Advances in Clinical and Experimental Medicine

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Advances in Clinical and Experimental Medicine

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doi: 10.17219/acem/159756

Publication type: original article

Language: English

License: Creative Commons Attribution 3.0 Unported (CC BY 3.0)

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Urbanowicz TK, Rodzki M, Michalak M, et al. Large unstained cell (LUC) count as a predictor of carotid artery occlusion [published online as ahead of print on March 15, 2023]. Adv Clin Exp Med. 2023. doi:10.17219/acem/159756

Large unstained cell (LUC) count as a predictor of carotid artery occlusion

Tomasz Kamil Urbanowicz1,A,B,C,D,F, Michał Rodzki1,B,C,F, Michał Michalak2,C,F, Anna Olasińska-Wiśniewska1,C,E,F, Anna Witkowska1,B,F, Beata Krasińska3,E,F, Michał Bociański1,B,F, Aleksandra Krasińska-Płachta4,B,F, Agnieszka Cieśla5,B,F, Sebastian Stefaniak1,E,F, Marek Jemielity1,E,F, Zbigniew Krasiński6,E,F

1 Department of Cardiac Surgery and Transplantology, Poznan University of Medical Sciences, Poland

2 Department of Computer Science and Statistics, Poznan University of Medical Sciences, Poland

3 Department of Hypertension, Angiology and Internal Diseases, Poznan University of Medical Sciences, Poland

4 Department of Ophthalmology, Poznan University of Medical Sciences, Poland

5 Poznan University of Medical Sciences, Poland

6 Department of Vascular and Endovascular Surgery, Angiology and Phlebology, Poznan University of Medical Sciences, Poland

Graphical abstract


Graphical abstracts

Abstract

Background. Carotid artery stenosis is often considered a stable clinical condition, and the underlying atherosclerosis is thought to have an inflammatory background.

Objectives. The aim of the study was to assess the value of different parameters obtained from whole blood counts for the prediction of advanced carotid artery atherosclerosis, including vessel occlusion, irrespective of symptom occurrence.

Materials and methods. The study group comprised 290 patients (84 (29%) females and 206 (71%) males) with a mean age of 68 ±8 years, who were admitted to the Vascular Surgery Department due to significant carotid artery disease. Patients were retrospectively divided into 2 subgroups regarding the presence or absence of artery occlusion. The demographic, clinical and laboratory preoperative data were compared between both groups.

Results. We found significant differences in preoperative large unstained cell (LUC) counts between patients with and without carotid artery occlusion (p = 0.003), when analyzed with the Mann–Whitney test for independent samples. The receiver operating characteristic (ROC) curve showed that LUC count has prognostic properties for carotid artery occlusion, with an area under the curve (AUC) of 0.637 (p = 0.033), yielding a 69.70% sensitivity and a 51.75% specificity.

Conclusions. Large unstained cells represent an acute inflammatory state related to artery occlusion. An LUC count below the cutoff value of 0.16×109/L may be a predictor of carotid artery occlusion. Therefore, carotid artery occlusion should not be regarded as a chronic state, but as a clinical challenge being promoted by active inflammatory processes.

Key words: inflammation, atherosclerosis, occlusion, carotid stenosis, large unstained cells

Background

Carotid artery disease is a challenging clinical problem that has recently been recognized as one of the most common causes of stroke.1 Instead of viewing age as the traditional risk factor, clinical considerations have assumed a greater significance for carotid plaques and stroke prediction.2 Asymptomatic carotid artery disease is diagnosed in less than 1% of those aged below 50 years, and in over 3% of patients over 80 years.3, 4 The monitoring of atherosclerotic plaques plays an important role in stroke prevention,5 with the prevalence of symptomatic intracranial stenosis being higher in elderly patients than in those younger than 70 years.6

