Predictive value of modified Thrive-c model in the prognosis of AIS patients after reperfusion therapy

Predictive value of modified Thrive-c model in the prognosis of AIS patients after reperfusion therapy | 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 Case Report Predictive value of modified Thrive-c model in the prognosis of AIS patients after reperfusion therapy Jianguang Liu, Shuyin Yang, Chang wen Le, Yueyue Qin, Jingjing Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4479122/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 Purpose To explore the predictive value of combining the TyG index with the THRIVE score at admission for AIS patients following reperfusion therapy. Methods This retrospective study enrolled 284 AIS patients who had undergone reperfusion therapy. Patients were classified into good or poor prognosis groups based on their modified Rankin Scale (mRS) scores. We analyzed the relationship between the TyG index, the THRIVE score at admission, and the prognosis of AIS. We applied Spearman correlation analysis to investigate the correlation between the TyG index and the THRIVE score at admission against the 90-day mRS scores of AIS patients post-reperfusion. The study developed a logistic regression analysis model to establish a combined predictive formula. ROC curves were constructed to evaluate the predictive power of the TyG index, the admission THRIVE score, their combined use, the THRIVE-c model, and the modified THRIVE-c model for AIS prognosis. Results There were significant differences observed between the groups with poor and good prognoses regarding diabetes, hypertension, atrial fibrillation, gender, NIHSS score at admission, admission THRIVE score, TyG index, triglycerides, and fasting blood glucose. Logistic regression analysis determined that both the TyG index and THRIVE score at admission serve as independent risk factors for poor 90-day prognosis in ischemic stroke patients. The combined predictive coefficient, the THRIVE-c model, and the modified THRIVE-c model yielded AUCs of 0.784, 0.81, and 0.847, respectively, indicating their reliable predictive efficacy. Conclusion Both the TyG index and the THRIVE score assessed at admission are significant independent predictors of adverse outcomes in AIS patients post-reperfusion therapy. The integration of these metrics, along with the THRIVE-c and modified THRIVE-c models, enhances the accuracy of prognosis prediction for AIS patients. TyG index THRIVE score Acute ischemic stroke Prognosis IR Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Stroke is recognized as the second leading cause of death and the third leading cause of disability in the world, and it has been reported that 11.6% of global deaths are attributable to stroke, with acute ischemic stroke (AIS) being the most common subtype of pathological stroke, accounting for 85% of all stroke cases[1] With the development of medical technology, early reperfusion therapy has become the key treatment to effectively reduce the disability and mortality of stroke. And the study of risk factors of AIS patients after revascularization and reperfusion therapy is of great significance in the early identification of high-risk groups and improvement of prognosis. Insulinresistance (IR) is considered to be a new risk factor for stroke [2]. Previous studies have shown that IR was independently associated with adverse clinical outcomes in AIS, exacerbated deterioration of neurological function during hospitalization, and triggered recurrence of AIS.[3] The triglyceride glucose (TyG) index, a novel index for assessing IR, has demonstrated high sensitivity and specificity in the diagnosis of IR[4]. The THRIVE score, derived from the results of the Mechanical Embolus Removal in Cerebral Ischemia (MERCI) and Multi MERCI study data, was combined age and NIHSS score with the Chronic Disease Scale to predict 90-day adverse outcomes [modified Rankin score (mRS) 3-6] or death in patients with AIS after the onset of the disease [5] .These variables were convenient so that the clinician using the predictive score spent the shortest time to assess the patient. However, these simplifications are accompanied by potential inaccuracies. Therefore, Flint et al. constructed a Thrive-c multivariate logistic regression model to generate predictive probabilities and create joint equations by entering age, NIHSS variables, and categorical variables (diabetes, hypertension, [6][7][8]and atrial fibrillation) that the Thrive-c model performed better in predicting adverse outcomes compared to the traditional THRIVE score[9] ] . Previous studies[10] found that the higher the THRIVE score, the higher the mortality rate at 3 months in patients with acute ischemic stroke. However, the THRIVE score did not include variables to assess IR, which contributed to the formation,development and prognosis of AIS by promoting platelet activation, triggering endothelial cell dysfunction, facilitating the expression of inflammatory processes, and disruption of the regulation of glycolipid metabolism[11][12] .Meanwhile,IR may exist in both diabetic and non-diabetic patients.Therefore, combining TyG index with THRIVE score may be better for assessing the prognosis of AIS patients. We reconstruct the modified Thrive-c model by replacing diabetes with the TyG index. ， Currently, studies combining the TyG index and the THRIVE score to assess the prognosis of AIS patients have not been reported. This study explores the predictive value of the THRIVE score, the TyG index, and the combination on the 90-day prognosis of AIS patients receiving reperfusion therapy, and explores whether the modified Thrive-c model has a better efficacy in evaluating the 90-day prognosis of AIS patients receiving reperfusion therapy. Information and methods 1.1 General data: Patients receiving reperfusion therapy for AIS in the Department of Neurology of Wuhan Third Hospital were retrospectively collected from January 2021 to December 2023. Inclusion criteria: 1)Patients eligible for acute ischemic stroke, diagnosed according to the Chinese Guidelines for the Diagnosis and Treatment of Acute Ischemic Stroke 2018[13] ; 2)Patients who received intravenous tissue-type plasminogen activator (tPA) and/or intra-arterial (IA) thrombolysis after admission; 3)All patients in this study signed an informed consent form. Exclusion criteria 1)Previous stroke with residual severe neurological disability, i.e., modified Rankin Scale (mRS) score >2; 2)Patients without reperfusion therapy (including IV tissue plasminogen activator (tPA) and/or intra-arterial (IA) thrombectomy); 3)Other neurological disorders such as intracranial hemorrhage, infections, and space-occupying lesions;4)Combined malignancy, hematologic system diseases. This study was approved by the Ethics Committee of the Third Hospital of Wuhan City (Approval No.WUSANYILUN2023-022). 