Correlation analysis between carotid atherosclerosis and post-stroke cognitive impairment

doi:10.21203/rs.3.rs-4693271/v1

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Correlation analysis between carotid atherosclerosis and post-stroke cognitive impairment

https://doi.org/10.21203/rs.3.rs-4693271/v1

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Background

Post-stroke cognitive impairment (PSCI) significantly impacts the quality of life and recovery of stroke survivors. An indicator capable of identifying the risk of cognitive decline upon admission can facilitate early identification and management of stroke patients.

Objective

Carotid ultrasound, being a low-cost, widely accessible, and easily measurable diagnostic tool, has drawn the interest of researchers. Consequently, we aimed to investigate the link between carotid atherosclerosis markers and PSCI in patients with mild ischemic stroke (AIS), with the goal of improving early identification and diagnosis of PSCI in patients.

Methods

We enrolled 181 patients diagnosed with acute ischemic stroke within seven days. Data analysis was conducted using baseline demographics, clinical risk factors, and carotid ultrasound parameters. Binary logistic regression was utilized to analyze the independent factors influencing PSCI. The receiver operating characteristic curve(ROC) was employed to assess the predictive value of carotid atherosclerosis indexes and models combining various independent influencing factors for PSCI.

Results

A total of 181 participants completed the 6-month follow-up, among whom 75 patients (41.1%) were diagnosed with PSCI. Logistic regression analysis revealed that carotid plaque Crouse score (OR = 1.111, 95% CI = 1.016–1.216) and severe stenosis of the carotid artery (OR = 2.774, 95% CI = 1.148–6.705) were independently associated with PSCI at 6–12 months. The predictive model for post-stroke cognitive impairment included carotid plaque Crouse score (AUC = 0.667) and degree of carotid artery stenosis (AUC = 0.596), along with other covariates, with an overall Area Under the Curve (AUC) of 0.818 (95% CI: 0.758–0.877). There were significant differences in the scores of AVLT-I, AVLT-II, VFT, TMT-B, CDT, and MoCA subdomains between PSCI and post-stroke non-cognitive impairment (PSNCI) patients at 6 months (P < 0.05).

Conclusion

The carotid atherosclerosis index in the acute phase of AIS is independently correlated with PSCI, and both the carotid plaque Crouse score and severe stenosis of the common carotid artery hold diagnostic value for PSCI.

Health sciences/Neurology

Health sciences/Risk factors

Mild acute ischemic stroke

Post-stroke cognitive impairment

Degree of common carotid artery stenosis

Carotid plaque Crouse score

Post-stroke cognitive impairment (PSCI) is a syndrome characterized by a series of cognitive symptoms that manifest within six months of a stroke event and meet the diagnostic criteria for cognitive impairment [1]. It occurs sequentially with the onset of stroke events. Despite its high incidence, patients and their families often focus on physical and language disorders in the acute or early stages of stroke, overlooking symptoms such as memory loss, unresponsiveness, and abnormal mental behavior, which are indicative of PSCI. This oversight may lead to missing the optimal window for cognitive intervention. Therefore, early detection and diagnosis of PSCI are crucial in clinical practice.

Currently, the exact pathophysiological mechanisms underlying vascular cognitive impairment are not fully understood. However, research suggests that hypoperfusion, oxidative stress, and inflammation may be integral components of its pathogenesis. These factors, either individually or in combination, may impact neurovascular units, leading to the onset of vascular cognitive impairment. Studies have indicated that inflammation and immune responses play crucial roles in the pathogenesis of ischemic stroke[2, 3]. Prior to disease onset, aberrant immune reactions may trigger inflammation within and around blood vessel walls, thereby promoting thrombosis, altering vascular reactivity, and accelerating the development of atherosclerosis. The gradual formation of carotid plaques, increased stiffness of arterial walls, and eventual narrowing of carotid artery lumens may accelerate cognitive dysfunction by reducing cerebral blood flow and disrupting neurovascular coupling[4].

Although PSCI typically accompanies pathological changes in large arteries, such as arterial stenosis, reduced blood flow, and arterial plaques, the relationship between large vessel diseases and PSCI remains unclear. While some studies indicate that large vessel stenosis can highly predict the occurrence of PSCI[5], not all studies demonstrate consistent results[6]. Understanding the relationship between features of atherosclerosis and PSCI will provide a deeper insight into the pathology of PSCI, facilitating early diagnosis and prevention.

Two types of risk prediction methods are recommended by domestic and foreign guidelines: one is the prediction method based on the assessment of neuropsychological scales in the acute phase of stroke, and the other is the prediction method based on the PSCI risk factor model. Administering a full set of cognitive scales is time-consuming, and patients in the acute stage may be irritable and less cooperative. On the other hand, prediction models based on risk factors often rely on numerous radiological indicators. Given the large population of stroke patients in China and the relative scarcity of medical resources, there is an urgent need for clinicians to identify a low-cost and easily obtainable marker. This study aims to investigate the value of atherosclerosis markers in diagnosing PSCI to enhance early identification and diagnosis of this condition.

2.1 Study Design and population

In this study, 181 patients with mild acute ischemic stroke (AIS) who were admitted to the Department of Neurology at Bethune First Hospital of Jilin University from October 2020 to October 2022 were consecutively enrolled. Subjects with a Montreal Cognitive Assessment (MoCA) score of < 22 were classified into the PSCI group, while those with a MoCA score of 22 were categorized as the post-stroke non-cognitive impairment group (PSNCI) based on a follow-up after six months [7]. The research flow chart is illustrated in Fig. 1 below. The inclusion criteria were: (i) meeting the WHO diagnostic criteria for AIS within seven days of onset [8] and being between 50 and 80 years of age; (ii) presenting with neurological deficits and having a detectable lesion in the diffusion phase of head MRI (DWI); (iii) having a National Institutes of Health Stroke Scale score of ≤ 6 points; (iv) obtaining informed consent from patients and family members, and both parties signing an informed consent form, and being able to cooperate in completing relevant examinations and cognitive scales. The exclusion criteria were as follows: (i) cognitive decline resulting from other systemic diseases, including medical and surgical conditions, psychiatric disorders, infectious diseases, toxic and metabolic disorders, etc.; (ii) individuals with a history of memory loss or a confirmed diagnosis of cognitive impairment prior to stroke, and those who have been using anticholinesterase drugs, sedatives, anti-anxiety medications, antipsychotics, or other drugs known to affect cognitive function within the past 2 weeks; (iii) patients unable to cooperate with cognitive scale assessments due to visual impairment, speech impediments, hearing loss, or physical disabilities; (iv) individuals unable to participate in study-related laboratory tests, cognitive assessments, and examinations; (v) presence of contraindications for MRI examination, such as having a pacemaker installed or metal implants in the body; (vi) patients with occlusion of the common carotid artery. The research protocol was approved by the Ethics Committee of the First Hospital of Jilin University and conducted in accordance with the principles outlined in the Declaration of Helsinki. Written informed consent was obtained from all participants.

