Inflammatory Blood Stability Links to Early Ischemic Brain Injury Running head: Inflammatory Blood Stability in Stroke

Inflammatory Blood Stability Links to Early Ischemic Brain Injury Running head: Inflammatory Blood Stability in Stroke | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Inflammatory Blood Stability Links to Early Ischemic Brain Injury Running head: Inflammatory Blood Stability in Stroke Wirginia Krzyściak, Paweł Brzegowy, Roman Pułyk, Bartłomiej Łasocha, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9216218/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Biological determinants of ischemic injury after reperfusion remain incompletely understood. Early inflammatory responses may influence infarct progression, but the reliability of leukogram measurements across vascular compartments is unclear. We assessed agreement between arterial and peripheral venous leukogram parameters and their association with imaging-defined ischemic injury. In this prospective study, 37 patients undergoing mechanical thrombectomy had paired blood samples collected from the proximal cerebral arterial circulation before reperfusion and from peripheral veins. Leukogram parameters and inflammatory indices were analyzed. Agreement was evaluated using Bland–Altman and Passing–Bablok methods, while associations with ischemic injury – measured by ASPECTS and infarct core volume (Brainomix, RAPID) – were examined using correlation and doubly robust regression models. Segmented neutrophils and lymphocytes differed between compartments, indicating limited interchangeability. In contrast, the neutrophil-to-lymphocyte ratio (NLR) showed strong agreement across sampling sites. Higher arterial NLR and ΔNLR were associated with lower ASPECTS and larger infarct core volumes. A higher proportion of neutrophil band forms was independently linked to smaller infarct cores. Leukogram parameters were not associated with angiographic reperfusion. These findings reveal compartment-specific inflammatory heterogeneity and identify NLR as a stable, clinically accessible biomarker associated with early ischemic injury, independent of sampling site. Health sciences/Biomarkers Health sciences/Medical research Health sciences/Neurology Biological sciences/Neuroscience acute ischemic stroke mechanical thrombectomy neutrophil-to-lymphocyte ratio inflammation perfusion imaging Figures Figure 1 Figure 2 1. Introduction Acute ischemic stroke remains a leading cause of mortality and long-term disability worldwide, despite substantial advances in reperfusion therapies, including intravenous thrombolysis and mechanical thrombectomy [ 1 ]. Although mechanical thrombectomy markedly improves survival and functional outcomes, a considerable proportion of patients fail to achieve proportional neurological recovery despite successful angiographic recanalization. This phenomenon – often described as futile recanalization or reperfusion without functional independence – illustrates that restoration of large-vessel patency alone does not guarantee favorable biological or clinical recovery [ 2 ]. Clinical trial data further demonstrate that reopening an occluded artery does not invariably translate into equivalent neurological improvement, underscoring the contribution of patient-specific and tissue-level factors such as age, baseline deficit severity, and microvascular reperfusion [ 3 ]. These observations highlight the importance of secondary injury mechanisms that shape infarct evolution following ischemia and reperfusion. Increasing evidence implicates inflammatory and immunological processes as central determinants of early tissue injury and subsequent neurological trajectory. Early neutrophil activation accompanied by relative lymphopenia represents a systemic stress response to cerebral ischemia and has emerged as a measurable marker of inflammatory burden [ 4 ]. In this context, leukogram-derived indices – particularly the neutrophil-to-lymphocyte ratio (NLR) – have gained attention as accessible biomarkers with potential prognostic relevance in acute stroke [ 5 , 6 ]. Inflammatory activation has been linked not only to clinical severity but also to imaging markers of irreversible brain injury. Studies suggest that systemic inflammatory signatures correlate with early ischemic changes quantified by neuroimaging measures such as ASPECTS (Alberta Stroke Program Early CT Score) and infarct core volume on perfusion imaging, parameters that reflect the biological extent of tissue damage and influence therapeutic response [ 7 , 8 ]. Integration of hematological and imaging biomarkers may therefore improve biological stratification in the hyperacute phase of stroke. During endovascular treatment, blood samples can be obtained from peripheral venous circulation and from arterial segments proximal to the site of occlusion, enabling compartment-specific assessment of systemic and locally conditioned inflammatory responses. Prior investigations demonstrate compartment-specific differences in leukocyte composition, suggesting that sampling site may influence interpretation of inflammatory markers [ 9 ]. However, the degree of agreement and clinical interchangeability of leukogram measurements between arterial and peripheral compartments remains insufficiently characterized [ 10 ]. Such uncertainty is clinically relevant because localized inflammatory responses, altered hemodynamics, and immune activation associated with ischemia-reperfusion may differentially shape cellular profiles in these compartments [ 11 ]. Advances in perfusion imaging now permit increasingly precise quantification of infarct core and penumbral tissue, enabling closer linkage between biological markers and tissue injury patterns [ 12 ]. Within this framework, composite indices such as NLR may offer methodological stability by integrating opposing leukocyte shifts, potentially reducing sensitivity to sampling variability. Whether leukogram parameters measured at different sampling sites are interchangeable – and whether they retain meaningful associations with imaging-defined injury – remains an important unresolved question. Study Aim This study aimed to evaluate the agreement between leukogram parameters measured in arterial blood sampled proximal to the occlusion and in peripheral venous blood, in patients undergoing mechanical thrombectomy for acute ischemic stroke, and to examine their associations with imaging-defined ischemic injury and early clinical course. Specifically, we sought to: assess systematic and proportional differences between arterial and peripheral measurements using agreement analyses (Bland–Altman and Passing–Bablok); determine relationships between inflammatory indices, including NLR and inter-compartment differences, and neuroimaging markers of ischemic injury (ASPECTS and infarct core/penumbra volumes); explore associations between leukogram parameters and early clinical outcomes following reperfusion. These objectives address a clinically relevant gap in understanding the biological interpretation and practical reliability of inflammatory biomarkers in the hyperacute stroke setting. 2. Materials and Methods 2.1. Participants The study included 37 consecutive patients admitted with acute ischemic stroke who underwent endovascular treatment with mechanical thrombectomy according to current standard-of-care stroke treatment protocols. Intravenous thrombolysis with alteplase was administered when clinically indicated and in accordance with established therapeutic guidelines; eligibility for inclusion was independent of prior thrombolysis and was based exclusively on fulfillment of endovascular treatment criteria and feasibility of protocol-defined arterial and peripheral venous blood sampling. The study was conducted in specialized comprehensive stroke centers in Kraków, including the Angiography and Interventional Radiology Unit, Department of Diagnostic Imaging, University Hospital in Kraków, where all endovascular procedures and arterial blood sampling were performed, and the Department of Radiology, Collegium Medicum, Jagiellonian University, which was responsible for imaging analysis, methodological oversight, and overall study coordination. Patient recruitment followed a prospective observational design. The study was specifically designed to assess the concordance of leukogram parameters between two anatomically and functionally distinct circulatory compartments: the proximal cerebral arterial circulation supplying the ischemic territory (sampled proximal to the site of occlusion) and the peripheral venous circulation, representing the systemic inflammatory compartment. This compartment-specific approach enabled direct comparison of inflammatory profiles between the local arterial inflow to the ischemic brain and the systemic circulation under standardized procedural conditions. Inclusion criteria age ≥ 18; clinical diagnosis of acute ischemic stroke with neurological deficits; occlusion of the ICA-T, M1 segment, or proximal M2 segment of the middle cerebral artery confirmed by vascular imaging (CTA); baseline NIHSS score ≥ 6; pre-stroke modified Rankin Scale score ≤ 2; ASPECTS ≥ 6 on initial CT; eligibility for treatment within the established therapeutic time windows; written informed consent obtained from the patient or a legally authorized representative; technical feasibility of safe arterial blood sampling proximal to the site of occlusion during mechanical thrombectomy. Exclusion criteria Patients were excluded if protocol-defined arterial or peripheral venous blood sampling could not be performed safely or reliably, or if key clinical or imaging data required for study endpoints were unavailable or of insufficient quality. Additional exclusion criteria included severe renal dysfunction precluding contrast-enhanced imaging, pregnancy, active systemic malignancy with potential effects on systemic inflammatory status, insufficient imaging quality preventing reliable infarct core assessment, and coexisting neurological disorders that could confound clinical or imaging interpretation. The study was conducted in accordance with the Declaration of Helsinki. The study protocol received approval from the Bioethics Committee of the Jagiellonian University (No. 1072.6120.18.2024, dated April 17, 2024). Written informed consent was obtained from all participants or their legally authorized representatives prior to study inclusion. 2.2. Clinical and Imaging Procedures All patients were treated in accordance with current guidelines for the management of acute ischemic stroke. After exclusion of intracerebral hemorrhage on noncontrast head CT, patients received intravenous thrombolysis with alteplase when eligible and subsequently underwent mechanical thrombectomy in cases of confirmed large-vessel occlusion. Depending on clinical indication and local protocol availability, diagnostic imaging included computed tomography with perfusion imaging. Assessment of infarct core extent and ischemic penumbra was performed using validated automated perfusion analysis software (Brainomix and RAPID) (Brainomix Ltd., Oxford, UK; and iSchemaView, Menlo Park, CA, USA). Particular emphasis was placed on quantitative measurement of infarct core volume and semi-quantitative assessment of early ischemic changes using the ASPECTS scale, which represent established imaging biomarkers reflecting the extent of irreversible ischemic brain injury [ 13 , 14 ]. Final angiographic reperfusion was assessed using the modified Thrombolysis in Cerebral Infarction (mTICI) scale, with mTICI ≥2b considered successful reperfusion [ 15 , 16 ]. 2.2.1. Vascular Access and Intraprocedural Management Vascular access for mechanical thrombectomy was obtained percutaneously via the common femoral artery (CFA) or, when clinically or anatomically appropriate, via the radial artery (RA), using the modified Seldinger technique under fluoroscopic guidance in accordance with standard endovascular stroke treatment protocols [ 17 , 18 ]. 2.2.2. Biological Material Collection Biological material was collected at a strictly defined intraprocedural time point – immediately after angiographic confirmation of large-vessel occlusion and before initiation of thrombectomy or reperfusion maneuvers. At the time of sampling, the embolus remained in situ and no reperfusion had yet been achieved. 2.2.3. Arterial Blood Sampling: Site and Technique Arterial blood samples were obtained from the proximal cerebral arterial compartment supplying the ischemic territory during mechanical thrombectomy performed under persistent large-vessel occlusion, with the embolic obstruction still present and prior to initiation of reperfusion maneuvers. Sampling was conducted using a microcatheter (Phenom 21, Medtronic) navigated under fluoroscopic guidance into the proximal middle cerebral artery (M1 or proximal M2 segment) while large-vessel occlusion was still present and before deployment of any thrombectomy device; catheter position relative to the embolic lesion was verified by digital subtraction angiography (Fig. 1 ). Arterial blood was aspirated directly through the positioned microcatheter before initiation of thrombectomy maneuvers, ensuring sampling under stable pre-reperfusion occlusion conditions. The microcatheter used for sampling was subsequently used for delivery of the thrombectomy device when technically appropriate. Accordingly, arterial samples represented blood within the proximal arterial inflow to the occluded vascular territory at the precise procedural time point of sampling. Sampling deliberately excluded post-occlusive arterial segments, distal branches, collateral-dependent compartments, and microcirculatory blood. This standardized protocol ensured anatomical and physiological consistency of arterial sampling and enabled valid comparison with peripheral venous leukogram parameters. 2.2.4. Peripheral Venous Blood Sampling Simultaneously, peripheral venous blood samples were collected into EDTA-anticoagulated tubes from an upper-extremity peripheral vein under standardized conditions. This enabled direct comparison of leukogram parameters between arterial and peripheral venous compartments. 2.2.5. Morphological and Cytometric Analysis of Neutrophils Peripheral and arterial blood smears were stained using the Wright method for morphological leukocyte assessment. Flow cytometric analysis was performed in a predefined representative subset of samples using CD16 immunophenotyping to confirm neutrophil identity and predominance. The gating strategy included debris exclusion, singlet discrimination, and identification of CD16⁺ granulocyte populations consistent with mature neutrophils (Fig. 2 ). This analysis was performed solely for methodological validation and was not included in statistical or outcome analyses. 2.2.6. Statistical Analysis Agreement between arterial and peripheral venous leukogram parameters was assessed using Bland–Altman analysis and Passing–Bablok regression. Associations between hematological parameters and clinical or imaging outcomes were evaluated using Spearman rank correlation coefficients and generalized estimating equation (GEE) models with doubly robust estimation methods to account for potential confounding and model misspecification bias [ 19 – 26 ]. A detailed description of the doubly robust estimation method is provided in the Supplementary Materials. 