Numerous clinical factors have been associated with the formation of atherosclerosis, including components of the metabolic syndrome, such as diabetes, obesity and hyperlipidemia, all of which show an elevated inflammatory response. In obesity, perivascular adipose tissue which surrounds blood vessels, where it becomes dysfunctional and secretes pro-inflammatory molecules, promotes the infiltration of inflammatory cells, and furthers the development of atherosclerosis.7, 8, 9

There is an increased body of evidence suggesting that the size of carotid artery atherosclerotic plaques, more so than their composition, plays a significant role in the clinical presentation of carotid artery disease.10 However, studies examining the makeup of plaques and subsequent remodeling and influence on mechanical forces should be taken into consideration with the use of ultrasound Doppler, computed tomography (CT) and positron emission tomography (PET), or magnetic resonance imaging (MRI) for predicting possible complications.11, 12, 13

Atherosclerosis is considered a lipid-derived disease with an inflammatory background.14 The inflammatory reactions initiate the formation of plaques if the endothelium becomes dysfunctional, while also facilitating disease progression.15, 16, 17 The infiltration of inflammatory cells has previously been presented as a hallmark feature of plaque instability,18 while a reduction of the inflammatory response was associated with plaque reduction in animal models.19 In our previous reports, we found a significant relationship between simple inflammatory indices obtained from the whole blood counts and overall mortality.20, 21, 22

Objectives

The current study aimed to assess the value of different morphological parameters obtained from whole blood counts for the prediction of advanced carotid artery atherosclerosis, including vessel occlusion and irrespective of symptom occurrence.

Materials and methods

Study patients

Three hundred ninety-one patients were admitted to the Department of Vascular Surgery at the Poznan University of Medical Sciences (Poland) between January 2018 and December 2020 due to significant carotid artery disease. Of this group, 290 patients (84 (29%) females and 206 (71%) males) with a mean age of 68 ±8 years underwent detailed laboratory evaluation and were enrolled in the final retrospective single-center analysis. The laboratory tests were performed upon hospital admission. The study group received carotid artery treatment including percutaneous (50 (17%)) and surgical (239 (82%)) interventions (Figure 1). One patient was disqualified due to a high perioperative mortality risk. Patients requiring unplanned intervention or concomitant surgery were also excluded from the study. Additional exclusion criteria encompassed inflammatory, autoimmune, oncological, or hematological proliferative diseases.

Thirty-one (11%) patients admitted for vascular intervention presented with carotid artery occlusion, while 259 (89%) had significant stenosis. Comorbidities included hypercholesterolemia 196 (68%)), arterial hypertension (178 (61%)), a history of stroke (146 (50%)), tobacco use (96 (33%)), diabetes mellitus (73 (25%)), and permanent atrial fibrillation (AF) (19 (7%)) (Table 1).

Study design – laboratory analysis

The blood samples for whole blood analysis were collected upon patient admission. The study group was divided retrospectively into 2 subgroups, namely those with carotid artery stenosis and those with carotid artery occlusion. The blood morphological results were compared between both subgroups, and the ability of various blood markers to predict carotid artery occlusion was analyzed.

Inflammatory indices were calculated, including neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), the systemic inflammatory index (SII) (the quotient of neutrophils and platelets divided by the lymphocyte count), the systemic inflammatory response index (SIRI) (the quotient of neutrophils and monocytes divided by the lymphocyte count), and the aggregate index of systemic inflammation (AISI) (the proportion of neutrophils, monocytes and platelets divided by the lymphocyte count).

Analysis

We analyzed demographic, clinical and laboratory data comprising whole blood count parameters with the use of a routine hematology analyzer (Sysmex Europe, Norderstedt, Germany).

The researchers adhered to the principles of good clinical practice and the Declaration of Helsinki, and the study was approved by the Local Ethics Committee of the Poznan University of Medical Sciences (approval No. 784/21, October 13, 2021).

