Arterial Spin Labeling Predicts Early Neurological Improvement After Endovascular Therapy in Acute Ischemic Stroke

Arterial Spin Labeling Predicts Early Neurological Improvement After Endovascular Therapy in Acute Ischemic 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 Research Article Arterial Spin Labeling Predicts Early Neurological Improvement After Endovascular Therapy in Acute Ischemic Stroke Yuan Zhang, Xiaotian Zhou, Mingliang Tong, Bin Song, Yi Sun, Xilin Sun, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9370575/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Acute ischemic stroke (AIS) ranks among the leading causes of morbidity and mortality worldwide. Although endovascular therapy (EVT) is the standard approach for large vessel occlusion, neurological recovery following post-recanalization varies widely. This study aimed to investigate the predictive value of arterial spin labeling (ASL) with different post-labeling delays (PLDs) for early neurological improvement (ENI) after EVT in patients with AIS. Methods A retrospective analysis was conducted on 91 patients with AIS who underwent EVT from January 2023 to April 2025. ASL was performed to quantify cerebral blood flow at three distinct PLDs (1500, 2000, and 2500 ms). Univariable and multivariable logistic regression were utilized to identify factors associated with ENI, which was defined as the complete resolution of neurological deficits or a ≥ 4-point reduction in the National Institutes of Health Stroke Scale score at 24 hours post-stroke. Results Clinical factors, including drinking, smoking, and diastolic blood pressure (DBP), were significantly correlated with ENI. ASL-derived cerebral blood flow values, particularly at a PLD of 1500 ms in the infarct core and affected hemispheres, were positively associated with ENI ( P < 0.05). A combined predictive model integrating clinical and ASL variables demonstrated an accuracy of 0.747, sensitivity of 0.782, and a positive predictive value of 0.796, thereby outperforming models based solely on clinical or imaging parameters. Conclusions ASL with different PLDs provides valuable hemodynamic insights for predicting ENI after EVT in patients with AIS. When combined with clinical characteristics, this noninvasive technique enhances predictive accuracy and shows promise as a biomarker for individualized prognosis. Stroke Endovascular therapy Post-labeling delay Prognosis Figures Figure 1 Figure 2 Figure 3 Introduction Acute ischemic stroke (AIS) poses a significant health burden worldwide, resulting in about 6.55 million deaths and 143 million disabilities each year.[ 1 – 3 ] Despite the emergence of endovascular therapy (EVT) as the standard approach for large vessel occlusion and its significant improvement in recanalization rates,[ 4 – 6 ] patient neurological recovery after successful reperfusion remains highly variable. An accurate assessment of cerebral perfusion is essential for predicting early neurological recovery and tailoring patient management. Although modalities, such as computed tomography perfusion (CTP) and dynamic susceptibility contrast imaging, are currently standard for delineating the ischemic penumbra and collateral status, they rely on exogenous contrast agents to estimate relative cerebral blood flow (CBF). [ 7 , 8 ] These methods carry risks of contrast-induced nephropathy and allergic reactions, and their quantitative accuracy can be compromised by contralateral compensatory flow variations, limiting their applicability in certain populations. Arterial spin labeling (ASL) overcomes the drawbacks of exogenous contrast agents by offering noninvasive CBF quantification, making it ideal for patients with renal or allergic contraindications. Acquiring ASL data at different post-labeling delays (PLDs) provides complementary perfusion information under varying hemodynamic conditions.[ 9 ] Although existing literature links ASL-detected post-EVT hyperperfusion to favorable 90-day outcomes,[ 10 ] and ASL can assess the ischemic penumbra and predict recovery in AIS,[ 11 , 12 ] the role of ASL with different PLDs, especially when integrated with clinical variables in predicting early neurological improvement (ENI) remains undefined. Therefore, this study aimed to determine the prognostic value of ASL with different PLDs for ENI following EVT in patients with AIS by combining perfusion parameters with clinical variables to optimize early outcome prediction. Methods 1.1 Study Population This single-center retrospective analysis was conducted at a Stroke Center from January 2023 to April 2025. Inclusion criteria: (1) age > 18 years; (2) diagnosis of AIS confirmed by clinical presentation and imaging; (3) symptom onset within 24 hours; (4) baseline National Institutes of Health Stroke Scale (NIHSS) score ≤ 1; and (5) infarct located in the anterior circulation, with the ischemic core defined by CT perfusion (CTP; relative CBF < 30%) or magnetic resonance imaging (MRI; apparent diffusion coefficient < 620 × 10⁻⁶ mm²/s).[ 13 ] Exclusion criteria: (1) transient ischemic attack; (2) intracerebral hemorrhage; (3) contraindications to MRI; (4) severe systemic diseases or malignant tumors; and (5) incomplete clinical or imaging data. Patients received intravenous thrombolysis, mechanical thrombectomy, or bridging therapy based on clinical indications. The NIHSS scores were evaluated and recorded at baseline (upon admission), 24 hours post-admission, and at discharge. 1.2 Outcome Measures The primary endpoint was neurological function, assessed using the NIHSS score at admission, 24 hours post-admission, and at discharge. ENI was identified by a drop of 4 or more points from the baseline score.[ 14 ] 2.1 MRI Data Acquisition Imaging was performed on a 3.0T MRI system (uMR 770, United Imaging Healthcare, Shanghai, China) equipped with a 32-channel head coil. The standardized imaging protocol included: T2-weighted imaging (T2WI; TR/TE, 4107/88.8 ms), fluid-attenuated inversion recovery (FLAIR; TR/TE/TI, 8000/104.1/2425 ms), and T1-weighted imaging (T1WI; TR/TE, 7.87/3.2 ms). Diffusion-weighted imaging was acquired with b-values of 0 and 1000 s/mm² (TR/TE, 2364/90.5 ms; spatial resolution, 1.0 × 1.0 × 6.5 mm³). The single PLD pseudo-continuous ASL (PCASL) sequence was repeated thrice with different PLDs of 1500, 2000, and 2500 ms. PCASL parameters were: TR/TE, 5500/13.58 ms; spatial resolution, 3.5 × 3.5 × 6.0 mm³; field of view, 224 × 224 mm²; scan duration, 3 min 18 s. 2.2 Image Analysis MATLAB R2021b (MathWorks, Natick, MA, USA) and Statistical Parametric Mapping (Wellcome Trust Centre for Neuroimaging, UCL, London, UK) were used for image processing and quantitative analysis. 2.2.1 PCASL Processing Motion correction was performed on the PCASL images to compensate for head motion. Individuals with excessive head motion (> 3 mm/3°) and/or other artifacts, such as severe signal dropout or incomplete field of view, were excluded. The label and control images were pairwise subtracted to produce perfusion-weighted images ( \(\:\text{Δ}\text{M}\) ). \(\:{\text{M}}_{\text{0}}\) images from each PLD with values of 1500, 2000, and 2500 ms were coregistered to the PCASL space to ensure that \(\:{\text{M}}_{\text{0}}\) signals and perfusion-weighted images were in the same anatomical space for CBF quantification. 2.2.2 CBF quantification For PCASL, the quantification of CBF were derived from the perfusion difference signal ΔM utilizing a single-compartment perfusion model[ 15 ]: $$\:\text{CBF=}\frac{\text{Δ}\text{M}}{{\text{M}}_{\text{0}}}\text{⋅}\frac{\text{6000⋅}\lambda\text{⋅}{\text{e}}^{\text{PLD}\text{/}\text{T}{\text{1}}_{\text{blood}}}}{\text{2⋅}\text{α}\text{⋅}{\text{α}}_{\text{bg}}\text{⋅}\text{T}{\text{1}}_{\text{blood}}\text{⋅(1-}{\text{e}}^{\text{-}\text{τ}\text{/}\text{T}{\text{1}}_{\text{blood}}}\text{)}}\text{\hspace{1em}(mL/100g/min)}$$ where \(\:\lambda\) is the partition coefficient of water in the brain brain" = 0.9 mL/g" is labeling efficiency, typically assumed to be 0.85, \(\:\text{T}{\text{1}}_{\text{blood}}\) is the longitudinal relaxation time of arterial blood = 1650 ms, \(\:\text{τ}\) is labeling duration = 1.8 s. \(\:\text{Δ}\text{M}\) and \(\:{\text{M}}_{\text{0}}\) denot the signal intensities of the perfusion difference image and the proton density-weighted image, respectively. Using a Gaussian kernel of 0.5 mm, the calculated CBF maps were spatially smoothed. 