The systemic immune-inflammation index as a superior predictor of short-term prognosis in acute ischemic stroke after mechanical thrombectomy: a retrospective cohort study

The systemic immune-inflammation index as a superior predictor of short-term prognosis in acute ischemic stroke after mechanical thrombectomy: a retrospective cohort study | 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 The systemic immune-inflammation index as a superior predictor of short-term prognosis in acute ischemic stroke after mechanical thrombectomy: a retrospective cohort study Bo ZHOU, Xiaoyu Xu, Menglu Zhang, Qingtao Xie, Shiqin Ju, Yanbo Cheng, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6635485/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: This study evaluated the role of machine learning models based on the Systemic Immunity Index (SII) in predicting short-term prognosis after mechanical thrombectomy (MT) in acute ischemic stroke (AIS). Methods: Data from 387 AIS patients who underwent MT were retrospectively analyzed, including clinical variables, inflammatory markers such as SII, platelet lymphocyte ratio (PLR), neutrophil lymphocyte ratio (NLR）and 90-day modified Rankin Scale (mRS) scores. Patients were categorized into good and poor prognosis groups based on mRS scores. Univariate and multifactorial logistic regression models were constructed to identify risk factors and compare predictive performance. Four models were developed: clinical baseline, SII+clinical baseline, PLR+clinical baseline, and NLR+clinical baseline. Model performance was assessed using ROC curves, NRI, IDI, calibration curves, and decision curve analysis (DCA). Results: Results showed that SII outperformed PLR and NLR, with AUCs of 0.834 (uncorrected) and 0.841 (corrected). The optimal model (SII+clinical baseline) achieved an AUC of 0.863, significantly improving prognosis prediction. SHAP analysis confirmed SII as the most influential variable (74.2%). The model demonstrated good fit, clinical utility, and effectiveness in identifying poor prognosis patients at a 15% probability threshold. Conclusion: In conclusion, SII-based models provide superior prognostic accuracy compared to traditional markers, offering a valuable tool for clinical decision-making in AIS patients post-MT. Acute Ischemic Stroke Mechanical Thrombolysis Systemic Immunoinflammatory Index Neutrophil Lymphocyte Ratio Platelet Lymphocyte Ratio Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction AIS is one of the leading causes of death and disability worldwide [1] Currently, MT has been shown to be one of the most effective treatments for patients with acute ischemic stroke due to large artery occlusion [2] . However, despite the effectiveness of MT in improving cerebral perfusion in the short term, the long-term prognosis of patients still varies widely [3] . Approximately 50% of patients who undergo mechanical thrombectomy have a poor prognosis [4] . Therefore accurate prognostic assessment is essential for individualized treatment and risk management.In recent years, researchers have been working to explore the pathomechanisms of the disease, and have found that the inflammatory response triggered by disruption of blood flow plays a crucial role, and that a whole-brain inflammatory response occurs and persists after stroke, and that this whole-brain inflammation may continue to influence post-stroke pathology and have an important impact on the patient's long-term neurological recovery [5] . In recent years, the systemic immunoinflammatory index (SII), as an emerging inflammatory marker, has demonstrated significant value in the prognostic assessment of a variety of diseases [6-8] . SII reflects the immune-inflammatory state of the body by integrating neutrophil, platelet, and lymphocyte counts, and it has been used with some success in the prognostic assessment of cardiovascular diseases [9, 10] and many types of cancer [11, 12] . In addition to this, relevant studies have shown that a high systemic immunoinflammatory index is associated with early neurological deterioration (END) and 3-month adverse outcome, which can be used as a potential prognostic predictor in patients with acute ischemic stroke treated with intravenous thrombolysis [13] . Available studies have confirmed that the admission systemic immunoinflammatory index (SII) is positively correlated with symptomatic intracranial hemorrhage (sICH) in patients with acute large vessel occlusive stroke (AIS-LVO) undergoing endovascular therapy (EVT), suggesting that it can be used as an important indicator for early risk stratification of complications [14] , that there is a relationship between it and the patient patient's prognosis, and that the dynamics of SII was significantly associated with a high risk of poor prognosis in patients with AIS [15] , which provides a theoretical basis for the introduction of inflammatory parameters into the model. Meanwhile, elevated SII was shown to independently predict 30-day all-cause mortality risk with the integration of platelet, immune and inflammatory pathways [16] , which provides a multidimensional biomarker framework for machine learning models to parse AIS pathophysiological interactions and prognostic prediction. The above findings collectively build the scientific basis for an SII-based inflammation monitoring system in the prognostic assessment after mechanical thrombus extraction, laying the foundation for feature engineering and clinical interpretability of the model in this study. The aim of this study was to predict the short-term prognosis of patients after mechanical thrombectomy for acute ischemic stroke by using an SII-based machine learning model.Relevant clinical studies have shown that other inflammatory markers, such as the neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR)), are closely associated with END in patients with AIS [17] . Therefore, our study also compared the value and model performance of SII with NLR and PLR in predicting patient clinical outcomes after mechanical thrombolysis in patients with AIS, and explored the machine learning model based on SII as the best prediction model after mechanical thrombolysis in acute ischemic stroke by NRI and IDI analysis. Information and methods 1.1 Study population: retrospective collection Patients with AIS between January 2022 and December 2024 at the Affiliated Hospital of Xuzhou Medical University. The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Affiliated Hospital of Xuzhou Medical University. Inclusion criteria:(1) Inclusion criteria:(1) diagnosed with AIS according to World Health Organization criteria with complete baseline and follow-up data; (2) confirmed large vessel occlusion by imaging and treated with mechanical thrombolysis; and (3) time from onset to start of thrombolysis was 18 years. Exclusion criteria: (1) CT of the head suggests the presence of bleeding and high bleeding risk diseases (such as intracranial aneurysm, etc.) affecting the operation; (2) severe allergy to contrast media; (3) pregnant and breastfeeding females; (4) patients with serious diseases, such as renal failure, severe hepatic insufficiency, or cancer; (5) patients with autoimmune diseases; (6) patients with immunosuppressive drugs or antibiotic use ; (5) 90d postoperative loss of visit. 1.2 Data collection: general information and past medical history were collected; including age, gender, history of diabetes mellitus, history of hypertension, history of atrial fibrillation; preoperative National Institutes of Health Stroke Scale (NIHSS) scores, modified Rankin Scale (mRS) scores, and mRS scores at 90d postoperatively, TOAST typing and Site of onset. Data related to mechanical thrombolysis were collected: time from onset to arterial puncture, duration of the procedure, number of thrombolysis attempts, method of thrombolysis, whether thrombolytic therapy was performed before the procedure, whether arterial thrombolysis was performed, whether a stent was inserted, and the grade of revascularization after the procedure. Laboratory indices were collected: platelet count, neutrophil count, and lymphocyte count at baseline; and calculations were made to measure PLR, NLR, and SII values. 1.3 Follow-up and grouping: patients or their family members were followed up by telephone or outpatient clinic at 90d postoperatively, and the prognosis was assessed according to the mRS score. mRS ≤2 points were included in the good prognosis group, and mRS >2 points were included in the bad prognosis group, and the data of the two groups were statistically analyzed to explore the effects of NLR, PLR, and SII levels on the patients' poor prognosis at 90d postoperatively after mechanical thrombolysis. 1.4 Statistical methods: All the measured data were statistically described by mean±standard deviation to satisfy normality, and the t-test was used for the differences between the two groups; non-normal distribution was statistically described by median (percentile), and the Wilcoxon's rank sum test was used for the differences between the two groups. The statistical description of the count data was based on the number of cases (%), and the differences between groups were tested by X2 or Fisher's exact probability test. The Wilcoxon rank sum test was used for differences between the two groups for grade information. Based on the results of intergroup variability, in order to study the analysis of risk factors related to the prognosis of AIS patients after mechanical thrombolysis, a one-way logistic regression model was used to analyze the statistically significant differences between good and poor prognosis of AIS patients after mechanical thrombolysis, and then clinical baselines, SII+clinical baselines, PLR+clinical baselines, and NLR+clinical baselines, were constructed according to clinical practice. Then four multifactorial logistic regression prediction models were constructed based on clinical practice, including SII+clinical baseline, PLR+clinical baseline, and NLR+clinical baseline. The area under the ROC curve (AUC) was used to predict the performance of the models, and the optimal prediction model was constructed by using the NRI and IDI analyses, and the area under the ROC curve (AUC) was used to evaluate the value of the different prediction models and the performance of the models for predicting the clinical outcomes of patients with AIS after mechanical thrombus extraction. The calibration curves and the Hosmer-Lemeshow test were used to evaluate the model fit, and finally, the decision curve (DCA) was used to analyze the clinical value of the models. To further assess the predictive efficacy of SII, PLR and NLR on the prognosis of patients with AIS after mechanical thrombolysis, ROC curves were analyzed using uncorrected and corrected confounders. P<0.05 was considered statistically significant. R4.3.2 software was used for basic statistical analysis, and Python3.9 software was used for SHAP visualization. Outcomes 2.1 Overall results:Based on the inclusion and exclusion criteria, 387 cases were extracted from HIS database of AIS in our hospital, out of which 236 cases occurred with poor prognosis and the incidence of poor prognosis was 60.98%. The median age of the good prognosis group was 67.00 [58.00-73.00],of which 49 females accounted for 32.45% and 102 males accounted for 67.55%; the median age of the poor prognosis group was 69.00 [59.00-77.00],of which 78 females accounted for 33.05% and 158 males accounted for 66.95%; Table 1. The median SII in the adverse group was 1407.66 (965.41; 2358.12), the median PLR was 198.18 (137.25; 283.33), and the median NLR was 7.34 (4.68; 11.21); statistically significant differences were found in the onset-to-puncture time (min), operative duration (min), nihss before bolus removal, SII, PLR, and NLR were statistically different between the two groups (P < 0.05),Table 1. Table 1 Comparison of clinical baseline data between the two groups Variable Good prognosis （N=151 ） Poor prognosis （N=236） Total (N=387) Statistic P_Value gender 0.015 0.902 - female n% 49 (32.45%) 78 (33.05%) 127(32.82%) - male n% 102 (67.55%) 158 (66.95%) 260 (67.18%) Age median (years） 67.00 [58.00;73.00] 69.00 [59.00;77.00] 68.00 [59.00;75.00] -1.902 0.057 TOAST 1=ACI;2=CCI 0.988 0.320 - 1 107 (70.86%) 178 (75.42%) 285 (73.64%) - 2 44 (29.14%) 58 (24.58%) 102 (26.36%) Affected area 1=AC; 2=PC; 1.490 0.222 - 1 126 (83.44%) 185 (78.39%) 311 (80.36%) - 2 25 (16.56%) 51 (21.61%) 76 (19.64%) MT times 4.173 0.243 - 1 115 (76.16%) 160 (67.80%) 275 (71.06%) - 2 21 (13.91%) 51 (21.61%) 72 (18.60%) - 3 13 (8.61%) 23 (9.75%) 36 (9.30%) - 4 2 (1.32%) 2 (0.85%) 4 (1.03%) MT Techniques 1=Stent Retriever; 2=aspiration 3=Combined thrombectomy 4=Arterial thrombolysis; 6.320 0.097 - 1 128 (84.77%) 179 (75.85%) 307(79.33%) - 2 8 (5.30%) 25 (10.59%) 33 (8.53%) - 3 15 (9.93%) 29(12.29%) 44(11.37%) - 4 0(0.0%) 3(1.27%) 3 (0.78%) Intravenous thrombolysis(yes=1; no=0) 0.082 0.774 - 0 88 (58.28%) 141 (59.75%) 229 (59.17%) - 1 63 (41.72%) 95 (40.25%) 158 (40.83%) Onset to Puncture Time /min 300.00 [220.00;432.50] 365.00 [272.50;476.50] 340.00 [242.50;465.50] -3.166 0.002 Operating time/ min 90.00 [60.00;120.00] 100.00 [80.00;140.00] 97.00 [70.00;125.00] -3.351 0.001 Hypertension (yes=1; no=0) 1.576 0.209 - 0 86 (56.95%) 119 (50.42%) 205 (52.97%) - 1 65 (43.05%) 117 (49.58%) 182 (47.03%) Diabetes (yes=1; no=0) 0.936 0.333 - 0 128(84.77%) 191 (80.93%) 319 (82.43%) - 1 23(15.23%) 45(19.07%) 68 (17.57%) atrial fibrillation (yes=1; no=0) 1.302 0.254 - 0 108 (71.52%) 181 (76.69%) 289 (74.68%) - 1 43 (28.48%) 55 (23.31%) 98 (25.32%) TICI grading 5.388 0.145 - 0 3 (1.99%) 2 (0.85%) 5 (1.29%) - 1 1 (0.66%) 9 (3.81%) 10 (2.58%) - 2 16 (10.60%) 32 (13.56%) 48 (12.40%) - 3 131 (86.75%) 193 (81.78%) 324 (83.72%) Nihss 0 16.00 [ 11.00;25.00] 20.00 [ 14.00;30.00] 19.00 [12.00;27.00] -3.425 0.001 MRS 0 3.146 0.207 - 3 3(1.99%) 5 (2.12%) 8 (2.07%) - 4 68 (45.03%) 85 (36.02%) 153 (39.53%) - 5 80 (52.98%) 146 (61.86%) 226 (58.40%) SII 566.74 [383.78;891.60] 1407.66 [965.41;2358.12] 1012.23 [604.65;1748.30] -11.533 0.000 PLR 110.74 [79.24;155.11] 198.18 [ 137.25;283.33] 157.27 [108.50;235.83] -10.169 0.000 NLR 3.08 [2.31;4.58] 7.34[4.68;11.21] 5.36[3.13;8.68] -11.028 0.000 Abbreviation：TOAST=Transient Ischemic Attack of Symptomatic and Atherosclerotic Thrombosis; ACI=Arterial-derived cerebral infarction; CCI=Cardiac-derived cerebral infarction; AC=Anterior circulation; PC=Posterior circulation; TICI=Thrombolysis in Cerebral Infarction Classification; Nihss 0=National Institutes of Health Stroke Scale score at the time of admission; MRS0=0Modified Rankin Scale score at the time of admission. 