Statistical analyses

Numerical data were presented as mean ± standard deviation (M ±SD) when they followed normal distribution (Shapiro–Wilk test). Otherwise, data were reported as medians and interquartile range (Q1–Q3), where Q1 is the lower quartile and Q3 is the upper quartile. Categorical variables were presented as counts and percentages. The comparison between carotid stenosis patients and occlusion patients was performed with the Student’s t-test when data followed normal distribution (Table 2) and variation between groups was homogenous (Levene’s test). When data did not follow normal distribution, the Mann–Whitey test was used. Categorical data were compared using the χ2 test. The receiver operating characteristic (ROC) curve analysis was used to find the parameters that have prognostic properties for the presence of occlusion. The cutoff point was estimated with the Youden’s index. The uni- and multivariable logistic regression with backward stepwise selection was performed to find factors which increased the occlusion risk. The results are presented as odds ratio (OR) and 95% confidence interval (95% CI). The inclusion criteria for the study was whole blood count results in the normal range to prevent outliers. The lack of multicollinearity of explanatory variables was checked by assessing the correlation and by estimating variance inflation factor (VIF). For all analyzed predictors, the VIF < 1.7. The assumption regarding the linear relationship between explanatory variables and the logic of the response variable was checked using the Box–Tidwell test. The statistical analysis was performed using MedCalc® Statistical Software v. 20.027 (MedCalc Software Ltd., Ostend, Belgium). The assumptions regarding logistic regression were performed using Stata 17 software (StataCorp, College Station, USA). A p-value of 0.05 was considered statistically significant for all tests.

Results

A total of 290 patients were analyzed, including 155 (53%) who presented with clinical symptoms (visual disturbances in 29 (10%) patients and vertigo in 31 (11%) patients). Stroke was reported in 146 (50%) patients, and there were 178 (61%) and 112 (39%) patients diagnosed with significant (more than 70% of lumen narrowing) right and left carotid artery disease, respectively. Collateral atherosclerosis of carotid arteries was present in 108 (37%) patients.

A total of 169 (58%) and 16 (6%) patients underwent surgery, while 29 (10%) and 8 (4%) underwent angioplasty procedures, in the stenosis and occlusion groups, respectively. It was found that 13 (5%) episodes of perioperative neurological complications were reported, including 11 (4%) transient ischemic attacks (TIA) and 2 (1%) strokes.

The Mann–Whitney test for independent samples revealed significant differences in preoperative large unstained cells (LUCs) between patients with carotid artery occlusion and stenosis (p = 0.003). The ROC curve analysis showed that LUC count has prognostic properties for carotid artery occlusion featuring an area under the curve (AUC) = 0.637, 95% CI: 0.58–0.69, and a p-value of 0.033 with a 69.70% sensitivity and a 51.75% specificity (Figure 2).

Univariable analysis

According to the univariate logistic regression analysis (Table 3), significant preoperative factors obtained from the whole blood analysis included LUC count below the cutoff value of 0.16 (OR = 2.47, 95% CI: 1.13–5.39, p = 0.024).

Moreover, the univariable analysis of demographical data including gender (OR = 1.58, 95% CI: 0.66–3.81, p = 0.300) or obesity (OR = 0.63, 95% CI: 0.20–2.01, p = 0.438) did not reveal significance of these factors towards an increased risk of coronary artery disease (CAD). Neither preoperative symptoms (OR = 1.06, 95% CI: 0.38–2.91, p = 0.915) nor standard risk factors including smoking (OR = 1.06, 95% CI: 0.38–2.91, p = 0.915), diabetes mellitus (OR = 1.33, 95% CI: 0.60–2.96, p = 0.472), hypercholesterolemia (OR = 1.27, 95% CI: 0.78–2.11, p = 0.611), hypertension (OR = 1.29, 95% CI: 0.60–2.78, p = 0.508), or body mass index (BMI; OR = 0.99, 95% CI: 0.90–1.11, p = 0.992) showed statistical significance for either coexisting CAD (OR = 2.08, 95% CI: 0.98–4.42, p = 0.058) or peripheral artery disease (OR = 1.95, 95% CI: 0.78–4.88, p = 0.151) (Table 3). The only significant factor discovered in our analysis was AF (OR = 3.09, 95% CI: 1.03–9.25, p = 0.043). Similarly, laboratory results were not predictive, including serum cholesterol (OR = 0.86, 95% CI: 0.62–1.19, p = 0.363), low-density lipoprotein (LDL; OR = 0.93, 95% CI: 0.63–1.36, p = 0.698), or high-density lipoprotein (HDL; OR = 0.67, 95% CI: 0.28–1.63, p = 0.383).