2.2.3 Results evaluation T1-weighted images were processed with Statistical Parametric Mapping12 to create tissue probability maps for gray matter, white matter, and cerebrospinal fluid. The hyperintense region on diffusion weighted imaging at b = 1000 s/mm² defined the infarct core. The territory was manually delineated by two radiologists (Z.Y. with 10 years and S.X.L. with 12 years of experience) under the supervision of another experienced radiologist (W.H. with 21 years of experience), in consensus, using the open-source segmentation software ITK-SNAP version 4.4.0 ( https://www.itksnap.org/ ) (Penn Image Computing and Science Laboratory, Philadelphia, PA) to minimize inter-observer variability. The infarct core and CBF maps were normalized to the Montreal Neurological Institute space utilizing each participant's T1-weighted structural image as a reference. The resulting mask was then mirrored across the cerebral midline to generate the contralateral healthy region. From the normalized CBF maps, the mean CBF values were extracted for the affected and healthy regions, manually delineated infarct core, and the corresponding contralateral regions. The hemisphere containing the infarct was defined as the affected region, whereas the contralateral hemisphere was defined as the healthy region. All CBF values were calculated within these predefined regions of interest to assess alterations in cerebral perfusion, and the extracted data were used for statistical analysis. 3. Statistical Analysis Statistical analyses were conducted utilizing R 4.4.2 and MedCalc 16.0. Data are presented as mean ± SD, median (interquartile range), or frequencies (percentages). Group comparisons utilized the independent-samples t -test, Mann-Whitney U test, chi-square test, or Fisher's exact test, as appropriate following Kolmogorov-Smirnov normality testing. To identify predictors of neurological improvement, clinical variables with P < 0.05 in univariable logistic regression were entered into a multivariable model. For ASL parameters, Elastic Net regularization was applied to those with univariable P < 0.05 to minimize multicollinearity and isolate robust predictors. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated. Model discrimination and calibration were evaluated using receiver operating characteristic (ROC) curves and the Hosmer-Lemeshow test with calibration plots, respectively. The incremental value of ASL parameters was quantified using Net Reclassification Improvement Statistical significance was defined as a two-sided P < 0.05. Results Baseline Characteristics A total of 91 patients were included in the final analysis, as shown in the flowchart (Fig. 1 ). ENI was observed in 34 patients (37.4%) at 24 hours post-admission and in 55 patients (60.4%) at discharge. The patients’ demographic and clinical characteristics are summarized in Table S1 . At 24 hours post-admission, drinking was negatively associated with ENI ( P = 0.037, OR = 0.170, 95% CI: 0.032–0.896). At discharge, smoking ( P = 0.015, OR = 0.302, 95% CI: 0.114–0.796) and higher DBP ( P = 0.010, OR = 0.960, 95% CI: 0.930–0.990) were independent predictors of poor neurological improvement (Table 1 ). Table 1 Univariable logistic regression of clinical characteristics Items 24h neurologic improvement Discharge neurologic improvement P value OR Lower limit of 95% CI Upper limit of 95% CI P value OR Lower limit of 95% CI Upper limit of 95% CI Age (per year) 0.292 0.977 0.936 1.020 0.117 0.964 0.922 1.009 Sex (Female vs. Male) 0.240 0.573 0.226 1.452 0.308 1.606 0.647 3.989 Drinking (No vs. Yes) 0.037 * 0.170 0.032 0.896 0.386 0.480 0.091 2.524 Smoking Yes (reference) (-) 1.000 (-) (-) (-) 1.000 (-) (-) No 0.970 1.019 0.394 2.636 0.015 * 0.302 0.114 0.796 Unknown 0.903 0.926 0.268 3.198 0.113 0.371 0.109 1.266 Hypertension 0.324 0.615 0.234 1.615 0.278 0.604 0.243 1.503 Diabetes 0.732 1.181 0.457 3.053 0.417 1.464 0.583 3.678 Hyperlipemia 0.942 1.050 0.284 3.888 0.671 1.317 0.370 4.687 AF 0.611 1.447 0.348 6.014 0.515 0.623 0.150 2.587 Cerebral infarction 0.817 0.875 0.282 2.718 0.270 0.500 0.146 1.713 Normal to onset (per hour) 0.095 0.849 0.701 1.029 0.558 1.030 0.933 1.137 Affected side (Right vs. Left) 0.314 0.641 0.270 1.523 0.737 0.865 0.372 2.012 TOAST LAA (reference) (-) 1.000 (-) (-) CE 0.359 0.564 0.166 1.915 0.359 1.773 0.522 6.020 SAO 0.945 1.035 0.385 2.782 0.727 0.839 0.314 2.243 SOE 0.982 0.978 0.145 6.575 0.390 2.727 0.277 26.864 SUE 0.999 < 0.001 < 0.001 NR 0.999 < 0.001 < 0.001 NR LDL (per mmol/L) 0.807 1.062 0.657 1.714 0.313 0.781 0.484 1.262 HCY (per µmol/L) 0.250 0.982 0.952 1.013 0.867 0.999 0.983 1.015 FIB (per g/L) 0.653 1.129 0.666 1.912 0.966 1.012 0.598 1.711 PLT (per ×10⁹/L) 0.538 0.997 0.989 1.006 0.257 0.995 0.987 1.003 SBP (per mmHg) 0.905 0.999 0.982 1.016 0.124 0.987 0.970 1.004 DBP (per mmHg) 0.178 0.979 0.949 1.010 0.010 * 0.960 0.930 0.990 OR, odds ratio; CI, confidence interval; AF, atrial fibrillation; TOAST, Trial of Org 10172 in Acute Stroke Treatment; LAA, large-artery atherosclerosis; CE, cardioembolism; SAO, small-artery occlusion; SOE, stroke of other determined etiology; SUE, stroke of undetermined etiology; LDL, low-density lipoprotein; HCY, homocysteine; FIB, fibrinogen; PLT, platelets; SBP, systolic blood pressure; DBP, diastolic blood pressure. *: p < 0.05. ASL Variables Univariable analysis demonstrated that CBF values derived from ASL at different PLDs were significantly associated with ENI. At a PLD of 1500 ms, higher CBF in the infarct core (OR = 1.042, 95% CI: 1.006–1.080, P = 0.021), contralateral region (OR = 1.100, 95% CI: 1.011–1.197, P = 0.026), and affected region (OR = 1.102, 95% CI: 1.030–1.179, P = 0.005) were positively correlated with ENI. At longer PLDs (2000–2500 ms), CBF in the infarct core and affected regions remained significant predictors of ENI ( P < 0.05) (Table 2 ). Table 2 Univariable logistic regression analysis of different PLD parameters Items 24h neurologic improvement Discharge neurologic improvement P value OR Lower limit of 95% CI Upper limit of 95% CI P value OR Lower limit of 95% CI Upper limit of 95% CI PLD_1500_Infarct 0.404 1.013 0.982 1.046 0.021 * 1.042 1.006 1.080 PLD_1500_Contralateral 0.519 1.027 0.948 1.112 0.026 * 1.100 1.011 1.197 PLD_1500_Affected 0.533 1.019 0.961 1.080 0.005 * 1.102 1.030 1.179 PLD_1500_Healthy 0.920 1.003 0.949 1.060 0.048 * 1.060 1.001 1.123 PLD_2000_Infarct 0.127 1.025 0.993 1.057 0.252 1.020 0.986 1.054 PLD_2000_Contralateral 0.431 1.033 0.953 1.120 0.299 1.044 0.962 1.134 PLD_2000_Affected 0.676 1.014 0.951 1.081 0.037 * 1.079 1.005 1.158 PLD_2000_Healthy 0.958 1.002 0.942 1.065 0.306 1.033 0.971 1.099 PLD_2500_Infarct 0.924 0.998 0.963 1.035 0.018 * 1.055 1.009 1.103 PLD_2500_Contralateral 0.686 0.985 0.918 1.058 0.061 1.089 0.996 1.190 PLD_2500_Affected 0.848 0.994 0.938 1.054 0.032 * 1.083 1.007 1.164 PLD_2500_Healthy 0.415 0.975 0.917 1.037 0.098 1.064 0.989 1.145 PLD, post-labeling delay; OR, odds ratio; CI, confidence interval. *: p < 0.05. Predictive Modeling Three predictive models were established to facilitate the prognostication of ENI at discharge. The baseline clinical model (Model 1) incorporated smoking status and DBP. In Model 2, the ASL-based PLD model, variable selection was performed utilizing the Elastic Net regularization algorithm, and seven robust ASL-derived parameters identified at the optimal λ value through rigorous fitting and validation were included in the final model construction. Model 3, a combined multivariable logistic regression model, integrated both the clinical variables from Model 1 and the ASL-derived parameters from Model 2. Model 2 and Model 3 demonstrated improved discrimination compared to Model 1, with Net Reclassification Improvements of 0.14 and 0.23, respectively (Fig. 2 , Table 3 ). The developed nomogram effectively predicted the probability