3. Results 3.1. Characteristics of the sample The study cohort consisted of 37 patients with acute ischemic stroke undergoing endovascular treatment. Baseline demographic, clinical, imaging, interventional, and sampling-related characteristics are summarized in Table 1 . Table 1 Demographic, Hematological, and Clinical Characteristics of Patients Undergoing Endovascular Treatment for Acute Ischemic Stroke. Category / Variable N Mean (SD) or n (%) 95% CI Demographics Age (years) 37 74.16 (11.65) 70, 78 Sex 37 Female 25 (67.57%) 50%, 81% Male 12 (32.43%) 19%, 50% Arterial Blood Leukogram Parameters (%) Segmented granulocytes 37 79.38 (10.05) 76, 83 Band granulocytes 37 1.59 (2.87) 0.64, 2.6 Lymphocytes 37 15.32 (8.67) 12, 18 Reactive lymphocytes 37 0.95 (1.56) 0.42, 1.5 Monocytes 37 1.57 (1.63) 1.0, 2.1 Basophils 37 0.38 (0.79) 0.11, 0.64 Eosinophils 37 0.30 (0.62) 0.09, 0.50 Other 37 0.30 (0.62) 0.09, 0.50 NLR 36 6.90 (3.92) 5.6, 8.2 DLR 16 54.75 (30.47) 39, 71 MLR 24 0.19 (0.15) 0.13, 0.26 LMR 25 9.06 (7.44) 6.0, 12 Peripheral Blood Leukogram Parameters (%) Segmented granulocytes 37 76.65 (11.72) 73, 81 Band granulocytes 37 1.59 (2.44) 0.78, 2.4 Lymphocytes 37 17.76 (9.97) 14, 21 Reactive lymphocytes 37 1.11 (1.47) 0.62, 1.6 Monocytes 37 2.30 (2.21) 1.6, 3.0 Basophils 37 0.30 (0.62) 0.09, 0.50 Eosinophils 37 0.24 (0.49) 0.08, 0.41 Other 36 0.03 (0.17) 0.00, 0.08 NLR 37 6.99 (5.85) 5.0, 8.9 DLR 20 53.79 (28.98) 40, 67 MLR 28 0.23 (0.29) 0.12, 0.34 LMR 28 7.99 (4.81) 6.1, 9.9 Differences/ratios between arterial and peripheral blood ΔNLR 36 -0.11 (4.33) -1.6, 1.4 Neutrophil band form ratio 22 1.22 (1.52) 0.54, 1.9 Stroke Severity and Imaging Parameters NIHSS at admission 35 16.31 (5.24) 15, 18 NIHSS after procedure 30 11.03 (7.15) 8.4, 14 NIHSS at discharge 34 6.82 (6.14) 4.7, 9.0 Core volume Brainomix (mL) 36 34.50 (29.00) 25, 44 Core volume Rapid (mL) 36 26.81 (35.45) 15, 39 Penumbra volume Brainomix (mL) 36 123.00 (70.61) 99, 147 Penumbra volume Rapid (mL) 36 115.50 (65.61) 93, 138 ASPECTS Brainomix 29 7.76 (2.32) 6.9, 8.6 ASPECTS Rapid 30 7.10 (2.52) 6.2, 8.0 Treatment Details mTICI after procedure 36 2b 8.00 (22.22%) 11%, 40% 2c 6.00 (16.67%) 7.0%, 33% 3 22.00 (61.11%) 44%, 76% Intravenous thrombolysis 36 14.00 (38.89%) 24%, 56% Time of r-tPA administration from symptom onset (min) 10 128.40 (45.49) 96, 161 Time of r-tPA administration from last seen well (min) 10 155.40 (63.05) 110, 201 Baseline hematological profiles, stroke severity, imaging-derived infarct and penumbra volumes, and treatment-related variables are summarized in Table 1 . Treatment details demonstrated high reperfusion success, with mTICI scores predominantly grade 3 (61.1%, 95% CI 44%–76%; n = 36). Intravenous thrombolysis was administered in 38.9% of cases (95% CI 24%–56%; n = 36), with mean onset-to-needle times of 128.4 minutes (95% CI 96–161; n = 10) from symptom onset and 155.4 minutes (95% CI 110–201; n = 10) from last known well. Overall, the cohort exhibited moderate-to-severe baseline stroke severity and underwent contemporary endovascular treatment with high rates of angiographic reperfusion. 3.2. Associations Between Hematological Parameters and Core Infarct Volume (Brainomix) In doubly robust generalized estimating equation models adjusted for age and sex, an inverse association was observed between the neutrophil band form ratio and core infarct volume assessed by Brainomix (adjusted mean difference: −6.23 mL per unit increase, 95% CI: −11.21 to − 1.26; p = 0.014). A borderline, non-significant positive association was observed for the difference in neutrophil-to-lymphocyte ratio (adjusted mean difference: 1.58 mL per unit increase, 95% CI: −0.11 to 3.27; p = 0.067). No statistically significant associations were identified for arterial neutrophil-to-lymphocyte ratio, arterial monocyte percentage, or time from symptom onset to recombinant tissue plasminogen activator administration, with effect estimates close to zero and p-values exceeding 0.05. Detailed results are presented in Table 2 . Table 2 Adjusted Mean Differences in Core Infarct Volume (Brainomix) Associated with Selected Exposures in Patients Undergoing Endovascular Treatment for Acute Ischemic Stroke. Exposure N Adjusted Mean Difference (95% CI), mL P-value Arterial neutrophil-to-lymphocyte ratio 35 1.26 (-1.40, 3.92) 0.354 Difference in neutrophil-to-lymphocyte ratio 35 1.58 (-0.11, 3.27) 0.067 Neutrophil band form ratio 22 -6.23 (-11.21, -1.26) 0.014 Arterial monocytes 36 2.72 (-2.87, 8.31) 0.341 Time of recombinant tissue plasminogen activator administration from symptom onset 10 0.09 (-0.25, 0.42) 0.617 Notes : CI: confidence interval; N: number of complete observations used in the model. Mean differences represent the adjusted change in core infarct volume (in milliliters) per one-unit increase in the exposure, estimated using doubly robust generalized estimating equation models with an identity link function, adjusted for age (centered at the sample mean) and sex, unless otherwise specified. For time of recombinant tissue plasminogen activator administration, the model was unadjusted due to the small sample size (N = 10). For neutrophil band form ratio, the model was adjusted only for sex due to the insufficient sample size (N = 22) for two confounders. Estimates quantify the conditional exposure-outcome association given the included covariates. Variations in N reflect missing data for specific exposures. All analyses assume independence of observations, with robust standard errors accounting for potential heteroscedasticity and model misspecification. 3.3. Associations Between Hematological Parameters and Core Infarct Volume (Rapid) In doubly robust generalized estimating equation models adjusted for age and sex, a statistically significant positive association was observed between the difference in neutrophil-to-lymphocyte ratio (ΔNLR) and core infarct volume assessed by Rapid (Table 3 ). Table 3 Adjusted Mean Differences in Core Infarct Volume (Rapid) Associated with Selected Exposures in Patients Undergoing Endovascular Treatment for Acute Ischemic Stroke. Exposure N Adjusted Mean Difference (95% CI), mL P-value Arterial neutrophil-to-lymphocyte ratio 35 2.19 (-1.01, 5.39) 0.180 Difference in neutrophil-to-lymphocyte ratio 35 2.97 (0.64, 5.30) 0.013 Neutrophil band form ratio 22 -4.62 (-11.06, 1.81) 0.159 Arterial monocytes 36 2.57 (-5.03, 10.16) 0.508 Time of recombinant tissue plasminogen activator administration from symptom onset 10 0.07 (-0.32, 0.46) 0.726 Notes : CI: confidence interval; N: number of complete observations used in the model. Mean differences represent the adjusted change in core infarct volume (in milliliters) per one-unit increase in the exposure, estimated using doubly robust generalized estimating equation models with an identity link function, adjusted for age (centered at the sample mean) and sex, unless otherwise specified. For time of recombinant tissue plasminogen activator administration, the model was unadjusted due to the small sample size (N = 10). For neutrophil band form ratio, the model was adjusted only for sex due to the insufficient sample size (N = 22) for two confounders. Estimates quantify the conditional exposure-outcome association given the included covariates. Variations in N reflect missing data for specific exposures. All analyses assume independence of observations, with robust standard errors accounting for potential heteroscedasticity and model misspecification. No statistically significant associations were identified for arterial neutrophil-to-lymphocyte ratio, neutrophil band form ratio, arterial monocytes, or time from symptom onset to recombinant tissue plasminogen activator administration. Detailed results are summarized in Table 3 . 3.4. Analysis of Agreement Between Arterial and Peripheral Blood Leukogram Parameters As shown in Table 4 , significant mean biases were observed for segmented granulocytes and lymphocytes, whereas NLR showed no significant bias between arterial and peripheral samples. Passing–Bablok regression confirmed the absence of proportional and constant bias for NLR (Supplementary Table S1 ). Table 4 Summary of Bland-Altman Statistics for Leukogram Parameters Comparing Arterial and Peripheral Blood Measurements. Parameter N Bias (95% CI) SD of Bias Lower LOA (95% CI) Upper LOA (95% CI) p-value Segmented Granulocytes 37 2.73 (0.02 to 5.44) 8.12 -13.19 (-17.86 to -8.52) 18.65 (13.98 to 23.32) 0.048 Band Granulocytes 37 0.00 (-0.94 to 0.94) 2.83 -5.54 (-7.17 to -3.92) 5.54 (3.92 to 7.17) 1.000 Lymphocytes 37 -2.43 (-4.54 to -0.32) 6.33 -14.83 (-18.47 to -11.20) 9.97 (6.33 to 13.61) 0.025 Reactive Lymphocytes 37 -0.16 (-0.71 to 0.39) 1.64 -3.38 (-4.32 to -2.44) 3.06 (2.11 to 4.00) 0.552 Monocytes 37 -0.73 (-1.62 to 0.17) 2.68 -5.99 (-7.53 to -4.45) 4.53 (2.99 to 6.07) 0.107 Basophils 37 0.08 (-0.22 to 0.38) 0.89 -1.67 (-2.18 to -1.16) 1.83 (1.32 to 2.35) 0.585 Eosinophils 37 0.05 (-0.17 to 0.28) 0.66 -1.25 (-1.63 to -0.87) 1.36 (0.97 to 1.74) 0.624 NLR 36 -0.11 (-1.58 to 1.35) 4.33 -8.59 (-11.12 to -6.07) 8.37 (5.84 to 10.89) 0.878 DLR 14 -5.34 (-23.53 to 12.85) 31.51 -67.09 (-98.96 to -35.22) 56.42 (24.55 to 88.28) 0.537 LMR 19 1.33 (-1.36 to 4.03) 5.59 -9.63 (-14.32 to -4.94) 12.29 (7.60 to 16.98) 0.313 Notes : N = number of paired comparisons. Bias = mean difference between arterial and peripheral measurements (arterial minus peripheral). SD = standard deviation. LOA = limit of agreement. CI = confidence interval. p-value derived from one-sample t-test assessing whether bias differs significantly from zero (alternative hypothesis: true bias ≠ 0); values < 0.05 indicate statistically significant bias. NLR = neutrophil-to-lymphocyte ratio; DLR = derived lymphocyte ratio; LMR = lymphocyte-to-monocyte ratio. Regarding sex stratification, the subgroup analyses reveal no substantial evidence of differential effects, as confidence intervals for slopes and intercepts broadly overlap between males and females across parameters, with no consistent patterns of deviation from the line of identity. For instance, in NLR, the male slope (1.204, 95% CI 0.744–2.097) and female slope (0.695, 95% CI 0.422–1.276) overlap despite numerical differences, inferring that sex does not meaningfully moderate the arterial-peripheral relationship. Wider confidence intervals in subgroups (due to reduced sample sizes, e.g., N = 12 for males in several parameters) limit definitive conclusions, but this absence of clear stratification effects implies that sex-specific adjustments are unnecessary for these measurements in clinical practice. In summary, segmented granulocytes and lymphocytes exhibit statistically significant biases in arterial versus peripheral blood measurements, indicating that agreement between sampling sites differs across leukocyte subpopulations. Regarding sex stratification, the analyses reveal no substantial disagreements between males and females. Consequently, there is no need to account for sex in clinical routines when interpreting these leukogram parameters, though larger studies may further validate this in diverse populations. 3.5. Correlation Analysis of Hematological and Clinical Outcomes in Patients Undergoing Endovascular Treatment for Acute Ischemic Stroke As illustrated in the correlation matrix (Fig. 2 ), moderate negative correlations were observed between arterial NLR and ASPECTS on Brainomix (ρ = -0.47, p = 0.011) as well as between ΔNLR and ASPECTS on Brainomix (ρ = -0.40, p = 0.037), both achieving statistical significance (p < 0.05). A weak positive correlation was noted between ΔNLR and core infarct volume on Rapid (ρ = 0.34, p = 0.048). Additionally, a borderline moderate negative correlation emerged between arterial NLR and ASPECTS on Rapid (ρ = -0.37, p = 0.050). These findings demonstrate that elevated arterial NLR and ΔNLR may associate with greater early ischemic changes (lower ASPECTS) and, in the case of ΔNLR, larger core infarct volumes, indicating associations between inflammatory markers and imaging-derived measures of ischemic injury. No other correlations reached statistical significance, including those involving arterial band granulocytes, reactive lymphocytes, or monocytes with NIHSS scores, infarct volumes, or mTICI grade, though some displayed numerically weak to moderate strengths (e.g., arterial band granulocytes with penumbra volume on Brainomix: ρ = -0.30, p = 0.079; arterial band granulocytes with ASPECTS on Rapid: ρ = 0.31, p = 0.099). These findings further characterize the associations between early inflammatory markers and imaging-derived measures of ischemic injury. 4. Discussion Long-term randomized trials and meta-analyses have unequivocally demonstrated that endovascular treatment of acute ischemic stroke with mechanical thrombectomy significantly improves functional outcomes compared with best medical therapy alone, including intravenous thrombolysis, representing one of the most important advances in contemporary stroke care [ 15 , 27 ]. Despite this progress, clinical response to reperfusion therapy remains highly heterogeneous, and achievement of complete angiographic reperfusion (eg, mTICI 3) does not translate uniformly into favorable functional recovery. Previous studies by van Horn et al. and others have shown that a substantial proportion of patients with full recanalization experience unfavorable outcomes at 90 days, with age, baseline stroke severity, and extent of ischemic injury remaining independent prognostic determinants [ 28 – 30 ]. These findings highlight that restoration of macrovascular patency does not fully determine tissue fate after stroke and underscore the importance of biological determinants operating at the microvascular and cellular level. In particular, inflammatory responses occurring at the interface between the systemic circulation and the ischemic cerebral vasculature may critically influence infarct evolution independently of angiographic reperfusion status. This discrepancy underscores the complexity of secondary brain injury mechanisms and highlights the role of inflammatory, immunological, and metabolic processes in shaping infarct evolution and clinical trajectory [ 31 , 32 ]. Increasing evidence suggests that the very early inflammatory response – characterized by neutrophil predominance accompanied by lymphopenia – may influence infarct progression, blood–brain barrier integrity, and microvascular perfusion in ways not captured by angiographic assessment of large-vessel recanalization [ 33 , 34 ]. Consequently, identification of accessible hematological biomarkers capable of early risk stratification has attracted growing interest. Leukogram-derived indices, particularly the neutrophil-to-lymphocyte ratio (NLR), represent simple and widely available markers of systemic inflammation [ 4 ]. However, in clinical practice these parameters may be obtained from peripheral blood or from arterial samples collected during endovascular procedures, and their agreement across sampling sites has remained insufficiently characterized, introducing interpretative uncertainty in hyperacute stroke studies [ 35 ]. Against this background, the present study systematically evaluated agreement between arterial and peripheral leukogram parameters while examining the stability of NLR as a sampling-site–independent biomarker associated with early ischemic injury. Our findings demonstrate that leukogram parameters are not universally interchangeable. This observation provides direct in vivo evidence that inflammatory cell distributions differ between the systemic and cerebral arterial compartments during hyperacute ischemia, supporting the concept that stroke induces spatially heterogeneous immune responses within the vascular system supplying the injured brain. Selective, statistically significant systematic differences were observed for segmented granulocytes and lymphocytes, whereas other leukocyte fractions and derived indices showed no clinically meaningful bias (Table 4 ). These results suggest that interpretation of neutrophilia or lymphopenia during the hyperacute phase of stroke may depend on sampling location, a factor with potential implications for assessment of inflammatory activation [ 9 ]. Taken together, these observations support the concept that leukocyte distribution becomes compartmentalized within the cerebral circulation during