2.2 Research on the value of comprehensive inflammation indexes in predicting clinical outcomes: Since the present study found statistically significant differences in the distribution of demographic and clinical indexes such as onset-to-puncture time (min), operating time (min), and nihss before bolus removal between the two groups, in order to better assess the effectiveness of SII, PLR, and NLR in predicting clinical outcomes, the present study plotted the ROC curves of corrected covariates and uncorrected covariates to illustrate in depth the predictive value of each comprehensive inflammation index. In order to better assess the effectiveness of SII, PLR, and NLR in predicting clinical outcomes, this study plotted the ROC curves of corrected covariates and uncorrected covariates to illustrate in depth the predictive value of each composite inflammatory index. As shown in Figure 1, SIl had the greatest predictive efficacy, with an AUC=0.834 (95% C1=0.790-0.874) for uncorrected confounders and 0.841 (95% C1=0.841-0.844) for corrected covariates; NLR was the next most effective predictor with an AUC=0.820 (95% C1=0.774-0.859) for uncorrected covariates; and NLR, with an AUC=0.820 (95% C1=0.774-0.859) for uncorrected covariates. -0.859), and after correcting for other influences its AUC=0.818 (95% C1=0.818-0.821); and finally PLR, with an AUC=0.800 (95% C1=0.754-0.843) uncorrected for confounders, and after correcting for other influences its AUC=0.810 (95% C1=0.809-0.812). 0.812). See Figure 1. The area under the ROC curve based on the above indicators was greater than 0.7, proving that the indicators SII, PLR, and NLR all have some predictive value. Outcomes 2.3 Analysis and modeling of factors affecting prognosis: With the dependent variable of whether the prognosis is poor (good=0, poor=1), and the independent variables of continuous variables such as clinical baseline indicators of onset to puncture time (min), operating time (min), and nihss before bolus removal, four multifactorial logistic regression prediction models were constructed according to clinical practice, namely, Model1 (basic clinical model), Model2 (basic+SII), Model3 (basic+PLR), and Model4 (basic+NLR), which showed the highest effectiveness. Four multifactorial logistic regression prediction models were constructed according to clinical practice: Model1 (clinical base model), Model2 (base + SII), Model3 (base + PLR), and Model4 (base + NLR), respectively, and as shown in Table 3 ; Table 4; Table 5 andFigure 2, Model2 was the most effective. After the Delong test, it was found that the difference in the area of the ROC curve of Model2 was statistically significant (P0.05) compared with that of Model4, and the difference in the area of the ROC curve of Model2 was statistically significant (P>0.05). However, after NRI and IDI analyses, it was found that Model4, when compared with Model2, had an NRI(Categorical) of -0.1568 ([95% CI: -0.2383 to -0.0753 ] ; P<0.001); an NRI(Continuous) of -0.2662 ([95% CI: -0.468 to - -0.0643 ] ; P= 0.010); an IDI of -0.0247 [[95% CI: -0.0484 to -0.001 ] ; P=0.041); and an IDI of -0.041. 0.0643 ] ; P= 0.010); IDI was -0.0247 [[95% CI: -0.0484 to -0.001 ] ; P=0.041), which shows that model2 has better comprehensive performance than model4 and is the optimal prediction model. The result is the formula for the optimal prediction equation: prognostic bad =-3.576+0.001*OnsettoPunctureTime+0.007*OPTime+0.031*nihss0+0.002*SII. And, OnsettoPunctureTime, OPTime, nihss0 and SII were prognostic risk factors (OR all >1, P<0.05). For details, see Table 2. Table 2 Results of unifactorial and multifactorial logistic regression analyses influencing prognosis variables Single factor logistic Model1 Model2 Model3 Model4 OR（95%CI） Pvalue OR（95%CI） Pvalue OR（95%CI） Pvalue OR（95%CI） Pvalue OR（95%CI） Pvalue OTPT 1.001 (1.000,1.002) 0.030 1.001 (1.000,1.002) 0.039 1.001 (1.000,1.002) 0.046 1.001 (1.000, 1.003) 0.041 1.001 (1.000, 1.002) 0.051 OPTime 1.006 (1.002,1.011) 0.007 1.005 (1.001, 1.010) 0.028 1.007 (1.001,1.012) 0.017 1.007 (1.002, 1.013) 0.011 1.006 (1.001,1.011) 0.024 nihss0 1.039 (1.015,1,064) 0.001 1.041 (1.016, 1.066) 0.001 1.031 (1.003,1.060) 0.028 1.041 (1.013,1.070) 0.004 1.025 (0.997,1.053) 0.082 SII 1.002 (1.002,1.003) 0.000 1.002 (1.002, 1.003) 0.000 PLR 1.016 (1.012,1.020) 0.000 1.017 (1.013, 1.021) 0.000 NLR 1.505 (1.370, 1.672) 0.000 1.513 (1.366, 1.677) 0.000 Abbreviation：OTPT=Onset to puncture time; OPTime=Operating time. Table 3 Results of AUC, sensitivity, specificity and other evaluation indexes of ROC of different models Variable Cutoff AUC（95%CI） P_Value ACC Sensitivity Specificity PPV NPV Model1 0.113 0.655 (0.597,0.712) <0.0001 0.669 0.826 0.424 0.691 0.610 Model2 0.497 0.863 (0.825,0.901) <0.0001 0.806 0.839 0.755 0.843 0.750 Model3 0.017 0.830 (0.789,0.871) <0.0001 0.762 0.809 0.689 0.803 0.698 Model4 0.084 0.844 (0.804,0.883) <0.0001 0.775 0.771 0.781 0.847 0.686 Table 4 ROC two-by-two comparison results for different models Comparative models1 Comparative model 2 statistic Z p_value Model1 Model2 -7.208 <0.0001 Model1 Model3 -6.075 <0.0001 Model1 Model4 -6.515 <0.0001 Model2 Model3 2.520 0.012 Model2 Model4 1.813 0.070 Model3 Model4 -0.896 0.370 Table 5 Results of two-by-two comparison of the performance of different models Comparison NRI(Categorical) [95% CI] NRI(Continuous) [95% CI] IDI [95% CI] Model1 vs Model2 0.5349 [ 0.4497 - 0.6201 ] ; P<0.001 1.0778 [ 0.9153 - 1.2403 ] ; P<0.001 0.2901 [ 0.2483 - 0.3318] P<0.001 Model1 vs Model3 0.4167 [ 0.3243 - 0.5092 ] ; P<0.001 0.8585 [ 0.6776 - 1.0393 ] ; P<0.001 0.2477 [ 0.2059 - 0.2894] P<0.001 Model1 vs Model4 0.4443 [ 0.3543 - 0.5343]; P<0.001 0.9284 [ 0.7523 - 1.1045]; P<0.001 0.2654 [ 0.2235 - 0.3072] P<0.001 Model2 vs Model3 -0.1356 [ -0.2362 - -0.0351]; P= 0.008 -0.3398 [ -0.541 - -0.1386]; P= 0.001 -0.0424[ -0.0704 - -0.0144]; P= 0.003 Model2 vs Model4 -0.1568 [ -0.2383- -0.0753]; P<0.001 -0.2662 [ -0.468 - -0.0643]; P= 0.010 -0.0247[-0.0484 --0.001] P=0.041 Model3 vs Model4 -0.0212 [ -0.1238 - 0.0815]; P= 0.686 0.2699 [ 0.0675 - 0.4723]; P= 0.009 0.0177[-0.0171 - 0.0524] P= 0.319 2.4 Visual construction and evaluation of the optimal prediction model: the optimal prediction model in this study was a multifactorial logistic regression model constructed based on the SII index, with an area under the ROC curve AUC = 0.954 (95% CI : 0.925-0.983), a sensitivity of 0.839 when the model cutoff = 0.497, a specificity of 0.755 , PPV of 0.843, NPV of 0.750, and accuracy of 0.806 (95% CI :0.763-0.844), as detailed in Figure 3. The model was analyzed for goodness-of-fit, and it was found that the model had a good ability to match between the predicted risk and the actual results (Hosmer-Lemeshow test, X 2 = 4.899, P= 0.265>0.05,Figure 4). 2.5 Visualization of SHAP values for the importance of variables in the prediction model: The relative importance and impact of variables in the model in predicting postoperative prognosis were analyzed by SHAP values, and it can be seen in Figure 5 that different variable characteristics have different impacts on prognostic prediction. In the present study, we found that SII, operative duration (min), nihss before bolus removal, and onset-to-puncture time (min) were associated with an increased risk of poor prognosis, and the higher the value, the higher the probability of poor prognosis. In addition, we can find that the characteristics of different variables affect the model differently, with SII having the highest influence on the predictive model, accounting for 74.2% of the total model, followed by the length of the procedure (min) and the nihss prior to the removal of the embolus, accounting for 9.6% and 9.3% of the total model, and lastly, the time from onset of the procedure to the puncture (min), at 6.9%. 2.6 Results of DCA analysis: As can be seen in Figure 6, when the probability threshold (Pt) is ≥15%, the use of the constructed predictive model for this column of graphs leads to a progressively higher net benefit compared to performing all other relevant tests on the patients, i.e., in clinical practice, by setting Pt = 15%, the predictive model is able to detect an additional 15 patients per 100 screened without over-increasing the number of other tests. patients with poor prognosis without increasing the number of false positives. That is, within the threshold probability range of 0.15 to 0.90, there is a high net benefit of using this line plot model to predict the risk of poor prognosis in patients. Discussion In this study, we found that the systemic immunoinflammatory index (SII) demonstrated a significant advantage in predicting the 90-day prognosis after mechanical thrombectomy in patients with acute ischemic stroke (AIS), and its predictive efficacy was superior to that of the traditional inflammatory markers, PLR and NLR.A multifactorial regression model constructed based on the SII (AUC=0.863) significantly enhanced the accuracy of prognostic assessment, which was confirmed by SHAP analysis. SII contributed up to 74.2% to the model. The significance of the study, mechanism exploration, clinical value, limitations and future directions are discussed below. 3.1 Exploration of the advantages and mechanisms of SII as a novel inflammatory marker The present study demonstrated that SII has higher sensitivity and specificity in predicting poor prognosis after mechanical thrombolysis. This result may be closely related to its property of integrating the dynamic balance of neutrophils, platelets and lymphocytes. Neutrophils, as a core component of innate immunity, release pro-inflammatory factors and exacerbate neuroinflammatory responses in ischemia-reperfusion injury [21] , and peripheral blood cells show significant temporal fluctuations in gene expression in the acute stroke state in which neutrophil-specific transcriptional signatures predominate [22] ; changes in the homeostatic state of neutrophils are closely related to the severity of the stroke condition, possibly by stimulating the systemic inflammatory response in which they play a key role. They exacerbate the severity of stroke through a variety of pathways, including triggering capillary occlusion, generating oxygen free radicals, releasing inflammatory factors, and promoting thrombosis through neutrophil and platelet aggregation and the formation of Neutrophil Extracellular Traps (NETs) [23-25] . Platelet activation is not only involved in thrombosis, but also exacerbates vascular endothelial damage by releasing inflammatory mediators. Platelets and lymphocytes have been shown to play key roles in immune regulation and inflammatory response. As an important participant in the inflammatory response in thrombopathic areas, platelets show rapid activation in stroke pathology. When a cerebrovascular accident occurs, activated platelets specifically bind to damaged vascular endothelial cells through surface adhesion molecules, while releasing a variety of pro-inflammatory mediators. These mediators can not only chemotaxis leukocytes and other immune cells to the focal area, but also enhance the intensity of the local inflammatory response, forming a cascade amplification effect [26, 27] . However, lymphocytes control inflammatory pathways by coordinating, healing, and repairing inflammation [28, 29] , and lymphopenia suggests a state of immunosuppression, which is associated with risk of infection and poor prognosis. Taken together, by integrating changes in these three types of cells, SII is able to reflect more comprehensively the systemic inflammatory and immune imbalance status of the organism, and thus more accurately predict potential barriers to neurological recovery. In contrast, PLR and NLR involve the ratio of only two types of cells, which may miss the pathologic significance of platelet or neutrophil abnormalities alone, resulting in a relative lack of predictive efficacy. In addition, this study revealed the dominant role of SII in the model by SHAP analysis, further supporting its status as a core biomarker. This finding is consistent with previous studies, such as Huang et al [10] who found that SII predicted long-term outcome after hemodialysis in patients with coronary artery disease, and Yang et al [14] who demonstrated that SII was associated with the risk of symptomatic intracranial hemorrhage in patients with AIS. However, this study is the first to include SII in a prognostic model after mechanical thrombolysis and to validate its association with clinical outcomes, providing a new perspective on the monitoring of inflammation after stroke. 3.2 In this study, we constructed the best predictive model in which, in addition to SII, Onset-to-Puncture Time, Baseline NIHSS Score, and Procedure Duration played varying degrees of roles in the model, and we next attempted to analyze these three metrics 。 