of ENI, and the calibration curve exhibited strong concordance between the predicted and observed probabilities (Fig. 3 A and B). Decision curve analysis indicated that using the nomogram for clinical decisions provided net clinical benefit across threshold probabilities from 10% to 98% for predicting ENI (Fig. 3 C). Table 3 Predictive performance of clinical, PLD, and combined models Models ACC SEN SPE PPV NPV Model 1 (Clinical) 0.714 0.782 0.611 0.754 0.647 Model 2 (PLD) 0.703 0.710 0.694 0.780 0.610 Model 3 (Clinical + PLD) 0.747 0.782 0.694 0.796 0.676 ACC, accuracy; SEN, sensitivity; SPE, specificity; PPV, positive predictive value; NPV, negative predictive value. The performance indicators were calculated based on the optimal cut-off value corresponding to the Youden index. Discussion This study demonstrates that ASL with different PLDs provides significant prognostic value for predicting ENI after EVT in patients with AIS. CBF derived from a PLD of 1500 ms in the infarct core and affected hemispheres exhibited the most robust correlation with early recovery. Integrating ASL-derived perfusion parameters with clinical variables enhanced predictive accuracy. To date, this is a novel effort to integrate ASL acquisitions at different PLDs with clinical variables for the early prognostication of post-EVT outcomes. Interpretation of Findings Although successful EVT achieves macrovascular recanalization, tissue-level reperfusion remains the ultimate determinant of neuronal survival and functional recovery. Single-delay ASL is often confounded by delayed arterial transit times typical of the post-ischemic cerebrovasculature. By utilizing multiple PLDs, our approach enables a more nuanced assessment of microvascular reperfusion dynamics.[ 16 ] The strong predictive performance of CBF at a 1500 ms PLD likely reflects an optimal temporal window for capturing capillary-level tissue perfusion and early microvascular recovery in this cohort. Shorter PLDs are adept at detecting perfusion in viable tissue with minimal signal decay, whereas longer PLDs (e.g., 2000–2500 ms) may primarily reflect markedly delayed collateral flow or venous outflow, rather than signifying effective microvascular-level tissue reperfusion, thereby slightly attenuating their predictive strength. Comparison With Prior Work and Clinical Implications These findings build upon prior evidence establishing ASL-derived CBF as a viable biomarker for post-stroke evaluation.[ 17 , 18 ] However, in contrast to the majority of studies that have focused on long-term functional independence (e.g., the 90-day modified Rankin Scale),[ 19 , 20 ] the present study specifically targets ENI, which is a highly relevant immediate surrogate for treatment efficacy. The ability of ASL with different PLDs to capture delayed collateral flow, which is often misclassified as a perfusion deficit by single-delay techniques, highlights its advantage over conventional dynamic susceptibility contrast or CTP imaging.[ 21 , 22 ] Clinically, the incorporation of ASL parameters into predictive nomograms could refine patient stratification immediately following EVT. Identifying individuals who exhibit poor microvascular reperfusion despite successful large-vessel recanalization (i.e., the "no-reflow" phenomenon) opens critical windows for adjunctive therapies, such as targeted hemodynamic augmentation or early neuroprotective interventions.[ 23 ] Furthermore, the non-contrast nature of ASL bypasses the risk of contrast-induced nephropathy, making it uniquely suited for acute stroke patients who may require serial imaging or have compromised renal function. Limitations and Future Directions Several limitations of the present study warrant consideration. First, given the retrospective, single-center design and the specific cohort size (n = 91), the generalizability of our results may be restricted. Future multicenter studies with larger, diverse populations are required to robustly validate these findings. Second, heterogeneity in EVT techniques, baseline collateral status, and final reperfusion grades inherently influence clinical trajectories; these variables were not exhaustively stratified in our current models. Finally, our primary endpoint was restricted to early neurological outcomes. Future prospective multicenter studies incorporating long-term follow-up and multimodal imaging (such as diffusion-perfusion mismatch or vessel wall imaging) are necessary to fully elucidate the extended prognostic utility of ASL with different PLDs. Conclusions ASL with different PLDs provides a noninvasive and promising imaging biomarker for evaluating cerebral perfusion recovery following EVT in AIS. When integrated with routine clinical characteristics, ASL significantly enhances the accurate prediction of ENI. Incorporating ASL with different PLDs into standard post-EVT imaging protocols holds substantial promise for facilitating early, individualized prognostic assessments and guiding acute post-stroke management. Abbreviations AIS Acute ischemic stroke EVT Endovascular therapy ASL Arterial spin labeling PLD Post-labeling delay ENI Early neurological improvement NIHSS National Institutes of Health Stroke Scale DBP Diastolic blood pressure CTP Computed tomography perfusion CBF Cerebral blood flow MRI Magnetic resonance imaging T2WI T2-weighted imaging FLAIR Fluid-attenuated inversion recovery T1WI T1-weighted imaging PCASL Pseudo-continuous ASL OR Odds ratio CI Confidence interval ROC Receiver operating characteristic TIA Transient ischemic attack AF Atrial fibrillation TOAST Trial of Org 10172 in Acute Stroke Treatment LAA Large-artery atherosclerosis CE Cardioembolism SAO Small-artery occlusion SOE Stroke of other determined etiology SUE Stroke of undetermined etiology LDL Low-density lipoprotein HCY Homocysteine FIB Fibrinogen PLT Platelets SBP Systolic blood pressure ACC Accuracy SEN Sensitivity SPE Specificity PPV Positive predictive value NPV Negative predictive value. Declarations Ethical approval and consent to participate The Institutional Review Board of Minhang Hospital approved this retrospective study and waived the requirement for written informed consent due to its retrospective nature. The study was conducted in accordance with the Declaration of Helsinki. Consent for publication Not applicable. Availability of data and materials The corresponding author can provide the datasets upon a reasonable request and with institutional approval. Competing interests The authors declare that they have no competing interests. Funding This study was funded by the Natural Science Foundation of Minhang Hospital, Fudan University (2022MHBJ04), and the Science and Technology Commission of Minhang District, Shanghai (2024MHZ077). Author contributions YZ, YS, and XlS collected images. XtZ and HW conducted quantitative measurements and wrote part of the manuscript. MlT and BS conducted statistical analysis and created charts. 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Thamm T, Guo J, Rosenberg J, Liang T, Marks MP, Christensen S, Do HM, Kemp SM, Adair E, Eyngorn I, et al. Contralateral Hemispheric Cerebral Blood Flow Measured With Arterial Spin Labeling Can Predict Outcome in Acute Stroke. Stroke. 2019;50(12):3408–15. Raynald SD, Huo X, Jia B, Tong X, Ma G, Wang A, Mo D, Ma N, Gao F, et al. The Safety and Efficacy of Endovascular Treatment in Acute Ischemic Stroke Patients Caused by Large-Vessel Occlusion with Different Etiologies of Stroke: Data from ANGEL-ACT Registry. Neurotherapeutics. 2022;19(2):501–12. Baek JH, Kim BM, Suh SH, Jeon HJ, Ihm EH, Park H, Kim CH, Cha SH, Choi CH, Yi KS, et al. First-Pass Recanalization with EmboTrap II in Acute Ischemic Stroke (FREE-AIS): A Multicenter Prospective Study. Korean J Radiol. 2023;24(2):145–54. Debatisse J, Chalet L, Eker OF, Cho TH, Becker G, Wateau O, Wiart M, Costes N, Mérida I, Léon C et al. Quantitative imaging outperforms No-reflow in predicting functional outcomes in a translational stroke model. Neurotherapeutics 2025:e00529. Törteli A, Tóth R, Bari F, Farkas E, Menyhárt Á. Collateral is brain: Low perfusion triggers spreading depolarization and futile reperfusion after acute ischemic stroke. J Cereb Blood Flow Metab. 