2.2 Study variables

General clinical data were collected during hospitalization, which included information on age, gender, and education level. The international Acute Stroke Treatment Trial (TOAST) classification was used to categorize cerebral infarction based on its etiology, including large artery atherosclerosis (LAA), cardioembolism (CE), small artery occlusion (SAA), other determined causes (OD), and undetermined causes (UD). The severity of neurological deficits was assessed using the National Institutes of Health Stroke Scale (NIHSS) score[9], which ranged from 0 to 6 points.

On the day of admission, blood routine tests were conducted, along with measurements of fasting blood glucose, triglycerides, total cholesterol, HDL-C, LDL-C, Hcy, C-reactive protein, trace elements, cystatin C, glycosylated hemoglobin, folic acid, vitamin B12, and other relevant parameters. All blood samples were collected from the biobank of the First Hospital of Jilin University.

All patients underwent cranial MRI within 7 days of the onset of stroke. The main MRI sequences included T1-weighted, T2-weighted, and diffusion-weighted imaging. White matter hyperintensities (WMH) were diagnosed based on the criteria for reporting vascular changes on neuroimaging, as outlined in the neuroimaging criteria [10]. WMH was categorized into three groups depending on the total Fazekas scale score (combining periventricular and deep changes): mild (0–2), moderate (3–4), and severe (5–6). The locations of infarcts (anterior/posterior circulation, supratentorial/infratentorial) were documented. The anterior circulation refers to cerebral infarction occurring in the region supplied by the internal carotid artery, while the posterior circulation involves cerebral infarction in the area supplied by the vertebrobasilar artery. The anterior circulation encompasses the frontal, parietal, temporal cortex, subcortical regions, internal capsule, and basal ganglia, whereas the posterior circulation includes the brainstem, cerebellum, thalamus, medial temporal lobe, and occipital lobe regions. Supratentorial denotes the cerebral hemispheres, while infratentorial pertains to the brainstem and cerebellar regions. All the aforementioned imaging data were assessed by experienced radiologists.

During this examination, a linear probe with a frequency of 7.5Hz was utilized to examine unilateral blood vessels, focusing primarily on the common carotid artery segment, carotid artery bifurcation, internal carotid artery segment, and external carotid artery segment. The thickness of the smooth muscle layer of the common carotid artery intima-media, known as the Carotid intima-media thickness (IMT) index, was documented. Additionally, the size of carotid plaques was expressed in terms of length (mm) × thickness (mm). The carotid plaque Crouse score was calculated as the sum of bilateral carotid plaque thicknesses. The degree of carotid artery stenosis is categorized as mild stenosis (stenosis rate < 50 percent), moderate stenosis (stenosis rate 50 to 69 percent), and severe stenosis (stenosis rate 70 to 99 percent) based on expert consensus [11]. In this study, detailed records were maintained for the IMT thickness of the left common carotid artery, the IMT thickness of the right common carotid artery, the total number of plaques in the bilateral carotid arteries, the location of carotid artery plaques (none, unilateral, bilateral), the length, thickness, and area of the plaques at the carotid artery bifurcation, the location of the plaques at the carotid artery bifurcation (none, unilateral, bilateral), the carotid plaque Crouse score, and the degree of common carotid artery stenosis. Patients with carotid artery occlusion were not included in this study.

Between the 7th and 10th day after the onset of the disease, when the symptoms of cerebral infarction were relatively stable, a psychometric evaluator conducted a cognitive psychological assessment of all selected patients in a relatively quiet environment (with no other patients present at the same time). The overall cognitive function was assessed using the MOCA, with a total score of 30. There is a strong correlation between MOCA scores and educational attainment, with studies indicating that the corrected MOCA score increases by 1 point for every < 12 years of schooling. We recorded the initial score and the score at six months of follow-up. Based on the corrected MOCA score after 6 months, participants were divided into PSCI (MOCA < 22) and PSNCI (MOCA ≥ 22) groups [7]. The Auditory Verbal Learning Test-I (AVLT-I) and Auditory Verbal Learning Test-I (AVLT-II) were used to evaluate learning and memory function. The Verbal Fluency Test (VFT) was utilized to assess language function, while the Trail Making Test (TMT-B) was employed to evaluate attention and executive function. Furthermore, Item 1 in MoCA, the Clock Drawing Test (CDT), was used to assess visuospatial ability. Scores for the MoCA subdomains, including visuospatial/executory/name, attention, read-knock, computational, language, abstraction, delayed recall, and orientation, were recorded separately [12]. Additionally, changes in 6-month MoCA scores were assessed in all patients.

2.3 Statistical analysis

Continuous variables (e.g., age) are represented as mean (± standard deviation) and compared between groups using independent t-tests. Non-parametric continuous variables (e.g., years of education, blood lipids, fasting blood glucose, cystatin C, uric acid, homocysteine, folic acid, magnesium, copper, ferritin, Crouse) are represented as median (interquartile range, 25th-75th percentile) and compared using the Mann-Whitney U test. Categorical variables (e.g., gender, medical history, TOAST, degree of carotid stenosis) are presented as numbers (n) and percentages (%) and compared using the chi-square test. The relationship between carotid atherosclerosis and PSCI was explored, with demographic and vascular risk factors adjusted as covariates. Model 1 included age, gender, and years of education, while Model 2 further adjusted for hypertension, previous stroke, and WMH on the basis of Model 1. The researchers conducted univariate analysis initially, and then included variables with p < 0.1 from the univariate analysis and those requiring correction in the binary logistic regression analysis. Finally, the predictive value of models containing different carotid atherosclerotic features for PSCI was assessed using receiver operating characteristic curve (ROC) analysis. A significance level of P < 0.05 was used to determine statistical significance.