3.2.1 The onset-to-puncture time is a critical factor in determining the prognosis of AIS patients undergoing MT. In this study, the onset-to-puncture time was significantly associated with poor outcomes, as evidenced by its inclusion as a key variable in the optimal predictive model (Model 2). Mechanistic Insight: Delayed reperfusion leads to mitochondrial dysfunction, reactive oxygen species (ROS) bursts, and inflammatory cascade responses that exacerbate tissue damage [30] . Relevant studies have demonstrated that hypoxia activates microglia and endothelial cells, releases pro-inflammatory factors such as IL-1β and TNF-α, recruits neutrophil infiltration, and exacerbates blood-brain barrier disruption [31] . Moreover, delayed reperfusion leads to a systemic inflammatory response (e.g., elevated SII), which amplifies secondary brain injury through the “brain-peripheral immune axis” [5] . The "time is brain" principle [32] underscores that earlier intervention reduces infarct volume and mitigates secondary damage, such as blood-brain barrier disruption and neuronal apoptosis. Clinical Implications: These findings reinforce the importance of streamlining pre-hospital and in-hospital workflows to minimize delays. Future studies could explore dynamic SII changes relative to onset-to-puncture time to refine time-sensitive interventions. 3.2.2 The study identified procedure duration as an independent predictor of poor outcomes, with longer durations correlating with higher risks (SHAP analysis: 9.6% contribution to the model).Mechanistic Insight: Prolonged MT may reflect procedural complexities (e.g., difficult vascular access, multiple retrieval attempts), which can aggravate endothelial injury and inflammatory responses. Extended ischemia-reperfusion cycles also amplify oxidative stress and neutrophil activation, worsening neuroinflammation.Clinical Implications: Optimizing procedural efficiency (e.g., advanced imaging guidance, operator experience) and monitoring intraprocedural SII dynamics could mitigate risks. The interplay between SII and procedure duration warrants further investigation to identify thresholds for intervention. 3.2.3 The baseline NIHSS score is also a robust predictor of poor prognosis (SHAP analysis: 9.3% contribution), aligning with its established role in stroke severity assessment. Mechanistic Insight: Higher NIHSS scores indicate more extensive neurological deficits, often reflecting larger infarct cores or critical perfusion deficits [2, 33] . These patients may exhibit exaggerated systemic inflammation (e.g., elevated SII), exacerbating secondary injury.Clinical Implications: Integrating NIHSS with SII enhances risk stratification. Patients with high NIHSS and SII may benefit from adjunctive anti-inflammatory therapies or intensified post-MT monitoring. 3.3 Comparison with traditional inflammatory markers and predictive models In this study, the superiority of SII was clarified by comparing the predictive efficacy of SII, PLR and NLR. After correction for confounders, the AUC of SII (0.841) was significantly higher than that of PLR (0.810) and NLR (0.818). This difference may stem from the focus of PLR and NLR on a single pathologic process: while PLR mainly reflects platelet activation and immune depletion, NLR focuses on acute stress and infection risk. However, the inflammatory response after AIS involves multi-pathway interactions, and SII is better able to capture complex pathophysiologic changes due to its multidimensional integration properties. For example, high SII may simultaneously suggest platelet overactivation, neutrophil infiltration, and lymphocyte depletion, which together exacerbate blood-brain barrier disruption and secondary brain injury, ultimately leading to poor neurological recovery. Notably, the multifactorial model (Model 2) that included SII significantly outperformed the other models in both NRI and IDI analyses. This not only confirms the independent predictive value of SII, but also suggests its synergistic effect with traditional clinical indicators (e.g., onset-to-puncture time, length of surgery). This finding contrasts with the study by Sun et al [34] , which found that NLR was associated with early neurologic deterioration after intravenous thrombolysis, but did not address the mechanical thrombolysis population. The innovation of this study is to combine SII with endovascular treatment-specific indicators (e.g., revascularization grade, number of thrombus removals) to construct a risk stratification tool that is more relevant to clinical practice. 3.4 Clinical significance and practical value The prediction model in this study demonstrated a good net benefit at the 15% probability threshold (DCA analysis), suggesting its potential for important applications in clinical decision making. For patients after mechanical thrombectomy, early identification of high-risk groups can help optimize monitoring strategies (e.g., intensified anti-inflammatory therapy, infection prevention) and rehabilitation programs. For example, in patients with significantly elevated SII, dynamic monitoring of inflammatory markers and early intervention may be considered to reduce the risk of secondary injury. In addition, the interpretability of the model (quantifying variable contributions through SHAP values) enhances clinician confidence and provides an evidence-based basis for individualized treatment. On the other hand, this study provides theoretical support for the dual-targeted inflammation-immunity intervention. Targeted therapies (e.g., IL-1β inhibitors, neutrophil extracellular trap inhibitors) for post-stroke inflammatory response have made some progress in recent years [35, 36] , and SII, as a comprehensive inflammatory marker, may become a biomarker for the evaluation of the efficacy of such therapies, and may even guide the timing of treatment. 3.5 Research limitations and directions for improvement Despite the innovative nature of the findings, the following limitations need to be addressed: Risk of bias in retrospective design: the data were derived from a single center, and the calculation of some laboratory indicators (e.g., SII) relied on baseline data and did not incorporate postoperative dynamic changes. Future multicenter prospective studies are needed to verify the generalizability of the model.Sample size limitation: although the sample of 387 cases meets the needs of statistical analysis, the efficacy of subgroup analyses (e.g., different TOAST subtypes, grade of revascularization) is insufficient. Expanding the sample size could further refine the risk stratification. Complexity of machine learning models: the current model is based on logistic regression, which is interpretable but advanced algorithms such as deep learning have not been attempted. Neural network models could be explored in the future to capture non-linear associations. 3.6 Directions for future research To explore the association between the trajectory of postoperative SII changes and long-term prognosis, and to establish a dynamic prediction model.In addition, SII can be combined with perfusion imaging (e.g., CTP) and diffusion-weighted imaging (DWI) to create multimodal prognostic systems. Randomized controlled trials based on SII stratified design to assess the efficacy of anti-inflammatory therapy in high-risk populations. It also explores the predictive value of SII in hemorrhagic stroke or cardioembolic stroke and expands its clinical scenarios. Conclusion This study confirms the validity of SII in the prognostic assessment after mechanical thrombectomy in acute ischemic stroke. Its integration of multidimensional inflammatory and immune information significantly improved the accuracy and clinical utility of the prediction model. Future studies need to further optimize the model design and explore precise intervention strategies under the guidance of SII to ultimately improve the long-term neurological outcome of AIS patients. Statements and Declarations 1.Ethical approval and informed consent This study was conducted in accordance with the Declaration of Helsinki and was approved by the Local Ethics Committee of the Affiliated Hospital of Xuzhou Medical University (ethics approval number XYFY2019-KL042-01).Considering the retrospective nature of the study, the requirement for patient consent was waived by the ethics committee.All patient data were anonymized or maintained confidentially. 2.Data availability statement Data from this study are available from the corresponding author upon request. 3.Conflict of Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 4.Funding Supported by National Health Commission WKLX2023CZ0143 and "Pairing Assistance" Research Program of the Affiliated Hospital of Xuzhou Medical University. 5.Authorship Contribution All authors contributed to the study's conception and design. Data collection and analysis were performed by Bo Zhou, Xiaoyu Xu, Menglu Zhang, Qingtao Xie,and Shiqin Ju. The initial draft of the manuscript was written by Bo Zhou and all authors commented on previous versions of the manuscript. The contributions made by Yanbo Cheng and Yu Feng in the research include providing research ideas and guidance on article writing.All authors read and approved the final manuscript.Please note that Bo Zhou and Xiaoyu Xu are designated as co-first authors, as they contributed equally to this work. Both authors participated in data acquisition, and manuscript writing. Yanbo Cheng and Yu Feng contributed equally as co-corresponding authors. They will provide guidance and necessary assistance throughout the publication process of the manuscript. References Haupt M, Gerner ST, Bähr M, Doeppner TR. Neuroprotective Strategies for Ischemic Stroke-Future Perspectives. Int J Mol Sci. 2023. 24(5): 4334. Powers WJ, Rabinstein AA, Ackerson T, et al. Guidelines for the Early Management of Patients With Acute Ischemic Stroke: 2019 Update to the 2018 Guidelines for the Early Management of Acute Ischemic Stroke: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association. Stroke. 2019. 50(12): e344-e418. Marto JP, Strambo D, Hajdu SD, et al. Twenty-Four-Hour Reocclusion After Successful Mechanical Thrombectomy: Associated Factors and Long-Term Prognosis. Stroke. 2019. 50(10): 2960-2963. Goyal M, Menon BK, van Zwam WH, et al. Endovascular thrombectomy after large-vessel ischaemic stroke: a meta-analysis of individual patient data from five randomised trials. Lancet. 2016. 387(10029): 1723-31. Shi K, Tian DC, Li ZG, Ducruet AF, Lawton MT, Shi FD. Global brain inflammation in stroke. Lancet Neurol. 2019. 18(11): 1058-1066. Zhao J, Lv H, Yin D, et al. Systemic Immune-Inflammation Index Predicts Long-Term Outcomes in Patients with Three-Vessel Coronary Disease After Revascularization: Results from a Large Cohort of 3561 Patients. J Inflamm Res. 2022. 15: 5283-5292. Parmana I, Boom CE, Poernomo H, et al. High Preoperative Systemic Immune-Inflammation Index Values Significantly Predicted Poor Outcomes After on-Pump Coronary Artery Bypass Surgery. J Inflamm Res. 2024. 17: 755-764. Chen R, Su S, Wang C, et al. Systemic immune-inflammation index predicts the clinical outcomes in patients with acute uncomplicated type-B aortic dissection undergoing optimal medical therapy. BMC Cardiovasc Disord. 2024. 24(1): 7. Yang YL, Wu CH, Hsu PF, et al. Systemic immune-inflammation index (SII) predicted clinical outcome in patients with coronary artery disease. Eur J Clin Invest. 2020. 50(5): e13230. Huang J, Zhang Q, Wang R, et al. Systemic Immune-Inflammatory Index Predicts Clinical Outcomes for Elderly Patients with Acute Myocardial Infarction Receiving Percutaneous Coronary Intervention. Med Sci Monit. 2019. 25: 9690-9701. Huang H, Liu Q, Zhu L, et al. Prognostic Value of Preoperative Systemic Immune-Inflammation Index in Patients with Cervical Cancer. Sci Rep. 2019. 9(1): 3284. Li J, Cao D, Huang Y, et al. The Prognostic and Clinicopathological Significance of Systemic Immune-Inflammation Index in Bladder Cancer. Front Immunol. 2022. 13: 865643. Wei CJ, Xue JJ, Zhou X, Xia XS, Li X. Systemic Immune-Inflammation Index is a Prognostic Predictor for Patients With Acute Ischemic Stroke Treated With Intravenous Thrombolysis. Neurologist. 2024. 29(1): 22-30. Yang Y, Cui T, Bai X, et al. Association Between Systemic Immune-Inflammation Index and Symptomatic Intracranial Hemorrhage in Acute Ischemic Stroke Patients Undergoing Endovascular Treatment. Curr Neurovasc Res. 2022. 19(1): 83-91. Huang S, Xie W, Gao Y, et al. A Role for Systemic Inflammation in Stroke-Associated Infection and the Long-Term Prognosis of Acute Ischemic Stroke: A Mediation Analysis. J Inflamm Res. 2024. 17: 6533-6545. Wu S, Shi X, Zhou Q, Duan X, Zhang X, Guo H. The Association between Systemic Immune-Inflammation Index and All-Cause Mortality in Acute Ischemic Stroke Patients: Analysis from the MIMIC-IV Database. Emerg Med Int. 2022. 2022: 4156489. Gong P, Liu Y, Gong Y, et al. The association of neutrophil to lymphocyte ratio, platelet to lymphocyte ratio, and lymphocyte to monocyte ratio with post-thrombolysis early neurological outcomes in patients with acute ischemic stroke. J Neuroinflammation. 2021. 18(1): 51. Albers GW, Marks MP, Kemp S, et al. Thrombectomy for Stroke at 6 to 16 Hours with Selection by Perfusion Imaging. N Engl J Med. 2018. 378(8): 708-718. Tao C, Nogueira RG, Zhu Y, et al. Trial of Endovascular Treatment of Acute Basilar-Artery Occlusion. N Engl J Med. 2022. 387(15): 1361-1372. Nogueira RG, Jadhav AP, Haussen DC, et al. Thrombectomy 6 to 24 Hours after Stroke with a Mismatch between Deficit and Infarct. N Engl J Med. 2018. 