1.2 Clinical data collection (1) Baseline characteristics The general data of 284 patients with AIS after reperfusion therapy were collected in detail, such as gender, age, NIHSS score on admission, etc., which were categorized according to the classification criteria of Oxfordshire community stroke project (OCSP)[14]. The patients were categorized into total anterior circulation infarction (TACI), partial anterior circulation infarction (PACI), posterior circulation infarction (POCI), postertior circulation infarction (POCI) and lacunar infarction (LACI), according to the classification criteria ofOCSP. The THRIVE scale, medical history, drinking or smoking history, and systolic and diastolic blood pressure at admission were collected within 24 h of admission. (2) Laboratory data collection Fasting blood samples were collected within 24 hours of admission, including fasting blood glucose (FBG), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), homocysteine (Hcy), uric acid (UA). (3) TyG Index Calculation TyG index = ln[fasting triglycerides (mg/dl) x fasting blood glucose (mg/dl)/2]. [15] (Unit 1mmol/LFBG = 18.00mg/dl;1mmol/LTG = 88.57mg/dl) (4) Evaluation of prognosis The patients were followed up 90 d after discharge and their prognosis was assessed using the modified Rankin scale, with scores of 0-2 as a good prognosis group (206 patients) and 3-6 as a poor prognosis group (78 patients). (5) Follow-up All subjects were followed up for 3 months by clinical visits and/or telephone calls, excluding patients who were lost to follow-up and disabled or died due to other illnesses. Standardized records were kept for patients who deteriorated and died. 1.3 Statistical processing . SPSS27.0 was applied to process the data statistically, and the count data were firstly tested for normality, and those conforming to normal distribution were expressed as mean ± standard deviation x±s, and two independent samples t-test was used for comparison between groups; those with non-normal distribution were expressed as median (M) and quartile (P25, P75), and Mann-Whitney U-test was used for comparison between groups. Categorical data were expressed as the number of cases (%), and comparisons between groups were made using the χ2 test or Fisher's exact probability method. Spearman correlation analysis was used to analyze the correlation between TyG index, THRIVE score and mRS score of AIS patients after 90 d of reperfusion therapy. Multifactorial Logistic regression was used to determine the independent risk factors for poor prognosis of AIS patients at 90 days after discharge from the hospital, based on which, TyG index combined with admission THRIVE score was established, Thrive-c model, modified Thrive-c model and the prognosis of AIS patients by analyzing the correlation between the parameters and constructing joint equations, and calculating the OR values of independent risk factors and their 95% confidence interval (95% CI); using the subjects' work characteristics (ROC) curve To evaluate the predictive performance of TyG index at admission and admission THRIVE score and their combination, Thrive-c model, and modified Thrive-c model on the 90-day prognosis of AIS patients discharged from the hospital.all statistical tests performed in this study were two-tailed tests, and p < 0.05 was defined as a statistically significant difference. Results 2.1 Comparison of baseline characteristics for different prognoses A total of 284 study subjects were included in this study, with 90-day mRS scores of 0–2 categorized as good prognosis group and 3–6 as poor prognosis group.Diabetes mellitus was higher in the poor prognosis group compared to the good prognosis group (p < 0.05); the admission NIHSS score, admission THRIVE score, TyG index, FBG, TC indicators were significantly higher (p < 0.05), and HDL-C was lower.Gender, atrial fibrillation, previous stroke, hyperlipidemia, coronary heart disease, smoking history, drinking history, Hcy, UA, LDL-C, systolic blood pressure, diastolic blood pressure and other baseline data were not statistically different (p > 0.05) (Table 1 ). Table 1 Baseline characteristics of 2 groups Variables Good prognosis group ( n = 206) Poor prognosis group ( n = 78) p Women 60 (29.1) 27 (34.6) 0.370 Age/years 65.31 ± 11.79 71.08 ± 10.18 0.000 Hypertension 137 (66.5) 57 (73.1) 0.288 Diabetes 57 (27.7) 35 (44.9) 0.006 Hyperlipidemia 38 (18.4) 13 (16.7) 0.727 Coronary disease 11 (5.3) 9 (11.5) 0.068 Atrial fibrillation 37 (18.0) 16 (20.5) 0.622 Previous stroke 25 (12.1) 12 (15.4) 0.468 Smoking history 98 (47.6) 36 (46.2) 0.831 Drinking history 66 (32.0) 23 (29.5) 0.679 Hcy (umol/L) 15.98 (12.90,22.08) 17.08 (13.00,24.81) 0.473 UA (umol/L) 340 (281.75, 397.75) 351.5 (239.25, 419.00) 0.776 TC (mmol/L) 4.32 (3.61, 5.09) 4.40 (3.40, 5.29) 0.914 TG (mmol/L) 1.22 (0.88, 1.75) 1.69 (1.09, 2.29) 0.000 HDL-C (mmol/L) 1.13 ± 0.30 1.06 ± 0.36 0.095 LDL-C (mmol/L) 2.48 (1.96, 3.03) 2.44 (1.80, 3.07) 0.639 FBG (mmol/L) 5.77 (5.08, 7.33) 7.79 (6.63, 9.53) 0.000 TyG index 8.73 ± 0.72 9.24 ± 0.67 0.000 Systolic blood pressure (mmHg) 152.16 ± 22.70 156.95 ± 21.03 0.106 Diastolic blood pressure (mmHg) 90.26 ± 14.98 88.39 ± 18.27 0.378 Admission NIHSS score/s 4 (2, 7.5) 9.5 (6, 14) 0.000 Admission THRIVE score/s 2 (1, 3) 3 (2, 4.25) 0.000 Data are expressed as IQR, mean ± standard deviation, or n (%) Hcy,homocysteine;UA,uric acid;TC,total cholesterol;TG,total glyceride;HDL-C,high density lipoprotein cholesterol;LDL-C,low density lipoprotein cholesterol;FBG,fasting blood glucose;TyG,triglyceride-glucose Index; NIHSS, National Institute of Health Stroke Scale;THRIVE score, Totaled Heath Risks in Vascular Events Score. 