3.1 Basic characteristics of patients with mild AIS

The characteristics of the study population are summarized in Table 1. The mean age of participants was 62.3 ± 7.6 years. The average number of years of schooling in the study population was 12.0 (9.0, 12.0). Among all individuals, 162 participants (89.5%) exhibited unilateral or bilateral supracarotid atherosclerotic plaques, while forty participants (22.1%) had severe carotid artery stenosis. According to the TOAST classification, patients with large artery atherosclerotic AIS accounted for the largest proportion at 45.9%, followed by arteriole occlusion type at 34.8%. Among the common risk factors for cerebrovascular disease, 57.5% of patients had a history of hypertension, and 29.9% had experienced a previous stroke. According to the baseline MoCA assessment conducted within 1 week after the onset of AIS, 51.9% of these patients exhibited acute cognitive impairment, with 41.4% still experiencing it six months after the onset of AIS. The prevalence of PSCI at 6 months post-AIS (51.9%) was significantly higher than that observed at baseline (41.4%). Out of the total patient population, 31 individuals regained normal cognitive status at 6 months post-AIS, while 13 patients initially with normal cognitive status experienced deterioration and developed PSCI by that time. Overall, the prevalence of PSCI at 6 months after AIS was 41.4%, which was significantly lower than the proportion of patients with acute cognitive impairment (51.9%).

Table 1

General data were compared between PSCI and PSNCI groups
	PSCI	PSNCI	\(\:{}^{2}\)/Z/t	P value
	(n = 75)	(n = 106)	\(\:{}^{2}\)/Z/t	P value
Age	63.4 ± 7.7	61.6 ± 7.5	-1.570	0.116
Male(%)	51(68.0)	84(79.2)	2.93	0.087
Hypertension(%)	54(72.0)	50(47.2)	11.079	0.001^**
Diabetes(%)	22(29.3)	26(24.5)	0.520	0.471
Heart disease(%)	5(6.7)	14(13.2)	2.000	0.157
History of stroke(%)	30(40.0)	24(23.1)	5.924	0.015^*
Smoking(%)	34(45.3)	56(52.8)	0.987	0.32
Drinking(%)	32(42.7)	55(51.9)	1.496	0.221
Microbleeds(%)	18(24.0)	24(22.6)	0.045	0.831
Years of education	9(6.0–12.0)	12(9.0–15.0)	-3.604	0.001^**
Apoe carries E4(%)	7(9.7)	10(9.4)	0.004	0.949
HDL cholesterol (mmol/L)	1.0(0.9–1.2)	0.9(0.9–1.1)	-1.164	0.245
CystatinC(mg/L)	1.0(0.9–1.1)	1.0(0.9–1.1)	-0.323	0.746
Fasting blood glucose (mmol/L)	5.4(5.0–7.0)	5.4(5.1–6.2)	-0.067	0.946
Triglycerides (mmol/L)	1.8(1.3-2.0)	1.5(1.2–1.9)	-2.065	0.039^*
LDL cholesterol (mmol/L)	2.9(2.4–3.6)	2.7(2.3–3.3)	-1.943	0.052
VitaminB12(pmol/L)	248(168.0-336.0)	281(217.0-421.0)	-1.488	0.137
Homocysteine (umol/L)	11.8(10.3–16.1)	11.4(9.4–14.7)	-1.323	0.186
Folic acid (nmol/L)	5.6(4-8.3)	6.5(3.7–8.9)	-0.149	0.881
Iron (umol/L)	14.0 ± 6.1	15.4 ± 6.6	-1.418	0.158
Ferritin (ng/ml)	195.5(152.0-287.8)	147.9(100.2-242.4)	-1.132	0.258
Total iron binding (umol/L)	47(41.3–51.0)	48(43.0–54.0)	-0.8	0.424
Magnesium (mol/L)	0.9(0.7–0.87)	0.8(0.7–0.88)	-0.539	0.59
Zinc (umol/L)	13.4 ± 2.3	13.5 ± 2.9	-0.388	0.698
Copper (umol/L)	15(13.0–16.0)	14.5(13.1–17.1)	-0.637	0.524
Glycosylated hemoglobin (%)	6(5.6–6.8)	5.9(5.5–6.7)	-0.087	0.931
C-reactive protein (mg/L)	2.6(1.7–3.5)	2.9(1.7–3.8)	-0.072	0.942
D-dimer (ug/L)	0.4(0.3–0.7)	0.46(0.3–0.6)	-0.734	0.463
Cholesterol (mmol/L)	4.6(3.9–5.2)	4.5(3.6-5.0)	-1.457	0.145
WMH(%)			10.484	0.005^**
Mild	18(24.0)	30(28.3)
Moderate	21(38.7)	49(46.2)
Severe	36(34.8)	27(25.5)
TOAST分型(%)			4.631	0.327
Aortic atherosclerotic type	38(50.7)	45(42.5)
Cardiogenic embolic type	5(6.7)	14(13.2)
Arteriolar occlusion type	23(30.7)	40(37.7)
Other causes	6(8.0)	5(4.7)
Unexplained type	3(4.0)	2(1.9)
Infarction above and below the tentorial(%)			0.764	0.682
Supratentorial infarction	57(76.0)	83(78.3)
Infratentorial infarction	13(17.3)	19(17.9)
Supratentorial Infratentorial	9(6.7)	4(3.8)
Anterior and posterior circulation infarction(%)			1.324	0.516
Anterior circulation	52(69.3)	65(61.3)
Posterior circulation	16(21.3)	30(28.3)
Anterior and Posterior circulation	7(9.3)	11(10.4)
MMSE	24(21.0–26.0)	28(27.0–29.0)	-7.936	0.001^**
MoCA	17(14.0–20.0)	17(14.0–20.0)	-8.229	0.001^**
CDR	0.5(0.5-1.0)	0.5(0-0.5)	-6.869	0.001^**
ADL	22(20.0–29.0)	20(20.0–22.0)	-3.487	0.001^**
Abbreviation: HDL, High density lipoprotein. LDL,Low density lipoprotein .WMH, white matter hyperintensities. TOAST, Trial of Org 10172 in Acute Stroke Treatment. MMSE, Mini-mental State Examination. MoCA, Montreal Cognitive Assessment. CDR, Montreal Cognitive Assessment. ADL, Activity of Daily Living.
P<0.05, *P<0.01 indicates that the difference is statistically significant .

3.2 Comparison between the PSCI and PSNCI groups in the acute phase of AIS

As depicted in Table 1, PSCI comprised 41.4% of the cognitive function assessments at the 6-month follow-up. Individuals with PSCI exhibited lower levels of education, more pronounced WMH, elevated triglycerides, and lower MMSE, MoCA, CDR, and ADL scores compared to cognitively normal participants. No significant differences were observed between the two groups regarding other baseline serological, imaging, and demographic data.

Carotid atherosclerosis and intracranial artery characteristics are illustrated in Table 2. The carotid plaque Crouse score was notably higher in the PSCI group compared to the PSNCI group (P = 0.001). Furthermore, the proportion of common carotid artery stenosis was greater in the PSCI group than in the PSNCI group (P = 0.002). Additionally, the intima-media thickness of the right common carotid artery was observed to be higher in the PSCI group compared to the PSNCI group (P = 0.006). Individuals with PSCI displayed more severe common carotid artery stenosis than those with normal cognition. Moreover, there was a positive correlation observed between higher carotid plaque Crouse scores and increased thickness of the intima-media layer in the right common carotid artery. However, no significant differences were noted in other markers of carotid atherosclerosis and intracranial artery characteristics.