the hyperacute phase of ischemia, likely reflecting local endothelial activation and collateral flow dynamics. Such compartmentalization challenges the assumption that leukogram parameters measured simultaneously in arterial blood from the proximal cerebral circulation and in peripheral venous blood are biologically and clinically interchangeable [ 10 , 36 , 37 ]. Notably, despite these compartment-specific differences, NLR remained remarkably stable across sampling sites. Agreement was confirmed by Bland–Altman and Passing–Bablok analyses without evidence of constant or proportional bias (Table 4 ; Supplementary Table S1 ). This stability is consistent with prior reports suggesting that NLR, as a composite parameter, partially compensates for opposing shifts in neutrophil and lymphocyte counts, thereby demonstrating resistance to preanalytical variability [ 38 ]. From a methodological perspective, this positions NLR as a robust inflammatory marker in acute ischemic stroke, particularly relevant given the central role of neutrophils in early ischemic cascades. Beyond methodological robustness, this stability suggests that NLR reflects a fundamental biological equilibrium between innate immune activation and adaptive immune suppression that is preserved across vascular compartments. This property distinguishes NLR from individual leukocyte counts and supports its interpretation as an integrative biomarker of systemic inflammatory state relevant to ischemic brain injury. A second major observation is the association between early inflammatory markers and imaging-defined ischemic injury. Arterial NLR correlated inversely with ASPECTS, and the difference in NLR between sampling sites (ΔNLR) was positively associated with infarct core volume. These relationships, initially identified in correlation analyses (Fig. 1 ), were supported by multivariable models, indicating that early inflammatory activation dominated by neutrophils may contribute to tissue injury severity. Importantly, the association between ΔNLR and infarct core volume was demonstrated across two independent automated imaging platforms. A borderline association was observed using Brainomix (Table 2 ), whereas a statistically significant relationship was confirmed using Rapid software (Table 3 ). Consistency across segmentation systems strengthens confidence that these findings are biologically meaningful rather than platform-specific artifacts. Importantly, the partial differences observed between RAPID and Brainomix are unlikely to reflect fundamentally different biological definitions of infarct core in the present study, as both platforms were applied to the same CT perfusion datasets using harmonized perfusion thresholds (rCBF 6 s for hypoperfused tissue). Rather, these differences most likely reflect vendor-specific variability in proprietary image-processing pipelines, including deconvolution algorithms, automated arterial input and venous output function selection, motion and noise correction procedures, and voxel-level classification strategies. Such technical factors are known to influence quantitative infarct core estimation, particularly in the hyperacute phase when perfusion abnormalities evolve dynamically and signal-to-noise characteristics may affect threshold-based tissue classification. Within this context, the association between ΔNLR and infarct core volume was statistically significant when infarct core was quantified using RAPID, whereas a borderline association was observed with Brainomix-derived measurements. These findings suggest that the strength of observed associations may be sensitive to platform-specific measurement variability and limited statistical power inherent to modest sample sizes. Importantly, however, the overall directionality of associations was consistent across both segmentation systems, supporting the biological coherence of the observed relationship between early inflammatory activation and ischemic tissue injury. These observations have important methodological implications. They indicate that inflammatory biomarkers such as NLR and ΔNLR demonstrate biologically meaningful associations with tissue injury that are detectable across independent imaging platforms, while also emphasizing that quantitative effect estimates may vary depending on the specific implementation of perfusion analysis software. This platform sensitivity underscores the importance of interpreting imaging–biomarker relationships within the context of known technical variability and reinforces the need for cautious cross-study comparisons when different segmentation tools are used. At the same time, the convergence of findings across analytically distinct imaging platforms strengthens the inference that early inflammatory activation is intrinsically linked to ischemic tissue injury rather than representing a software-specific analytical artifact. The association between ΔNLR, derived from paired arterial samples obtained proximal to the occlusion and peripheral blood, and infarct core volume supports the concept that immune activation is closely coupled to local vascular and microcirculatory dysfunction during hyperacute ischemia. This relationship is biologically plausible, given the established role of neutrophils in promoting endothelial injury, microvascular obstruction, and secondary infarct expansion through release of reactive oxygen species, proteolytic enzymes, and proinflammatory mediators. Taken together, these findings support a model in which early inflammatory responses are not merely epiphenomena but reflect biologically relevant processes linked to tissue-level injury in acute ischemic stroke. The consistency of these associations across independent perfusion analysis platforms, despite expected technical variability, strengthens confidence in the robustness and translational relevance of leukogram-derived inflammatory markers –particularly NLR and ΔNLR – as accessible indicators of ischemic tissue injury in patients undergoing reperfusion therapy. Mechanistically, neutrophils accumulating within ischemic microvasculature represent a potent source of matrix metalloproteinases, reactive oxygen species, and proinflammatory mediators that may exacerbate endothelial dysfunction and contribute to microvascular no-reflow, thereby facilitating secondary infarct expansion despite successful macrovascular recanalization [ 39 , 40 ]. This mechanistic framework provides biological plausibility for the observed association between arterial inflammatory indices and imaging-defined infarct core volume, reinforcing the interpretation that leukogram-derived markers reflect active pathophysiological processes rather than epiphenomenal systemic responses. In parallel, stroke-associated lymphopenia has been interpreted as a manifestation of systemic immunosuppression driven by lymphocyte apoptosis and immune dysregulation, potentially increasing susceptibility to infection and influencing recovery trajectories [ 41 ]. Within this framework, the observed associations between NLR, ΔNLR, and imaging-derived infarct measures align with contemporary models linking immune responses to ischemic brain injury while remaining associative rather than causal. An additional, intriguing finding is the inverse association between the proportion of band neutrophils and infarct core volume assessed with Brainomix (Table 2 ). Although immature neutrophil forms are traditionally considered markers of heightened inflammatory activity, emerging evidence indicates functional heterogeneity within neutrophil subsets, including potential regulatory and reparative roles depending on activation state and local microenvironment [ 42 ]. This observation further underscores the biological complexity of early immune responses in ischemic stroke and highlights that qualitative features of immune activation – rather than absolute leukocyte counts alone – may influence tissue-level injury. While this observation requires validation in larger cohorts, it aligns with evolving concepts emphasizing immune cell phenotype and functional plasticity rather than absolute counts. Given the observational design, these findings should be interpreted cautiously and not as evidence of direct causal protection. In contrast, multivariable analyses did not demonstrate significant associations between inflammatory parameters and achievement of complete angiographic reperfusion (mTICI 3; Table 5 ). This is consistent with prior thrombectomy literature showing that procedural success is predominantly determined by anatomical and technical factors – including occlusion characteristics, time to reperfusion, and device performance – rather than systemic inflammatory status. This dissociation between inflammatory biomarkers and angiographic reperfusion further supports the concept that immune activation primarily influences downstream tissue-level injury rather than the mechanical success of large-vessel recanalization. Immune responses appear more relevant to secondary tissue injury and longer-term outcomes than to immediate procedural metrics [ 43 ]. Table 5 Adjusted Risk Ratios for the Association Between Selected Exposures and Achievement of mTICI Grade 3 in Patients Undergoing Endovascular Treatment for Acute Ischemic Stroke. Exposure N Adjusted RR (95% CI) P-value Arterial neutrophil-to-lymphocyte ratio 35 0.993 (0.885, 1.115) 0.911 Difference in neutrophil-to-lymphocyte ratio 35 1.006 (0.962, 1.053) 0.791 Neutrophil band form ratio 22 0.830 (0.567, 1.218) 0.342 Arterial monocytes 36 0.991 (0.798, 1.231) 0.936 Time of recombinant tissue plasminogen activator administration from symptom onset 10 1.002 (0.992, 1.012) 0.729 Notes : RR: risk ratio; CI: confidence interval; N: number of complete observations used in the model. Risk ratios represent the multiplicative change in the probability of achieving mTICI grade 3 per one-unit increase in the exposure, adjusted for age (centered at the sample mean) and sex using doubly robust generalized estimating equation models. For time of recombinant tissue plasminogen activator administration, the model was unadjusted due to the small sample size (N = 10), for Neutrophil band form ratio the model was adjusted only for Sex due to the insufficient sample size (N = 22) for two confounders. Estimates quantify the conditional exposure-outcome association given the included covariates. Variations in N reflect missing data for specific exposures. All analyses assume independence of observations, with robust standard errors accounting for potential heteroscedasticity and model misspecification. Collectively, these findings reinforce the importance of integrating clinical, laboratory, and imaging data when evaluating acute ischemic stroke. Growing evidence suggests that angiographic recanalization alone is insufficient to predict biological recovery and that individualized therapeutic strategies may benefit from incorporation of inflammatory biomarkers [ 44 ]. In this context, simple hematological indices such as NLR and ΔNLR may provide practical adjuncts for early biological stratification. Importantly, the stability of NLR across vascular compartments, combined with its consistent association with imaging-defined tissue injury across independent perfusion analysis platforms, supports its interpretation as a biologically robust and technically reliable biomarker. Because leukogram measurements are universally available in routine clinical practice, these findings suggest that NLR could serve as an immediately translatable biomarker for biological risk stratification in reperfusion-treated stroke. Such stratification may ultimately help identify patients at increased risk of progressive tissue injury despite successful recanalization and guide development of adjunctive immunomodulatory strategies. Several limitations merit consideration. The modest sample size restricts statistical power and increases susceptibility to type II error, particularly in multivariable analyses (Tables 2 , 3 , 5 ). Single-time-point sampling precludes assessment of dynamic inflammatory trajectories. Furthermore, absence of direct measurements within cerebral microcirculation limits differentiation between systemic and localized immune processes, and lack of long-term functional follow-up prevents direct linkage of inflammatory markers to delayed clinical outcomes. Additionally, although associations were directionally consistent across imaging platforms, quantitative differences between software implementations highlight the importance of considering technical variability when interpreting perfusion-derived infarct metrics. Despite these constraints, the present study provides coherent evidence that early inflammatory markers –particularly NLR – complement standard clinical and imaging assessment in acute ischemic stroke. The stability of NLR across sampling compartments and its association with imaging-defined injury support the concept that early neutrophil-dominant immune responses participate in shaping infarct evolution. By demonstrating compartment-specific inflammatory heterogeneity in direct proximity to the ischemic vascular territory and linking these findings to quantitative imaging biomarkers, this study provides in vivo evidence connecting vascular immune activation with tissue-level injury in humans. These findings identify compartment-specific inflammatory heterogeneity as a previously underrecognized feature of acute ischemic stroke and establish NLR as a biologically stable biomarker linked to early tissue injury. Future prospective studies incorporating longitudinal immune profiling and functional outcomes are warranted to determine whether inflammatory biomarkers can improve biological risk stratification and inform precision therapeutic strategies in acute stroke. Declarations Funding: This study was supported by internal research grants from the Jagiellonian University Medical College: Project no. N41/DBS/001158 (Principal Investigator: Tadeusz Popiela) and Project no. N42/DBS/000452 (Principal Investigator: Wirginia Krzysciak). The funding sources had no role in the study design, data collection, analysis, interpretation of results, manuscript preparation, or the decision to submit the work for publication. Author Contributions: W.K. and T.P. conceived the study, developed the conceptual framework, and designed the compartment-specific arterial and peripheral venous sampling protocol. B.Ł., P.B., R.P., P.W. and T.P. performed endovascular procedures, obtained arterial blood samples during mechanical thrombectomy, and contributed to acquisition and verification of clinical and procedural data. W.K., M.P., and B.N. performed hematological analyses, conducted laboratory data validation, and contributed to biological interpretation of inflammatory parameters. W.K. designed and performed the statistical analyses, including agreement modeling and multivariable regression, and contributed to methodological development and interpretation of analytical results. W.K. and P.M drafted the manuscript and integrated clinical, imaging, and laboratory findings into a unified biological framework. T.P. provided overall scientific supervision and critically revised the manuscript for important intellectual content. All authors contributed to interpretation of the data, critically revised the manuscript, approved the final version, and agree to be accountable for all aspects of the work. Data Availability: 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 approval: This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Bioethics Committee of the Jagiellonian University (No. 1072.6120.18.2024, dated April 17, 2024). Consent to participate: Informed consent was obtained from all individual participants included in the study. Competing Interests: The authors declare no competing interests. References Saini, V., Guada, L. & Yavagal, D. R. Global Epidemiology of Stroke and Access to Acute Ischemic Stroke Interventions. Neurology 97 , 6–16 (2021). Gomis, M. & Dávalos, A. Recanalization and Reperfusion Therapies of Acute Ischemic Stroke: What have We Learned, What are the Major Research Questions, and Where are We Headed? Front Neurol 5 , (2014). Hussein, H. M. et al. Occurrence and predictors of futile recanalization following endovascular treatment among patients with acute ischemic stroke: a multicenter study. Am. J. Neuroradiol. 