2.2 Secondary outcomes, stroke site and typing of 2 groups The incidence of the secondary outcomes including short-term neurological deterioration (END), hemorrhagic transformation and death was significantly greater in the poor prognosis group, as well as the type of reperfusion therapy(p < 0.05).Patients receiving IA thrombectomy obtained poorer prognosis.And there was no statistically significant difference in the other types including TOAST typing, responsible vascularization and OCSP typing(p > 0.05) ( (Table 2 ). Table 2 Comparison of secondary outcome,site of lesion and stroke types Variables Good prognosis group ( n = 166) Poor prognosis group ( n = 118) p Secondary outcomes Short-term neurological deterioration 25 (12.1) 30 (38.5) < 0.001 Death 0 (zero) 5 (6.4) < 0.001 Hemorrhagic transformation 8 (3.9) 10 (12.8) 0.006 TOAST classification LAA 90 (43.7) 39 (50.0) 0.135 SVO 77 (37.4) 19 (24.4) CE 34 (16.5) 19 (24.4) Other 5 (2.4) 1 (1.3) Responsible vascularization internal carotid artery 28 (13.6) 11 (14.1) 0.449 basilar artery 29 (14.1) 9 (11.5) vertebral artery 6 (2.9) 4 (5.1) anterior cerebral artery 12 (5.8) 4 (1.3) middle cerebral artery 121 (58.7) 51 (65.4) Other artery 10 (4.9) 2 (2.6) Reperfusion therapy IA thrombectomy only 27 (13.1) 21 (26.9) 0.009 IV tPA only 165 (80.1) 49 (62.8) IV tPA + IA thrombectomy 14 (6.8) 8 (10.3) OCSP typing TACI 14 (6.8) 6 (7.7) 0.128 PACI 104 (50.5) 43 (55.1) POCI 52 (25.2) 10 (12.8) LACI 36 (17.5) 19 (24.7) Data are expressed as IQR, mean ± standard deviation, or n (%) TOAST,Trial of. Org 10172 in Acute Stroke Treatment;LAA, large artery Atherosclerosis;SVO, small vessel occlusion;CE, cardioembolism; IA, intra-arterial;IV tPA, intravenous tissue plasminogen activator; OCSP,Oxfordshire community stroke project;TACI,total anterior circulation infarction;PACI,partial anterior circulation infarction;POCI,posterior circulation infarction;POCI,postertior circulation infarction;LACI,lacunar infarction. 2.3 Baseline for TYG groups The baseline characteristics of four groups are significantly different in all parameters (Table 3 ). Diabetes, hyperlipidemia, bleeding conversion and proportion of adverse outcomes were significantly increased in patients with elevated TyG index (p < 0.05). As TyG index elevates, several physiological parameters such as total cholesterol (TC), triglycerides (TG), fasting blood glucose (FBG), systolic blood pressure (SBP), and diastolic blood pressure (DBP) increase while age and age and incidence of atrial fibrillation decrease. (P < 0.05). Table 3 Comparison of baseline data for different TYG levels Variables Q1(n = 71) Q2(n = 71) Q3(n = 71) Q4(n = 71) P M/F 51/20 48/23 49/22 49/22 0.957 Age/years 70.03 ± 11.937 68.97 ± 12.00 65.18 ± 10.15 63.38 ± 11.36 0.001 Hypertension 45(23.2) 44(22.7) 45(23.2) 60(30.9) 0.179 Diabetes 11(15.5) 15(21.1) 20(31.3) 46(59.0) <0.001 Hyperlipidemia 7(9.9) 8(11.3) 17(23.9) 19(26.8) 0.013 Coronary disease 7(9.9) 3(4.2) 7(9.9) 7(9.9) 0.969 Atrial fibrillation 19(26.8) 18(25.4) 7(9.9) 9(12.7) 0.006 Previous stroke Hemorrhage 4(5.6) 14(9.7) 8(12.5) 11(14.1) 0.097 Smoking history 26(36.6) 33(46.5) 38(53.5) 37(52.1) 0.170 Drinking history 18(25.4) 20(28.2) 22(31.0) 29(40.8) 0.212 Hcy (umol/L) 15.14(11.44,19.83) 17.32(13.34,22.29) 17.26(13.23,26.27) 15.57(14.13,21.41) 0.114 UA (umol/L) 335.41 ± 104.54 343.04 ± 98.20 343.48 ± 108.00 367.72 ± 116.05 0.321 TC (mmol/L) 3.79(3.27,4.67) 3.88(3.27,4.78) 4.58(3.595,5.47) 4.69(4.165,5.6) <0.001 TG (mmol/L) 0.78(0.6,0.96) 1.12(0.905,1.26) 1.62(1.42,1.91) 2.8(1.965,3.41) <0.001 HDL-C (mmol/L) 1.26 ± 0.30 1.0941 ± 0.32 1.07 ± 0.31 1.04 ± 0.32 0.095 LDL-C (mmol/L) 2.28 ± 0.76 2.3915 ± 0.93 2.90 ± 0.96 2.7328 ± 0.89 0.743 FBG (mmol/L) 5.36(4.61,5.86) 5.94(5.24,7.35) 6.1(5.53,7.73) 8.3(7.02,11.25) <0.001 Systolic blood pressure (mmHg) 149.99 ± 24.44 148.92 ± 18.57 155.51 ± 22.65 159.48 ± 22.04 0.015 Diastolic blood pressure (mmHg) 89(75,100) 86(78,98) 91(85,100.5) 90(83.5,104) 0.014 Admission NIHSS 5(2,9) 4(2,10) 5(2,8) 6(3,9.5) 0.48 Admission THRIVE score 2(1,4) 2(1,4) 2(1,3) 2(2,3) 0.357 Primary endpoints mRS 1(0,1) 1(0,2) 1(1,3) 2(1,3) <0.001 Poor prognosis 7(9.9) 10(14.1) 22(34.4) 39(50.0) <0.001 Secondary endpoints Short-term neurological deterioration 10(14.4) 11(15.5) 19(26.8) 15(21.1) 0.205 Bleeding Conversion 3(4.2) 4(5.6) 1(1.4) 10(14.1) 0.014 Death 1(1.4) 1(1.4) 1(1.4) 2(2.8) 0.894 Data are expressed as IQR, mean ± standard deviation, or n (%) CHD,Coronary heart disease;Hcy,homocysteine;UA,uric acid;TC,total cholesterol;TG,total glyceride; HDL-C,high density lipoprotein cholesterol; LDL-C,low density lipoprotein cholesterol;FBG,fasting blood glucose;TyG,triglyceride-glucose Index; NIHSS, National Institute of Health Stroke Scale;THRIVE, Totaled Heath Risks in Vascular Events; mRS,modified Rankin score. 2.4 Spearman correlation analysis Both TyG index and admission THRIVE score were positively correlated with mRS score after 90d in AIS patients after reperfusion therapy (r = 0.360, P < 0.001), suggesting that TyG index and THRIVE score are both correlated with the prognosis of AIS patients firmly. 2.5.1 Multifactor Logistic Regression Analysis In the univariate analysis for 90 days mRS, the differences in age, diabetes, admission NIHSS, admission THRIVE, TyG index, FBG, TG, and type of reperfusion therapy were statistically significant (p < 0.05). Because of the covariance between TG and TyG index, all single factors with significant differences were included in the Logistic regressive analysis except for TG. And it showed that age, THRIVE score, TyG index, NIHSS score, and diabetes were all independent risk factors for poor prognosis (p < 0.05), as shown in Table 4 . Table 4 Multifactorial Logistic Regression Analysis of Factors Influencing Prognosis in Ischemic Stroke Patients Variables unadjusted adjusted OR (95% CI) p OR (95% CI) p Age 1.047 (1.021, 1.073) < 0.001 1.09 (1.044, 1.138) < 0.001 THRIVE score 1.646 (1.368, 1.98) < 0.001 0.635 (0.427, 0.944) 0.025 TyG index 2.531 (1.728, 3.707) < 0.001 3.211 (0.944, 6.081) < 0.001 NIHSS 1.188 (1.125, 1.255) < 0.001 1.292 (1.162, 1.438) < 0.001 Diabetes 2.128 (1.239, 3.653) 0.006 3.462 (1.382, 8.674) 0.008 Note: TG was not included due to covariance with the study index TyG index 2.5.2 Subgroup analysis Patients were stratified according to THRIVE score (≤ 2, > 2), stroke severity (NHISS score 0–4 for mild stroke, 5–42 for moderate-to-severe stroke), diabetes, age (< 65, ≥ 65), and type of reperfusion therapy(intravenous thrombolysis, non-intravenous thrombolysis), and adjusted for age, diabetes, admission NIHSS score, admission THRIVE score, TyG index, FBG, and reperfusion therapy, a subgroup analysis of TyG index and the risk of poor 90-day prognosis at discharge in patients with AIS was performed, and the TyG index was associated with an increased risk of poor 90-day prognosis at discharge in patients with AIS in all stratified populations except for mild stroke and diabetes (p < 0.05) (e.g., Fig. 1 ). 