378(1): 11-21. Xu X, Chen M, Zhu D. Reperfusion and cytoprotective agents are a mutually beneficial pair in ischaemic stroke therapy: an overview of pathophysiology, pharmacological targets and candidate drugs focusing on excitotoxicity and free radical. Stroke Vasc Neurol. 2024. 9(4): 351-359. Tang Y, Xu H, Du X, et al. Gene expression in blood changes rapidly in neutrophils and monocytes after ischemic stroke in humans: a microarray study. J Cereb Blood Flow Metab. 2006. 26(8): 1089-102. Laridan E, Denorme F, Desender L, et al. Neutrophil extracellular traps in ischemic stroke thrombi. Ann Neurol. 2017. 82(2): 223-232. Aronowski J, Roy-O'Reilly MA. Neutrophils, the Felons of the Brain. Stroke. 2019. 50(3): e42-e43. del Zoppo GJ, Schmid-Schönbein GW, Mori E, Copeland BR, Chang CM. Polymorphonuclear leukocytes occlude capillaries following middle cerebral artery occlusion and reperfusion in baboons. Stroke. 1991. 22(10): 1276-83. Rawish E, Nording H, Münte T, Langer HF. Platelets as Mediators of Neuroinflammation and Thrombosis. Front Immunol. 2020. 11: 548631. Zuo K, Yang X. Decreased platelet-to-lymphocyte ratio as predictor of thrombogenesis in nonvalvular atrial fibrillation. Herz. 2020. 45(7): 684-688. Miró-Mur F, Urra X, Gallizioli M, Chamorro A, Planas AM. Antigen Presentation After Stroke. Neurotherapeutics. 2016. 13(4): 719-728. Schwartz M, Moalem G. Beneficial immune activity after CNS injury: prospects for vaccination. J Neuroimmunol. 2001. 113(2): 185-92. Eltzschig HK, Eckle T. Ischemia and reperfusion--from mechanism to translation. Nat Med. 2011. 17(11): 1391-401. Anrather J, Iadecola C. Inflammation and Stroke: An Overview. Neurotherapeutics. 2016. 13(4): 661-670. Saver JL. Time is brain--quantified. Stroke. 2006. 37(1): 263-6. Phipps MS, Cronin CA. Management of acute ischemic stroke. BMJ. 2020. 368: l6983. Sun YY, Wang MQ, Wang Y, et al. Platelet-to-lymphocyte ratio at 24h after thrombolysis is a prognostic marker in acute ischemic stroke patients. Front Immunol. 2022. 13: 1000626. Galea J, Brough D. The role of inflammation and interleukin-1 in acute cerebrovascular disease. J Inflamm Res. 2013. 6: 121-8. Gao X, Zhao X, Li J, et al. Neutrophil extracellular traps mediated by platelet microvesicles promote thrombosis and brain injury in acute ischemic stroke. Cell Commun Signal. 2024. 22(1): 50. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-6635485","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":463208797,"identity":"7f212734-58f3-4a2e-b5d2-edd060a4f383","order_by":0,"name":"Bo ZHOU","email":"","orcid":"","institution":"Xuzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"ZHOU","suffix":""},{"id":463208799,"identity":"347b4f04-5c5f-4746-973e-58e257b62f4d","order_by":1,"name":"Xiaoyu Xu","email":"","orcid":"","institution":"Feng County People's 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Clinical\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6635485/v1/a0f2b547f0b8d6fed28b8be5.png"},{"id":83676414,"identity":"50a8c6f6-3b1f-40fe-90c9-e866ff28c9a4","added_by":"auto","created_at":"2025-05-30 14:56:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":99000,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curve analysis of different clinical prediction models\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6635485/v1/c30499b27bcab5836b911784.png"},{"id":83676415,"identity":"a069f5b7-2fe0-444a-a501-20638f9d1b14","added_by":"auto","created_at":"2025-05-30 14:56:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":93234,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredictive Model Effectiveness Evaluation Chart ROC Curve\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6635485/v1/00bc2c00bbae8607c56bb3bf.png"},{"id":83676419,"identity":"c1e6321a-6ea9-4b34-897a-c823376b11d2","added_by":"auto","created_at":"2025-05-30 14:56:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":151815,"visible":true,"origin":"","legend":"\u003cp\u003ePredictive Modeling Effectiveness Evaluation Chart Correction Curve bootstrap method.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6635485/v1/0e46dae38ffb98fea00c830b.png"},{"id":83676417,"identity":"9fa0ed0f-d751-4bc3-ba4f-781bac072445","added_by":"auto","created_at":"2025-05-30 14:56:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":101325,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSHAP Abstract Map of Predictive Models. \u003c/strong\u003eNote: In the SHAP diagram, the width of the horizontal bars in the summary on the left indicates the degree of influence, suggesting a greater contribution to a wider range. The color gradient from blue (low) to red (high) reflects the magnitude of each predictor, indicating their relative importance. On the right is the percentage contribution of the different variable characteristics of the predictive model to the model.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6635485/v1/c861a4bd7ee248cb48092e0c.png"},{"id":83677245,"identity":"77d1f3e4-44ce-4ca4-9a83-9a49f5960ce4","added_by":"auto","created_at":"2025-05-30 15:04:07","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":109895,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDecision curves for predictive modeling\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6635485/v1/b0f30c890c3977218d69a215.png"},{"id":102410957,"identity":"d70f4fed-f0d9-4e49-ac6b-476e12335e86","added_by":"auto","created_at":"2026-02-11 11:57:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1842564,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6635485/v1/0b0da845-2072-442e-8bb1-9a2de98c429b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eThe systemic immune-inflammation index as a superior predictor of short-term prognosis in acute ischemic stroke after mechanical thrombectomy: a retrospective cohort study\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAIS is one of the leading causes of death and disability worldwide\u003csup\u003e[1]\u003c/sup\u003e Currently, MT has been shown to be one of the most effective treatments for patients with acute ischemic stroke due to large artery occlusion\u003csup\u003e[2]\u003c/sup\u003e. However, despite the effectiveness of MT in improving cerebral perfusion in the short term, the long-term prognosis of patients still varies widely\u003csup\u003e[3]\u003c/sup\u003e. Approximately 50% of patients who undergo mechanical thrombectomy have a poor prognosis\u003csup\u003e[4]\u003c/sup\u003e. Therefore accurate prognostic assessment is essential for individualized treatment and risk management.In recent years, researchers have been working to explore the pathomechanisms of the disease, and have found that the inflammatory response triggered by disruption of blood flow plays a crucial role, and that a whole-brain inflammatory response occurs and persists after stroke, and that this whole-brain inflammation may continue to influence post-stroke pathology and have an important impact on the patient\u0026apos;s long-term neurological recovery\u003csup\u003e[5]\u003c/sup\u003e .\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn recent years, the systemic immunoinflammatory index (SII), as an emerging inflammatory marker, has demonstrated significant value in the prognostic assessment of a variety of diseases\u003csup\u003e[6-8]\u003c/sup\u003e. SII reflects the immune-inflammatory state of the body by integrating neutrophil, platelet, and lymphocyte counts, and it has been used with some success in the prognostic assessment of cardiovascular diseases\u003csup\u003e[9, 10]\u003c/sup\u003e and many types of cancer\u003csup\u003e[11, 12]\u003c/sup\u003e. In addition to this, relevant studies have shown that a high systemic immunoinflammatory index is associated with early neurological deterioration (END) and 3-month adverse outcome, which can be used as a potential prognostic predictor in patients with acute ischemic stroke treated with intravenous thrombolysis\u0026nbsp;\u003csup\u003e[13]\u003c/sup\u003e. Available studies have confirmed that the admission systemic immunoinflammatory index (SII) is positively correlated with symptomatic intracranial hemorrhage (sICH) in patients with acute large vessel occlusive stroke (AIS-LVO) undergoing endovascular therapy (EVT), suggesting that it can be used as an important indicator for early risk stratification of complications\u0026nbsp;\u003csup\u003e[14]\u003c/sup\u003e, that there is a relationship between it and the patient patient\u0026apos;s prognosis, and that the dynamics of SII was significantly associated with a high risk of poor prognosis in patients with AIS\u003csup\u003e[15]\u003c/sup\u003e, which provides a theoretical basis for the introduction of inflammatory parameters into the model. Meanwhile, elevated SII was shown to independently predict 30-day all-cause mortality risk with the integration of platelet, immune and inflammatory pathways\u0026nbsp;\u003csup\u003e[16]\u003c/sup\u003e, which provides a multidimensional biomarker framework for machine learning models to parse AIS pathophysiological interactions and prognostic prediction.\u003c/p\u003e\n\u003cp\u003eThe above findings collectively build the scientific basis for an SII-based inflammation monitoring system in the prognostic assessment after mechanical thrombus extraction, laying the foundation for feature engineering and clinical interpretability of the model in this study. The aim of this study was to predict the short-term prognosis of patients after mechanical thrombectomy for acute ischemic stroke by using an SII-based machine learning model.Relevant clinical studies have shown that other inflammatory markers, such as the neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR)), are closely associated with END in patients with AIS\u003csup\u003e[17]\u003c/sup\u003e . Therefore, our study also compared the value and model performance of SII with NLR and PLR in predicting patient clinical outcomes after mechanical thrombolysis in patients with AIS, and explored the machine learning model based on SII as the best prediction model after mechanical thrombolysis in acute ischemic stroke by NRI and IDI analysis.\u003c/p\u003e"},{"header":"Information and methods","content":"\u003cp\u003e1.1 Study population: retrospective collection Patients with AIS between January 2022 and December 2024 at the Affiliated Hospital of Xuzhou Medical University. The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Affiliated Hospital of Xuzhou Medical University. Inclusion criteria:(1) Inclusion criteria:(1) diagnosed with AIS according to World Health Organization criteria with complete baseline and follow-up data; (2) confirmed large vessel occlusion by imaging and treated with mechanical thrombolysis; and (3) time from onset to start of thrombolysis was \u0026lt;6 h, which could be extended to 16 h or 24 h by meeting the inclusion criteria for the DEFUSE 3, DAWN, or BAOCHE studies; \u003csup\u003e[18-20]\u003c/sup\u003e(4) Age \u0026gt; 18 years. Exclusion criteria: (1) CT of the head suggests the presence of bleeding and high bleeding risk diseases (such as intracranial aneurysm, etc.) affecting the operation; (2) severe allergy to contrast media; (3) pregnant and breastfeeding females; (4) patients with serious diseases, such as renal failure, severe hepatic insufficiency, or cancer; (5) patients with autoimmune diseases; (6) patients with immunosuppressive drugs or antibiotic use ; (5) 90d postoperative loss of visit.\u003c/p\u003e\n\u003cp\u003e1.2 Data collection: general information and past medical history were collected; including age, gender, history of diabetes mellitus, history of hypertension, history of atrial fibrillation; preoperative National Institutes of Health Stroke Scale (NIHSS) scores, modified Rankin Scale (mRS) scores, and mRS scores at 90d postoperatively, TOAST typing and Site of onset. Data related to mechanical thrombolysis were collected: time from onset to arterial puncture, duration of the procedure, number of thrombolysis attempts, method of thrombolysis, whether thrombolytic therapy was performed before the procedure, whether arterial thrombolysis was performed, whether a stent was inserted, and the grade of revascularization after the procedure. Laboratory indices were collected: platelet count, neutrophil count, and lymphocyte count at baseline; and calculations were made to measure PLR, NLR, and SII values.\u003c/p\u003e\n\u003cp\u003e1.3 Follow-up and grouping: patients or their family members were followed up by telephone or outpatient clinic at 90d postoperatively, and the prognosis was assessed according to the mRS score. mRS \u0026le;2 points were included in the good prognosis group, and mRS \u0026gt;2 points were included in the bad prognosis group, and the data of the two groups were statistically analyzed to explore the effects of NLR, PLR, and SII levels on the patients\u0026apos; poor prognosis at 90d postoperatively after mechanical thrombolysis.