2024;44(10):1881–7. Liu Z, Shou Q, Jann K, Zhao C, Wang DJ, Shao X. A Test-Retest Study of Single- and Multi-Delay pCASL for Choroid Plexus Perfusion Imaging in Healthy Subjects Aged 19 to 87 Years. NeuroImage 2025:121048. Additional Declarations No competing interests reported. Supplementary Files TableS1.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 14 May, 2026 Reviews received at journal 26 Apr, 2026 Reviewers agreed at journal 25 Apr, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviewers invited by journal 20 Apr, 2026 Editor assigned by journal 16 Apr, 2026 Submission checks completed at journal 15 Apr, 2026 First submitted to journal 15 Apr, 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. We do this by developing innovative software and high quality services for the global research community. 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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-9370575","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":629949584,"identity":"889d483d-b7a3-4ea6-ba44-2bed6ed7b777","order_by":0,"name":"Yuan Zhang","email":"","orcid":"","institution":"Minhang Hospital, Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Zhang","suffix":""},{"id":629949585,"identity":"390ba0d0-a97b-4340-bfa3-5c0ac60e3ccb","order_by":1,"name":"Xiaotian Zhou","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Xiaotian","middleName":"","lastName":"Zhou","suffix":""},{"id":629949586,"identity":"d7a5c3bf-4f82-4d75-9f0c-57d55ff7ee4b","order_by":2,"name":"Mingliang Tong","email":"","orcid":"","institution":"Minhang Hospital, Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Mingliang","middleName":"","lastName":"Tong","suffix":""},{"id":629949592,"identity":"b63cf641-85bf-47ca-b950-ed482d899a3a","order_by":3,"name":"Bin Song","email":"","orcid":"","institution":"Minhang Hospital, Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Song","suffix":""},{"id":629949593,"identity":"728b5ef0-adac-4658-bb31-60e7403cfc69","order_by":4,"name":"Yi Sun","email":"","orcid":"","institution":"Minhang Hospital, Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Sun","suffix":""},{"id":629949599,"identity":"398569e2-acf3-4e60-a016-3dca14fc0f8c","order_by":5,"name":"Xilin Sun","email":"","orcid":"","institution":"Minhang Hospital, Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Xilin","middleName":"","lastName":"Sun","suffix":""},{"id":629949600,"identity":"2a224cf1-2fe4-43ce-a4e4-49b8449114a1","order_by":6,"name":"He Wang","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"He","middleName":"","lastName":"Wang","suffix":""},{"id":629949601,"identity":"f7637a07-faa2-4031-a90d-2bdfd570d94e","order_by":7,"name":"Lan Zheng","email":"","orcid":"","institution":"Minhang Hospital, Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Lan","middleName":"","lastName":"Zheng","suffix":""},{"id":629949602,"identity":"ebacd7c8-a7c1-4aa4-b5e3-b176d9bc184e","order_by":8,"name":"Hao Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIie3NsQrCMBCA4RwFXYquV8R3CBSqUNFXSRHsEjqKg0imrH0bcSwU7BJ1Lbg4iUOHTk4itoNrjJtg/ukO7uMIsdl+NRCTPiGsHR1jsvDEtyT/gtDikN9gd0JazK8Xsgoj0T1keqKSxRjUGam6jihRcSTchGlJkPGAgjxvaMkCBJlHAl2qJ6eqJUekZXxHeJqQkvsXkFlDePNFGJBZWQUE5Bw9VS2R7WNfulxPvJT7Ncgp9op4i/U6HKZdpSdNncHjPbJ2/XTf5NQGRzabzfbPvQAbLkZXuNf8qwAAAABJRU5ErkJggg==","orcid":"","institution":"Minhang Hospital, Fudan University","correspondingAuthor":true,"prefix":"","firstName":"Hao","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-04-09 15:39:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9370575/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9370575/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108028036,"identity":"ff84744a-7658-4871-bed4-8dd4fdea2e0e","added_by":"auto","created_at":"2026-04-28 15:29:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":796982,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCohort selection flowchart.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAIS, acute ischemic stroke; NIHSS, National Institutes of Health Stroke Scale; TIA, transient ischemic attack; ENI, early neurological improvement.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9370575/v1/4b03691f5cfee9e3d5fdb997.png"},{"id":108028037,"identity":"54bf6c63-b7bd-4bf3-8733-fb2ecb22817d","added_by":"auto","created_at":"2026-04-28 15:29:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":664900,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curves of the three models for predicting ENI at discharge.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePLD indicates ASL with different PLDs; PLD, post-labeling delay; ASL, arterial spin labeling; ENI, early neurological improvement.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9370575/v1/47a2f304daaab971845fd536.png"},{"id":108181363,"identity":"f2fc2ac1-8bde-451b-9ed4-85e8e0b38df1","added_by":"auto","created_at":"2026-04-30 08:58:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":8059647,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram and calibration curve of the combined prediction model.\u003c/strong\u003e (A) Clinical PLD nomogram, (B) calibration curves, (C) decision curve analysis of the combined prediction model. PLD_1500_A, PLD_1500_affected; PLD_1500_H, PLD_1500_ healthy; PLD, post-labeling delay; DBP, diastolic blood pressure.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9370575/v1/c9e64abb589cca69a418451b.png"},{"id":108183492,"identity":"39d79cbf-12f3-408f-9acb-f6172327d504","added_by":"auto","created_at":"2026-04-30 09:01:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7133301,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9370575/v1/7166d797-027f-4d5e-900a-27a881f8a660.pdf"},{"id":108028039,"identity":"6ead4c2c-a994-4baf-aa0b-ddabdcf7981b","added_by":"auto","created_at":"2026-04-28 15:29:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16159,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9370575/v1/38629643beb90ca7a10afd32.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Arterial Spin Labeling Predicts Early Neurological Improvement After Endovascular Therapy in Acute Ischemic Stroke","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute ischemic stroke (AIS) poses a significant health burden worldwide, resulting in about 6.55\u0026nbsp;million deaths and 143\u0026nbsp;million disabilities each year.[\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e] Despite the emergence of endovascular therapy (EVT) as the standard approach for large vessel occlusion and its significant improvement in recanalization rates,[\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e] patient neurological recovery after successful reperfusion remains highly variable.\u003c/p\u003e \u003cp\u003eAn accurate assessment of cerebral perfusion is essential for predicting early neurological recovery and tailoring patient management. Although modalities, such as computed tomography perfusion (CTP) and dynamic susceptibility contrast imaging, are currently standard for delineating the ischemic penumbra and collateral status, they rely on exogenous contrast agents to estimate relative cerebral blood flow (CBF). [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e] These methods carry risks of contrast-induced nephropathy and allergic reactions, and their quantitative accuracy can be compromised by contralateral compensatory flow variations, limiting their applicability in certain populations.\u003c/p\u003e \u003cp\u003eArterial spin labeling (ASL) overcomes the drawbacks of exogenous contrast agents by offering noninvasive CBF quantification, making it ideal for patients with renal or allergic contraindications. Acquiring ASL data at different post-labeling delays (PLDs) provides complementary perfusion information under varying hemodynamic conditions.