2.5.3 Construction of multivariate logistic regression THRIVE-c model, modified THRIVE-c model and TyG index THRIVE joint equation Thrive-c multivariate logistic regression model was constructed with age, admission NIHSS score, diabetes, atrial fibrillation, hypertension as independent variables to generate predictive probabilities and joint equations with the outcome of "Prognosis of patients with AIS after reperfusion therapy": \(\text{p}（\text{T}\text{H}\text{R}\text{I}\text{V}\text{E}-\text{c} \text{m}\text{o}\text{d}\text{e}\text{l}）=\frac{1}{1+{\text{e}}^{-（-5.741+0.045\times \text{a}\text{g}\text{e}+0.211\times \text{N}\text{I}\text{H}\text{S}\text{S} \text{s}\text{c}\text{o}\text{r}\text{e}-1.21\times \text{d}\text{i}\text{a}\text{b}\text{e}\text{t}\text{e}\text{s}+1.129\times \text{a}\text{t}\text{r}\text{i}\text{a}\text{l} \text{f}\text{i}\text{b}\text{r}\text{i}\text{l}\text{l}\text{a}\text{t}\text{i}\text{o}\text{n}-0.092\times \text{h}\text{y}\text{p}\text{e}\text{r}\text{t}\text{e}\text{n}\text{s}\text{i}\text{o}\text{n}）}}\) ; We construct a modified Thrive-c model with age, admission NIHSS score, TyG index, atrial fibrillation and hypertension as independent variables: $$\text{p}（\text{M}\text{o}\text{d}\text{i}\text{f}\text{i}\text{e}\text{d} \text{T}\text{H}\text{R}\text{I}\text{V}\text{E}-\text{c}\text{m}\text{o}\text{d}\text{e}\text{l}）=\frac{1}{1+{\text{e}}^{-（-18.859+0.063\times \text{a}\text{g}\text{e}+0.192\times \text{N}\text{I}\text{H}\text{S}\text{S} \text{s}\text{c}\text{o}\text{r}\text{e}+1.265\times \text{T}\text{y}\text{G} \text{i}\text{n}\text{d}\text{e}\text{x}+1.029\times \text{a}\text{t}\text{r}\text{i}\text{a}\text{l} \text{f}\text{i}\text{b}\text{r}\text{i}\text{l}\text{l}\text{a}\text{t}\text{i}\text{o}\text{n}-0.11\times \text{h}\text{y}\text{p}\text{e}\text{r}\text{t}\text{e}\text{n}\text{s}\text{i}\text{o}\text{n}）}}$$ ; Because age, diabetes, and NHISS score are all components of the THRIVE score, they were not included in the joint equation, and a joint predictive equation was constructed for TyG index and THRIVE score[ 16 ]: \(\text{p}（\text{T}\text{y}\text{G} \text{i}\text{n}\text{d}\text{e}\text{x} \text{c}\text{o}\text{m}\text{b}\text{i}\text{n}\text{e}\text{d} \text{w}\text{i}\text{t}\text{h} \text{T}\text{H}\text{R}\text{I}\text{V}\text{E} \text{s}\text{c}\text{o}\text{r}\text{e}）=\frac{1}{1+{\text{e}}^{\begin{array}{c}-（-11.667+0.546\times THRIVE score+1.02\times TyG index\\ ）\end{array}}}\) , and the results of the regression coefficients for each model are shown in Table 5 . Table 5 Results of regression coefficients for each model Modified THRIVE-c model THRIVE-c model TyG index combiend with THRIVE score variable Regression coefficient variable Regression coefficient variable Regression coefficient age 0.063 age 0.045 TyG index 1.02 NIHSS score 0.192 NIHSS score 0.211 THRIVE score 0.546 TyG index 1.265 diabetes -1.21 atrial fibrillation 1.029 atrial fibrillation 1.129 high blood pressure -0.11 high blood pressure -0.092 intercept -18.859 -5.741 -11.667 Note: The AF/Hypertension/Diabetes categorical variable is assigned as the assignment: no = 0 and yes = 1; 2.6.1 Efficacy of TyG index, THRIVE score, the combination, THRIVE-c model, and modified THRIVE-c model on prognosis of AIS patients ROC curve analysis showed that TyG index, THRIVE score, combination of both, THRIVE-c model, and modified THRIVE-c model had the applied value of predicting the prognosis of patients with AIS (p < 0.05), (Fig. 2 . Table 6 ). Delong analysis was used to analyze whether there was a statistical difference in the AUC of each index of appeal, and it was found that there was no statistically significant difference between the AUCs of the other metrics, except for TyG index - modified THRIVE-c model, THRIVE score - TyG index combined with THRIVE score, THRIVE score - THRIVE-c model, THRIVE score - modified THRIVE-c model, TyG index combined THRIVE score - modified THRIVE-c model, and THRIVE-c model - modified THRIVE-c model were found to be statistically significant (|z| > 1.96 and p < 0.05). This suggests that the modified THRIVE-c model, THRIVE-c model, and TyG index combined with THRIVE score predictive efficacy were all superior to single THRIVE score and TyG index indicators, and that the predictive efficacy of the modified THRIVE-c model was highest while the predictive efficacy of the TyG index combined with THRIVE score was lowest (Table 7 ). Table 6 Results of ROC curve analysis for each indicator and prediction model Variables AUC threshold value sensitivity specificity 95% CI P TyG index 0.724 8.976 0.705 0.723 0.66 to 0.789 < 0.001 THRIVE score 0.71 2.5 0.705 0.66 0.643to 0.778 < 0.001 THRIVE score combined with TyG index 0.784 0.229 0.833 0.66 0.729 to 0.84 < 0.001 THRIVE-c model 0.81 0.207 0.859 0.65 0.755 to 0.865 < 0.001 Modified THRIVE-c model 0.847 0.227 0.833 0.743 0.801 to 0.894 < 0.001 Table 7 Delong analysis parameters sports event z P AUC Variance TyG Index - THRIVE Score 0.274 0.784 0.014 TyG Index - TyG Index United THRIVE Score -1.861 0.063 -0.06 TyG index - THRIVE-c model -1.909 0.056 -0.086 TyG Index - Modified THRIVE-c Model -3.677 < 0.001 -0.123 THRIVE Score - TyG Index Joint THRIVE Score -3.158 0.002 -0.074 THRIVE scores - THRIVE-c modeling -4.244 < 0.001 -0.1 THRIVE Scoring - Modified THRIVE-c Modeling -4.749 < 0.001 -0.137 TyG Index Joint THRIVE Score - THRIVE-c Model -1.022 0.307 -0.026 TyG index United THRIVE score - modified THRIVE-c model -3.364 0.001 -0.063 THRIVE-c Model - Modified THRIVE-c Model -2.034 0.042 -0.038 2.6.3 Analysis of the results of the predictive value of each indicator of different reperfusion therapies on the prognosis of patients with AIS Patients were divided into intravenous thrombolysis group and non-intravenous thrombolysis group (mechanical thrombolysis and both) and the ROC curve was established to test the predictive value of each index on the prognosis of AIS patients. The TyG index, the THRIVE score, the combination of both, the THRIVE-c model and the modified THRIVE-c model in the intravenous thrombolysis group all had predicting value for