31 , 454–458 (2010). 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Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 327 , 307–310 (1986). H, P. & None, B. A new biometrical procedure for testing the equality of measurements from two different analytical methods. Application of linear regression procedures for method comparison studies in clinical chemistry, Part I. J. Clin. Chem. Clin. Biochem. 21 , 709–720 (1983). Schober, P. & Schwarte, L. A. Correlation Coefficients: Appropriate Use and Interpretation. Anesth. Analg . 126 , 1763–1768 (2018). Rothman, K. ]. No Adjustments Are Needed for Multiple Comparisons. Epidemiology 1 , 43–46 (1990). Bang, H. & Robins, J. M. Doubly robust estimation in missing data and causal inference models. Biometrics 61 , 962–973 (2005). Zeger, S. L., Liang, K. Y. & Albert, P. S. Models for Longitudinal Data: A Generalized Estimating Equation Approach. Biometrics 44 , 1049 (1988). Schnabel, R. B. et al. Multiple inflammatory biomarkers in relation to cardiovascular events and mortality in the community. Arterioscler. Thromb. Vasc Biol. 33 , 1728–1733 (2013). Lambertsen, K. L., Biber, K. & Finsen, B. Inflammatory cytokines in experimental and human stroke. J. Cereb. Blood Flow. Metab. 32 , 1677–1698 (2012). R Core Team. R: A language and environment for statistical computing (R Foundation, 2025). Badhiwala, J. H. et al. Endovascular Thrombectomy for Acute Ischemic Stroke: A Meta-analysis. JAMA 314 , 1832–1843 (2015). Rinkel, L. A. et al. Effect of Intravenous Alteplase Treatment on First-Line Stent Retriever Versus Aspiration Alone During Endovascular Treatment. Stroke 53 , 3278–3288 (2022). Van Horn, N. et al. Predictors of poor clinical outcome despite complete reperfusion in acute ischemic stroke patients. J. Neurointerv Surg. 13 , 14–18 (2021). Rabinstein, A. A., Albers, G. W., Brinjikji, W. & Koch, S. Factors that may contribute to poor outcome despite good reperfusion after acute endovascular stroke therapy. Int. J. Stroke . 14 , 23–31 (2019). Dirnagl, U., Iadecola, C. & Moskowitz, M. A. Pathobiology of ischaemic stroke: An integrated view. Trends Neurosci. 22 , 391–397 (1999). Iadecola, C. & Anrather, J. The immunology of stroke: from mechanisms to translation. Nat. Med. 17 , 796–808 (2011). Perez-de-Puig, I. et al. Neutrophil recruitment to the brain in mouse and human ischemic stroke. Acta Neuropathol. 129 , 239–257 (2015). Lambertsen, K. L., Finsen, B. & Clausen, B. H. Post-stroke inflammation-target or tool for therapy? Acta Neuropathol. 137 , 693–714 (2019). Buck, B. H. et al. Early neutrophilia is associated with volume of ischemic tissue in acute stroke. Stroke 39 , 355–360 (2008). Jin, R., Yang, G. & Li, G. Inflammatory mechanisms in ischemic stroke: role of inflammatory cells. J. Leukoc. Biol. 87 , 779–789 (2010). Li, S. J. et al. Post-operative neutrophil-to-lymphocyte ratio and outcome after thrombectomy in acute ischemic stroke. Front Neurol 13 , (2022). Mun-Bryce, S. & Rosenberg, G. A. Matrix metalloproteinases in cerebrovascular disease. J. Cereb. Blood Flow. Metab. 18 , 1163–1172 (1998). Rosell, A. et al. MMP-9-positive neutrophil infiltration is associated to blood-brain barrier breakdown and basal lamina type IV collagen degradation during hemorrhagic transformation after human ischemic stroke. Stroke 39 , 1121–1126 (2008). Shi, K., Wood, K., Shi, F. D., Wang, X. & Liu, Q. Stroke-induced immunosuppression and poststroke infection. Stroke Vasc Neurol. 3 , 34–41 (2018). Silvestre-Roig, C., Hidalgo, A. & Soehnlein, O. Neutrophil heterogeneity: implications for homeostasis and pathogenesis. Blood 127 , 2173–2181 (2016). Khan, F., Yogendrakumar, V. & Menon, B. K. Endovascular Thrombectomy for Ischemic Stroke With Large Infarct. Stroke 56 , 1655–1658 (2025). Shen, H., Killingsworth, M. C. & Bhaskar, S. M. M. Comprehensive Meta-Analysis of Futile Recanalization in Acute Ischemic Stroke Patients Undergoing Endovascular Thrombectomy: Prevalence, Factors, and Clinical Outcomes. Life (Basel) 13, (2023). (1965). Additional Declarations No competing interests reported. Supplementary Files SuplemmentaryfilesScientificReportsKrzysciakWetal09042026.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 05 May, 2026 Editor invited by journal 16 Apr, 2026 Editor assigned by journal 09 Apr, 2026 Submission checks completed at journal 09 Apr, 2026 First submitted to journal 24 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9216218","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":638723405,"identity":"ab6a7004-219c-4187-b529-376d7df35b2f","order_by":0,"name":"Wirginia Krzyściak","email":"data:image/png;base64,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","orcid":"","institution":"Jagiellonian University Medical College","correspondingAuthor":true,"prefix":"","firstName":"Wirginia","middleName":"","lastName":"Krzyściak","suffix":""},{"id":638723408,"identity":"d8fde3ea-a174-4493-928f-5241727d8f1d","order_by":1,"name":"Paweł Brzegowy","email":"","orcid":"","institution":"Jagiellonian University Medical College","correspondingAuthor":false,"prefix":"","firstName":"Paweł","middleName":"","lastName":"Brzegowy","suffix":""},{"id":638723409,"identity":"387555d0-0788-4d44-ab6b-79a152da6b4d","order_by":2,"name":"Roman Pułyk","email":"","orcid":"","institution":"Jagiellonian University Medical College","correspondingAuthor":false,"prefix":"","firstName":"Roman","middleName":"","lastName":"Pułyk","suffix":""},{"id":638723411,"identity":"8deb0131-bd6c-4ebf-8bd9-a7eb11c4e9be","order_by":3,"name":"Bartłomiej Łasocha","email":"","orcid":"","institution":"Jagiellonian University Medical College","correspondingAuthor":false,"prefix":"","firstName":"Bartłomiej","middleName":"","lastName":"Łasocha","suffix":""},{"id":638723414,"identity":"ead4bbec-b9b5-4cad-a32f-1ad2c613f7e9","order_by":4,"name":"Monika Papież","email":"","orcid":"","institution":"Jagiellonian University Medical College","correspondingAuthor":false,"prefix":"","firstName":"Monika","middleName":"","lastName":"Papież","suffix":""},{"id":638723420,"identity":"3e882a90-524f-41e6-bc36-51f2531d1727","order_by":5,"name":"Barbara Nowak","email":"","orcid":"","institution":"Jagiellonian University Medical College","correspondingAuthor":false,"prefix":"","firstName":"Barbara","middleName":"","lastName":"Nowak","suffix":""},{"id":638723426,"identity":"300c8208-292f-4983-9a38-84c684aa6445","order_by":6,"name":"Paulina Mazur","email":"","orcid":"","institution":"Jagiellonian University Medical College","correspondingAuthor":false,"prefix":"","firstName":"Paulina","middleName":"","lastName":"Mazur","suffix":""},{"id":638723433,"identity":"3ea33ea6-fec6-4fcb-8989-c323858fab95","order_by":7,"name":"Paweł Wrona","email":"","orcid":"","institution":"Jagiellonian University Medical College","correspondingAuthor":false,"prefix":"","firstName":"Paweł","middleName":"","lastName":"Wrona","suffix":""},{"id":638723434,"identity":"bf02f280-1de3-40dc-ad87-55144e793806","order_by":8,"name":"Tadeusz Popiela","email":"","orcid":"","institution":"Jagiellonian University Medical College","correspondingAuthor":false,"prefix":"","firstName":"Tadeusz","middleName":"","lastName":"Popiela","suffix":""}],"badges":[],"createdAt":"2026-03-24 21:54:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9216218/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9216218/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109216476,"identity":"f494336f-7bda-4dce-bbbb-1b4ec0378085","added_by":"auto","created_at":"2026-05-13 18:05:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1463037,"visible":true,"origin":"","legend":"\u003cp\u003eMultimodal imaging and cytological validation of arterial blood sampling site and neutrophil identification in acute ischemic stroke.\u003c/p\u003e\n\u003cp\u003e(A) Automated collateral circulation assessment using computed tomography angiography (CTA) with RAPID software is presented as a color-coded Blood Vessel Density map. The highlighted region corresponds to the vascular territory supplied by the left middle cerebral artery (MCA) in the setting of M2 segment occlusion. The color scale reflects relative vessel density (%), demonstrating reduced vascular opacification within the affected territory and confirming impaired downstream perfusion.\u003c/p\u003e\n\u003cp\u003e(B) Digital subtraction angiography (DSA) during mechanical thrombectomy shows the intermediate aspiration catheter positioned in the C5 segment of the left internal carotid artery (red arrow). Persistent occlusion of the left MCA M2 segment is confirmed by absence of distal contrast opacification beyond the embolic obstruction (blue arrows), indicating lack of antegrade perfusion distal to the occlusion. These findings confirm that arterial blood sampling was performed proximal to the site of vascular occlusion.\u003c/p\u003e\n\u003cp\u003e(C) Fluoroscopic imaging demonstrates a Phenom 21 microcatheter advanced across the embolic lesion (green arrow). This microcatheter was used to aspirate arterial blood from the proximal cerebral arterial compartment prior to thrombectomy and subsequently served for thrombectomy device delivery, confirming the anatomical relationship of the sampling site to the occlusion.\u003c/p\u003e\n\u003cp\u003e(D–H) Flow cytometric analysis of leukocytes: viable cells (P1) were identified based on forward scatter (FSC-A) and side scatter (SSC-A), enabling exclusion of debris and noncellular events. Singlet discrimination using FSC-A versus FSC-H within the P1 population allowed exclusion of doublets and cellular aggregates. Neutrophils were defined as CD16⁺ events with intermediate-to-high SSC-A signal, reflecting characteristic cytoplasmic granularity, whereas CD16⁺/SSC-low events were classified as non-neutrophil leukocytes and excluded.\u003c/p\u003e\n\u003cp\u003e(I) Wright-stained arterial blood smear demonstrates a segmented neutrophil with a multilobed nucleus and granular cytoplasm, confirming morphological identification in arterial samples collected proximal to the occlusion.\u003c/p\u003e\n\u003cp\u003eTogether, these imaging, cytological, and flow cytometric findings confirm both the anatomical location of arterial blood sampling and the reproducible identification of neutrophils using standardized morphological and immunophenotypic criteria.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9216218/v1/07137bf04ced73a85d1c11a3.png"},{"id":109216478,"identity":"15e684eb-883a-4033-8372-70a478bb453f","added_by":"auto","created_at":"2026-05-13 18:05:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":145511,"visible":true,"origin":"","legend":"\u003cp\u003eSpearman rank correlation heatmap showing pairwise associations between hematological parameters and clinical and imaging outcomes in patients undergoing endovascular treatment for acute ischemic stroke. Colors represent Spearman rank correlation coefficients (ρ), with green indicating positive correlations and red indicating negative correlations; color intensity reflects correlation strength. Asterisks indicate statistically significant correlations (p \u0026lt; 0.05). Hematological variables include neutrophil-to-lymphocyte ratio (Art. NLR), inter-compartment difference in NLR (ΔNLR), band neutrophil proportion, reactive lymphocytes, and monocytes measured in arterial blood. Clinical and imaging outcomes include National Institutes of Health Stroke Scale (NIHSS) scores at admission, post-procedure, and discharge; infarct core and penumbra volumes measured using Brainomix and RAPID software; Alberta Stroke Program Early CT Score (ASPECTS); and modified Thrombolysis in Cerebral Infarction (mTICI) grade.\u003c/p\u003e\n\u003cp\u003eAbbreviations: NLR = neutrophil-to-lymphocyte ratio; NIHSS = National Institutes of Health Stroke Scale; ASPECTS = Alberta Stroke Program Early CT Score; mTICI = modified Thrombolysis in Cerebral Infarction.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9216218/v1/f0d1db08995af45e3b277ec2.png"},{"id":109252467,"identity":"3e60e4c2-7c5f-4de2-b4e1-b80070ce053e","added_by":"auto","created_at":"2026-05-14 09:26:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1125577,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9216218/v1/4094d6b5-f6ab-4c31-b4ef-85d25043b715.pdf"},{"id":109222291,"identity":"71d7294c-4871-400c-8965-8f2eb56e7d29","added_by":"auto","created_at":"2026-05-13 21:06:56","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":77714,"visible":true,"origin":"","legend":"","description":"","filename":"SuplemmentaryfilesScientificReportsKrzysciakWetal09042026.docx","url":"https://assets-eu.researchsquare.com/files/rs-9216218/v1/1ab299db7ea417e443331e17.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Inflammatory Blood Stability Links to Early Ischemic Brain Injury Running head: Inflammatory Blood Stability in Stroke","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAcute ischemic stroke remains a leading cause of mortality and long-term disability worldwide, despite substantial advances in reperfusion therapies, including intravenous thrombolysis and mechanical thrombectomy [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although mechanical thrombectomy markedly improves survival and functional outcomes, a considerable proportion of patients fail to achieve proportional neurological recovery despite successful angiographic recanalization. This phenomenon \u0026ndash; often described as futile recanalization or reperfusion without functional independence \u0026ndash; illustrates that restoration of large-vessel patency alone does not guarantee favorable biological or clinical recovery [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]. Clinical trial data further demonstrate that reopening an occluded artery does not invariably translate into equivalent neurological improvement, underscoring the contribution of patient-specific and tissue-level factors such as age, baseline deficit severity, and microvascular reperfusion [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eThese observations highlight the importance of secondary injury mechanisms that shape infarct evolution following ischemia and reperfusion. Increasing evidence implicates inflammatory and immunological processes as central determinants of early tissue injury and subsequent neurological trajectory. Early neutrophil activation accompanied by relative lymphopenia represents a systemic stress response to cerebral ischemia and has emerged as a measurable marker of inflammatory burden [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]. In this context, leukogram-derived indices \u0026ndash; particularly the neutrophil-to-lymphocyte ratio (NLR) \u0026ndash; have gained attention as accessible biomarkers with potential prognostic relevance in acute stroke [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eInflammatory activation has been linked not only to clinical severity but also to imaging markers of irreversible brain injury. Studies suggest that systemic inflammatory signatures correlate with early ischemic changes quantified by neuroimaging measures such as ASPECTS (Alberta Stroke Program Early CT Score) and infarct core volume on perfusion imaging, parameters that reflect the biological extent of tissue damage and influence therapeutic response [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]. Integration of hematological and imaging biomarkers may therefore improve biological stratification in the hyperacute phase of stroke.