) of the joint prediction model was 0.827(95% confidence interval [CI]: 0.756-0.899). The prediction model constructed by the joint indicators had good prediction accuracy. The average density of perivascular fat had a significant impact on the properties of vascular plaques(χ²=20.84, P<0.001). Conclusion The combination of cervical vascular CTA imaging indicators and blood lipid indicators has important predictive value for ischemic stroke, helping to identify high-risk populations early and providing a basis for clinical intervention.

Key words: stroke, neck vascular CTA, blood lipid indicators, predictive value

CLC Number:

R743

Wang Hui, Xia Xinjian, Liu Sai, Wei Xifu, Zhang Shuzhong. The association of imaging indicators on computed tomography angiography of the carotid arteries and blood lipid indicators with ischemic stroke[J]. Clinical Focus, 2025, 40(9): 790-795.

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URL: https://www.lchc.cn/EN/10.3969/j.issn.1004-583X.2025.09.003

https://www.lchc.cn/EN/Y2025/V40/I9/790

Figures/Tables 7

Fig.1 Magnetic resonance DWI-ADC map and cervical vascular CTA map of the patients a. DWI; b.ADC; c,d. CTA

Tab.1 Differences in imaging indicators and lipid indicators between the two groups

特征	非卒中组( $n$ =71)	卒中组( $n$ =51)	统计值	$P$ 值
颈内动脉斑块性质[例(%)]
无斑块	12(16.9)	0(0.0)
非钙化斑块	12(16.9)	13(25.5)	10.57	0.014
钙化斑块	8(11.3)	9(17.6)
混合斑块	39(54.9)	29(56.9)
颈内动脉狭窄程度[例(%)]
无狭窄	12(16.9)	0(0.0)
轻度狭窄	23(32.4)	12(23.5)	15.08	0.002
中度狭窄	23(32.4)	18(35.3)
重度狭窄	13(18.3)	21(41.2)
周围脂肪
平均密度	-96.20±13.98	-81.67±16.58	-5.09	<0.001
峰值	-38.18±14.55	-35.84±14.81	-0.87	0.388
谷值	-137.99±18.55	-117.39±25.98	-4.84	<0.001
总胆固醇	5.27±1.06	5.39±1.34	-0.55	0.586
甘油三酯	1.74±1.11	1.88±0.93	-0.76	0.447
高密度脂蛋白胆固醇	1.35±0.31	1.39±0.33	-0.55	0.583
低密度脂蛋白胆固醇	2.66±0.65	3.39±0.82	-5.25	<0.001

Tab.1 Differences in imaging indicators and lipid indicators between the two groups

特征	非卒中组( $n$ =71)	卒中组( $n$ =51)	统计值	$P$ 值
颈内动脉斑块性质[例(%)]
无斑块	12(16.9)	0(0.0)
非钙化斑块	12(16.9)	13(25.5)	10.57	0.014
钙化斑块	8(11.3)	9(17.6)
混合斑块	39(54.9)	29(56.9)
颈内动脉狭窄程度[例(%)]
无狭窄	12(16.9)	0(0.0)
轻度狭窄	23(32.4)	12(23.5)	15.08	0.002
中度狭窄	23(32.4)	18(35.3)
重度狭窄	13(18.3)	21(41.2)
周围脂肪
平均密度	-96.20±13.98	-81.67±16.58	-5.09	<0.001
峰值	-38.18±14.55	-35.84±14.81	-0.87	0.388
谷值	-137.99±18.55	-117.39±25.98	-4.84	<0.001
总胆固醇	5.27±1.06	5.39±1.34	-0.55	0.586
甘油三酯	1.74±1.11	1.88±0.93	-0.76	0.447
高密度脂蛋白胆固醇	1.35±0.31	1.39±0.33	-0.55	0.583
低密度脂蛋白胆固醇	2.66±0.65	3.39±0.82	-5.25	<0.001