\u003c/p\u003e\n\u003cp\u003e1.4 Statistical methods: All the measured data were statistically described by mean\u0026plusmn;standard deviation to satisfy normality, and the t-test was used for the differences between the two groups; non-normal distribution was statistically described by median (percentile), and the Wilcoxon\u0026apos;s rank sum test was used for the differences between the two groups. The statistical description of the count data was based on the number of cases (%), and the differences between groups were tested by X2 or Fisher\u0026apos;s exact probability test. The Wilcoxon rank sum test was used for differences between the two groups for grade information. Based on the results of intergroup variability, in order to study the analysis of risk factors related to the prognosis of AIS patients after mechanical thrombolysis, a one-way logistic regression model was used to analyze the statistically significant differences between good and poor prognosis of AIS patients after mechanical thrombolysis, and then clinical baselines, SII+clinical baselines, PLR+clinical baselines, and NLR+clinical baselines, were constructed according to clinical practice. Then four multifactorial logistic regression prediction models were constructed based on clinical practice, including SII+clinical baseline, PLR+clinical baseline, and NLR+clinical baseline. The area under the ROC curve (AUC) was used to predict the performance of the models, and the optimal prediction model was constructed by using the NRI and IDI analyses, and the area under the ROC curve (AUC) was used to evaluate the value of the different prediction models and the performance of the models for predicting the clinical outcomes of patients with AIS after mechanical thrombus extraction. The calibration curves and the Hosmer-Lemeshow test were used to evaluate the model fit, and finally, the decision curve (DCA) was used to analyze the clinical value of the models. To further assess the predictive efficacy of SII, PLR and NLR on the prognosis of patients with AIS after mechanical thrombolysis, ROC curves were analyzed using uncorrected and corrected confounders. P\u0026lt;0.05 was considered statistically significant. R4.3.2 software was used for basic statistical analysis, and Python3.9 software was used for SHAP visualization.\u003c/p\u003e"},{"header":"Outcomes","content":"\u003cp\u003e2.1 Overall results:Based on the inclusion and exclusion criteria, 387 cases were extracted from HIS database of AIS in our hospital, out of which 236 cases occurred with poor prognosis and the incidence of poor prognosis was 60.98%. The median age of the good prognosis group was 67.00 [58.00-73.00],of which 49 females accounted for 32.45% and 102 males accounted for 67.55%; the median age of the poor prognosis group was 69.00 [59.00-77.00],of which 78 females accounted for 33.05% and 158 males accounted for 66.95%; Table 1. The median SII in the adverse group was 1407.66 (965.41; 2358.12), the median PLR was 198.18 (137.25; 283.33), and the median NLR was 7.34 (4.68; 11.21); statistically significant differences were found in the onset-to-puncture time (min), operative duration (min), nihss before bolus removal, SII, PLR, and NLR were statistically different between the two groups (P \u0026lt; 0.05),Table 1.\u003c/p\u003e\n\u003cp\u003eTable 1\u003cstrong\u003eComparison of clinical baseline data between the two groups\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eGood prognosis （N=151 ）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003ePoor prognosis （N=236）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003e(N=387)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eStatistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eP_Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003egender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.902\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e- female n%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e49 (32.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e78 (33.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e127(32.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e- male n%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e102 (67.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e158 (66.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e260 (67.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eAge median\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(years）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e67.00\u003c/p\u003e\n \u003cp\u003e[58.00;73.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e69.00\u003c/p\u003e\n \u003cp\u003e[59.00;77.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e68.00\u003c/p\u003e\n \u003cp\u003e[59.00;75.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-1.902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eTOAST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1=ACI;2=CCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.320\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e- 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e107 (70.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e178 (75.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e285 (73.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e- 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e44 (29.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e58 (24.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e102 (26.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eAffected area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1=AC; 2=PC;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e1.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e- 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e126 (83.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e185 (78.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e311 (80.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e- 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e25 (16.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e51 (21.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e76 (19.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eMT times\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e4.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e- 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e115 (76.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e160 (67.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e275 (71.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e- 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e21 (13.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e51 (21.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e72 (18.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e- 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e13 (8.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e23 (9.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e36 (9.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e- 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e2 (1.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e2 (0.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e4 (1.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eMT Techniques\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1=Stent\u003c/p\u003e\n \u003cp\u003eRetriever;\u003c/p\u003e\n \u003cp\u003e2=aspiration\u003c/p\u003e\n \u003cp\u003e3=Combined thrombectomy\u0026nbsp;4=Arterial thrombolysis;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e6.320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e- 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e128 (84.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e179 (75.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e307(79.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e- 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e8 (5.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e25 (10.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e33 (8.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e- 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e15 (9.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e29(12.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e44(11.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e- 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0(0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e3(1.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e3 (0.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eIntravenous thrombolysis(yes=1; no=0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.774\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e- 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e88 (58.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e141 (59.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e229 (59.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e- 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e63 (41.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e95 (40.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e158 (40.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eOnset to Puncture Time /min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e300.00 [220.00;432.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e365.00 [272.50;476.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e340.00 [242.50;465.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-3.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eOperating\u003c/p\u003e\n \u003cp\u003etime/ min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e90.00 [60.00;120.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e100.00 [80.00;140.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e97.00 [70.00;125.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-3.351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003cp\u003e(yes=1; no=0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e1.576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e- 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e86 (56.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e119 (50.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e205 (52.97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e- 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e65 (43.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e117 (49.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e182 (47.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003cp\u003e(yes=1; no=0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e- 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e128(84.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e191 (80.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e319 (82.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e- 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e23(15.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e45(19.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e68 (17.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eatrial fibrillation\u003c/p\u003e\n \u003cp\u003e(yes=1; no=0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e1.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e- 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e108 (71.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e181 (76.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e289 (74.68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e- 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e43 (28.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e55 (23.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e98 (25.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eTICI grading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e5.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e- 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e3 (1.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e2 (0.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e5 (1.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e- 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e1 (0.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e9 (3.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e10 (2.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e- 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e16 (10.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e32 (13.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e48 (12.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e- 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e131 (86.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e193 (81.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e324 (83.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eNihss 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e16.00 [ 11.00;25.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e20.00 [ 14.00;30.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e19.00 [12.00;27.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-3.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eMRS 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e3.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e- 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e3(1.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e5 (2.12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e8 (2.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e- 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e68 (45.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e85 (36.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e153 (39.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e- 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e80 (52.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e146 (61.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e226 (58.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eSII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e566.74 [383.78;891.60]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e1407.66 [965.41;2358.12]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e1012.23 [604.65;1748.30]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-11.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e110.74 [79.24;155.11]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e198.18 [ 137.25;283.33]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e157.27 [108.50;235.83]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-10.