\u003c/p\u003e\n\u003cp\u003eDuring endovascular treatment, blood samples can be obtained from peripheral venous circulation and from arterial segments proximal to the site of occlusion, enabling compartment-specific assessment of systemic and locally conditioned inflammatory responses. Prior investigations demonstrate compartment-specific differences in leukocyte composition, suggesting that sampling site may influence interpretation of inflammatory markers [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, the degree of agreement and clinical interchangeability of leukogram measurements between arterial and peripheral compartments remains insufficiently characterized [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]. Such uncertainty is clinically relevant because localized inflammatory responses, altered hemodynamics, and immune activation associated with ischemia-reperfusion may differentially shape cellular profiles in these compartments [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eAdvances in perfusion imaging now permit increasingly precise quantification of infarct core and penumbral tissue, enabling closer linkage between biological markers and tissue injury patterns [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]. Within this framework, composite indices such as NLR may offer methodological stability by integrating opposing leukocyte shifts, potentially reducing sensitivity to sampling variability. Whether leukogram parameters measured at different sampling sites are interchangeable \u0026ndash; and whether they retain meaningful associations with imaging-defined injury \u0026ndash; remains an important unresolved question.\u003c/p\u003e\n\u003cp\u003eStudy Aim\u003c/p\u003e\n\u003cp\u003eThis study aimed to evaluate the agreement between leukogram parameters measured in arterial blood sampled proximal to the occlusion and in peripheral venous blood, in patients undergoing mechanical thrombectomy for acute ischemic stroke, and to examine their associations with imaging-defined ischemic injury and early clinical course. Specifically, we sought to:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eassess systematic and proportional differences between arterial and peripheral measurements using agreement analyses (Bland\u0026ndash;Altman and Passing\u0026ndash;Bablok);\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003edetermine relationships between inflammatory indices, including NLR and inter-compartment differences, and neuroimaging markers of ischemic injury (ASPECTS and infarct core/penumbra volumes);\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eexplore associations between leukogram parameters and early clinical outcomes following reperfusion.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThese objectives address a clinically relevant gap in understanding the biological interpretation and practical reliability of inflammatory biomarkers in the hyperacute stroke setting.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1. Participants\u003c/h2\u003e\n\u003cp\u003eThe study included 37 consecutive patients admitted with acute ischemic stroke who underwent endovascular treatment with mechanical thrombectomy according to current standard-of-care stroke treatment protocols. Intravenous thrombolysis with alteplase was administered when clinically indicated and in accordance with established therapeutic guidelines; eligibility for inclusion was independent of prior thrombolysis and was based exclusively on fulfillment of endovascular treatment criteria and feasibility of protocol-defined arterial and peripheral venous blood sampling.\u003c/p\u003e\n\u003cp\u003eThe study was conducted in specialized comprehensive stroke centers in Krak\u0026oacute;w, including the Angiography and Interventional Radiology Unit, Department of Diagnostic Imaging, University Hospital in Krak\u0026oacute;w, where all endovascular procedures and arterial blood sampling were performed, and the Department of Radiology, Collegium Medicum, Jagiellonian University, which was responsible for imaging analysis, methodological oversight, and overall study coordination.\u003c/p\u003e\n\u003cp\u003ePatient recruitment followed a prospective observational design. The study was specifically designed to assess the concordance of leukogram parameters between two anatomically and functionally distinct circulatory compartments: the proximal cerebral arterial circulation supplying the ischemic territory (sampled proximal to the site of occlusion) and the peripheral venous circulation, representing the systemic inflammatory compartment. This compartment-specific approach enabled direct comparison of inflammatory profiles between the local arterial inflow to the ischemic brain and the systemic circulation under standardized procedural conditions.\u003c/p\u003e\n\u003cp\u003eInclusion criteria\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eage\u0026thinsp;\u0026ge;\u0026thinsp;18;\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eclinical diagnosis of acute ischemic stroke with neurological deficits;\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eocclusion of the ICA-T, M1 segment, or proximal M2 segment of the middle cerebral artery confirmed by vascular imaging (CTA);\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ebaseline NIHSS score\u0026thinsp;\u0026ge;\u0026thinsp;6;\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003epre-stroke modified Rankin Scale score\u0026thinsp;\u0026le;\u0026thinsp;2;\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eASPECTS\u0026thinsp;\u0026ge;\u0026thinsp;6 on initial CT;\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eeligibility for treatment within the established therapeutic time windows;\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ewritten informed consent obtained from the patient or a legally authorized representative;\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003etechnical feasibility of safe arterial blood sampling proximal to the site of occlusion during mechanical thrombectomy.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eExclusion criteria\u003c/p\u003e\n\u003cp\u003ePatients were excluded if protocol-defined arterial or peripheral venous blood sampling could not be performed safely or reliably, or if key clinical or imaging data required for study endpoints were unavailable or of insufficient quality. Additional exclusion criteria included severe renal dysfunction precluding contrast-enhanced imaging, pregnancy, active systemic malignancy with potential effects on systemic inflammatory status, insufficient imaging quality preventing reliable infarct core assessment, and coexisting neurological disorders that could confound clinical or imaging interpretation.\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki. The study protocol received approval from the Bioethics Committee of the Jagiellonian University (No. 1072.6120.18.2024, dated April 17, 2024). Written informed consent was obtained from all participants or their legally authorized representatives prior to study inclusion.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e2.2. Clinical and Imaging Procedures\u003c/h2\u003e\n\u003cp\u003eAll patients were treated in accordance with current guidelines for the management of acute ischemic stroke. After exclusion of intracerebral hemorrhage on noncontrast head CT, patients received intravenous thrombolysis with alteplase when eligible and subsequently underwent mechanical thrombectomy in cases of confirmed large-vessel occlusion.\u003c/p\u003e\n\u003cp\u003eDepending on clinical indication and local protocol availability, diagnostic imaging included computed tomography with perfusion imaging.\u003c/p\u003e\n\u003cp\u003eAssessment of infarct core extent and ischemic penumbra was performed using validated automated perfusion analysis software (Brainomix and RAPID) (Brainomix Ltd., Oxford, UK; and iSchemaView, Menlo Park, CA, USA). Particular emphasis was placed on quantitative measurement of infarct core volume and semi-quantitative assessment of early ischemic changes using the ASPECTS scale, which represent established imaging biomarkers reflecting the extent of irreversible ischemic brain injury [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eFinal angiographic reperfusion was assessed using the modified Thrombolysis in Cerebral Infarction (mTICI) scale, with mTICI \u0026ge;2b considered successful reperfusion [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\n\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n\u003ch2\u003e2.2.1. Vascular Access and Intraprocedural Management\u003c/h2\u003e\n\u003cp\u003eVascular access for mechanical thrombectomy was obtained percutaneously via the common femoral artery (CFA) or, when clinically or anatomically appropriate, via the radial artery (RA), using the modified Seldinger technique under fluoroscopic guidance in accordance with standard endovascular stroke treatment protocols [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n\u003ch2\u003e2.2.2. Biological Material Collection\u003c/h2\u003e\n\u003cp\u003eBiological material was collected at a strictly defined intraprocedural time point \u0026ndash; immediately after angiographic confirmation of large-vessel occlusion and before initiation of thrombectomy or reperfusion maneuvers. At the time of sampling, the embolus remained \u003cem\u003ein situ\u003c/em\u003e and no reperfusion had yet been achieved.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n\u003ch2\u003e2.2.3. Arterial Blood Sampling: Site and Technique\u003c/h2\u003e\n\u003cp\u003eArterial blood samples were obtained from the proximal cerebral arterial compartment supplying the ischemic territory during mechanical thrombectomy performed under persistent large-vessel occlusion, with the embolic obstruction still present and prior to initiation of reperfusion maneuvers.\u003c/p\u003e\n\u003cp\u003eSampling was conducted using a microcatheter (Phenom 21, Medtronic) navigated under fluoroscopic guidance into the proximal middle cerebral artery (M1 or proximal M2 segment) while large-vessel occlusion was still present and before deployment of any thrombectomy device; catheter position relative to the embolic lesion was verified by digital subtraction angiography (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eArterial blood was aspirated directly through the positioned microcatheter before initiation of thrombectomy maneuvers, ensuring sampling under stable pre-reperfusion occlusion conditions.\u003c/p\u003e\n\u003cp\u003eThe microcatheter used for sampling was subsequently used for delivery of the thrombectomy device when technically appropriate.\u003c/p\u003e\n\u003cp\u003eAccordingly, arterial samples represented blood within the proximal arterial inflow to the occluded vascular territory at the precise procedural time point of sampling.\u003c/p\u003e\n\u003cp\u003eSampling deliberately excluded post-occlusive arterial segments, distal branches, collateral-dependent compartments, and microcirculatory blood.\u003c/p\u003e\n\u003cp\u003eThis standardized protocol ensured anatomical and physiological consistency of arterial sampling and enabled valid comparison with peripheral venous leukogram parameters.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n\u003ch2\u003e2.2.4. Peripheral Venous Blood Sampling\u003c/h2\u003e\n\u003cp\u003eSimultaneously, peripheral venous blood samples were collected into EDTA-anticoagulated tubes from an upper-extremity peripheral vein under standardized conditions. This enabled direct comparison of leukogram parameters between arterial and peripheral venous compartments.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n\u003ch2\u003e2.2.5. Morphological and Cytometric Analysis of Neutrophils\u003c/h2\u003e\n\u003cp\u003ePeripheral and arterial blood smears were stained using the Wright method for morphological leukocyte assessment.\u003c/p\u003e\n\u003cp\u003eFlow cytometric analysis was performed in a predefined representative subset of samples using CD16 immunophenotyping to confirm neutrophil identity and predominance. The gating strategy included debris exclusion, singlet discrimination, and identification of CD16⁺ granulocyte populations consistent with mature neutrophils (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). This analysis was performed solely for methodological validation and was not included in statistical or outcome analyses.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n\u003ch2\u003e2.2.6. Statistical Analysis\u003c/h2\u003e\n\u003cp\u003eAgreement between arterial and peripheral venous leukogram parameters was assessed using Bland\u0026ndash;Altman analysis and Passing\u0026ndash;Bablok regression.\u003c/p\u003e\n\u003cp\u003eAssociations between hematological parameters and clinical or imaging outcomes were evaluated using Spearman rank correlation coefficients and generalized estimating equation (GEE) models with doubly robust estimation methods to account for potential confounding and model misspecification bias [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eA detailed description of the doubly robust estimation method is provided in the Supplementary Materials.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Characteristics of the sample\u003c/h2\u003e\n \u003cp\u003eThe study cohort consisted of 37 patients with acute ischemic stroke undergoing endovascular treatment. Baseline demographic, clinical, imaging, interventional, and sampling-related characteristics are summarized in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDemographic, Hematological, and Clinical Characteristics of Patients Undergoing Endovascular Treatment for Acute Ischemic Stroke.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory / Variable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003cp\u003eor n (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eDemographics\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.16 (11.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70, 78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (67.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50%, 81%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (32.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19%, 50%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eArterial Blood Leukogram Parameters (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSegmented granulocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.38 (10.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76, 83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBand granulocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.59 (2.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64, 2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLymphocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.32 (8.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12, 18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReactive lymphocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95 (1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42, 1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.57 (1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0, 2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasophils\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38 (0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11, 0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEosinophils\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30 (0.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09, 0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30 (0.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09, 0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.90 (3.