prognosis of AIS patients (p 0.05, AUC = 0.637) (Table 8 , Fig. 3 , Fig. 4 ). Table 8 Parameters of ROC curves for different reperfusion therapies Variables Intravenous thrombolysis group Non-intravenous thrombolysis group AUC P 95% CI AUC P 95% CI TyG index 0.7 < 0.001 0.621–0.78 0.805 < 0.001 0.697–0.913 Admission THRIVE score 0.707 < 0.001 0.623–0.791 0.637 0.052 0.507–0.767 THRIVE score + TyG index 0.766 < 0.001 0.695–0.837 0.827 < 0.001 0.732–0.922 THRIVE-c model 0.803 < 0.001 0.736–0.869 0.814 < 0.001 0.713–0.915 Modified THRIVE-c model 0.836 < 0.001 0.778–0.894 0.869 < 0.001 0.783–0.955 Intravenous thrombolysis group, Non-intravenous thrombolysis group Discussion Insulin resistance (IR) is a new risk factor for stroke in more than 50% of non-diabetic patients with acute ischemic stroke (AIS) or transient ischemic attack (TIA)[ 17 ].Initial research had indicated that IR might be a trigger for AIS, by promoting atherosclerosis and inducing a prothrombotic condition, which led to poor outcomes[ 18 ]. Recently, the link between IR and cerebrovascular disease has been revealed. IR activated inflammatory genes and hindered insulin signal[ 5 ], which resulted in different levels of chronic inflammation, oxidative stress and endothelial dysfunction.These conditions could impair vascular function, thereby leading to cerebrovascular diseases[ 19 ][ 20 ]. Additionally, endothelial cells released more nitric oxide ( NO) and coagulant factors, which promoted platelet aggregation and thrombosis[ 21 ]], both led to cerebrovascular diseases[ 22 ][ 23 ]. Also IR triggered endoplasmic reticulum stress and macrophage apoptosis, which increased the development of vulnerable plaques, plaque necrosis and atherosclerosis[ 24 ]. Furthermore, IR was associated with adverse reactions in AIS patients with intravenous (IV) thrombolytic therapy[ 25 ][ 26 ]. The triglyceride-glucose (TyG) index, derived from fasting plasma glucose (FPG) and triglycerides (TG), was recognized as an accessible marker of insulin resistance[ 27 ].The TyG index was associated with neurological severity and early recurrence in AIS[ 28 ][ 29 ]. A higher TyG index was linked with an increased risk of early neurological deterioration and a decreased early neurological improvement in AIS patients with IV thrombolytic therapy. Particularly, the TyG index was closely correlated with early neurological deterioration in patients with subcortical infarcts near the middle cerebral artery[30]. It was reported that higher TyG index was related to increased mortality and poorer functional outcomes in both diabetic and non-diabetic patients[ 31 ], which confirmed our observation in this study. We found that more early neurological deterioration and hemorrhagic transformation and higher mortality in the high-TyG-index group. Our analysis also showed that both the THRIVE score and TyG index could independently predict 90-day outcomes for AIS patients with reperfusion therapy after adjusting factors such as diabetes, hypertension, age, NIHSS scores, the THRIVE scores at admission, TyG index, FBG, and types of reperfusion therapy.In general, the TyG index was a significant prognostic marker in AIS[ 32 ]. The THRIVE score had been verified to evaluate long-term outcomes in AIS patients with endovascular treatment and rt-PA IV thrombolysis [ 33 ]. Higher THRIVE score was correlated with poorer outcomes, higher mortality and more hemorrhagic transformation [ 34 ]. A retrospective research suggested that the THRIVE score could predict outcomes in Chinese AIS patients with or without IV thrombolytic treatment including those with cardioembolic strokes [ 35 ][ 36 ]].Further research had found that the THRIVE score could accurately predict long-term outcomes in AIS patients, regardless of whether they received thrombolytic or endovascular treatments [ 37 ]. In our research, it was determined that the admission THRIVE score and TyG index were both independent risk factors for long-term prognosis by multivariate regression analysis.AUC of the ROC curves for both indicators was above 0.7. And AUC of the ROC curves for the combination was 0.784, showing higher sensitivity (83.3%) and specificity (66%). The THRIVE score was composed of clinical metrics which were easily accessed but was not so precise. Therefore, Flint et al. created THRIVE-c model based on the THRIVE score, which was more precise. Our study modified THRIVE-c model by replacing diabetes with Tyg index, yielding the highest predictive precision (AUC = 0.847), sensitivity (83.3%), and specificity (74.3%). According to our observation, individuals with moderate to severe stroke demonstrated poorer outcomes. And patients without IV thrombolysis therapy showed particularly worse outcomes, which was likely attributable to more severe neurological impairments at admission. The TyG index was not significantly different in various THRIVE score groups which meant the TyG index was not affected by the THRIVE score. Furthermore, the TyG index appeared to be more pronounced in individuals under 65 or who had no history of diabetes, owing to the prevalent use of hypoglycemic agents and antihyperlipidemic drugs among the older, which could potentially influence the TyG index. These observations required further investigation to validate. In conclusion, for AIS patients receiving reperfusion therapy, both the TyG index and the THRIVE score measured at admission were independent predictors for short-term prognosis. Since the THRIVE score did not include IR—which was an important part in AIS development and also a significant predictor.The modified THRIVE-c model with the combination of both enhanced the predictive capability for short-term outcomes of AIS and was practical in clinical use. However, merging the TyG index with the THRIVE score, or modified THRIVE-c model lacked medical imaging and other essential laboratory markers, which may undermine the predictive performance and introduce constraints in our study.Our study was retrospective with limited population and partial clinical data.So, future research should build a more accurate model by more prospective, multicenter researches with large population and combine modified THRIVE-c model with medical imaging and other markers. Abbreviations IR Insulin resistance TyG index Triglyceride–glucose index AIS acute ischemic stroke SBP Systolic blood pressure DBP Diastolic blood pressure FPG Fasting plasma