Tab.2 Results of univariate and multivariate logistic regression analyses

特征	单因素			多因素
特征	比值比	95%置信区间	$P$ 值	比值比	95%置信区间	$P$ 值
颈内动脉斑块性质	1.28	0.90~1.82	0.167	0.77	0.43~1.37	0.368
颈内动脉狭窄程度	2.21	1.43~3.41	<0.001	3.28	1.68~6.42	<0.001
周围脂肪
平均密度	1.08	1.04~1.11	<0.001	1.06	1.01~1.12	0.029
峰值	1.01	0.99~1.04	0.385	0.98	0.94~1.03	0.464
谷值	1.05	1.03~1.07	<0.001	1.03	0.99~1.07	0.108
总胆固醇	1.09	0.80~1.49	0.568	0.53	0.26~1.09	0.086
甘油三酯	1.14	0.81~1.61	0.459	1.81	0.97~3.38	0.064
高密度脂蛋白胆固醇	1.38	0.45~4.25	0.577	10.31	1.00~105.85	0.051
低密度脂蛋白胆固醇	3.86	2.13~6.99	<0.001	6.06	2.26~16.26	<0.001

Tab.2 Results of univariate and multivariate logistic regression analyses

特征	单因素			多因素
特征	比值比	95%置信区间	$P$ 值	比值比	95%置信区间	$P$ 值
颈内动脉斑块性质	1.28	0.90~1.82	0.167	0.77	0.43~1.37	0.368
颈内动脉狭窄程度	2.21	1.43~3.41	<0.001	3.28	1.68~6.42	<0.001
周围脂肪
平均密度	1.08	1.04~1.11	<0.001	1.06	1.01~1.12	0.029
峰值	1.01	0.99~1.04	0.385	0.98	0.94~1.03	0.464
谷值	1.05	1.03~1.07	<0.001	1.03	0.99~1.07	0.108
总胆固醇	1.09	0.80~1.49	0.568	0.53	0.26~1.09	0.086
甘油三酯	1.14	0.81~1.61	0.459	1.81	0.97~3.38	0.064
高密度脂蛋白胆固醇	1.38	0.45~4.25	0.577	10.31	1.00~105.85	0.051
低密度脂蛋白胆固醇	3.86	2.13~6.99	<0.001	6.06	2.26~16.26	<0.001

Tab.3 Model coefficients for predicting ischemic stroke by combining imaging indicators with lipid indicators

特征	估算	标准误	$Z$ 值	$P$ 值	$O R$ 值	95%置信区间
颈内动脉狭窄程度	1.19	0.34	3.469	<0.001	3.28	0.52~1.86
周围脂肪平均密度	0.06	0.03	2.178	0.029	1.06	0.01~0.12
低密度脂蛋白胆固醇	1.80	0.50	3.574	<0.001	6.06	0.81~2.79

Tab.3 Model coefficients for predicting ischemic stroke by combining imaging indicators with lipid indicators

特征	估算	标准误	$Z$ 值	$P$ 值	$O R$ 值	95%置信区间
颈内动脉狭窄程度	1.19	0.34	3.469	<0.001	3.28	0.52~1.86
周围脂肪平均密度	0.06	0.03	2.178	0.029	1.06	0.01~0.12
低密度脂蛋白胆固醇	1.80	0.50	3.574	<0.001	6.06	0.81~2.79

Fig.2 ROC curve analysis of imaging indicators combined with lipid indicators for predicting ischemic stroke

Tab.4 Likelihood ratio test table for the nature and degree of stenosis of vascular plaques

特征	血管斑块性质			血管狭窄程度
特征	χ²值	df	$P$ 值	χ²值	df	$P$ 值
周围脂肪平均密度	20.84	3	<0.001	1.17	1	0.278
总胆固醇	1.69	3	0.637	0.01	1	0.965
甘油三酯	3.50	3	0.320	0.11	1	0.736
高密度脂蛋白胆固醇	5.93	3	0.115	1.05	1	0.304
低密度脂蛋白胆固醇	1.56	3	0.668	0.91	1	0.340

Tab.4 Likelihood ratio test table for the nature and degree of stenosis of vascular plaques

特征	血管斑块性质			血管狭窄程度
特征	χ²值	df	$P$ 值	χ²值	df	$P$ 值
周围脂肪平均密度	20.84	3	<0.001	1.17	1	0.278
总胆固醇	1.69	3	0.637	0.01	1	0.965
甘油三酯	3.50	3	0.320	0.11	1	0.736
高密度脂蛋白胆固醇	5.93	3	0.115	1.05	1	0.304
低密度脂蛋白胆固醇	1.56	3	0.668	0.91	1	0.340

Fig.3 A statistical chart of the estimated marginal average probability of perivascular fat density

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