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.6, 8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.75 (30.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39, 71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19 (0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13, 0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.06 (7.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.0, 12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeripheral Blood Leukogram Parameters (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSegmented granulocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.65 (11.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73, 81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBand granulocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.59 (2.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78, 2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLymphocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.76 (9.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14, 21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReactive lymphocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11 (1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62, 1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.30 (2.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6, 3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasophils\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30 (0.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09, 0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEosinophils\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24 (0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08, 0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03 (0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00, 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.99 (5.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.0, 8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.79 (28.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40, 67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23 (0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12, 0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.99 (4.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.1, 9.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDifferences/ratios between arterial and peripheral blood\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;NLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.11 (4.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.6, 1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeutrophil band form ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22 (1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54, 1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eStroke Severity and Imaging Parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNIHSS at admission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.31 (5.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15, 18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNIHSS after procedure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.03 (7.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.4, 14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNIHSS at discharge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.82 (6.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.7, 9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCore volume Brainomix (mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.50 (29.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25, 44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCore volume Rapid (mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.81 (35.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15, 39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePenumbra volume Brainomix (mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e123.00 (70.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99, 147\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePenumbra volume Rapid (mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115.50 (65.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93, 138\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASPECTS Brainomix\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.76 (2.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.9, 8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASPECTS Rapid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.10 (2.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.2, 8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatment Details\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emTICI after procedure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.00 (22.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11%, 40%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.00 (16.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.0%, 33%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.00 (61.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44%, 76%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntravenous thrombolysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.00 (38.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24%, 56%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime of r-tPA administration from symptom onset (min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128.40 (45.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96, 161\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime of r-tPA administration from last seen well (min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155.40 (63.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110, 201\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eBaseline hematological profiles, stroke severity, imaging-derived infarct and penumbra volumes, and treatment-related variables are summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eTreatment details demonstrated high reperfusion success, with mTICI scores predominantly grade 3 (61.1%, 95% CI 44%\u0026ndash;76%; n\u0026thinsp;=\u0026thinsp;36). Intravenous thrombolysis was administered in 38.9% of cases (95% CI 24%\u0026ndash;56%; n\u0026thinsp;=\u0026thinsp;36), with mean onset-to-needle times of 128.4 minutes (95% CI 96\u0026ndash;161; n\u0026thinsp;=\u0026thinsp;10) from symptom onset and 155.4 minutes (95% CI 110\u0026ndash;201; n\u0026thinsp;=\u0026thinsp;10) from last known well.\u003c/p\u003e\n \u003cp\u003eOverall, the cohort exhibited moderate-to-severe baseline stroke severity and underwent contemporary endovascular treatment with high rates of angiographic reperfusion.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Associations Between Hematological Parameters and Core Infarct Volume (Brainomix)\u003c/h2\u003e\n \u003cp\u003eIn doubly robust generalized estimating equation models adjusted for age and sex, an inverse association was observed between the neutrophil band form ratio and core infarct volume assessed by Brainomix (adjusted mean difference: \u0026minus;6.23 mL per unit increase, 95% CI: \u0026minus;11.21 to \u0026minus;\u0026thinsp;1.26; p\u0026thinsp;=\u0026thinsp;0.014). A borderline, non-significant positive association was observed for the difference in neutrophil-to-lymphocyte ratio (adjusted mean difference: 1.58 mL per unit increase, 95% CI: \u0026minus;0.11 to 3.27; p\u0026thinsp;=\u0026thinsp;0.067).\u003c/p\u003e\n \u003cp\u003eNo statistically significant associations were identified for arterial neutrophil-to-lymphocyte ratio, arterial monocyte percentage, or time from symptom onset to recombinant tissue plasminogen activator administration, with effect estimates close to zero and p-values exceeding 0.05. Detailed results are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAdjusted Mean Differences in Core Infarct Volume (Brainomix) Associated with Selected Exposures in Patients Undergoing Endovascular Treatment for Acute Ischemic Stroke.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdjusted Mean Difference (95% CI), mL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArterial neutrophil-to-lymphocyte ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"−\"\u003e\n \u003cp\u003e1.26 (-1.40, 3.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.354\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDifference in neutrophil-to-lymphocyte ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"−\"\u003e\n \u003cp\u003e1.58 (-0.11, 3.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeutrophil band form ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"−\"\u003e\n \u003cp\u003e-6.23 (-11.21, -1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArterial monocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"−\"\u003e\n \u003cp\u003e2.72 (-2.87, 8.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime of recombinant tissue plasminogen activator administration from symptom onset\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"−\"\u003e\n \u003cp\u003e0.09 (-0.25, 0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cem\u003eNotes\u003c/em\u003e: CI: confidence interval; N: number of complete observations used in the model. Mean differences represent the adjusted change in core infarct volume (in milliliters) per one-unit increase in the exposure, estimated using doubly robust generalized estimating equation models with an identity link function, adjusted for age (centered at the sample mean) and sex, unless otherwise specified. For time of recombinant tissue plasminogen activator administration, the model was unadjusted due to the small sample size (N\u0026thinsp;=\u0026thinsp;10). For neutrophil band form ratio, the model was adjusted only for sex due to the insufficient sample size (N\u0026thinsp;=\u0026thinsp;22) for two confounders. Estimates quantify the conditional exposure-outcome association given the included covariates. Variations in N reflect missing data for specific exposures. All analyses assume independence of observations, with robust standard errors accounting for potential heteroscedasticity and model misspecification.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Associations Between Hematological Parameters and Core Infarct Volume (Rapid)\u003c/h2\u003e\n \u003cp\u003eIn doubly robust generalized estimating equation models adjusted for age and sex, a statistically significant positive association was observed between the difference in neutrophil-to-lymphocyte ratio (\u0026Delta;NLR) and core infarct volume assessed by Rapid (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAdjusted Mean Differences in Core Infarct Volume (Rapid) Associated with Selected Exposures in Patients Undergoing Endovascular Treatment for Acute Ischemic Stroke.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdjusted Mean Difference (95% CI), mL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArterial neutrophil-to-lymphocyte ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.19 (-1.01, 5.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDifference in neutrophil-to-lymphocyte ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.97 (0.64, 5.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeutrophil band form ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.62 (-11.06, 1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArterial monocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.57 (-5.03, 10.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.508\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime of recombinant tissue plasminogen activator administration from symptom onset\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07 (-0.32, 0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.726\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cem\u003eNotes\u003c/em\u003e: CI: confidence interval; N: number of complete observations used in the model. Mean differences represent the adjusted change in core infarct volume (in milliliters) per one-unit increase in the exposure, estimated using doubly robust generalized estimating equation models with an identity link function, adjusted for age (centered at the sample mean) and sex, unless otherwise specified. For time of recombinant tissue plasminogen activator administration, the model was unadjusted due to the small sample size (N\u0026thinsp;=\u0026thinsp;10). For neutrophil band form ratio, the model was adjusted only for sex due to the insufficient sample size (N\u0026thinsp;=\u0026thinsp;22) for two confounders. Estimates quantify the conditional exposure-outcome association given the included covariates. Variations in N reflect missing data for specific exposures. All analyses assume independence of observations, with robust standard errors accounting for potential heteroscedasticity and model misspecification.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eNo statistically significant associations were identified for arterial neutrophil-to-lymphocyte ratio, neutrophil band form ratio, arterial monocytes, or time from symptom onset to recombinant tissue plasminogen activator administration.\u003c/p\u003e\n \u003cp\u003eDetailed results are summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Analysis of Agreement Between Arterial and Peripheral Blood Leukogram Parameters\u003c/h2\u003e\n \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, significant mean biases were observed for segmented granulocytes and lymphocytes, whereas NLR showed no significant bias between arterial and peripheral samples. Passing\u0026ndash;Bablok regression confirmed the absence of proportional and constant bias for NLR (Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary of Bland-Altman Statistics for Leukogram Parameters Comparing Arterial and Peripheral Blood Measurements.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBias\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD of Bias\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLower LOA\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUpper LOA\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSegmented Granulocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.73\u003c/p\u003e\n \u003cp\u003e(0.02 to 5.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e8.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-13.19\u003c/p\u003e\n \u003cp\u003e(-17.86 to -8.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.65\u003c/p\u003e\n \u003cp\u003e(13.98 to 23.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.048\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBand Granulocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003cp\u003e(-0.94 to 0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.54\u003c/p\u003e\n \u003cp\u003e(-7.17 to -3.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.54\u003c/p\u003e\n \u003cp\u003e(3.92 to 7.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLymphocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.43\u003c/p\u003e\n \u003cp\u003e(-4.54 to -0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e6.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-14.83\u003c/p\u003e\n \u003cp\u003e(-18.47 to -11.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.97\u003c/p\u003e\n \u003cp\u003e(6.33 to 13.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.025\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReactive Lymphocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003cp\u003e(-0.71 to 0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.38\u003c/p\u003e\n \u003cp\u003e(-4.32 to -2.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003cp\u003e(2.11 to 4.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.552\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.73\u003c/p\u003e\n \u003cp\u003e(-1.62 to 0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.99\u003c/p\u003e\n \u003cp\u003e(-7.53 to -4.