Table 2

Comparison of atherosclerosis characteristics between PSCI and PSNCI groups
	PSCI(n = 75)	PSNCI(n = 106)	c²/Z/t	P value
Bifurcation plaque length(mm)	9(6.7–12.6)	8.5(6.1–11.5)	-0.159	0.874
Bifurcation plaque thick(mm)	2.1(1.6–2.6)	1.8(1.7–2.7)	-0.179	0.858
Carotid artery bifurcation plaque area(mm²)	20.0(12.5–28.3)	15.8(9.8–29.7)	-0.027	0.978
Carotid plaque Crouse score	8.2(6.2–11.4)	5.8(3.2–9.6)	-3.819	0.001^**
Number of plaques	3(2.0–5.0)	2(1.0–3.0)	-1.316	0.188
Plaque location(%)			1.100	0.577
No plaques	10(13.3)	9(8.5)
Unilateral plaques	15(20.0)	22(20.8)
Bilateral plaques	50(66.7)	75(70.8)
Left carotid artery IMT	1.1(0.6–1.7)	0.7(0.6–1.3)	-1.289	0.197
Right carotid artery IMT	0.8(0.6–1.2)	0.6(0.5–1.2)	-2.735	0.006^**
Bifurcation plaque location(%)			-0.851	0.396
No plaques	12(16)	15(14.2)
Unilateral bifurcated plaques	25(33.3)	41(38.7)
Bifurcated plaques on both sides	38(50.7)	50(47.2)
Degree of stenosis of the common carotid artery(%)			9.388	0.002^**
Mild-moderate stenosis	50(66.7)	91(85.8)
Severe stenosis	25(33.3)	15(14.2)
RMCA velocity of flow(cm/s)	95(85.0-116.0)	103(87.0-152.0)	-1.083	0.279
RMCA pulsatility index	0.9(0.8-1.0)	0.9(0.8-1.0)	-0.883	0.377
LMCA velocity of flow(cm/s)	101(86.0-156.0)	104(88.0-153.0)	-0.444	0.657
LMCA pulsatility index	1.0(0.8-1.0)	0.9(0.9–1.1)	-0.433	0.665
RACA velocity of flow(cm/s)	80.0(69.0–90.0)	85.0(67.0-106.0)	-1.411	0.158
RACA pulsatility index	0.87(0.8-1.0)	0.87(0.82-1.0)	-0.665	0.506
LACA velocity of flow(cm/s)	87.0(69.0-115.0)	86.0(69.0-106.0)	-0.744	0.457
LACA pulsatility index	0.9(0.8-1.0)	0.9(0.8-1.0)	-0.258	0.797
RPCA velocity of flow(cm/s)	54.0(45.0–70.0)	54.0(39.0–67.0)	-0.87	0.384
RPCA pulsatility index	0.9(0.8–0.9)	0.9(0.8-1.0)	-1.333	0.183
LPCA velocity of flow(cm/s)	52.0(45.0–65.0)	52.0(43.0–65.0)	-0.142	0.887
LPCA pulsatility index	0.9(0.8-1.0)	0.9(0.8-1.0)	-0.381	0.703
RVA velocity of flow(cm/s)	51.0(40.0–61.0)	44.0(35.0–62.0)	-0.199	0.842
RVA pulsatility index	0.9(0.9-1.0)	0.9(0.8-1.0)	-0.016	0.31
LVA velocity of flow(cm/s)	44.0(36.0–56.0)	44.0(35.0–57.0)	-0.568	0.57
LVA pulsatility index	0.9(0.8-1.0)	0.9(0.8-1.0)	-0.085	0.932
BA velocity of flow(cm/s)	58.0(48.0–65.0)	61.0(41.0–69.0)	-0.299	0.765
BA pulsatility index	0.85(0.8–0.9)	0.9(0.8-1.0)	-1.454	0.146
Abbreviation: IMT, intima-media thickness. RMCA, Right middle cerebral artery. LMCA, Left middle cerebral artery .RACA, Right anterior cerebral artery. LACA, Left anterior cerebral artery. RPCA, Right posterior cerebral artery. LPCA, Left posterior cerebral artery. RVA, Right vertebral artery. LVA, Left vertebral artery. BA, Basilar artery.
P<0.05, *P<0.01 indicate that the differences are statistically significant.

3.3 Association between carotid plaque characteristics and cognitive Post-stroke cognitive impairment

In order to comprehensively investigate the association between carotid plaque characteristics and PSCI, two models, Model 1 and Model 2, were developed. Model 1 was adjusted for age, gender, and years of schooling, while Model 2, an extension of Model 1, included additional adjustments for hypertension, previous stroke, and WMH.

Table 3 displays the association between carotid atherosclerotic plaque characteristics and PSCI. In logistic regression analysis, the carotid plaque Crouse score was found to predict an increased risk of PSCI (OR = 1.153, 95% CI: 1.063–1.251, P = 0.001). Furthermore, severe stenosis of the common carotid artery was associated with a higher risk of PSCI (OR = 3.532, 95% CI: 1.635–7.632, P = 0.001). However, carotid artery bifurcation plaque length, thickness, area, total plaque number, intimal thickness of the left and right common carotid arteries, plaque location, and bifurcation plaque location did not show significant associations with PSCI.