glucose TC Total cholesterol TG Triglycerides HDL-C High-density lipoprotein cholesterol LDL-C Low-density lipoprotein cholesterol Hcy Homocysteine mRS modified Rankin Scale scores TPA tissue-type plasminogen activator IA intra-arterial OCSP Oxfordshire community stroke project (OCSP) TACI total anterior circulation infarction PACI partial anterior circulation infarction POCI posterior circulation infarction LACL lacunar infarction UA uric acid END short-term neurological deterioration end Declarations Acknowledgements We thank all the investigators and subjects who participated in this project. Author contributions LC conceived and designed the experiments and wrote the manuscript.QY and LJ organized the data and LC performed the analyses.LJ and YS contributed to the quality control of the data and the finalization of the manuscript. All authors read and approved the final manuscript. Funding This study received the support of from the National Natural Science Foundation of China (Research Grant #81871088) and The construction and practice of a regionalized health management consortium with the integration of medical treatment and prevention（Research Grant WY22A08）. Availability of data and materials The datasets used and/or analyzed in the study are available from the cor- responding author upon reasonable request. Ethics approval and consent to participate The study was approved by the medical ethics committee of Wuhan Third Hospital and all methods were performed in accordance with the applicable guidelines and regulations. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Pluta R, Januszewski S, Czuczwar SJ. The Role of Gut microbiota in an ischemic stroke. Int J Mol Sci. 2021;22(2):915. Kernan WN, Inzucchi SE, Viscoli CM, BrassLM, Bravata DM, Horwitz RI. Insulin resistanceand risk for stroke. Neurology. 2002; 59(6):809- 15. Zhou Y, Pan Y, Yan H, et al. Triglyceride glucose index and prognosis of patients with ischemic stroke [J]. Front Neurol, 2020, 11: 456. GUERRERO-ROMERO F, SIMENTAL-MENDÍA LE, GONZÁLEZ-ORTIZ M, et al. The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp[J]. Clin Endocrinol Metab,2010; 95(7):3347-3351. Kamel H, Patel N, Rao V A, et al. The totaled health risks in vascular events ( THRIVE) score predicts ischemic stroke outcomes independent of thrombolytic therapy in the NINDS tPA trial[J]. J Stroke Cerebrovasc Dis, 2013, 22( 7) : 1111-1116. Flint AC, Kamel H, Rao VA, Cullen SP, Faigeles BS, Smith WS. Vali-dation of the Totaled Heath Risks in Vascular Events (THRIVE) score for outcome prediction in endovascular stroke treatment. IntJStroke 2014; 9:32-9. Flint AC, Faigeles BS, Cullen SP et al. on behalf of the VISTA Collabo-ration. THRIVE score predicts ischemic stroke outcomes and throm-bolytic hemorrhage risk in VISTA. Stroke 2013; 44:3365-9. Flint AC, Cullen SP, Rao VA et al. The THRIVE score strongly predicts outcomes in patients treated with the Solitaire device in the SWIFT and STAR trials. IntJStroke 2014; 9:705-10. Flint, AC, Rao, VA, Chan, SL, et al. Improved ischemic stroke outcome prediction using model estimation of outcome probability: the THRIVE-c calculation. int J Stroke. 2015; 10 (6): 815-21. doi: 10.1111/ijs.12529. Flint AC, Cullen SP, Faigeles BS, et al. Predicting long-term outcome after endovascular stroke treatment: the totaled health risks in vascular events score[J]. AJNR Am J Neuroradiol, 2010, 31: 1192-1196. Di Pino, A, DeFronzo, RA. Insulin Resistance and Atherosclerosis: Implications for Insulin-Sensitizing Agents. endocr rev. 2019; 40 (6): 1447-1467. doi: 10.1210/er.2018-00141. Kazlauskien L, Butnorien J, Norkus A. Metabolic syndrome related to cardiovascular events in a 1 O-year prospective study [J].Diabet01 Metab syndr, 2015, 7: 102. DOI: 10.1186/ s13098 a015-0096-2. Peng B, Wu B. Chinese acute ischemic stroke diagnosis and treatment guidelines 2018 [J]. Chinese Journal of Neurology, 2018, 51( 9) : 666 - 682. Bamford J, Sandercock P, Dennis M, et al. Classification and natural history of clinically identifiable subtypes of cerebral infarction[J]. Lancet, 1991, 337: 1521-1526. MAZIDI M, KENGNE AP, KATSIKI N, et al. Lipid accumulation product andtriglycerides/glucose index are useful predictors of insulin resistance. j Diabetes Complications. 2018 Mar;32(3):266--270. QIN Zhengji, SHEN Yi, CUI Xiaoli, et al. Application of logistic regression in joint diagnosis of multiple indicators of disease [J]. China Health Statistics, 2014, 31(1): 116-117. DOI: CNKI: SUN: ZGWT.0.2014-01-036. Gast KB, Smit JW, den Heijer M, Middeldorp S, Rippe RC, le Cessie S, et al. Abdominal adiposity largely explains associations between insulin resistance, hyperglycemia and subclinical atherosclerosis: the NEO study. Atherosclerosis. 2013; 229(2): 423- 9. Alessi MC, Juhan-Vague I. Metabolic syndrome, haemostasis and thrombosis. Thromb Haemost. 2008; 99(6): 995-1000. 6 Bas DF, Ozdemir AO, Colak E, Kebapci N. Higher insulin resistance level is associated with worse clinical response in acute ischemic stroke patients treated with intravenous thrombolysis. Transl Stroke Res. 2016; 7(3): 167-71. Bornfeldt, KE, Tabas, I. Insulin resistance, hyperglycemia, and atherosclerosis. CELL METAB. 2011; 14 (5): 575-85. doi: 10.1016/j.cmet.2011.07.015 Wu G, Meininger CJ. Nitric oxide and vascular insulin resistance. BioFactors. 2009; 35 (1):21-7. doi: 10.1002/biof.3. Wang, CC, Gurevich, I, Draznin, B. Insulin affects vascular smooth muscle cell phenotype and migration via distinct signaling pathways. diabetes. 2003. ; 52 (10): 2562-9. doi: 10.2337/diabetes.52.10.2562. Moore, SF, Williams, CM, Brown, E, et al. Loss of the insulin receptor in murine megakaryocytes/platelets causes thrombocytosis and alterations in IGF signalling. cardiovasc res. 2015; 107 (1): 9-19. doi: 10.1093/cvr/cvv132. Qizilbash N. Fibrinogen and cerebrovascular disease Eur Heart J, 1995.16 Suppl A: p.42-5; discussion 45-6. Calleja AI, García-Bermejo P, Cortijo E, Bustamante R, Rojo Martínez E, González Sarmiento E, et al. Insulin resistance is associated with a poor response to intravenous thrombolysis in acute ischemic stroke. Diabetes Care. 2011; 34(11): 2413-7. Guerrero-Romero, F, Simental-Mendía, LE, González-Ortiz, M, et al. The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp. J CLIN ENDOCR METAB. 