[\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e] Although existing literature links ASL-detected post-EVT hyperperfusion to favorable 90-day outcomes,[\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e] and ASL can assess the ischemic penumbra and predict recovery in AIS,[\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e] the role of ASL with different PLDs, especially when integrated with clinical variables in predicting early neurological improvement (ENI) remains undefined. Therefore, this study aimed to determine the prognostic value of ASL with different PLDs for ENI following EVT in patients with AIS by combining perfusion parameters with clinical variables to optimize early outcome prediction.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003e1.1 Study Population\u003c/h2\u003e\u003cp\u003eThis single-center retrospective analysis was conducted at a Stroke Center from January 2023 to April 2025. Inclusion criteria: (1) age \u0026gt; 18 years; (2) diagnosis of AIS confirmed by clinical presentation and imaging; (3) symptom onset within 24 hours; (4) baseline National Institutes of Health Stroke Scale (NIHSS) score ≤ 1; and (5) infarct located in the anterior circulation, with the ischemic core defined by CT perfusion (CTP; relative CBF \u0026lt; 30%) or magnetic resonance imaging (MRI; apparent diffusion coefficient \u0026lt; 620 × 10⁻⁶ mm²/s).[\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e] Exclusion criteria: (1) transient ischemic attack; (2) intracerebral hemorrhage; (3) contraindications to MRI; (4) severe systemic diseases or malignant tumors; and (5) incomplete clinical or imaging data.\u003c/p\u003e\u003cp\u003ePatients received intravenous thrombolysis, mechanical thrombectomy, or bridging therapy based on clinical indications. The NIHSS scores were evaluated and recorded at baseline (upon admission), 24 hours post-admission, and at discharge.\u003c/p\u003e\u003ch2\u003e1.2 Outcome Measures\u003c/h2\u003e\u003cp\u003eThe primary endpoint was neurological function, assessed using the NIHSS score at admission, 24 hours post-admission, and at discharge. ENI was identified by a drop of 4 or more points from the baseline score.[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e\u003ch2\u003e2.1 MRI Data Acquisition\u003c/h2\u003e\u003cp\u003eImaging was performed on a 3.0T MRI system (uMR 770, United Imaging Healthcare, Shanghai, China) equipped with a 32-channel head coil. The standardized imaging protocol included: T2-weighted imaging (T2WI; TR/TE, 4107/88.8 ms), fluid-attenuated inversion recovery (FLAIR; TR/TE/TI, 8000/104.1/2425 ms), and T1-weighted imaging (T1WI; TR/TE, 7.87/3.2 ms). Diffusion-weighted imaging was acquired with b-values of 0 and 1000 s/mm² (TR/TE, 2364/90.5 ms; spatial resolution, 1.0 × 1.0 × 6.5 mm³).\u003c/p\u003e\u003cp\u003eThe single PLD pseudo-continuous ASL (PCASL) sequence was repeated thrice with different PLDs of 1500, 2000, and 2500 ms. PCASL parameters were: TR/TE, 5500/13.58 ms; spatial resolution, 3.5 × 3.5 × 6.0 mm³; field of view, 224 × 224 mm²; scan duration, 3 min 18 s.\u003c/p\u003e\u003ch2\u003e2.2 Image Analysis\u003c/h2\u003e\u003cp\u003eMATLAB R2021b (MathWorks, Natick, MA, USA) and Statistical Parametric Mapping (Wellcome Trust Centre for Neuroimaging, UCL, London, UK) were used for image processing and quantitative analysis.\u003c/p\u003e\u003ch2\u003e2.2.1 PCASL Processing\u003c/h2\u003e\u003cp\u003eMotion correction was performed on the PCASL images to compensate for head motion. Individuals with excessive head motion (\u0026gt; 3 mm/3°) and/or other artifacts, such as severe signal dropout or incomplete field of view, were excluded. The label and control images were pairwise subtracted to produce perfusion-weighted images (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{Δ}\\text{M}\\)\u003c/span\u003e\u003c/span\u003e). \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{M}}_{\\text{0}}\\)\u003c/span\u003e\u003c/span\u003e images from each PLD with values of 1500, 2000, and 2500 ms were coregistered to the PCASL space to ensure that \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{M}}_{\\text{0}}\\)\u003c/span\u003e\u003c/span\u003e signals and perfusion-weighted images were in the same anatomical space for CBF quantification.\u003c/p\u003e\u003ch2\u003e2.2.2 CBF quantification\u003c/h2\u003e\u003cp\u003eFor PCASL, the quantification of CBF were derived from the perfusion difference signal ΔM utilizing a single-compartment perfusion model[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]:\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{CBF=}\\frac{\\text{Δ}\\text{M}}{{\\text{M}}_{\\text{0}}}\\text{⋅}\\frac{\\text{6000⋅}\\lambda\\text{⋅}{\\text{e}}^{\\text{PLD}\\text{/}\\text{T}{\\text{1}}_{\\text{blood}}}}{\\text{2⋅}\\text{α}\\text{⋅}{\\text{α}}_{\\text{bg}}\\text{⋅}\\text{T}{\\text{1}}_{\\text{blood}}\\text{⋅(1-}{\\text{e}}^{\\text{-}\\text{τ}\\text{/}\\text{T}{\\text{1}}_{\\text{blood}}}\\text{)}}\\text{\\hspace{1em}(mL/100g/min)}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\lambda\\)\u003c/span\u003e\u003c/span\u003e is the partition coefficient of water in the brain brain\" = 0.9 mL/g\" is labeling efficiency, typically assumed to be 0.85, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{T}{\\text{1}}_{\\text{blood}}\\)\u003c/span\u003e\u003c/span\u003e is the longitudinal relaxation time of arterial blood = 1650 ms, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{τ}\\)\u003c/span\u003e\u003c/span\u003e is labeling duration = 1.8 s. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{Δ}\\text{M}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{M}}_{\\text{0}}\\)\u003c/span\u003e\u003c/span\u003e denot the signal intensities of the perfusion difference image and the proton density-weighted image, respectively. Using a Gaussian kernel of 0.5 mm, the calculated CBF maps were spatially smoothed.\u003c/p\u003e\u003ch2\u003e2.2.3 Results evaluation\u003c/h2\u003e\u003cp\u003eT1-weighted images were processed with Statistical Parametric Mapping12 to create tissue probability maps for gray matter, white matter, and cerebrospinal fluid.\u003c/p\u003e\u003cp\u003eThe hyperintense region on diffusion weighted imaging at b = 1000 s/mm² defined the infarct core. The territory was manually delineated by two radiologists (Z.Y. with 10 years and S.X.L. with 12 years of experience) under the supervision of another experienced radiologist (W.H. with 21 years of experience), in consensus, using the open-source segmentation software ITK-SNAP version 4.4.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.itksnap.org/\u003c/span\u003e\u003cspan class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Penn Image Computing and Science Laboratory, Philadelphia, PA) to minimize inter-observer variability. The infarct core and CBF maps were normalized to the Montreal Neurological Institute space utilizing each participant's T1-weighted structural image as a reference. The resulting mask was then mirrored across the cerebral midline to generate the contralateral healthy region.\u003c/p\u003e\u003cp\u003eFrom the normalized CBF maps, the mean CBF values were extracted for the affected and healthy regions, manually delineated infarct core, and the corresponding contralateral regions. The hemisphere containing the infarct was defined as the affected region, whereas the contralateral hemisphere was defined as the healthy region. All CBF values were calculated within these predefined regions of interest to assess alterations in cerebral perfusion, and the extracted data were used for statistical analysis.\u003c/p\u003e\u003ch3\u003e3. Statistical Analysis\u003c/h3\u003e\u003cp\u003eStatistical analyses were conducted utilizing R 4.4.2 and MedCalc 16.0. Data are presented as mean ± SD, median (interquartile range), or frequencies (percentages). Group comparisons utilized the independent-samples \u003cem\u003et\u003c/em\u003e-test, Mann-Whitney \u003cem\u003eU\u003c/em\u003e test, chi-square test, or Fisher's exact test, as appropriate following Kolmogorov-Smirnov normality testing.\u003c/p\u003e\u003cp\u003eTo identify predictors of neurological improvement, clinical variables with \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 in univariable logistic regression were entered into a multivariable model. For ASL parameters, Elastic Net regularization was applied to those with univariable \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 to minimize multicollinearity and isolate robust predictors. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated.\u003c/p\u003e\u003cp\u003eModel discrimination and calibration were evaluated using receiver operating characteristic (ROC) curves and the Hosmer-Lemeshow test with calibration plots, respectively. The incremental value of ASL parameters was quantified using Net Reclassification Improvement Statistical significance was defined as a two-sided \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eBaseline Characteristics\u003c/p\u003e \u003cp\u003eA total of 91 patients were included in the final analysis, as shown in the flowchart (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). ENI was observed in 34 patients (37.4%) at 24 hours post-admission and in 55 patients (60.4%) at discharge. The patients\u0026rsquo; demographic and clinical characteristics are summarized in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt 24 hours post-admission, drinking was negatively associated with ENI (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037, OR\u0026thinsp;=\u0026thinsp;0.170, 95% CI: 0.032\u0026ndash;0.896). At discharge, smoking (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015, OR\u0026thinsp;=\u0026thinsp;0.302, 95% CI: 0.114\u0026ndash;0.796) and higher DBP (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010, OR\u0026thinsp;=\u0026thinsp;0.960, 95% CI: 0.930\u0026ndash;0.990) were independent predictors of poor neurological improvement (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariable logistic regression of clinical characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e24h neurologic improvement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eDischarge neurologic improvement\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLower limit of 95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUpper limit of 95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLower limit of 95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUpper limit of 95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (per year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Female vs. Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.989\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking (No vs. Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.037\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.524\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.266\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.503\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.678\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.687\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.587\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebral infarction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.713\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal to onset (per hour)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAffected side (Right vs. Left)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTOAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAA (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.243\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26.864\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (per mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCY (per \u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFIB (per g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.711\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT (per \u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP (per mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP (per mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eOR, odds ratio; CI, confidence interval; AF, atrial fibrillation; TOAST, Trial of Org 10172 in Acute Stroke Treatment; LAA, large-artery atherosclerosis; CE, cardioembolism; SAO, small-artery occlusion; SOE, stroke of other determined etiology; SUE, stroke of undetermined etiology; LDL, low-density lipoprotein; HCY, homocysteine; FIB, fibrinogen; PLT, platelets; SBP, systolic blood pressure; DBP, diastolic blood pressure. *: p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eASL Variables\u003c/p\u003e \u003cp\u003eUnivariable analysis demonstrated that CBF values derived from ASL at different PLDs were significantly associated with ENI. At a PLD of 1500 ms, higher CBF in the infarct core (OR\u0026thinsp;=\u0026thinsp;1.042, 95% CI: 1.006\u0026ndash;1.080, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021), contralateral region (OR\u0026thinsp;=\u0026thinsp;1.100, 95% CI: 1.011\u0026ndash;1.197, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026), and affected region (OR\u0026thinsp;=\u0026thinsp;1.102, 95% CI: 1.030\u0026ndash;1.179, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) were positively correlated with ENI. At longer PLDs (2000\u0026ndash;2500 ms), CBF in the infarct core and affected regions remained significant predictors of ENI (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariable logistic regression analysis of different PLD parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e24h neurologic improvement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eDischarge neurologic improvement\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLower limit of 95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUpper limit of 95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLower limit of 95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUpper limit of 95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLD_1500_Infarct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.021\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLD_1500_Contralateral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.026\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLD_1500_Affected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLD_1500_Healthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.048\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLD_2000_Infarct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLD_2000_Contralateral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLD_2000_Affected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.037\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.158\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLD_2000_Healthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLD_2500_Infarct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.018\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLD_2500_Contralateral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLD_2500_Affected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.032\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.164\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLD_2500_Healthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003ePLD, post-labeling delay; OR, odds ratio; CI, confidence interval. *: p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePredictive Modeling\u003c/p\u003e \u003cp\u003eThree predictive models were established to facilitate the prognostication of ENI at discharge. The baseline clinical model (Model 1) incorporated smoking status and DBP. In Model 2, the ASL-based PLD model, variable selection was performed utilizing the Elastic Net regularization algorithm, and seven robust ASL-derived parameters identified at the optimal λ value through rigorous fitting and validation were included in the final model construction. Model 3, a combined multivariable logistic regression model, integrated both the clinical variables from Model 1 and the ASL-derived parameters from Model 2. Model 2 and Model 3 demonstrated improved discrimination compared to Model 1, with Net Reclassification Improvements of 0.14 and 0.23, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The developed nomogram effectively predicted the probability of ENI, and the calibration curve exhibited strong concordance between the predicted and observed probabilities (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and B). Decision curve analysis indicated that using the nomogram for clinical decisions provided net clinical benefit across threshold probabilities from 10% to 98% for predicting ENI (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePredictive performance of clinical, PLD, and combined models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSEN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSPE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1 (Clinical)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2 (PLD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.610\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3 (Clinical\u0026thinsp;+\u0026thinsp;PLD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eACC, accuracy; SEN, sensitivity; SPE, specificity; PPV, positive predictive value; NPV, negative predictive value. The performance indicators were calculated based on the optimal cut-off value corresponding to the Youden index.