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.53\u003c/p\u003e\n \u003cp\u003e(2.99 to 6.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasophils\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003cp\u003e(-0.22 to 0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.67\u003c/p\u003e\n \u003cp\u003e(-2.18 to -1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.83\u003c/p\u003e\n \u003cp\u003e(1.32 to 2.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.585\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEosinophils\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003cp\u003e(-0.17 to 0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.25\u003c/p\u003e\n \u003cp\u003e(-1.63 to -0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003cp\u003e(0.97 to 1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.624\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003cp\u003e(-1.58 to 1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e4.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.59\u003c/p\u003e\n \u003cp\u003e(-11.12 to -6.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.37\u003c/p\u003e\n \u003cp\u003e(5.84 to 10.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.34\u003c/p\u003e\n \u003cp\u003e(-23.53 to 12.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e31.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-67.09\u003c/p\u003e\n \u003cp\u003e(-98.96 to -35.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.42\u003c/p\u003e\n \u003cp\u003e(24.55 to 88.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.537\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003cp\u003e(-1.36 to 4.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e5.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.63\u003c/p\u003e\n \u003cp\u003e(-14.32 to -4.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.29\u003c/p\u003e\n \u003cp\u003e(7.60 to 16.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cem\u003eNotes\u003c/em\u003e: N\u0026thinsp;=\u0026thinsp;number of paired comparisons. Bias\u0026thinsp;=\u0026thinsp;mean difference between arterial and peripheral measurements (arterial minus peripheral). SD\u0026thinsp;=\u0026thinsp;standard deviation. LOA\u0026thinsp;=\u0026thinsp;limit of agreement. CI\u0026thinsp;=\u0026thinsp;confidence interval. p-value derived from one-sample t-test assessing whether bias differs significantly from zero (alternative hypothesis: true bias\u0026thinsp;\u0026ne;\u0026thinsp;0); values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicate statistically significant bias. NLR\u0026thinsp;=\u0026thinsp;neutrophil-to-lymphocyte ratio; DLR\u0026thinsp;=\u0026thinsp;derived lymphocyte ratio; LMR\u0026thinsp;=\u0026thinsp;lymphocyte-to-monocyte ratio.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eRegarding sex stratification, the subgroup analyses reveal no substantial evidence of differential effects, as confidence intervals for slopes and intercepts broadly overlap between males and females across parameters, with no consistent patterns of deviation from the line of identity. For instance, in NLR, the male slope (1.204, 95% CI 0.744\u0026ndash;2.097) and female slope (0.695, 95% CI 0.422\u0026ndash;1.276) overlap despite numerical differences, inferring that sex does not meaningfully moderate the arterial-peripheral relationship. Wider confidence intervals in subgroups (due to reduced sample sizes, e.g., N\u0026thinsp;=\u0026thinsp;12 for males in several parameters) limit definitive conclusions, but this absence of clear stratification effects implies that sex-specific adjustments are unnecessary for these measurements in clinical practice.\u003c/p\u003e\n \u003cp\u003eIn summary, segmented granulocytes and lymphocytes exhibit statistically significant biases in arterial versus peripheral blood measurements, indicating that agreement between sampling sites differs across leukocyte subpopulations.\u003c/p\u003e\n \u003cp\u003eRegarding sex stratification, the analyses reveal no substantial disagreements between males and females. Consequently, there is no need to account for sex in clinical routines when interpreting these leukogram parameters, though larger studies may further validate this in diverse populations.\u003c/p\u003e\n \u003ch3\u003e3.5. Correlation Analysis of Hematological and Clinical Outcomes in Patients Undergoing Endovascular Treatment for Acute Ischemic Stroke\u003c/h3\u003e\n \u003cp\u003eAs illustrated in the correlation matrix (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), moderate negative correlations were observed between arterial NLR and ASPECTS on Brainomix (\u0026rho; = -0.47, p\u0026thinsp;=\u0026thinsp;0.011) as well as between \u0026Delta;NLR and ASPECTS on Brainomix (\u0026rho; = -0.40, p\u0026thinsp;=\u0026thinsp;0.037), both achieving statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A weak positive correlation was noted between \u0026Delta;NLR and core infarct volume on Rapid (\u0026rho;\u0026thinsp;=\u0026thinsp;0.34, p\u0026thinsp;=\u0026thinsp;0.048). Additionally, a borderline moderate negative correlation emerged between arterial NLR and ASPECTS on Rapid (\u0026rho; = -0.37, p\u0026thinsp;=\u0026thinsp;0.050). These findings demonstrate that elevated arterial NLR and \u0026Delta;NLR may associate with greater early ischemic changes (lower ASPECTS) and, in the case of \u0026Delta;NLR, larger core infarct volumes, indicating associations between inflammatory markers and imaging-derived measures of ischemic injury.\u003c/p\u003e\n \u003cp\u003eNo other correlations reached statistical significance, including those involving arterial band granulocytes, reactive lymphocytes, or monocytes with NIHSS scores, infarct volumes, or mTICI grade, though some displayed numerically weak to moderate strengths (e.g., arterial band granulocytes with penumbra volume on Brainomix: \u0026rho; = -0.30, p\u0026thinsp;=\u0026thinsp;0.079; arterial band granulocytes with ASPECTS on Rapid: \u0026rho;\u0026thinsp;=\u0026thinsp;0.31, p\u0026thinsp;=\u0026thinsp;0.099).\u003c/p\u003e\n \u003cp\u003eThese findings further characterize the associations between early inflammatory markers and imaging-derived measures of ischemic injury.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eLong-term randomized trials and meta-analyses have unequivocally demonstrated that endovascular treatment of acute ischemic stroke with mechanical thrombectomy significantly improves functional outcomes compared with best medical therapy alone, including intravenous thrombolysis, representing one of the most important advances in contemporary stroke care [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Despite this progress, clinical response to reperfusion therapy remains highly heterogeneous, and achievement of complete angiographic reperfusion (eg, mTICI 3) does not translate uniformly into favorable functional recovery. Previous studies by van Horn et al. and others have shown that a substantial proportion of patients with full recanalization experience unfavorable outcomes at 90 days, with age, baseline stroke severity, and extent of ischemic injury remaining independent prognostic determinants [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. These findings highlight that restoration of macrovascular patency does not fully determine tissue fate after stroke and underscore the importance of biological determinants operating at the microvascular and cellular level. In particular, inflammatory responses occurring at the interface between the systemic circulation and the ischemic cerebral vasculature may critically influence infarct evolution independently of angiographic reperfusion status.\u003c/p\u003e \u003cp\u003eThis discrepancy underscores the complexity of secondary brain injury mechanisms and highlights the role of inflammatory, immunological, and metabolic processes in shaping infarct evolution and clinical trajectory [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Increasing evidence suggests that the very early inflammatory response \u0026ndash; characterized by neutrophil predominance accompanied by lymphopenia \u0026ndash; may influence infarct progression, blood\u0026ndash;brain barrier integrity, and microvascular perfusion in ways not captured by angiographic assessment of large-vessel recanalization [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Consequently, identification of accessible hematological biomarkers capable of early risk stratification has attracted growing interest. Leukogram-derived indices, particularly the neutrophil-to-lymphocyte ratio (NLR), represent simple and widely available markers of systemic inflammation [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, in clinical practice these parameters may be obtained from peripheral blood or from arterial samples collected during endovascular procedures, and their agreement across sampling sites has remained insufficiently characterized, introducing interpretative uncertainty in hyperacute stroke studies [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAgainst this background, the present study systematically evaluated agreement between arterial and peripheral leukogram parameters while examining the stability of NLR as a sampling-site\u0026ndash;independent biomarker associated with early ischemic injury. Our findings demonstrate that leukogram parameters are not universally interchangeable. This observation provides direct in vivo evidence that inflammatory cell distributions differ between the systemic and cerebral arterial compartments during hyperacute ischemia, supporting the concept that stroke induces spatially heterogeneous immune responses within the vascular system supplying the injured brain.\u003c/p\u003e \u003cp\u003eSelective, statistically significant systematic differences were observed for segmented granulocytes and lymphocytes, whereas other leukocyte fractions and derived indices showed no clinically meaningful bias (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These results suggest that interpretation of neutrophilia or lymphopenia during the hyperacute phase of stroke may depend on sampling location, a factor with potential implications for assessment of inflammatory activation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTaken together, these observations support the concept that leukocyte distribution becomes compartmentalized within the cerebral circulation during the hyperacute phase of ischemia, likely reflecting local endothelial activation and collateral flow dynamics. Such compartmentalization challenges the assumption that leukogram parameters measured simultaneously in arterial blood from the proximal cerebral circulation and in peripheral venous blood are biologically and clinically interchangeable [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Notably, despite these compartment-specific differences, NLR remained remarkably stable across sampling sites. Agreement was confirmed by Bland\u0026ndash;Altman and Passing\u0026ndash;Bablok analyses without evidence of constant or proportional bias (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). This stability is consistent with prior reports suggesting that NLR, as a composite parameter, partially compensates for opposing shifts in neutrophil and lymphocyte counts, thereby demonstrating resistance to preanalytical variability [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. From a methodological perspective, this positions NLR as a robust inflammatory marker in acute ischemic stroke, particularly relevant given the central role of neutrophils in early ischemic cascades. Beyond methodological robustness, this stability suggests that NLR reflects a fundamental biological equilibrium between innate immune activation and adaptive immune suppression that is preserved across vascular compartments. This property distinguishes NLR from individual leukocyte counts and supports its interpretation as an integrative biomarker of systemic inflammatory state relevant to ischemic brain injury.\u003c/p\u003e \u003cp\u003eA second major observation is the association between early inflammatory markers and imaging-defined ischemic injury. Arterial NLR correlated inversely with ASPECTS, and the difference in NLR between sampling sites (ΔNLR) was positively associated with infarct core volume. These relationships, initially identified in correlation analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), were supported by multivariable models, indicating that early inflammatory activation dominated by neutrophils may contribute to tissue injury severity. Importantly, the association between ΔNLR and infarct core volume was demonstrated across two independent automated imaging platforms. A borderline association was observed using Brainomix (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), whereas a statistically significant relationship was confirmed using Rapid software (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Consistency across segmentation systems strengthens confidence that these findings are biologically meaningful rather than platform-specific artifacts.\u003c/p\u003e \u003cp\u003eImportantly, the partial differences observed between RAPID and Brainomix are unlikely to reflect fundamentally different biological definitions of infarct core in the present study, as both platforms were applied to the same CT perfusion datasets using harmonized perfusion thresholds (rCBF\u0026thinsp;\u0026lt;\u0026thinsp;30% for infarct core and Tmax\u0026thinsp;\u0026gt;\u0026thinsp;6 s for hypoperfused tissue). Rather, these differences most likely reflect vendor-specific variability in proprietary image-processing pipelines, including deconvolution algorithms, automated arterial input and venous output function selection, motion and noise correction procedures, and voxel-level classification strategies. Such technical factors are known to influence quantitative infarct core estimation, particularly in the hyperacute phase when perfusion abnormalities evolve dynamically and signal-to-noise characteristics may affect threshold-based tissue classification.\u003c/p\u003e \u003cp\u003eWithin this context, the association between ΔNLR and infarct core volume was statistically significant when infarct core was quantified using RAPID, whereas a borderline association was observed with Brainomix-derived measurements. These findings suggest that the strength of observed associations may be sensitive to platform-specific measurement variability and limited statistical power inherent to modest sample sizes. Importantly, however, the overall directionality of associations was consistent across both segmentation systems, supporting the biological coherence of the observed relationship between early inflammatory activation and ischemic tissue injury.\u003c/p\u003e \u003cp\u003eThese observations have important methodological implications. They indicate that inflammatory biomarkers such as NLR and ΔNLR demonstrate biologically meaningful associations with tissue injury that are detectable across independent imaging platforms, while also emphasizing that quantitative effect estimates may vary depending on the specific implementation of perfusion analysis software. This platform sensitivity underscores the importance of interpreting imaging\u0026ndash;biomarker relationships within the context of known technical variability and reinforces the need for cautious cross-study comparisons when different segmentation tools are used.