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e3.08 [2.31;4.58]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e7.34[4.68;11.21]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e5.36[3.13;8.68]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-11.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviation：TOAST=Transient Ischemic Attack of Symptomatic and Atherosclerotic Thrombosis;\u003c/p\u003e\n\u003cp\u003eACI=Arterial-derived cerebral infarction; CCI=Cardiac-derived cerebral infarction; AC=Anterior circulation; PC=Posterior circulation; TICI=Thrombolysis in Cerebral Infarction Classification; Nihss 0=National Institutes of Health Stroke Scale score at the time of admission; MRS0=0Modified Rankin Scale score at the time of admission.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.2 Research on the value of comprehensive inflammation indexes in predicting clinical outcomes: Since the present study found statistically significant differences in the distribution of demographic and clinical indexes such as onset-to-puncture time (min), operating time (min), and nihss before bolus removal between the two groups, in order to better assess the effectiveness of SII, PLR, and NLR in predicting clinical outcomes, the present study plotted the ROC curves of corrected covariates and uncorrected covariates to illustrate in depth the predictive value of each comprehensive inflammation index. In order to better assess the effectiveness of SII, PLR, and NLR in predicting clinical outcomes, this study plotted the ROC curves of corrected covariates and uncorrected covariates to illustrate in depth the predictive value of each composite inflammatory index. As shown in Figure 1, SIl had the greatest predictive efficacy, with an AUC=0.834 (95% C1=0.790-0.874) for uncorrected confounders and 0.841 (95% C1=0.841-0.844) for corrected covariates; NLR was the next most effective predictor with an AUC=0.820 (95% C1=0.774-0.859) for uncorrected covariates; and NLR, with an AUC=0.820 (95% C1=0.774-0.859) for uncorrected covariates. -0.859), and after correcting for other influences its AUC=0.818 (95% C1=0.818-0.821); and finally PLR, with an AUC=0.800 (95% C1=0.754-0.843) uncorrected for confounders, and after correcting for other influences its AUC=0.810 (95% C1=0.809-0.812). 0.812). See Figure 1. The area under the ROC curve based on the above indicators was greater than 0.7, proving that the indicators SII, PLR, and NLR all have some predictive value.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e2.3 Analysis and modeling of factors affecting prognosis: With the dependent variable of whether the prognosis is poor (good=0, poor=1), and the independent variables of continuous variables such as clinical baseline indicators of onset to puncture time (min), operating time (min), and nihss before bolus removal, four multifactorial logistic regression prediction models were constructed according to clinical practice, namely, Model1 (basic clinical model), Model2 (basic+SII), Model3 (basic+PLR), and Model4 (basic+NLR), which showed the highest effectiveness. Four multifactorial logistic regression prediction models were constructed according to clinical practice: Model1 (clinical base model), Model2 (base + SII), Model3 (base + PLR), and Model4 (base + NLR), respectively, and as shown in Table 3 ; Table 4; Table 5 andFigure 2, Model2 was the most effective.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;After the Delong test, it was found that the difference in the area of the ROC curve of Model2 was statistically significant (P\u0026lt;0.05) compared with that of Model1 and Model3, and the difference was not statistically significant (P\u0026gt;0.05) compared with that of Model4, and the difference in the area of the ROC curve of Model2 was statistically significant (P\u0026gt;0.05). However, after NRI and IDI analyses, it was found that Model4, when compared with Model2, had an NRI(Categorical) of -0.1568 ([95% CI: -0.2383 to -0.0753 ] ; P\u0026lt;0.001); an NRI(Continuous) of -0.2662 ([95% CI: -0.468 to - -0.0643 ] ; P= 0.010); an IDI of -0.0247 [[95% CI: -0.0484 to -0.001 ] ; P=0.041); and an IDI of -0.041. 0.0643 ] ; P= 0.010); IDI was -0.0247 [[95% CI: -0.0484 to -0.001 ] ; P=0.041), which shows that model2 has better comprehensive performance than model4 and is the optimal prediction model. The result is the formula for the optimal prediction equation: prognostic bad =-3.576+0.001*OnsettoPunctureTime+0.007*OPTime+0.031*nihss0+0.002*SII.\u003c/p\u003e\n\u003cp\u003eAnd, OnsettoPunctureTime, OPTime, nihss0 and SII were prognostic risk factors (OR all \u0026gt;1, P\u0026lt;0.05). For details, see Table 2.\u003c/p\u003e\n\u003cp\u003eTable 2 \u003cstrong\u003eResults of unifactorial and multifactorial logistic regression analyses influencing prognosis\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"585\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003evariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eSingle factor logistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eModel1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eModel2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eModel3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eModel4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eOR（95%CI）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003ePvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eOR（95%CI）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003ePvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003eOR（95%CI）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003ePvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eOR（95%CI）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003ePvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eOR（95%CI）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003ePvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eOTPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.001 (1.000,1.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.001\u003c/p\u003e\n \u003cp\u003e(1.000,1.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e1.001\u003c/p\u003e\n \u003cp\u003e(1.000,1.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.001\u003c/p\u003e\n \u003cp\u003e(1.000, 1.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1.001\u003c/p\u003e\n \u003cp\u003e(1.000, 1.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eOPTime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.006 (1.002,1.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.005\u003c/p\u003e\n \u003cp\u003e(1.001, 1.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e1.007\u003c/p\u003e\n \u003cp\u003e(1.001,1.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.007\u003c/p\u003e\n \u003cp\u003e(1.002, 1.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1.006\u003c/p\u003e\n \u003cp\u003e(1.001,1.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003enihss0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.039 (1.015,1,064)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.041\u003c/p\u003e\n \u003cp\u003e(1.016, 1.066)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e1.031\u003c/p\u003e\n \u003cp\u003e(1.003,1.060)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.041\u003c/p\u003e\n \u003cp\u003e(1.013,1.070)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1.025\u003c/p\u003e\n \u003cp\u003e(0.997,1.053)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eSII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.002 (1.002,1.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e1.002\u003c/p\u003e\n \u003cp\u003e(1.002, 1.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.016 (1.012,1.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.017\u003c/p\u003e\n \u003cp\u003e(1.013, 1.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.505\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1.370, 1.672)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1.513\u003c/p\u003e\n \u003cp\u003e(1.366, 1.677)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Abbreviation：OTPT=Onset to puncture time; OPTime=Operating time.\u003c/p\u003e\n\u003cp\u003eTable 3 \u003cstrong\u003eResults of AUC, sensitivity, specificity and other evaluation indexes of ROC of different models\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"591\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eCutoff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eAUC（95%CI）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eP_Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003eACC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003ePPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eNPV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eModel1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.655\u003c/p\u003e\n \u003cp\u003e(0.597,0.712)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.610\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eModel2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.863\u003c/p\u003e\n \u003cp\u003e(0.825,0.901)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.750\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eModel3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.830\u003c/p\u003e\n \u003cp\u003e(0.789,0.871)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eModel4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.844\u003c/p\u003e\n \u003cp\u003e(0.804,0.883)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.686\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 4 \u003cstrong\u003eROC two-by-two comparison results for different models\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"502\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eComparative models1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eComparative model 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003estatistic Z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003ep_value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eModel1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eModel2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e-7.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eModel1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eModel3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e-6.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eModel1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eModel4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e-6.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eModel2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eModel3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2.520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eModel2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eModel4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eModel3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eModel4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e-0.896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e0.370\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 5 \u003cstrong\u003eResults of two-by-two comparison of the performance of different models\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"516\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eComparison\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eNRI(Categorical)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;[95% CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eNRI(Continuous) [95% CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eIDI [95% CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eModel1 vs Model2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.5349 [ 0.4497 - 0.6201 ] ; P\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e1.0778 [ 0.9153 - 1.2403 ] ; P\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.2901 [ 0.2483 - 0.3318] P\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eModel1 vs Model3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.4167 [ 0.3243 - 0.5092 ] ; P\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e0.8585 [ 0.6776 - 1.0393 ] ; P\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.2477 [ 0.2059 - 0.2894] P\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eModel1 vs Model4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.4443 [ 0.3543 - 0.5343]; P\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e0.9284 [ 0.7523 - 1.1045]; P\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.2654 [ 0.2235 - 0.3072] P\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eModel2 vs Model3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e-0.1356 [ -0.2362 - -0.0351]; P= 0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e-0.3398 [ -0.541 - -0.1386]; P= 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-0.0424[ -0.0704 - -0.0144]; P= 0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eModel2 vs Model4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e-0.1568 [ -0.2383- -0.0753]; P\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e-0.2662 [ -0.468 - -0.0643]; P= 0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-0.0247[-0.0484 --0.001]\u0026nbsp;P=0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eModel3 vs Model4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e-0.0212 [ -0.1238 - 0.0815]; P= 0.686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e0.2699 [ 0.0675 - 0.4723]; P= 0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.0177[-0.0171 - 0.0524] P= 0.319\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e2.4 Visual construction and evaluation of the optimal prediction model: the optimal prediction model in this study was a multifactorial logistic regression model constructed based on the SII index, with an area under the ROC curve AUC = 0.954 (95% CI : 0.925-0.983), a sensitivity of 0.839 when the model cutoff = 0.497, a specificity of 0.755 , PPV of 0.843, NPV of 0.750, and accuracy of 0.806 (95% CI :0.763-0.844), as detailed in Figure 3. The model was analyzed for goodness-of-fit, and it was found that the model had a good ability to match between the predicted risk and the actual results (Hosmer-Lemeshow test, X\u003csup\u003e2\u003c/sup\u003e= 4.899, P= 0.265\u0026gt;0.05,Figure 4).