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrates that ASL with different PLDs provides significant prognostic value for predicting ENI after EVT in patients with AIS. CBF derived from a PLD of 1500 ms in the infarct core and affected hemispheres exhibited the most robust correlation with early recovery. Integrating ASL-derived perfusion parameters with clinical variables enhanced predictive accuracy. To date, this is a novel effort to integrate ASL acquisitions at different PLDs with clinical variables for the early prognostication of post-EVT outcomes.\u003c/p\u003e \u003cp\u003eInterpretation of Findings\u003c/p\u003e \u003cp\u003eAlthough successful EVT achieves macrovascular recanalization, tissue-level reperfusion remains the ultimate determinant of neuronal survival and functional recovery. Single-delay ASL is often confounded by delayed arterial transit times typical of the post-ischemic cerebrovasculature. By utilizing multiple PLDs, our approach enables a more nuanced assessment of microvascular reperfusion dynamics.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] The strong predictive performance of CBF at a 1500 ms PLD likely reflects an optimal temporal window for capturing capillary-level tissue perfusion and early microvascular recovery in this cohort. Shorter PLDs are adept at detecting perfusion in viable tissue with minimal signal decay, whereas longer PLDs (e.g., 2000\u0026ndash;2500 ms) may primarily reflect markedly delayed collateral flow or venous outflow, rather than signifying effective microvascular-level tissue reperfusion, thereby slightly attenuating their predictive strength.\u003c/p\u003e \u003cp\u003eComparison With Prior Work and Clinical Implications\u003c/p\u003e \u003cp\u003eThese findings build upon prior evidence establishing ASL-derived CBF as a viable biomarker for post-stroke evaluation.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] However, in contrast to the majority of studies that have focused on long-term functional independence (e.g., the 90-day modified Rankin Scale),[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] the present study specifically targets ENI, which is a highly relevant immediate surrogate for treatment efficacy. The ability of ASL with different PLDs to capture delayed collateral flow, which is often misclassified as a perfusion deficit by single-delay techniques, highlights its advantage over conventional dynamic susceptibility contrast or CTP imaging.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eClinically, the incorporation of ASL parameters into predictive nomograms could refine patient stratification immediately following EVT. Identifying individuals who exhibit poor microvascular reperfusion despite successful large-vessel recanalization (i.e., the \"no-reflow\" phenomenon) opens critical windows for adjunctive therapies, such as targeted hemodynamic augmentation or early neuroprotective interventions.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] Furthermore, the non-contrast nature of ASL bypasses the risk of contrast-induced nephropathy, making it uniquely suited for acute stroke patients who may require serial imaging or have compromised renal function.\u003c/p\u003e \u003cp\u003eLimitations and Future Directions\u003c/p\u003e \u003cp\u003eSeveral limitations of the present study warrant consideration. First, given the retrospective, single-center design and the specific cohort size (n\u0026thinsp;=\u0026thinsp;91), the generalizability of our results may be restricted. Future multicenter studies with larger, diverse populations are required to robustly validate these findings. Second, heterogeneity in EVT techniques, baseline collateral status, and final reperfusion grades inherently influence clinical trajectories; these variables were not exhaustively stratified in our current models. Finally, our primary endpoint was restricted to early neurological outcomes. Future prospective multicenter studies incorporating long-term follow-up and multimodal imaging (such as diffusion-perfusion mismatch or vessel wall imaging) are necessary to fully elucidate the extended prognostic utility of ASL with different PLDs.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eASL with different PLDs provides a noninvasive and promising imaging biomarker for evaluating cerebral perfusion recovery following EVT in AIS. When integrated with routine clinical characteristics, ASL significantly enhances the accurate prediction of ENI. Incorporating ASL with different PLDs into standard post-EVT imaging protocols holds substantial promise for facilitating early, individualized prognostic assessments and guiding acute post-stroke management.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAIS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcute ischemic stroke\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEVT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEndovascular therapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArterial spin labeling\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePLD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePost-labeling delay\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eENI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEarly neurological improvement\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNIHSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Institutes of Health Stroke Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiastolic blood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCTP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComputed tomography perfusion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCBF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCerebral blood flow\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMagnetic resonance imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eT2WI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eT2-weighted imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFLAIR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFluid-attenuated inversion recovery\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eT1WI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eT1-weighted imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCASL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePseudo-continuous ASL\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTIA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTransient ischemic attack\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAtrial fibrillation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTOAST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTrial of Org 10172 in Acute Stroke Treatment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLAA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLarge-artery atherosclerosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCardioembolism\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSAO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSmall-artery occlusion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSOE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStroke of other determined etiology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSUE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStroke of undetermined etiology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow-density lipoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHCY\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHomocysteine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFIB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFibrinogen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePLT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePlatelets\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSystolic blood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eACC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSEN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSPE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePositive predictive value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNPV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNegative predictive value.