2010; 95 (7): 3347-51. doi: 10.1210/jc.2010-0288. Wang A, Wang G, Liu Q, et al. Triglyceride-glucose index and the risk of stroke and its subtypes in the general population: an 11-year follow-up [J]. Cardiovasc Diabetol, 2021, 20(1):46. HUANG Yan, FU Xuejun, SHI Huijie, et al. Correlation analysis of triacylglycerol-glucose index and neurological deficit in acute ischemic stroke［J］. Chinese Journal of Stroke, 2022, 17(3):251-257. Nam KW, Kwon HM, Lee YS. High triglyceride-glucose index is associated with early recurrent ischemic lesion in acute ischemic stroke [J]. Sci Rep, 2021, 11(1):15335. Zhang B, Lei H, Ambler G, et al. Association between triglycerideglucose index and early neurological outcomes after thrombolysis in patients with acute ischemic stroke[J]. J Clin Med, 2023, 12(10): 3471. Nam KW, Kang MK, Jeong HY, et al. Triglyceride-glucose index is associated with early neurological deterioration in single subcortical infarction: Early prognosis in single subcortical infarctions [J]. Int J Stroke, 2021, 16(8):944-952. Ma X, Han Y, Jiang L, et al. Triglyceride-glucose index and the prognosis of patients with acute ischemic stroke: a meta-analysis ［J］. Horm Metab Res, 2022, 54(6):361-370. Toh EMS, Lim AYL, Ming C, et al. Association of triglycerideglucose index with clinical outcomes in patients with acute ischemic stroke receiving intravenous thrombolysis[J]. Sci Rep, 2022, 12(1):1596. Lee, M, Kim, CH, Kim, Y, et al. High Triglyceride Glucose Index Is Associated with Poor Outcomes in Ischemic Stroke Patients after Reperfusion Therapy. CEREBROVASC DIS. 2021; 50 (6): 691-699. Flint A C, Kamel H, Rao V A, et al. Validation of the Totaled Health Risks In Vascular Events ( THRIVE) score for outcome prediction in endovascular stroke treatment [J].Int J Stroke, 2014, 9( 1) : 32-39. Kamel H, Patel N, Rao V A, et al. The totaled health risks in vascular events ( THRIVE) score predicts ischemic stroke outcomes independent of thrombolytic therapy in the NINDS tPA trial[J]. J Stroke Cerebrovasc Dis, 2013, 22( 7) : 1111-1116. Lei C, Wu B, Liu M, et al. Totaled health risks in vascular events score predicts clinical outcomes in patients with cardioembolic and other subtypes of ischemic stroke［J］. Stroke, 2014, 45( 6):1689-1694. You Shoujiang, The predictive value of THRIVE score on the prognosis of AIS patients with atrial fibrillation [J]. Chinese Journal of Internal Medicine, 2014, 53(7) : 532-536. Kamel, H, Patel, N, Rao, VA, et al. The totaled health risks in vascular events (THRIVE) score predicts ischemic stroke outcomes independent of thrombolytic therapy in the NINDS tPA trial. j STROKE CEREBROVASC. 2012; 22 (7): 1111-6. Additional Declarations No competing interests reported. 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07:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4479122/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4479122/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59033658,"identity":"3b318e1a-1b5d-471e-96f0-86838f6b99f9","added_by":"auto","created_at":"2024-06-25 14:36:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4446824,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis of TyG index and prognosis of AIS patients after reperfusion therapy\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4479122/v1/fd27fe94560ede1a009530af.png"},{"id":59033656,"identity":"74d4ca80-6d93-4d41-9c25-19c3a923fce5","added_by":"auto","created_at":"2024-06-25 14:36:13","extension":"png","order_by":2,"title":"Figure 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14:36:13","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":67206,"visible":true,"origin":"","legend":"","description":"","filename":"rawdata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4479122/v1/285b26e6677c3855f7cadbcd.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive value of modified Thrive-c model in the prognosis of AIS patients after reperfusion therapy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStroke is recognized as the second leading cause of death and the third leading cause of disability in the world, and it has been reported that 11.6% of global deaths are attributable to stroke, with acute ischemic stroke (AIS) being the most common subtype of pathological stroke, accounting for 85% of all stroke cases[1] With the development of medical technology, early reperfusion therapy has become the key treatment to effectively reduce the disability and mortality of stroke. And the study of risk factors of AIS patients after revascularization and reperfusion therapy is of great significance in the early identification of high-risk groups and improvement of prognosis. Insulinresistance (IR) is considered to be a new risk factor for stroke [2]. Previous studies have shown that IR was independently associated with adverse clinical outcomes in AIS, exacerbated deterioration of neurological function during hospitalization, and triggered recurrence of AIS.[3] The triglyceride glucose (TyG) index, a novel index for assessing IR, has demonstrated high sensitivity and specificity in the diagnosis of IR[4].\u003c/p\u003e\n\u003cp\u003eThe THRIVE score, derived from the results of the Mechanical Embolus Removal in Cerebral Ischemia (MERCI) and Multi MERCI study data, was combined age and NIHSS score with the Chronic Disease Scale to predict 90-day adverse outcomes [modified Rankin score (mRS) 3-6] or death in patients with AIS after the onset of the disease\u003csup\u003e[5]\u003c/sup\u003e .These variables were convenient so that the clinician using the predictive score spent the shortest time to assess the patient. However, these simplifications are accompanied by potential inaccuracies. Therefore, Flint et al. constructed a Thrive-c multivariate logistic regression model to generate predictive probabilities and create joint equations by entering age, NIHSS variables, and categorical variables (diabetes, hypertension, [6][7][8]and atrial fibrillation) that the Thrive-c model performed better in predicting adverse outcomes compared to the traditional THRIVE score[9]\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003ePrevious studies[10] found that the higher the THRIVE score, the higher the mortality rate at 3 months in patients with acute ischemic stroke. However, the THRIVE score did not include variables to assess IR, which contributed to the formation,development and prognosis of AIS by promoting platelet activation, triggering endothelial cell dysfunction, facilitating the expression of inflammatory processes, and disruption of the regulation