Multivariable analysis

According to the multivariate logistic regression analysis (Table 3), the only significant preoperative factor was LUC count below the cutoff value of 0.16 × 109/L (OR = 2.70, 95% CI: 1.22–6.03, p = 0.015) and AF (OR = 3.75, 95% CI: 1.22–11.65, p = 0.022).

Moreover, the logistic regression with the Hosmer–Lemeshow test for goodness-of-fit, log-likelihood ratio test p-values, and Nagelkerke pseudo R2 test of analyzed parameters were performed (Table 4). The tests revealed significance of LUC count and CAD co-existance for carotid artery occulsion.

Discussion

To the best of our knowledge, our study is the first to reveal the predictive value of LUC count for carotid artery occlusion. We present the results of a multivariable analysis of preoperative whole blood count, with a cutoff value of 0.16 × 109/L as a predictive factor, regardless of symptoms.

Contrary to popular belief, our analysis revealed neither predictive value of poten­tial comorbidities of atherosclerosis, nor predictive values of laboratory re­sults of serum cholesterol fractions for carotid artery obstruction. Moreover, while some studies23, 24 have shown gender dependence of peripheral atherosclerotic disease, we did not find such correlation with carotid artery occlusion. While our study examined the whole blood count analysis and compared its results to diagnostic tools such as ultrasound imaging in patients with defined carotid disease, our rationale was to find predictive indicators for carotid artery disease progression that could be performed during routine check-ups. Based on our results, we believe that LUCs can be regarded as a simple marker within whole blood that can help distinguish patients with more advanced stages of carotid artery disease.

The LUC population reflects activated lymphocytes and peroxidase-negative large cells that do not contain morphological features of lymphocytes, eosinophils, basophils, or neutrophils.25, 26 This population can include virally activated lymphocytes, plasma cells, pediatric lymphocytes, hairy cells, and peroxidase-negative blasts. Those cells are beyond clear classification but have been postulated to be clinically relevant during inflammatory states, viral infections and hematological malignancies.27, 28, 29. Their increased amount in whole blood analysis was found to be correlated with immunological activation.27 Vanker and Ipp have indicated LUCs as a valuable marker of both innate immunity and CD8+ lymphocyte activation.30 Previously, LUCs have only been analyzed in a small number of studies related to leukemia, myelodysplastic syndromes and viral infection.31 Though LUCs reflect organism activation to variable factors, we are the first to present their association with atherosclerosis.

Since LUCs represent activated lymphocytes, they can also be regarded as a carotid stenosis progression indicator and the underlying inflammatory activation indicator.32 The inflammatory origin of atherosclerosis has been previously presented and suggests this origin is involved in both disease initiation and progression.33, 34, 35 Simple parameters from whole blood count, including NLR, MLR and SIRI, were postulated to be related to atherosclerosis progression.36, 37, 38, 39, 40, 41, 42 However, in our analysis, none of these indices were related to carotid artery occlusion, although we previously presented the prognostic value of MLR for collateral carotid artery involvement.32 The significant difference between LUCs and lymphocyte, monocyte and neutrophil counts is based on the characteristics of the cells; LUC counts take into account activated cells, while other cell counts measure the concentration of the cells.