Table 3

Association between carotid plaque characteristics and Post-stroke cognitive impairment
		B(Partial regression coefficient)	P	OR	95% CI
		B(Partial regression coefficient)	P	OR	lower limit	upper limit
Crouse	model 1	0.143	0.001^**	1.153	1.063	1.251
Crouse	model 2	0.146	0.002^**	1.157	1.055	1.269
Number of plaques	model 1	0.063	0.421	1.065	0.913	1.242
Number of plaques	model 2	0.055	0.521	1.057	0.893	1.251
Left carotid artery IMT	model 1	0.250	0.244	1.283	0.843	1.953
Left carotid artery IMT	model 2	0.289	0.219	1.325	0.842	2.117
Right carotid artery IMT	model 1	0.238	0.331	1.269	0.785	2.050
Right carotid artery IMT	model 2	0.322	0.223	1.380	0.822	2.318
Bifurcation plaque length	model 1	-0.029	0.301	0.971	0.920	1.026
Bifurcation plaque length	model 2	-0.037	0.217	0.962	0.904	1.023
Bifurcation plaque thick	model 1	-0.150	0.311	0.861	0.644	1.150
Bifurcation plaque thick	model 2	-0.150	0.359	0.861	0.625	1.186
Carotid artery bifurcation plaque area	model 1	-0.005	0.553	0.995	0.980	1.011
Carotid artery bifurcation plaque area	model 2	-0.007	0.426	0.993	0.976	1.010
Plaque location	model 1		0.782
No plaques		-	-	1	-	-
Unilateral plaques		-1.181	0.763	0.835	0.257	2.708
Bilateral plaques		-0.337	0.520	0.714	0.256	1.994
Plaque location	model 2		0.168
No plaques		-	-	1	-	-
Unilateral plaques		-0.932	0.160	0.394	0.107	1.445
Bilateral plaques		-1.101	0.059	0.333	0.106	1.042
Bifurcation plaque location	model 1		0.509
No plaques		-	-	1	-	-
Unilateral bifurcated plaques		0.311	0.516	1.364	0.534	3.486
Plaques on both sides		-0.250	0.475	0.779	0.393	1.546
Bifurcation plaque location	model 2		0.674
No plaques		-	-	1	-	-
Unilateral bifurcated plaques		-0.372	0.507	0.689	0.230	2.070
Plaques on both sides		-0.063	0.908	0.939	0.325	2.716
Degree of stenosis	model 1
Mild-moderate		-	-	1	-	-
Severe stenosis		1.262	0.001**	3.532	1.635	7.632
Degree of stenosis	model 2
Mild-moderate		-	-	1	-	-
Severe stenosis		1.317	0.003**	3.733	1.582	8.811
Abbreviation: Model 1 was adjusted for age, gender, and years of schooling; Model 2 involved adjusting for hypertension, previous stroke, and WMH based on model 1. **P < 0.01 indicates that the difference is statistically significant.

3.4 The features of carotid atherosclerosis were included in the logistic regression analysis of PSCI.

Taking into account potential correlations between carotid atherosclerosis indicators, binary logistic regression analysis was conducted for different carotid atherosclerosis indicators. According to Fig. 2, the risk of PSCI increases by 81.1% for each additional year of schooling. The risk of PSCI in AIS patients with a history of hypertension was 3.862 times that of AIS patients without a history of hypertension, while the risk of PSCI in AIS patients with a history of stroke was 2.269 times that of AIS patients without a history of stroke. Furthermore, the risk of PSCI was 2.774 times higher for severe carotid artery stenosis, and the probability of PSCI was 2.748 times higher for severe WMH. Additionally, the risk of PSCI increased by 1.111 times for every 1-point increase in carotid plaque Crouse score.

According to Fig. 2, the history of hypertension, previous stroke, severe carotid artery stenosis, severe WMH, and carotid plaque Crouse score were identified as independent risk factors for the occurrence of PSCI, while a higher education level was deemed an independent protective factor against PSCI.

3.5 The combination of multiple indicators in the model proves to be valuable in predicting the ROC curve of PSCI.

When analyzing ROC curves separately, we discovered that the sensitivity and specificity of a single carotid atherosclerosis index in predicting PSCI had certain limitations. As a result, six indicators, including years of education, WMH, degree of carotid artery stenosis, carotid plaque Crouse score, previous stroke history, and previous hypertension history, were identified as independent influencing factors of PSCI. Consequently, we endeavored to construct three new models based on the carotid plaque Crouse score, the common carotid artery stenosis model, and the combined carotid plaque Crouse score and common carotid artery stenosis, incorporating other variables such as age, years of education, WMH, previous history of hypertension, and previous stroke history. Subsequently, we analyzed the diagnostic value of each new model for predicting PSCI.

The AUC of the carotid plaque Crouse score was 0.667 (95% CI: 0.589–0.745, P < 0.001), with a sensitivity of 0.434 and a specificity of 0.893. Meanwhile, the AUC of common carotid artery stenosis was 0.596 (95% CI: 0.589–0.750, P = 0.001), with a sensitivity of 0.333 and a specificity of 0.858. In contrast, the AUC of the model combining the carotid plaque Crouse score, common carotid artery stenosis, and other variables was 0.818 (95% CI: 0.758–0.877, P < 0.001), with a sensitivity of 0.853, specificity of 0.642, and a cut-off of 0.320. These results demonstrate that compared to a single marker, the new model combining multiple indicators significantly improves the area under the curve and sensitivity for predicting PSCI (Fig. 3).

3.6 Neuropsychological assessment scale between PSCI group and PSNCI group

The Patient Neuropsychological Assessment Scale is outlined in Table 4. Notably, significant differences were observed in the mean scores of AVLT-I, AVLT-II, VFT, TMT-B, and CDT between PSCI patients and PSNCI patients at 6 months (P < 0.001). Specifically, at 6 months, the scores of AVLT-II, AVLT-I, VFT, and CDT were lower in the PSCI group compared to the PSNCI group (P < 0.001). Moreover, at 6 months, the TMT-B score was higher in the PSCI group compared to the PSNCI group (P < 0.001). For patients with PSCI, notable improvements were observed in AVLT-I, AVLT-II, and VFT at 6 months compared to baseline. Specifically, there was a significant enhancement in AVLT-I (baseline 9.58 ± 4.49 vs. at 6 months 12.06 ± 8.05, P < 0.001), AVLT-II (baseline 3.58 ± 2.45 vs. at 6 months 4.10 ± 2.71, P < 0.001), and VFT (baseline 31.82 ± 10.70 vs. at 6 months 34.70 ± 8.43, P < 0.001), while TMT-B and CDT scores remained stable.

Table 4

Neuropsychological assessment scale between PSCI group and PSNCI group
	All patients (N = 181)			PSCI(N = 75)			PSNCI(N = 106)			P^# value
	Baseline	6 month	P value	Baseline	6 month	P value	Baseline	6 month	P value	P^# value
AVLT-II	4.76 ± 2.57	5.57 ± 2.70	< 0.001	3.58 ± 2.45	4.10 ± 2.71	< 0.001	5.51 ± 2.37	6.51 ± 2.25	< 0.001	< 0.001
AVLT-I	13.65 ± 8.29	16.43 ± 8.26	< 0.001	9.58 ± 7.49	12.06 ± 8.05	< 0.001	16.23 ± 7.75	19.20 ± 7.15	< 0.001	< 0.001
VFT	39.37 ± 12.49	41.84 ± 11.64	< 0.001	31.82 ± 10.70	34.70 ± 8.43	< 0.001	44.15 ± 11.15	46.37 ± 11.15	< 0.001	< 0.001
TMT-B	211.88 ± 82.50	195.06 ± 62.08	< 0.001	252.26 ± 74.51	232.33 ± 61.51	0.628	186.77 ± 73.99	171.89 ± 50.30	< 0.001	< 0.001
CDT	2.93 ± 1.48	3.22 ± 1.23	< 0.001	2.19 ± 1.37	2.36 ± 0.95	0.426	3.45 ± 1.32	3.82 ± 1.04	0.001	< 0.001
Abbreviation: AVLT-II, Auditory verbal learning test-II. AVLT-I, Auditory verbal learning test-I. VFT, Verbal fluency tests .TMT-B, Trail making test-B. CDT, Clock Drawing Test.
The P^# value represents the comparison between the PSCI group and the PSNCI group at 6 months, and the P value represents the comparison between baseline and 6 months. P<0.05 indicates that the difference is statistically significant .