\u003c/p\u003e\n\u003cp\u003e2.5 Visualization of SHAP values for the importance of variables in the prediction model: The relative importance and impact of variables in the model in predicting postoperative prognosis were analyzed by SHAP values, and it can be seen in Figure 5 that different variable characteristics have different impacts on prognostic prediction. In the present study, we found that SII, operative duration (min), nihss before bolus removal, and onset-to-puncture time (min) were associated with an increased risk of poor prognosis, and the higher the value, the higher the probability of poor prognosis. In addition, we can find that the characteristics of different variables affect the model differently, with SII having the highest influence on the predictive model, accounting for 74.2% of the total model, followed by the length of the procedure (min) and the nihss prior to the removal of the embolus, accounting for 9.6% and 9.3% of the total model, and lastly, the time from onset of the procedure to the puncture (min), at 6.9%.\u003c/p\u003e\n\u003cp\u003e2.6 Results of DCA analysis: As can be seen in Figure 6, when the probability threshold (Pt) is \u0026ge;15%, the use of the constructed predictive model for this column of graphs leads to a progressively higher net benefit compared to performing all other relevant tests on the patients, i.e., in clinical practice, by setting Pt = 15%, the predictive model is able to detect an additional 15 patients per 100 screened without over-increasing the number of other tests. patients with poor prognosis without increasing the number of false positives. That is, within the threshold probability range of 0.15 to 0.90, there is a high net benefit of using this line plot model to predict the risk of poor prognosis in patients.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we found that the systemic immunoinflammatory index (SII) demonstrated a significant advantage in predicting the 90-day prognosis after mechanical thrombectomy in patients with acute ischemic stroke (AIS), and its predictive efficacy was superior to that of the traditional inflammatory markers, PLR and NLR.A multifactorial regression model constructed based on the SII (AUC=0.863) significantly enhanced the accuracy of prognostic assessment, which was confirmed by SHAP analysis. SII contributed up to 74.2% to the model. The significance of the study, mechanism exploration, clinical value, limitations and future directions are discussed below.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1 Exploration of the advantages and mechanisms of SII as a novel inflammatory marker\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study demonstrated that SII has higher sensitivity and specificity in predicting poor prognosis after mechanical thrombolysis. This result may be closely related to its property of integrating the dynamic balance of neutrophils, platelets and lymphocytes. Neutrophils, as a core component of innate immunity, release pro-inflammatory factors and exacerbate neuroinflammatory responses in ischemia-reperfusion injury\u003csup\u003e[21]\u003c/sup\u003e, and peripheral blood cells show significant temporal fluctuations in gene expression in the acute stroke state in which neutrophil-specific transcriptional signatures predominate\u003csup\u003e[22]\u003c/sup\u003e; changes in the homeostatic state of neutrophils are closely related to the severity of the stroke condition, possibly by stimulating the systemic inflammatory response in which they play a key role. They exacerbate the severity of stroke through a variety of pathways, including triggering capillary occlusion, generating oxygen free radicals, releasing inflammatory factors, and promoting thrombosis through neutrophil and platelet aggregation and the formation of Neutrophil Extracellular Traps (NETs)\u003csup\u003e[23-25]\u003c/sup\u003e. Platelet activation is not only involved in thrombosis, but also exacerbates vascular endothelial damage by releasing inflammatory mediators. Platelets and lymphocytes have been shown to play key roles in immune regulation and inflammatory response. As an important participant in the inflammatory response in thrombopathic areas, platelets show rapid activation in stroke pathology. When a cerebrovascular accident occurs, activated platelets specifically bind to damaged vascular endothelial cells through surface adhesion molecules, while releasing a variety of pro-inflammatory mediators. These mediators can not only chemotaxis leukocytes and other immune cells to the focal area, but also enhance the intensity of the local inflammatory response, forming a cascade amplification effect\u003csup\u003e[26, 27]\u003c/sup\u003e. However, lymphocytes control inflammatory pathways by coordinating, healing, and repairing inflammation\u003csup\u003e[28, 29]\u003c/sup\u003e, and lymphopenia suggests a state of immunosuppression, which is associated with risk of infection and poor prognosis. Taken together, by integrating changes in these three types of cells, SII is able to reflect more comprehensively the systemic inflammatory and immune imbalance status of the organism, and thus more accurately predict potential barriers to neurological recovery. In contrast, PLR and NLR involve the ratio of only two types of cells, which may miss the pathologic significance of platelet or neutrophil abnormalities alone, resulting in a relative lack of predictive efficacy.\u003c/p\u003e\n\u003cp\u003eIn addition, this study revealed the dominant role of SII in the model by SHAP analysis, further supporting its status as a core biomarker. This finding is consistent with previous studies, such as Huang et al \u003csup\u003e[10]\u003c/sup\u003ewho found that SII predicted long-term outcome after hemodialysis in patients with coronary artery disease, and Yang et al \u003csup\u003e[14]\u003c/sup\u003ewho demonstrated that SII was associated with the risk of symptomatic intracranial hemorrhage in patients with AIS. However, this study is the first to include SII in a prognostic model after mechanical thrombolysis and to validate its association with clinical outcomes, providing a new perspective on the monitoring of inflammation after stroke.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 In this study, we constructed the best predictive model in which, in addition to SII, Onset-to-Puncture Time, Baseline NIHSS Score, and Procedure Duration played varying degrees of roles in the model, and we next attempted to analyze these three metrics\u003c/strong\u003e\u003cstrong\u003e。\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.1\u003c/strong\u003e The onset-to-puncture time is a critical factor in determining the prognosis of AIS patients undergoing MT. In this study, the onset-to-puncture time was significantly associated with poor outcomes, as evidenced by its inclusion as a key variable in the optimal predictive model (Model 2). Mechanistic Insight: Delayed reperfusion leads to mitochondrial dysfunction, reactive oxygen species (ROS) bursts, and inflammatory cascade responses that exacerbate tissue damage\u003csup\u003e[30]\u003c/sup\u003e. Relevant studies have demonstrated that hypoxia activates microglia and endothelial cells, releases pro-inflammatory factors such as IL-1\u0026beta; and TNF-\u0026alpha;, recruits neutrophil infiltration, and exacerbates blood-brain barrier disruption\u003csup\u003e[31]\u003c/sup\u003e. Moreover, delayed reperfusion leads to a systemic inflammatory response (e.g., elevated SII), which amplifies secondary brain injury through the \u0026ldquo;brain-peripheral immune axis\u0026rdquo;\u003csup\u003e[5]\u003c/sup\u003e. The \u0026quot;time is brain\u0026quot; principle\u003csup\u003e[32]\u003c/sup\u003e underscores that earlier intervention reduces infarct volume and mitigates secondary damage, such as blood-brain barrier disruption and neuronal apoptosis. Clinical Implications: These findings reinforce the importance of streamlining pre-hospital and in-hospital workflows to minimize delays. Future studies could explore dynamic SII changes relative to onset-to-puncture time to refine time-sensitive interventions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.2\u003c/strong\u003e The study identified procedure duration as an independent predictor of poor outcomes, with longer durations correlating with higher risks (SHAP analysis: 9.6% contribution to the model).Mechanistic Insight: Prolonged MT may reflect procedural complexities (e.g., difficult vascular access, multiple retrieval attempts), which can aggravate endothelial injury and inflammatory responses. Extended ischemia-reperfusion cycles also amplify oxidative stress and neutrophil activation, worsening neuroinflammation.Clinical Implications: Optimizing procedural efficiency (e.g., advanced imaging guidance, operator experience) and monitoring intraprocedural SII dynamics could mitigate risks. The interplay between SII and procedure duration warrants further investigation to identify thresholds for intervention.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.3\u0026nbsp;\u003c/strong\u003eThe baseline NIHSS score is also a robust predictor of poor prognosis (SHAP analysis: 9.3% contribution), aligning with its established role in stroke severity assessment. Mechanistic Insight: Higher NIHSS scores indicate more extensive neurological deficits, often reflecting larger infarct cores or critical perfusion deficits\u003csup\u003e[2, 33]\u003c/sup\u003e. These patients may exhibit exaggerated systemic inflammation (e.g., elevated SII), exacerbating secondary injury.Clinical Implications: Integrating NIHSS with SII enhances risk stratification. Patients with high NIHSS and SII may benefit from adjunctive anti-inflammatory therapies or intensified post-MT monitoring.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Comparison with traditional inflammatory markers and predictive models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, the superiority of SII was clarified by comparing the predictive efficacy of SII, PLR and NLR. After correction for confounders, the AUC of SII (0.841) was significantly higher than that of PLR (0.810) and NLR (0.818). This difference may stem from the focus of PLR and NLR on a single pathologic process: while PLR mainly reflects platelet activation and immune depletion, NLR focuses on acute stress and infection risk. However, the inflammatory response after AIS involves multi-pathway interactions, and SII is better able to capture complex pathophysiologic changes due to its multidimensional integration properties. For example, high SII may simultaneously suggest platelet overactivation, neutrophil infiltration, and lymphocyte depletion, which together exacerbate blood-brain barrier disruption and secondary brain injury, ultimately leading to poor neurological recovery. Notably, the multifactorial model (Model 2) that included SII significantly outperformed the other models in both NRI and IDI analyses. This not only confirms the independent predictive value of SII, but also suggests its synergistic effect with traditional clinical indicators (e.g., onset-to-puncture time, length of surgery). This finding contrasts with the study by Sun et al \u003csup\u003e[34]\u003c/sup\u003e, which found that NLR was associated with early neurologic deterioration after intravenous thrombolysis, but did not address the mechanical thrombolysis population. The innovation of this study is to combine SII with endovascular treatment-specific indicators (e.g., revascularization grade, number of thrombus removals) to construct a risk stratification tool that is more relevant to clinical practice.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Clinical significance and practical value\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe prediction model in this study demonstrated a good net benefit at the 15% probability threshold (DCA analysis), suggesting its potential for important applications in clinical decision making. For patients after mechanical thrombectomy, early identification of high-risk groups can help optimize monitoring strategies (e.g., intensified anti-inflammatory therapy, infection prevention) and rehabilitation programs. For example, in patients with significantly elevated SII, dynamic monitoring of inflammatory markers and early intervention may be considered to reduce the risk of secondary injury. In addition, the interpretability of the model (quantifying variable contributions through SHAP values) enhances clinician confidence and provides an evidence-based basis for individualized treatment.