The standard inflammatory markers such as C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) were analyzed in patients with carotid disease as possible simple markers. In the study by Liu et al., CRP combined with lipoprotein-associated phospholipase A2 was found to be a significant marker for carotid atherosclerosis.43 Moreover, Boaz et al. showed the relationship between intimal thickening and CRP.44 However, Schmidt et al. did not reveal any interactions between vascular risk factors for carotid atherosclerosis and CRP, as CRP was found to be related to the severity of small brain vessel injury.45 Finally, the CRP concentration as a serological determinant of carotid plaques vulnerability was postulated by Fittipaldi et al., who demonstrated the relation between atherosclerotic plaques vulnerability evaluated in histological examination and CRP values >5 mg/L.46

Large unstained cells have been described as peroxidase-negative cells. Myeloperoxidase (MPO) is a protein of 140 kDa molecular weight, composed of a tetramer from 2 α chains (60 kDa) and 2 β chains (14 kDa).47 This protein is commonly used for the classification of acute leukemia during diagnosis.48 The MPO deficiency has been reported as of either primary (genetic) or secondary origin as a disease consequence,49, 50 and has been described in renal failure, cardiovascular disease and diabetes mellitus.51, 52, 53 According to previous reports, MPO deficiency can be related to chronic disease development, including the extent of brain damage during stroke.54 An increased number of LUCs as peroxidase negative cells in peripheral blood test, especially in patients with carotid artery occlusion, may be regarded as a secondary type of deficiency. This cell type is activated by various factors, although not those typically associated with monocyte or lymphocyte activation, and may be associated with vasculitis.55 Moreover, LUCs were related to acute inflammatory reaction.31 According to previous reports, LUCs should be regarded as a mixture of activated lymphocytes, monocytes and lymphoblasts, and therefore we concluded that chronic carotid artery occlusion induces an active inflammatory response.32, 56

The role of monocytes in atherosclerosis has been described as facilitating increased cytokine release and being involved in plaque destabilization.57, 58 The inflammatory indices have been gaining scientific attention in recent years due to their low cost and easy availability as a possible predictive tool in cardiovascular disease.59, 60

The role of lymphoblasts in atherosclerosis has not been previously investigated in a general population, although their presence in premature atherosclerosis in hematological diseases has been postulated.61 The results of our study may shed new light on the role of lymphoblasts, especially in the narrowing of arteries during the atherosclerotic processes.

The second factor which appeared predictive for carotid artery occlusion was AF. Obviously, inflammatory activation, with the co-occurrence of AF, may serve as a trigger and result in carotid artery disease, as AF may enhance the inflammatory response.62 However, the inflammatory activation related to several conditions and diseases, such as carotid artery disease, heart failure and acute coronary syndrome, may trigger AF occurrence.63, 64 It has already been observed in patients after cardiac surgery that AF in the early postoperative period may occur even without the arrhythmia in the patient’s history.65

Limitations

The study was performed as a single-center, retrospective study and involved only patients with advanced stages of carotid artery disease referred for surgical intervention. Future studies including patients with a wide spectrum of carotid artery severity would be beneficial. The results of the study may be relevant to subgroups of patients with carotid disease, irrespectively of clinical symptoms or comorbidities, indicating those who present with artery occlusion. Second, our study lacked a healthy control group. Although the results of logistic regression model are significant, the pseudo R2 is low, possibly due to the relatively small sample size and lack of control group.

Conclusions

Large unstained cells represent an acute inflammatory state related to artery occlusion, and their concentration below a cutoff value of 0.16×109/L may predict carotid artery obstruction. Carotid artery occlusion should not be regarded as a chronic state, but as a clinical challenge promoting an active inflammatory process.