For patients with PSNCI, notable improvements were observed in AVLT-I, AVLT-II, TMT-B, VFT, and CDT at 6 months compared to baseline. Specifically, there was a significant enhancement in AVLT-I (baseline 16.23 ± 7.75 vs. at 6 months 19.20 ± 7.15, P < 0.001), AVLT-II (baseline 5.51 ± 2.37 vs. at 6 months 6.51 ± 2.25, P < 0.001), and VFT (baseline 44.15 ± 11.15 vs. at 6 months 46.37 ± 11.15, P < 0.001). Additionally, TMT-B showed improvement (baseline 186.77 ± 73.99 vs. at 6 months 171.89 ± 50.30, P < 0.001).

Overall, the performance of PSNCI patients on various neuropsychological scales was notably superior to that of PSCI patients at 6 months following the onset of AIS ( P < 0.001). Meanwhile, for patients with PSCI, there were significant improvements in AVLT-I, AVLT-II, and VFT from baseline to 6 months ( P < 0.001), while their TMT-B and CDT scores remained stable.

3.7 Different subdomains of MoCA in the PSCI group and PSNCI group

The MoCA subdomain scores reflecting the patients' cognitive performance are delineated in Table 5. Significant differences in the subdomains of MoCA scores were observed between PSCI patients and PSNCI patients at 6 months (P < 0.05), encompassing visual-spatial ability, naming, attention, language, abstraction, delayed recall, and orientation. At the 6-month mark, the scores for visual-spatial ability, naming, attention, language, abstraction, delayed recall, and orientation in the PSCI group were lower than those in the PSNCI group (P < 0.01).

Table 5

Different subdomain tables of MOCA in the PSCI group and PSNCI group
Subdomains	Score range	All patients (N = 181)			PSCI(N = 75)			PSNCI(N = 106)
Subdomains	Score range	Baseline	6 month	P value	Baseline	6 month	P value	Baseline	6 month	P value	P^# value
Visual space	0–5	2.93 ± 1.48	3.22 ± 1.23	< 0.001	2.19 ± 1.37	2.36 ± 0.95	0.426	3.45 ± 1.32	3.82 ± 1.04	0.001	< 0.001
Named	0–3	2.61 ± 0.71	2.57 ± 0.67	< 0.001	2.38 ± 0.90	2.28 ± 0.84	< 0.001	2.76 ± 0.49	2.76 ± 0.43	< 0.001	< 0.001
Attention	0–2	1.84 ± 0.38	1.87 ± 0.36	0.001	1.68 ± 0.50	1.74 ± 0.47	0.055	1.95 ± 0.21	1.95 ± 0.21	0.614	0.001
Read knock	0–1	0.71 ± 0.46	0.82 ± 0.39	0.002	0.57 ± 0.50	0.72 ± 0.45	0.042	0.81 ± 0.39	0.89 ± 0.32	0.838	0.006
Calculation	0–3	2.18 ± 1.04	2.33 ± 0.85	< 0.001	1.64 ± 1.05	1.85 ± 0.92	0.003	2.56 ± 0.84	2.67 ± 0.60	< 0.001	< 0.001
language	0–3	2.28 ± 0.88	2.12 ± 0.94	< 0.001	1.88 ± 0.99	1.59 ± 0.98	< 0.001	2.57 ± 0.66	2.49 ± 0.71	< 0.001	< 0.001
Abstract	0–2	1.10 ± 0.76	0.98 ± 0.74	< 0.001	0.70 ± 0.74	0.57 ± 0.64	0.008	1.38 ± 0.65	1.27 ± 0.66	0.011	< 0.001
Delayed recall	0–5	1.55 ± 1.53	2.13 ± 1.62	< 0.001	0.78 ± 1.10	0.85 ± 1.19	0.159	2.08 ± 1.56	3.01 ± 1.25	< 0.001	< 0.001
Orientation	0–6	5.23 ± 1.20	5.44 ± 1.11	< 0.001	4.68 ± 1.43	4.92 ± 1.34	0.014	5.61 ± 0.80	5.81 ± 0.81	0.964	< 0.001
Abbreviation : P^# represents the comparison between the PSCI group and the PSNCI group at 6 months, while P represents the comparison between baseline and
at 6 months. P<0.05 indicates that the difference is statistically significant .

Regarding patients with PSCI, there was a noteworthy improvement in orientation from baseline to 6 months (baseline 4.68 ± 1.43 vs. at 6 months 4.92 ± 1.34, P = 0.014), and a significant enhancement in calculation (baseline 1.64 ± 1.05 vs. at 6 months 1.85 ± 0.92, P = 0.003). However, there was a decline in naming, abstraction, and language skills, while their visuospatial ability, attention, and delayed recall remained stable.

For patients with PSNCI, there was a notable improvement in visual spatial skills from baseline to 6 months (baseline 3.45 ± 1.32 vs. at 6 months 3.82 ± 1.04, P = 0.001), as well as a significant enhancement in calculation abilities (baseline 2.56 ± 0.84 vs. at 6 months 2.67 ± 0.60, P < 0.001) and delayed recall performance (baseline 2.08 ± 1.56 vs. at 6 months 3.01 ± 1.25, P < 0.001). However, there was a decline in naming, language, abstraction, and attention skills, while reading, knocking, and orientation abilities remained stable.

Overall, non-PSCI patients exhibited significantly superior performance compared to PSCI patients across all 7 subdomains of the MoCA, and the scores for each subdomain of the MoCA at 6 months following AIS were notably better than those of PSCI patients (P < 0.05).

4.1 Correlation of carotid atherosclerosis and cognitive impairment

This study delved into the correlation between carotid atherosclerosis and PSCI among hospitalized stroke patients. Our findings revealed that the presence of carotid atherosclerosis, specifically the carotid plaque Crouse score and the extent of common carotid artery stenosis, remained associated with PSCI even after comprehensive adjustment for demographics, vascular risk factors, and WMH. In summary, our results suggest that the carotid atherosclerotic plaque Crouse score and the degree of common carotid artery stenosis could serve as independent indicators of PSCI.