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and\u003c/strong\u003e \u003cstrong\u003econsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Institutional Review Board of Minhang Hospital approved this retrospective study and waived the requirement for written informed consent due to its retrospective nature. The study was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003ch3\u003eThe corresponding author can provide the datasets upon a reasonable request and with institutional approval.\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Natural Science Foundation of Minhang Hospital, Fudan University (2022MHBJ04), and the Science and Technology Commission of Minhang District, Shanghai (2024MHZ077).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYZ, YS, and XlS collected images. XtZ and HW conducted quantitative measurements and wrote part of the manuscript. MlT and BS conducted statistical analysis and created charts. YZ performed the data management and wrote the manuscript. HW and LZ performed the mothed improvement. All authors reviewed the manuscript. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Dr. Hongwei Li for his guidance on the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ede Jong Y, Fu EL, van Diepen M, Trevisan M, Szummer K, Dekker FW, Carrero JJ, Ocak G. Validation of risk scores for ischaemic stroke in atrial fibrillation across the spectrum of kidney function. Eur Heart J. 2021;42(15):1476\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Z, Li J, Wang X, Yuan B, Li J, Ma Q. 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Magn Reson Med. 2015;73(1):102\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuijten SPR, Bos D, van Doormaal PJ, Goyal M, Dijkhuizen RM, Dippel DWJ, Roozenbeek B, van der Lugt A, Warnert EAH. Cerebral blood flow quantification with multi-delay arterial spin labeling in ischemic stroke and the association with early neurological outcome. Neuroimage Clin. 2023;37:103340.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHernandez Petzsche MR, B\u0026uuml;rkle J, Hoffmann G, Zimmer C, R\u0026uuml;hling S, Schwarting J, Wunderlich S, Maegerlein C, Boeckh-Behrens T, Kaczmarz S et al. Cerebral blood flow from arterial spin labeling as an imaging biomarker of outcome after endovascular therapy for ischemic stroke. J Cereb Blood Flow Metab 2024:271678x241267066.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThamm T, Guo J, Rosenberg J, Liang T, Marks MP, Christensen S, Do HM, Kemp SM, Adair E, Eyngorn I, et al. 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Korean J Radiol. 2023;24(2):145\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDebatisse J, Chalet L, Eker OF, Cho TH, Becker G, Wateau O, Wiart M, Costes N, M\u0026eacute;rida I, L\u0026eacute;on C et al. Quantitative imaging outperforms No-reflow in predicting functional outcomes in a translational stroke model. Neurotherapeutics 2025:e00529.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eT\u0026ouml;rteli A, T\u0026oacute;th R, Bari F, Farkas E, Menyh\u0026aacute;rt \u0026Aacute;. Collateral is brain: Low perfusion triggers spreading depolarization and futile reperfusion after acute ischemic stroke. J Cereb Blood Flow Metab. 2024;44(10):1881\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Z, Shou Q, Jann K, Zhao C, Wang DJ, Shao X. A Test-Retest Study of Single- and Multi-Delay pCASL for Choroid Plexus Perfusion Imaging in Healthy Subjects Aged 19 to 87 Years. NeuroImage 2025:121048.\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":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Stroke, Endovascular therapy, Post-labeling delay, Prognosis","lastPublishedDoi":"10.21203/rs.3.rs-9370575/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9370575/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcute ischemic stroke (AIS) ranks among the leading causes of morbidity and mortality worldwide. Although endovascular therapy (EVT) is the standard approach for large vessel occlusion, neurological recovery following post-recanalization varies widely. This study aimed to investigate the predictive value of arterial spin labeling (ASL) with different post-labeling delays (PLDs) for early neurological improvement (ENI) after EVT in patients with AIS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA retrospective analysis was conducted on 91 patients with AIS who underwent EVT from January 2023 to April 2025. ASL was performed to quantify cerebral blood flow at three distinct PLDs (1500, 2000, and 2500 ms). Univariable and multivariable logistic regression were utilized to identify factors associated with ENI, which was defined as the complete resolution of neurological deficits or a ≥ 4-point reduction in the National Institutes of Health Stroke Scale score at 24 hours post-stroke.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical factors, including drinking, smoking, and diastolic blood pressure (DBP), were significantly correlated with ENI. ASL-derived cerebral blood flow values, particularly at a PLD of 1500 ms in the infarct core and affected hemispheres, were positively associated with ENI (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). A combined predictive model integrating clinical and ASL variables demonstrated an accuracy of 0.747, sensitivity of 0.782, and a positive predictive value of 0.796, thereby outperforming models based solely on clinical or imaging parameters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eASL with different PLDs provides valuable hemodynamic insights for predicting ENI after EVT in patients with AIS. When combined with clinical characteristics, this noninvasive technique enhances predictive accuracy and shows promise as a biomarker for individualized prognosis.\u003c/p\u003e","manuscriptTitle":"Arterial Spin Labeling Predicts Early Neurological Improvement After Endovascular Therapy in Acute Ischemic Stroke","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-28 15:29:54","doi":"10.21203/rs.3.rs-9370575/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-14T13:03:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-27T02:54:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"19054917895245444883852316250973911224","date":"2026-04-25T06:55:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"140429204050517055997624570586793233664","date":"2026-04-20T11:07:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-20T05:57:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-16T09:31:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-16T02:01:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2026-04-16T01:55:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"111a829b-423f-4935-9141-de59dfd4af81","owner":[],"postedDate":"April 28th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-14T13:03:21+00:00","index":25,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T15:29:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-28 15:29:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9370575","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9370575","identity":"rs-9370575","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}