Tables


Table 1. Demographic and clinical data

Parameters

Carotid stenosis (n = 259)

Carotid occlusion (n = 31)

p-value

Demographic parameters

gender M/F, n (%)

183 (71)/76 (29)

23 (74)/8 (26)

0.682

age (M ±SD) [years]

68.2 ±9.1

68.4 ±7.8

0.906t

BMI (M ±SD) [kg/m2]

27.0 ±4.4

26.9 ±4.1

0.904t

obesity, n (%)/available data

34 (26)/131

4 (18)/22

0.4349

Comorbidities

arterial hypertension, n (%)

158 (61)

20 (65)

0.507

diabetes mellitus, n (%)

63 (24)

10 (32)

0.470

stroke, n (%)

130 (50)

16 (52)

0.886

hypercholesterolemia, n (%)

174 (67)

22 (68)

0.181

chronic kidney disease, n (%)

15 (5)

5 (16)

0.892

AF, n (%)

14 (5)

5 (16)

0.033*

tobacco use, n (%)

84 (32)

12 (39)

0.299

coexisting CAD

61 (24)

13 (42)

0.027*

coexisting peripheral artery disease (non-carotid)

31 (12)

7 (23)

0.098

Symptoms

all, n (%)

140 (54)

15 (48)

0.8131

visual disturbances, n (%)

25 (18)

4 (13)

0.2947

vertigo, n (%)

26 (10)

5 (16)

0.3782

Laboratory parameters

whole blood count

WBC × 109/L, median (Q1–Q3)

8.1 (7.0–9.9)

8.6 (6.8–9.7)

0.936

neutrophil × 109/L, median (Q1–Q3)

5.3 (4.3–6.7)

5.7 (4.3–7.1)

0.353

lymphocyte × 109/L, median (Q1–Q3)

1.9 (1.5–2.4)

1.7 (1.4–2.1)

0.107

monocyte × 109/L, median (Q1–Q3)

0.5 (0.4–0.6)

0.5 (0.3–0.6)

0.369

MLR, median (Q1–Q3)

0.3 (0.2–0.3)

0.3 (0.2–0.3)

0.658

NLR, median (Q1–Q3)

2.8 (2.1–3.7)

3.3 (2.7–3.8)

0.053

PLR, median (Q1–Q3)

124 (92–169)

128 (101–165)

0.423

SII, median (Q1–Q3)

654 (457–928)

745 (479–993)

0.295

SIRI, median (Q1–Q3)

1.4 (0.9–1.9)

1.2 (0.99–2.1)

0.615

AISI, median (Q1–Q3)

312 (192–490)

304 (219–467)

0.820

eosinophils × 109/L, median (Q1–Q3)

0.1 (0.1–0.2)

0.1 (0.01–0.2)

0.451

basophils × 109/L, median (Q1–Q3)

0.05 (0.03–0.05)

0.04 (0.02–0.05)

0.492

LUCs × 109/L, median (Q1–Q3)

0.17 (0.13–0.22)

0.14 (0.11–0.18)

0.010*

RBC × 109/L, median (Q1–Q3)

4.5 (4.2–4.8)

4.6 (4.3–4.9)

0.195

Hb [mmol/L], median (Q1–Q3)

8.7 (8.1–9.1)

8.6 (8.2–9.4)

0.563

hematocrit (%), median (Q1–Q3)

41 (38–43)

41 (39–44)

0.721

MCH × 109/L, median (Q1–Q3)

1.9 (1.9–2.0)

1.9 (1.9–2.0)

0.926

MCHC × 109/L, median (Q1–Q3)

21.3 (20.9–21.8)

21.2 (21–21.8)

0.677

RDW × 109/L, median (Q1–Q3)

13.5 (13.0–14.1)

13.1 (12.9–13.9)

0.172

platelets × 103/μL, median (Q1–Q3)

233 (188–294)

222 (201–286)

0.717

lipidemic profile

total serum cholesterol [mmol/L], median (Q1–Q3)

3.99 (3.41–4.73)

3.84 (3.35–4.36)

0.306

HDL fraction [mmol/L], median (Q1–Q3)

1.30 (1.05–1.60)

1.22 (1.08–1.50)