Research has demonstrated that various stages of carotid atherosclerosis are linked to cognitive impairment. This study seeks to investigate the connection between different stages of carotid atherosclerosis progression and cognitive impairment. Carotid atherosclerosis is a chronic condition encompassing multiple processes, from intima-media thickness (IMT) thickening to the development of carotid atheroplaques, along with different degrees of carotid artery stenosis. Previous studies have indicated cognitive associations at different stages of atherosclerosis.

Carotid IMT serves as a crucial ultrasound-based indicator for the early detection of carotid arteriosclerosis. Recent research has revealed that IMT is closely linked to cognitive impairment and is considered a potential independent risk factor for cognitive decline. A Chinese cross-sectional study conducted in 2016 identified a robust correlation between IMT and overall cognitive function in older individuals with lower levels of education [13]. Additionally, an investigation within the INVADE project examined vascular risk factors at baseline and after six years, finding a connection between cognitive decline at two years and an increase in baseline IMT among a subgroup without cognitive impairment initially. In patients experiencing cognitive decline, baseline IMT was independently associated with cognitive impairment after adjusting for various risk factors [14]. In a longitudinal study of aging in the United States, involving 538 participants who underwent initial carotid ultrasonography and were subjected to up to 11 neuropsychological tests repeated over a follow-up period of up to eight years, individuals with greater carotid intima-media thickness (IMT) displayed accelerated declines across multiple verbal and nonverbal memory tests, as well as in verbal fluency and executive function assessments [15]. While most of the studies conducted were of a cross-sectional nature, the findings from these studies strongly indicate a significant association between IMT thickening and cognitive impairment. However, this particular study did not definitively establish baseline IMT as an independent risk factor for cognitive impairment, possibly due to a limited sample size and a short follow-up duration. Our findings emphasize the importance of addressing modifiable vascular risk factors to mitigate the risk of cognitive impairment.

Utilizing ultrasound technology on the neck blood vessels enables the clear identification and measurement of the length and thickness of atherosclerotic plaques that form during the inflammatory progression of the carotid artery wall. These plaques consist of a growing accumulation of lipid deposits and inflammatory cells, closely tied to the advancement of inflammation within the carotid artery wall. In a follow-up examination of participants from the Dallas Heart Study after eight years using the MoCA, a notable linear trend was observed: a decrease in MoCA scores correlated with an increasing number of atherosclerotic plaques [16]. In a multicenter study conducted in China in 2017, it was found that the prevalence of cognitive impairment was 26.51 percent, with a higher incidence observed among patients exhibiting carotid plaque ≥ 2 and hypoechoic plaque [17]. Within this population of acute ischemic stroke patients, cognitive impairment was associated with the number of carotid plaques, the presence of plaques in multiple carotid arteries, and plaque stability. Another study, involving 6025 patients who were followed up via telephone calls and face-to-face assessments, revealed an independent association between the presence of carotid plaque and dementia occurrence after seven years [18]. Arntzen KA et al. [19] conducted ultrasonography of the neck vessels in 4274 asymptomatic patients and discovered that the number and total area of plaques were closely linked to cognitive decline in patients during a seven-year follow-up period. However, contrasting findings emerged from previous studies and the Rotterdam prospective study, which indicated no correlation between the number of carotid atherosclerotic plaques and cognitive impairment following an average follow-up of nine years [20]. While there may be an association between carotid atherosclerotic plaques and cognitive impairment, additional population-based studies are necessary to validate this relationship.

Our findings revealed a significant elevation in the carotid plaque Crouse score among subjects with PSCI compared to those in the PSNCI group. The carotid plaque Crouse score serves as a sensitive indicator of atherosclerosis. Furthermore, a meta-analysis encompassing 5856 cases of mild cognitive impairment and 7001 normal participants demonstrated a significant association between elevated carotid intima-media thickness and the risk of mild cognitive impairment through sensitivity analysis [21]. Numerous studies have consistently demonstrated a strong association between carotid plaque thickening and cognitive impairment. Among these, the Crouse score, a non-invasive index that can be accurately measured using color Doppler ultrasound of the neck blood vessels, holds promise for widespread clinical application in reflecting the status of carotid atherosclerosis. Presently, there is a scarcity of research assessing the correlation between carotid plaque Crouse scores and incident dementia. Our study contributes new evidence indicating that elevated carotid plaque Crouse scores are linked to an increased risk of PSCI .

As carotid atherosclerotic plaques increase in size and number, hemodynamics undergo alterations, which can result in carotid artery stenosis. Additionally, research literature has indicated a close association between different degrees of carotid artery stenosis and cognitive impairment. In a cross-sectional cohort study conducted by Johnston across four communities in the United States, asymptomatic severe stenosis of the left carotid artery was found to be linked to cognitive impairment following regression analysis [22]. In alignment with the aforementioned study, the findings of this research suggest that individuals with severe carotid artery stenosis are at an increased risk of developing post-stroke cognitive impairment. Severe carotid artery stenosis can be viewed as an indicator of accelerated cognitive decline in patients with dementia. Evaluating cerebral hemodynamics can aid in identifying individuals at a heightened risk of rapid cognitive deterioration who may benefit from screening for revascularization therapy. In a study involving Framingham's descendants, researchers observed a higher likelihood of executive dysfunction among participants with severe internal carotid artery stenosis [23]. Rocque found that visual-spatial impairments occur earliest in cognitive impairments caused by carotid artery stenosis [24]. Cheng investigated bilateral brain connectivity using magnetic resonance imaging and discovered that asymptomatic carotid artery stenosis (> 70%) not only diminishes interhemispheric connectivity but also precipitates cognitive decline[25]. Our investigation corroborates these findings, revealing a robust correlation between cognitive impairment and the severity of carotid artery stenosis. Moreover, severe carotid artery stenosis confers a heightened risk of cognitive impairment compared to mild to moderate stenosis. Exploring the relationship between cognitive impairment and intracranial and extracranial atherosclerosis underscores the significance of managing controllable vascular factors in early recognition and intervention of cognitive decline. For individuals with carotid atherosclerosis progressing to carotid artery stenosis, further research is imperative to ascertain whether treatment modalities can ameliorate cognitive impairment.