\u003c/p\u003e\n\u003cp\u003eOn the other hand, this study provides theoretical support for the dual-targeted inflammation-immunity intervention. Targeted therapies (e.g., IL-1\u0026beta; inhibitors, neutrophil extracellular trap inhibitors) for post-stroke inflammatory response have made some progress in recent years \u003csup\u003e[35, 36]\u003c/sup\u003e, and SII, as a comprehensive inflammatory marker, may become a biomarker for the evaluation of the efficacy of such therapies, and may even guide the timing of treatment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Research limitations and directions for improvement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDespite the innovative nature of the findings, the following limitations need to be addressed:\u003c/p\u003e\n\u003cp\u003eRisk of bias in retrospective design: the data were derived from a single center, and the calculation of some laboratory indicators (e.g., SII) relied on baseline data and did not incorporate postoperative dynamic changes. Future multicenter prospective studies are needed to verify the generalizability of the model.Sample size limitation: although the sample of 387 cases meets the needs of statistical analysis, the efficacy of subgroup analyses (e.g., different TOAST subtypes, grade of revascularization) is insufficient. Expanding the sample size could further refine the risk stratification.\u003c/p\u003e\n\u003cp\u003eComplexity of machine learning models: the current model is based on logistic regression, which is interpretable but advanced algorithms such as deep learning have not been attempted. Neural network models could be explored in the future to capture non-linear associations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Directions for future research\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the association between the trajectory of postoperative SII changes and long-term prognosis, and to establish a dynamic prediction model.In addition, SII can be combined with perfusion imaging (e.g., CTP) and diffusion-weighted imaging (DWI) to create multimodal prognostic systems.\u003c/p\u003e\n\u003cp\u003eRandomized controlled trials based on SII stratified design to assess the efficacy of anti-inflammatory therapy in high-risk populations. It also explores the predictive value of SII in hemorrhagic stroke or cardioembolic stroke and expands its clinical scenarios.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study confirms the validity of SII in the prognostic assessment after mechanical thrombectomy in acute ischemic stroke. Its integration of multidimensional inflammatory and immune information significantly improved the accuracy and clinical utility of the prediction model. Future studies need to further optimize the model design and explore precise intervention strategies under the guidance of SII to ultimately improve the long-term neurological outcome of AIS patients.\u003c/p\u003e"},{"header":" Statements and Declarations","content":"\u003cp\u003e\u003cstrong\u003e1.Ethical approval and informed consent \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and was approved by the Local Ethics Committee of the Affiliated Hospital of Xuzhou Medical University (ethics approval number XYFY2019-KL042-01).Considering the retrospective nature of the study, the requirement for patient consent was waived by the ethics committee.All patient data were anonymized or maintained confidentially.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.Data availability statement \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData from this study are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.Conflict of Interest\u003c/strong\u003e\u003cbr\u003e The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.Funding \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupported by National Health Commission WKLX2023CZ0143 and \u0026quot;Pairing Assistance\u0026quot; Research Program of the Affiliated Hospital of Xuzhou Medical University. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.Authorship Contribution\u003c/strong\u003e\u003cbr\u003e All authors contributed to the study\u0026apos;s conception and design. Data collection and analysis were performed by Bo Zhou, Xiaoyu Xu, Menglu Zhang, Qingtao Xie,and Shiqin Ju. The initial draft of the manuscript was written by Bo Zhou and all authors commented on previous versions of the manuscript. The contributions made by Yanbo Cheng and Yu Feng in the research include providing research ideas and guidance on article writing.All authors read and approved the final manuscript.Please note that Bo Zhou and Xiaoyu Xu are designated as co-first authors, as they contributed equally to this work. Both authors participated in data acquisition, and manuscript writing. Yanbo Cheng and Yu Feng contributed equally as co-corresponding authors. They will provide guidance and necessary assistance throughout the publication process of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eHaupt M, Gerner ST, B\u0026auml;hr M, Doeppner TR. Neuroprotective Strategies for Ischemic Stroke-Future Perspectives. 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Lancet Neurol. 2019. 18(11): 1058-1066.\u003c/li\u003e\n \u003cli\u003eZhao J, Lv H, Yin D, et al. Systemic Immune-Inflammation Index Predicts Long-Term Outcomes in Patients with Three-Vessel Coronary Disease After Revascularization: Results from a Large Cohort of 3561 Patients. J Inflamm Res. 2022. 15: 5283-5292.\u003c/li\u003e\n \u003cli\u003eParmana I, Boom CE, Poernomo H, et al. High Preoperative Systemic Immune-Inflammation Index Values Significantly Predicted Poor Outcomes After on-Pump Coronary Artery Bypass Surgery. J Inflamm Res. 2024. 17: 755-764.\u003c/li\u003e\n \u003cli\u003eChen R, Su S, Wang C, et al. Systemic immune-inflammation index predicts the clinical outcomes in patients with acute uncomplicated type-B aortic dissection undergoing optimal medical therapy. BMC Cardiovasc Disord. 2024. 24(1): 7.\u003c/li\u003e\n \u003cli\u003eYang YL, Wu CH, Hsu PF, et al. Systemic immune-inflammation index (SII) predicted clinical outcome in patients with coronary artery disease. Eur J Clin Invest. 2020. 50(5): e13230.\u003c/li\u003e\n \u003cli\u003eHuang J, Zhang Q, Wang R, et al. Systemic Immune-Inflammatory Index Predicts Clinical Outcomes for Elderly Patients with Acute Myocardial Infarction Receiving Percutaneous Coronary Intervention. Med Sci Monit. 2019. 25: 9690-9701.\u003c/li\u003e\n \u003cli\u003eHuang H, Liu Q, Zhu L, et al. Prognostic Value of Preoperative Systemic Immune-Inflammation Index in Patients with Cervical Cancer. Sci Rep. 2019. 9(1): 3284.\u003c/li\u003e\n \u003cli\u003eLi J, Cao D, Huang Y, et al. The Prognostic and Clinicopathological Significance of Systemic Immune-Inflammation Index in Bladder Cancer. Front Immunol. 2022. 13: 865643.\u003c/li\u003e\n \u003cli\u003eWei CJ, Xue JJ, Zhou X, Xia XS, Li X. Systemic Immune-Inflammation Index is a Prognostic Predictor for Patients With Acute Ischemic Stroke Treated With Intravenous Thrombolysis. Neurologist. 2024. 29(1): 22-30.\u003c/li\u003e\n \u003cli\u003eYang Y, Cui T, Bai X, et al. Association Between Systemic Immune-Inflammation Index and Symptomatic Intracranial Hemorrhage in Acute Ischemic Stroke Patients Undergoing Endovascular Treatment. Curr Neurovasc Res. 2022. 19(1): 83-91.\u003c/li\u003e\n \u003cli\u003eHuang S, Xie W, Gao Y, et al. A Role for Systemic Inflammation in Stroke-Associated Infection and the Long-Term Prognosis of Acute Ischemic Stroke: A Mediation Analysis. J Inflamm Res. 2024. 17: 6533-6545.\u003c/li\u003e\n \u003cli\u003eWu S, Shi X, Zhou Q, Duan X, Zhang X, Guo H. The Association between Systemic Immune-Inflammation Index and All-Cause Mortality in Acute Ischemic Stroke Patients: Analysis from the MIMIC-IV Database. Emerg Med Int. 2022. 2022: 4156489.\u003c/li\u003e\n \u003cli\u003eGong P, Liu Y, Gong Y, et al. The association of neutrophil to lymphocyte ratio, platelet to lymphocyte ratio, and lymphocyte to monocyte ratio with post-thrombolysis early neurological outcomes in patients with acute ischemic stroke. J Neuroinflammation. 2021. 18(1): 51.\u003c/li\u003e\n \u003cli\u003eAlbers GW, Marks MP, Kemp S, et al. Thrombectomy for Stroke at 6 to 16 Hours with Selection by Perfusion Imaging. N Engl J Med. 2018. 378(8): 708-718.\u003c/li\u003e\n \u003cli\u003eTao C, Nogueira RG, Zhu Y, et al. Trial of Endovascular Treatment of Acute Basilar-Artery Occlusion. N Engl J Med. 2022. 387(15): 1361-1372.\u003c/li\u003e\n \u003cli\u003eNogueira RG, Jadhav AP, Haussen DC, et al. Thrombectomy 6 to 24 Hours after Stroke with a Mismatch between Deficit and Infarct. N Engl J Med. 2018. 378(1): 11-21.\u003c/li\u003e\n \u003cli\u003eXu X, Chen M, Zhu D. Reperfusion and cytoprotective agents are a mutually beneficial pair in ischaemic stroke therapy: an overview of pathophysiology, pharmacological targets and candidate drugs focusing on excitotoxicity and free radical. Stroke Vasc Neurol. 2024. 9(4): 351-359.\u003c/li\u003e\n \u003cli\u003eTang Y, Xu H, Du X, et al. Gene expression in blood changes rapidly in neutrophils and monocytes after ischemic stroke in humans: a microarray study. J Cereb Blood Flow Metab. 2006. 26(8): 1089-102.\u003c/li\u003e\n \u003cli\u003eLaridan E, Denorme F, Desender L, et al. Neutrophil extracellular traps in ischemic stroke thrombi. Ann Neurol. 2017. 82(2): 223-232.\u003c/li\u003e\n \u003cli\u003eAronowski J, Roy-O\u0026apos;Reilly MA. Neutrophils, the Felons of the Brain. Stroke. 2019. 50(3): e42-e43.\u003c/li\u003e\n \u003cli\u003edel Zoppo GJ, Schmid-Sch\u0026ouml;nbein GW, Mori E, Copeland BR, Chang CM. Polymorphonuclear leukocytes occlude capillaries following middle cerebral artery occlusion and reperfusion in baboons. Stroke. 1991. 22(10): 1276-83.\u003c/li\u003e\n \u003cli\u003eRawish E, Nording H, M\u0026uuml;nte T, Langer HF. Platelets as Mediators of Neuroinflammation and Thrombosis. Front Immunol. 2020. 11: 548631.\u003c/li\u003e\n \u003cli\u003eZuo K, Yang X. Decreased platelet-to-lymphocyte ratio as predictor of thrombogenesis in nonvalvular atrial fibrillation. Herz. 2020. 45(7): 684-688.\u003c/li\u003e\n \u003cli\u003eMir\u0026oacute;-Mur F, Urra X, Gallizioli M, Chamorro A, Planas AM. Antigen Presentation After Stroke. Neurotherapeutics. 2016. 13(4): 719-728.\u003c/li\u003e\n \u003cli\u003eSchwartz M, Moalem G. Beneficial immune activity after CNS injury: prospects for vaccination. J Neuroimmunol. 2001. 113(2): 185-92.\u003c/li\u003e\n \u003cli\u003eEltzschig HK, Eckle T. Ischemia and reperfusion--from mechanism to translation. Nat Med. 2011. 17(11): 1391-401.\u003c/li\u003e\n \u003cli\u003eAnrather J, Iadecola C. Inflammation and Stroke: An Overview. Neurotherapeutics. 2016. 13(4): 661-670.\u003c/li\u003e\n \u003cli\u003eSaver JL. Time is brain--quantified. Stroke. 2006. 37(1): 263-6.\u003c/li\u003e\n \u003cli\u003ePhipps MS, Cronin CA. Management of acute ischemic stroke. BMJ. 2020. 368: l6983.\u003c/li\u003e\n \u003cli\u003eSun YY, Wang MQ, Wang Y, et al. Platelet-to-lymphocyte ratio at 24h after thrombolysis is a prognostic marker in acute ischemic stroke patients. Front Immunol. 2022. 13: 1000626.\u003c/li\u003e\n \u003cli\u003eGalea J, Brough D. The role of inflammation and interleukin-1 in acute cerebrovascular disease. J Inflamm Res. 2013. 6: 121-8.\u003c/li\u003e\n \u003cli\u003eGao X, Zhao X, Li J, et al. Neutrophil extracellular traps mediated by platelet microvesicles promote thrombosis and brain injury in acute ischemic stroke. Cell Commun Signal. 2024. 22(1): 50.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Acute Ischemic Stroke, Mechanical Thrombolysis, Systemic Immunoinflammatory Index, Neutrophil Lymphocyte Ratio, Platelet Lymphocyte Ratio","lastPublishedDoi":"10.21203/rs.3.rs-6635485/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6635485/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e This study evaluated the role of machine learning models based on the Systemic Immunity Index (SII) in predicting short-term prognosis after mechanical thrombectomy (MT) in acute ischemic stroke (AIS).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eData from 387 AIS patients who underwent MT were retrospectively analyzed, including clinical variables, inflammatory markers such as SII, platelet lymphocyte ratio (PLR), neutrophil lymphocyte ratio (NLR）and 90-day modified Rankin Scale (mRS) scores. Patients were categorized into good and poor prognosis groups based on mRS scores. Univariate and multifactorial logistic regression models were constructed to identify risk factors and compare predictive performance. Four models were developed: clinical baseline, SII+clinical baseline, PLR+clinical baseline, and NLR+clinical baseline. Model performance was assessed using ROC curves, NRI, IDI, calibration curves, and decision curve analysis (DCA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eResults showed that SII outperformed PLR and NLR, with AUCs of 0.834 (uncorrected) and 0.841 (corrected). The optimal model (SII+clinical baseline) achieved an AUC of 0.863, significantly improving prognosis prediction. SHAP analysis confirmed SII as the most influential variable (74.2%). The model demonstrated good fit, clinical utility, and effectiveness in identifying poor prognosis patients at a 15% probability threshold.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eIn conclusion, SII-based models provide superior prognostic accuracy compared to traditional markers, offering a valuable tool for clinical decision-making in AIS patients post-MT.\u003c/p\u003e","manuscriptTitle":"The systemic immune-inflammation index as a superior predictor of short-term prognosis in acute ischemic stroke after mechanical thrombectomy: a retrospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-30 14:56:02","doi":"10.21203/rs.3.rs-6635485/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"11610381-1d66-48be-bbb5-51acae9d7354","owner":[],"postedDate":"May 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-11T11:57:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-30 14:56:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6635485","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6635485","identity":"rs-6635485","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}