0.555

LDL fraction [mmol/L], median (Q1–Q3)

2.06 (1.66–2.75)

1.90 (1.50–2.70)

0.566

kidney function tests

creatinine [mg/dL], median (Q1–Q3)

85.2 (71.7–104.6)

78.8 (71.5–111)

0.999

GRF, median (Q1–Q3)

75 (60–90)

77 (57–90)

0.679

thrombotic risk

fibrinogen [mg/dL], median (Q1–Q3)

335 (287–407)

325 (273–376)

0.413

Preoperative pharmacotherapy

β-blockers, n (%)

87 (34)

13 (42)

0.008

statins, n (%)

174 (67)

22 (71)

0.083

ACE-I, n (%)

80 (31)

13 (42)

0.361

antiplatelet therapy, n (%)

259 (100)

31 (100)

1.000

Performed procedures

surgical, n (%)

215 (83)

20 (64)

0.136

percutaneous, n (%)

40 (15)

10 (3)

0.021

disqualified, n (%)

4 (2)

1 (3)

0.504

AF – atrial fibrillation; AISI – aggregate index of systemic inflammation; BMI – body mass index; CAD – coronary artery disease; F – female; GFR – glomerular filtration rate; HDL – high-density lipoprotein; LDL – low-density lipoprotein; LUCs – large unstained cells; M – male; MCH – mean hemoglobin concentration; MCHC – mean corpuscular hemoglobin concentration; MLR – monocyte-to-lymphocyte ratio; NLR – neutrophil-to-lymphocyte ratio; PLR – platelet-to-lymphocyte ratio; RBC – red blood cells; RDW – red blood cell distribution width; M ±SD – mean ± standard deviation; SII – systemic inflammatory index; SIRI – systemic inflammatory response index; WBC – white blood count; * statistically significant; t p-value of the Student’s t-test; ACE-I – angiotensin-converting-enzyme inhibitor.
Table 2. Shapiro–Wilk normality test results