4.2 Risk factors for PSCI

In a particular study, it was observed that patients with mild to moderate stroke and pre-existing white matter lesions were at a higher risk of developing cognitive impairment, irrespective of the presence of new ischemic lesions [26]. Furthermore, the findings from the SMART-MR study indicate that the interplay between cerebral atrophy and white matter hyperintensities (WMH) or infarction could amplify cognitive decline [27]. These findings align with the outcomes of the present study, underscoring white matter hyperintensity as an independent risk factor for PSCI .

Higher levels of education are indicative of better cognitive reserve, potentially resulting in a greater capacity for compensatory neural resources to counterbalance age-related pathophysiological changes [28]. For instance, individuals with lower literacy exhibit a 3.03-fold higher risk of developing PSCI in comparison to those with higher education levels [29]. In summary, this study affirms that a high level of education serves as an independent protective factor against PSCI, while a low education level increases the likelihood of PSCI development, aligning with the findings of prior research.

One study demonstrated that hypertension, as an independent risk factor, has a detrimental impact on cognitive function following an initial AIS [30]. Elevated systolic blood pressure may have negative effects on the hippocampus and dentate gyrus, consequently impacting brain reserves [31, 32]. Furthermore, the ramifications of elevated systolic blood pressure may commence in early adulthood and predict a reduction in hippocampal volume in later years [33]. Hypertension has the potential to disrupt connections in the temporal lobe, thalamus, prefrontal cortex, and particularly the hippocampus [34]. In a rat model, hypertension has been shown to increase amyloid deposition not only in the brain parenchyma, but also in the frontoparietal region and in the vascular walls of the hippocampus [35]. Among non-demented individuals with Apoe E4, heightened blood pressure is correlated with diminished hippocampal volume and cognitive processing [36]. These findings are consistent with the results of this study, signifying that pre-existing hypertension serves as an independent risk factor for PSCI.

This study established that a history of previous stroke constitutes an independent risk factor for PSCI, consistent with the findings of preceding research. A 2022 meta-analysis encompassing [37] patients with prior stroke revealed a swifter rate of cognitive decline. This meta-analysis incorporated data from nine longitudinal hospital cohorts spanning 7 countries, with a median follow-up duration of 2.68 years. The individual participant data from the Stroke and Cognition Alliance were utilized to evaluate the progression and determinants of cognitive change subsequent to stroke. The analysis indicated that individuals with a history of stroke exhibited a faster rate of cognitive decline within 1 to 3 years post-onset compared to control subjects without a history of stroke. Moreover, this accelerated rate of decline was associated with older age and recurrent stroke.

4.3 Performance on different cognitive scales of PSCI

PSCI is linked to the impairment of at least one cognitive domain. Commonly observed impairments in processing speed and attention during the acute post-stroke phase have been documented, with evidence suggesting an improvement at three months following the stroke [38]. Furthermore, a decline in visuospatial ability, attention, and processing speed has been reported in stroke survivors from the post-stroke period up to 24 months, indicating a stable decline. Additionally, individuals with recurrent stroke exhibit a more pronounced decline in memory, attention/processing speed, executive function, and orientation [39]. In patients deemed to have achieved "excellent" clinical recovery at three months, assessments at 15 months revealed impaired memory, visuospatial, executive, and language function [40, 41]. In the present study, individuals with PSCI exhibited significantly poorer performance across all cognitive domains compared to the PSNCI group at 6 months. Specifically, PSCI patients demonstrated notably improved performance in the AVLT-I and AVLT-II and VFT, while their performance on the TMT-B and CDT remained stable. On the other hand, the PSNCI group displayed significant improvements in AVLT-I, AVLT-II, VFT, TMT-B, and CDT. This suggests that the timing of the assessment appears to influence the performance of cognitive domains.

In this study, MoCA subdomain scores of the PSNCI group were found to be significantly superior to those of patients with PSCI at the 6-month mark. The changes in MoCA subdomains from baseline to 6 months were discussed. Nevertheless, up to this point, no studies have thoroughly investigated the relationship between the various subdomains of MoCA in detecting cognitive impairment at different follow-up times. This study aims to assist clinicians in gaining a better understanding and interpretation of the relationship between the various cognitive domains assessed by MoCA at the 6-month follow-up.

We have demonstrated that indicators of carotid atherosclerosis can serve as predictors for the development of PSCI and possess diagnostic value, offering a novel perspective for clinical research concerning cervical blood vessels and PSCI. However, several limitations exist within this study. Firstly, our data originate from a single center, resulting in an insufficient sample size. A larger sample size would enhance the accuracy and reliability of our conclusions. Secondly, we excluded patients unable to cooperate with cognitive scale completion due to language or physical disabilities, thus limiting our understanding of the cognitive status of these individuals. Lastly, our study only conducted a 6-month cognitive follow-up, preventing the assessment of dynamic changes in cognitive function over time.

In conclusion, severe carotid artery stenosis and carotid plaque Crouse scores during the acute phase of Acute Ischemic Stroke (AIS) were found to be independently correlated with PSCI. This suggests that carotid atherosclerosis holds significant diagnostic value for PSCI and carries important clinical implications.

PSCI

Post

stroke cognitive impairment. PSNCI

Post

stroke no cognitive impairment .

Data availability statement

The data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request.

Ethics statements

The studies involving human participants were reviewed and approved by The First Hospital of Jilin University. The patients/participants provided their written informed consent to participate in this study.

Author contributions

GN: concept and design (lead), methodology (lead), statistical analysis (lead), drafting of the manuscript (lead). ZW: drafting of the manuscript, statistical analysis. HZ: concept and design, critical revision of the manuscript for important intellectual content, and study supervision. All authors read and approved the final manuscript.

Funding

None

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We thank the Department of Biobank, Division of Clinical Research for providing human tissues

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No competing interests reported.

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Submission checks completed at journal
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First submitted to journal
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Correlation analysis between carotid atherosclerosis and post-stroke cognitive impairment

Status:

Version 1

Abstract

Background

Objective

Methods

Results

Conclusion

Figures

1.Introduction

2.Material Methods

2.1 Study Design and population

2.2 Study variables

2.3 Statistical analysis

3.Results

3.1 Basic characteristics of patients with mild AIS

3.2 Comparison between the PSCI and PSNCI groups in the acute phase of AIS

3.3 Association between carotid plaque characteristics and cognitive Post-stroke cognitive impairment

3.4 The features of carotid atherosclerosis were included in the logistic regression analysis of PSCI.

3.6 Neuropsychological assessment scale between PSCI group and PSNCI group

3.7 Different subdomains of MoCA in the PSCI group and PSNCI group

4. Discussions

4.1 Correlation of carotid atherosclerosis and cognitive impairment

4.2 Risk factors for PSCI

4.3 Performance on different cognitive scales of PSCI

5. Conclusion

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1