\u003c/p\u003e \u003cp\u003eAt the same time, the convergence of findings across analytically distinct imaging platforms strengthens the inference that early inflammatory activation is intrinsically linked to ischemic tissue injury rather than representing a software-specific analytical artifact. The association between ΔNLR, derived from paired arterial samples obtained proximal to the occlusion and peripheral blood, and infarct core volume supports the concept that immune activation is closely coupled to local vascular and microcirculatory dysfunction during hyperacute ischemia. This relationship is biologically plausible, given the established role of neutrophils in promoting endothelial injury, microvascular obstruction, and secondary infarct expansion through release of reactive oxygen species, proteolytic enzymes, and proinflammatory mediators.\u003c/p\u003e \u003cp\u003eTaken together, these findings support a model in which early inflammatory responses are not merely epiphenomena but reflect biologically relevant processes linked to tissue-level injury in acute ischemic stroke. The consistency of these associations across independent perfusion analysis platforms, despite expected technical variability, strengthens confidence in the robustness and translational relevance of leukogram-derived inflammatory markers \u0026ndash;particularly NLR and ΔNLR \u0026ndash; as accessible indicators of ischemic tissue injury in patients undergoing reperfusion therapy.\u003c/p\u003e \u003cp\u003eMechanistically, neutrophils accumulating within ischemic microvasculature represent a potent source of matrix metalloproteinases, reactive oxygen species, and proinflammatory mediators that may exacerbate endothelial dysfunction and contribute to microvascular no-reflow, thereby facilitating secondary infarct expansion despite successful macrovascular recanalization [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This mechanistic framework provides biological plausibility for the observed association between arterial inflammatory indices and imaging-defined infarct core volume, reinforcing the interpretation that leukogram-derived markers reflect active pathophysiological processes rather than epiphenomenal systemic responses. In parallel, stroke-associated lymphopenia has been interpreted as a manifestation of systemic immunosuppression driven by lymphocyte apoptosis and immune dysregulation, potentially increasing susceptibility to infection and influencing recovery trajectories [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Within this framework, the observed associations between NLR, ΔNLR, and imaging-derived infarct measures align with contemporary models linking immune responses to ischemic brain injury while remaining associative rather than causal.\u003c/p\u003e \u003cp\u003eAn additional, intriguing finding is the inverse association between the proportion of band neutrophils and infarct core volume assessed with Brainomix (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Although immature neutrophil forms are traditionally considered markers of heightened inflammatory activity, emerging evidence indicates functional heterogeneity within neutrophil subsets, including potential regulatory and reparative roles depending on activation state and local microenvironment [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. This observation further underscores the biological complexity of early immune responses in ischemic stroke and highlights that qualitative features of immune activation \u0026ndash; rather than absolute leukocyte counts alone \u0026ndash; may influence tissue-level injury. While this observation requires validation in larger cohorts, it aligns with evolving concepts emphasizing immune cell phenotype and functional plasticity rather than absolute counts. Given the observational design, these findings should be interpreted cautiously and not as evidence of direct causal protection.\u003c/p\u003e \u003cp\u003eIn contrast, multivariable analyses did not demonstrate significant associations between inflammatory parameters and achievement of complete angiographic reperfusion (mTICI 3; Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This is consistent with prior thrombectomy literature showing that procedural success is predominantly determined by anatomical and technical factors \u0026ndash; including occlusion characteristics, time to reperfusion, and device performance \u0026ndash; rather than systemic inflammatory status. This dissociation between inflammatory biomarkers and angiographic reperfusion further supports the concept that immune activation primarily influences downstream tissue-level injury rather than the mechanical success of large-vessel recanalization. Immune responses appear more relevant to secondary tissue injury and longer-term outcomes than to immediate procedural metrics [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAdjusted Risk Ratios for the Association Between Selected Exposures and Achievement of mTICI Grade 3 in Patients Undergoing Endovascular Treatment for Acute Ischemic Stroke.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjusted RR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArterial neutrophil-to-lymphocyte ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.993 (0.885, 1.115)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifference in neutrophil-to-lymphocyte ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.006 (0.962, 1.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil band form ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.830 (0.567, 1.218)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArterial monocytes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.991 (0.798, 1.231)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime of recombinant tissue plasminogen activator administration from symptom onset\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.002 (0.992, 1.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.729\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNotes\u003c/em\u003e: RR: risk ratio; CI: confidence interval; N: number of complete observations used in the model. Risk ratios represent the multiplicative change in the probability of achieving mTICI grade 3 per one-unit increase in the exposure, adjusted for age (centered at the sample mean) and sex using doubly robust generalized estimating equation models. For time of recombinant tissue plasminogen activator administration, the model was unadjusted due to the small sample size (N\u0026thinsp;=\u0026thinsp;10), for Neutrophil band form ratio the model was adjusted only for Sex due to the insufficient sample size (N\u0026thinsp;=\u0026thinsp;22) for two confounders. Estimates quantify the conditional exposure-outcome association given the included covariates. Variations in N reflect missing data for specific exposures. All analyses assume independence of observations, with robust standard errors accounting for potential heteroscedasticity and model misspecification.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCollectively, these findings reinforce the importance of integrating clinical, laboratory, and imaging data when evaluating acute ischemic stroke. Growing evidence suggests that angiographic recanalization alone is insufficient to predict biological recovery and that individualized therapeutic strategies may benefit from incorporation of inflammatory biomarkers [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In this context, simple hematological indices such as NLR and ΔNLR may provide practical adjuncts for early biological stratification. Importantly, the stability of NLR across vascular compartments, combined with its consistent association with imaging-defined tissue injury across independent perfusion analysis platforms, supports its interpretation as a biologically robust and technically reliable biomarker. Because leukogram measurements are universally available in routine clinical practice, these findings suggest that NLR could serve as an immediately translatable biomarker for biological risk stratification in reperfusion-treated stroke. Such stratification may ultimately help identify patients at increased risk of progressive tissue injury despite successful recanalization and guide development of adjunctive immunomodulatory strategies.\u003c/p\u003e \u003cp\u003eSeveral limitations merit consideration. The modest sample size restricts statistical power and increases susceptibility to type II error, particularly in multivariable analyses (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Single-time-point sampling precludes assessment of dynamic inflammatory trajectories. Furthermore, absence of direct measurements within cerebral microcirculation limits differentiation between systemic and localized immune processes, and lack of long-term functional follow-up prevents direct linkage of inflammatory markers to delayed clinical outcomes. Additionally, although associations were directionally consistent across imaging platforms, quantitative differences between software implementations highlight the importance of considering technical variability when interpreting perfusion-derived infarct metrics.\u003c/p\u003e \u003cp\u003eDespite these constraints, the present study provides coherent evidence that early inflammatory markers \u0026ndash;particularly NLR \u0026ndash; complement standard clinical and imaging assessment in acute ischemic stroke. The stability of NLR across sampling compartments and its association with imaging-defined injury support the concept that early neutrophil-dominant immune responses participate in shaping infarct evolution. By demonstrating compartment-specific inflammatory heterogeneity in direct proximity to the ischemic vascular territory and linking these findings to quantitative imaging biomarkers, this study provides in vivo evidence connecting vascular immune activation with tissue-level injury in humans. These findings identify compartment-specific inflammatory heterogeneity as a previously underrecognized feature of acute ischemic stroke and establish NLR as a biologically stable biomarker linked to early tissue injury. Future prospective studies incorporating longitudinal immune profiling and functional outcomes are warranted to determine whether inflammatory biomarkers can improve biological risk stratification and inform precision therapeutic strategies in acute stroke.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis study was supported by internal research grants from the Jagiellonian University Medical College: Project no. N41/DBS/001158 (Principal Investigator: Tadeusz Popiela) and Project no. N42/DBS/000452 (Principal Investigator: Wirginia Krzysciak). The funding sources had no role in the study design, data collection, analysis, interpretation of results, manuscript preparation, or the decision to submit the work for publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e W.K. and T.P. conceived the study, developed the conceptual framework, and designed the compartment-specific arterial and peripheral venous sampling protocol. B.Ł., P.B., R.P., P.W. and T.P. performed endovascular procedures, obtained arterial blood samples during mechanical thrombectomy, and contributed to acquisition and verification of clinical and procedural data. W.K., M.P., and B.N. performed hematological analyses, conducted laboratory data validation, and contributed to biological interpretation of inflammatory parameters. W.K. designed and performed the statistical analyses, including agreement modeling and multivariable regression, and contributed to methodological development and interpretation of analytical results. W.K. and P.M drafted the manuscript and integrated clinical, imaging, and laboratory findings into a unified biological framework. T.P. provided overall scientific supervision and critically revised the manuscript for important intellectual content. All authors contributed to interpretation of the data, critically revised the manuscript, approved the final version, and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u0026nbsp;\u003c/strong\u003eThe 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Bioethics Committee of the Jagiellonian University (No. 1072.6120.18.2024, dated April 17, 2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u0026nbsp;\u003c/strong\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSaini, V., Guada, L. \u0026amp; Yavagal, D. R. Global Epidemiology of Stroke and Access to Acute Ischemic Stroke Interventions. \u003cem\u003eNeurology\u003c/em\u003e \u003cb\u003e97\u003c/b\u003e, 6\u0026ndash;16 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGomis, M. \u0026amp; D\u0026Atilde;\u0026iexcl;valos, A. 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(1965).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"acute ischemic stroke, mechanical thrombectomy, neutrophil-to-lymphocyte ratio, inflammation, perfusion imaging","lastPublishedDoi":"10.21203/rs.3.rs-9216218/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9216218/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBiological determinants of ischemic injury after reperfusion remain incompletely understood. Early inflammatory responses may influence infarct progression, but the reliability of leukogram measurements across vascular compartments is unclear. We assessed agreement between arterial and peripheral venous leukogram parameters and their association with imaging-defined ischemic injury. In this prospective study, 37 patients undergoing mechanical thrombectomy had paired blood samples collected from the proximal cerebral arterial circulation before reperfusion and from peripheral veins. Leukogram parameters and inflammatory indices were analyzed. Agreement was evaluated using Bland\u0026ndash;Altman and Passing\u0026ndash;Bablok methods, while associations with ischemic injury \u0026ndash; measured by ASPECTS and infarct core volume (Brainomix, RAPID) \u0026ndash; were examined using correlation and doubly robust regression models. Segmented neutrophils and lymphocytes differed between compartments, indicating limited interchangeability. In contrast, the neutrophil-to-lymphocyte ratio (NLR) showed strong agreement across sampling sites. Higher arterial NLR and ΔNLR were associated with lower ASPECTS and larger infarct core volumes. A higher proportion of neutrophil band forms was independently linked to smaller infarct cores. Leukogram parameters were not associated with angiographic reperfusion. These findings reveal compartment-specific inflammatory heterogeneity and identify NLR as a stable, clinically accessible biomarker associated with early ischemic injury, independent of sampling site.\u003c/p\u003e","manuscriptTitle":"Inflammatory Blood Stability Links to Early Ischemic Brain Injury Running head: Inflammatory Blood Stability in Stroke","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-13 18:05:32","doi":"10.21203/rs.3.rs-9216218/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-05-05T11:18:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-16T05:18:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-09T14:24:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-09T14:23:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-24T21:50:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9a8b4bee-bc35-4141-b78d-5d7a891c1298","owner":[],"postedDate":"May 13th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewersInvited","content":"26","date":"2026-05-05T11:18:20+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67978940,"name":"Health sciences/Biomarkers"},{"id":67978941,"name":"Health sciences/Medical research"},{"id":67978942,"name":"Health sciences/Neurology"},{"id":67978943,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-05-13T18:05:33+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-13 18:05:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9216218","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9216218","identity":"rs-9216218","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}