Parameter

Carotid stenosis

Carotid occlusion

N

W

p-value

N

W

p-value

Age

259

0.9904

0.5035

31

0.9391

0.1895

BMI

259

0.9959

0.9713

31

0.9703

0.7184

WBC

259

0.5363

<0.0001

31

0.9739

0.5960

Neutrophils

259

0.9750

0.0002

31

0.9820

0.8437

Lymphocytes

259

0.1743

<0.0001

31

0.9379

0.0590

Monocytes

259

0.9351

<0.0001

31

0.9570

0.2125

MLR

259

0.7287

<0.0001

31

0.9154

0.0137

NLR

259

0.9131

<0.0001

31

0.9187

0.0169

PLR

259

0.9362

<0.0001

31

0.8484

0.0003

SII

259

0.8481

<0.0001

31

0.7705

<0.0001

SIRI

259

0.8153

<0.0001

31

0.8343

0.0002

AISI

259

0.6565

<0.0001

31

0.6506

<0.0001

Eosinophils

259

0.6633

<0.0001

31

0.7695

<0.0001

Basophils

259

0.4971

<0.0001

31

0.8010

<0.0001

LUCs

259

0.2135

<0.0001

31

0.9830

0.8719

RBC

259

0.9850

0.0084

31

0.9365

0.0538

Hb

259

0.9460

<0.0001

31

0.9758

0.6559

HCT

259

0.0690

<0.0001

31

0.9355

0.0502

MCH

259

0.9338

<0.0001

31

0.1756

0.0000

MCHC

259

0.9515

<0.0001

31

0.9516

0.1483

RDW

259

0.8790

<0.0001

31

0.8525

0.0004

PLT

259

0.9546

<0.0001

31

0.8182

0.0001

CHOL

214

0.2269

<0.0001

31

0.9035

0.0066

HDL

214

0.4782

<0.0001

31

0.9705

0.4938

LDL

214

0.8959

<0.0001

31

0.9028

0.0063

Creatinine

237

0.7715

<0.0001

31

0.9182

0.0164

GFR

237

0.8872

<0.0001

31

0.8475

0.0003

Fibrinogen

259

0.9720

0.0001

31

0.9333

0.0435

BMI – body mass index; WBC – white blood count; MLR – monocyte-to-lymphocyte ratio; NLR – neutrophil-to-lymphocyte ratio; PLR – platelet-to-lymphocyte ratio; SII – systemic inflammatory index; SIRI – systemic inflammatory response index; AISI – aggregate index of systemic inflammation; LUCs – large unstained cells; RBC – red blood cells; MCH – mean hemoglobin concentration; MCHC – mean corpuscular hemoglobin concentration; RDW – red blood cell distribution width; HDL – high-density lipoprotein; LDL – low-density lipoprotein; GFR – glomerular filtration rate; PLT – platelets; CHOL – total serum cholesterol; Hb – hemoglobin; HCT – hematocrit; GRF – glomerular filtration rate; Hb – hemoglobin.
Table 3. Univariate and multivariate analysis of clinical and laboratory parameters in occluded arteries

Parameters

Univariate analysis

Multivariate analysis

OR

95% CI

probability

OR

95% CI

probability

Clinical parameters

sex

1.58

0.66–3.81

0.300

obesity

0.63

0.20–2.01

0.438

coexisting CAD

2.08

0.98–4.42

0.058

coexisting PAD

1.95

0.78–4.88

0.151

AF

3.09

1.03–9.25

0.043

3.75

1.22–11.65

0.022

Whole blood count parameters

platelets

1.0

0.99–1.01

0.943

LUCs below cutoff value of 0.16

2.47

1.13–5.39

0.024

2.70

1.22–6.03

0.015

hematocrit

0.68

0.04–13.86

0.805

MPV

1.09

0.79–1.49

0.597

MHC

1.09

0.76–1.57

0.626

MCHC

1.23

0.74–2.05

0.408

AF – atrial fibrillation; CAD – coronary artery disease; 95% CI – 95% confidence interval; OR – odds ratio; LUCs – large unstained cells; MCHC – mean corpuscular hemoglobin concentration; MHC – mean hemoglobin concentration; MPV – mean platelet volume; OR – odds ratio; PAD – peripheral artery disease (non-carotid).
Table 4. Hosmer–Lemeshow logistic regression, log-likelihood ratio test p-values and Nagelkerke pseudo R2 of analyzed parameters

Parameters

Univariate analysis

Multivariate analysis

Hosmer–Lemeshow test probability

log-likelihood ratio test probability

Nagelkerke R2

Hosmer–Lemeshow test

log-likelihood ratio test

Nagelkerke R2

Clinical parameters

Gender

0.283

0.008

Obesity

0.421

0.008

Coexisting CAD

0.063

0.023

Coexisting PAD

0.171

0.013

AF

0.061

0.024

0.571

0.007

0.067

Whole blood count parameters

Platelets

0.054

0.943

0.000

LUCs below cutoff value of 0.16

0.018

0.037

0.571

0.007

0.067

Hematocrit

0.249

0.652

0.001

MPV

0.562

0.601

0.002

MHC

0.249

0.035

0.029

MCHC

0.608

0.402

0.005

AF – atrial fibrillation; CAD – coronary artery disease; OR – odds ratio; LUCs – large unstained cells; MCHC – mean corpuscular hemoglobin concentration; MHC – mean hemoglobin concentration; MPV – mean platelet volume; PAD – peripheral artery disease (non-carotid).

Figures


Fig. 1. Flowchart of conducted procedures
LICA – left internal carotid artery; PTA – percutaneous carotid angioplasty; pts – patients; RICA – right internal carotid artery.
Fig. 2. Receiver operating characteristic (ROC) curve of preoperative large unstained cells (LUCs) for predicting carotid artery occlusion
AUC – area under the curve.

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