of glycolipid metabolism[11][12] .Meanwhile,IR may exist in both diabetic and non-diabetic patients.Therefore, combining TyG index with THRIVE score may be better for assessing the prognosis of AIS patients. We reconstruct the modified Thrive-c model by replacing diabetes with the TyG index. \u003c/p\u003e\n\u003cp\u003e\u003csup\u003e，\u003c/sup\u003eCurrently, studies combining the TyG index and the THRIVE score to assess the prognosis of AIS patients have not been reported. This study explores the predictive value of the THRIVE score, the TyG index, and the combination on the 90-day prognosis of AIS patients receiving reperfusion therapy, and explores whether the modified Thrive-c model has a better efficacy in evaluating the 90-day prognosis of AIS patients receiving reperfusion therapy. \u003c/p\u003e"},{"header":"Information and methods","content":"\u003cp\u003e1.1 General data: Patients receiving reperfusion therapy for AIS in the Department of Neurology of Wuhan Third Hospital were\u0026nbsp;retrospectively\u0026nbsp;collected from January 2021 to December 2023.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInclusion criteria:\u0026nbsp;1)Patients eligible for acute ischemic stroke, diagnosed according to the Chinese Guidelines for the Diagnosis and Treatment of Acute Ischemic Stroke 2018[13]\u0026nbsp;; 2)Patients who received intravenous tissue-type plasminogen activator (tPA) and/or intra-arterial (IA) thrombolysis after admission; 3)All patients in this study signed an informed consent form.\u003c/p\u003e\n\u003cp\u003eExclusion criteria\u003c/p\u003e\n\u003cp\u003e1)Previous stroke with residual severe neurological disability, i.e., modified Rankin Scale (mRS) score \u0026gt;2; 2)Patients without reperfusion therapy (including\u0026nbsp;IV tissue plasminogen activator (tPA) and/or intra-arterial (IA) thrombectomy); 3)Other neurological disorders such as intracranial hemorrhage, infections, and space-occupying lesions;4)Combined malignancy, hematologic system diseases.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of the Third Hospital of Wuhan City (Approval No.WUSANYILUN2023-022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2 Clinical data collection\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(1) Baseline characteristics\u003c/p\u003e\n\u003cp\u003eThe general data of 284 patients with AIS\u0026nbsp;after reperfusion therapy were\u0026nbsp;collected in detail, such as gender, age, NIHSS score on admission, etc., which were categorized\u0026nbsp;according to the classification criteria of Oxfordshire community stroke project (OCSP)[14]. The patients were categorized into total anterior circulation infarction (TACI), partial anterior circulation infarction (PACI), posterior circulation infarction (POCI), postertior circulation infarction (POCI)\u0026nbsp;and\u0026nbsp;lacunar infarction (LACI), according to the classification criteria ofOCSP. The\u0026nbsp;THRIVE scale,\u0026nbsp;medical history,\u0026nbsp;drinking or smoking history, and systolic and diastolic blood pressure at\u0026nbsp;admission were\u0026nbsp;collected\u0026nbsp;within 24 h of admission.\u003c/p\u003e\n\u003cp\u003e(2) Laboratory data collection\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFasting blood samples were\u0026nbsp;collected within 24 hours of admission, including fasting blood glucose (FBG), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), homocysteine (Hcy), uric acid (UA).\u003c/p\u003e\n\u003cp\u003e(3)\u0026nbsp;TyG Index\u0026nbsp;Calculation\u003c/p\u003e\n\u003cp\u003eTyG index = ln[fasting triglycerides (mg/dl) x fasting blood glucose (mg/dl)/2].\u0026nbsp;[15]\u0026nbsp;(Unit 1mmol/LFBG = 18.00mg/dl;1mmol/LTG = 88.57mg/dl)\u003c/p\u003e\n\u003cp\u003e(4) Evaluation of prognosis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe patients were followed up 90 d after discharge and their prognosis was assessed using the modified Rankin scale, with scores of 0-2 as a good prognosis group (206 patients) and 3-6 as a poor prognosis group (78 patients).\u003c/p\u003e\n\u003cp\u003e(5) Follow-up\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll subjects were followed up for 3 months by clinical\u0026nbsp;visits and/or\u0026nbsp;telephone calls, excluding patients who were lost to follow-up and disabled or died due to other illnesses. Standardized records were kept for patients who deteriorated and died.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3 Statistical processing .\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSPSS27.0 was applied to process the data statistically, and the count data were firstly tested for normality, and those conforming to normal distribution were expressed as mean \u0026plusmn; standard deviation x\u0026plusmn;s, and two independent samples t-test was used for comparison between groups; those with non-normal distribution were expressed as median (M) and quartile (P25, P75), and Mann-Whitney U-test was used for comparison between groups. Categorical data were expressed as the number of cases (%), and comparisons between groups were made using the \u003csup\u003e\u0026chi;2\u003c/sup\u003e test or Fisher\u0026apos;s exact probability method. Spearman correlation analysis was used to analyze the correlation between TyG index, THRIVE score and mRS score of AIS patients after 90 d of reperfusion therapy. Multifactorial Logistic regression was used to determine the independent risk factors for poor prognosis of AIS patients at 90 days after discharge from the hospital, based on which, TyG index combined with admission THRIVE score was established, Thrive-c model, modified Thrive-c model and the prognosis of AIS patients by analyzing the correlation between the parameters and constructing joint equations, and calculating the OR values of independent risk factors and their 95% confidence interval (95% CI); using the subjects\u0026apos; work characteristics (ROC) curve To evaluate the predictive performance of TyG index at admission and admission THRIVE score and their combination, Thrive-c model, and modified Thrive-c model on the 90-day prognosis of AIS patients discharged from the hospital.all statistical tests performed in this study were two-tailed tests, and p \u0026lt; 0.05 was defined as a statistically significant difference.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Comparison of baseline characteristics for different prognoses\u003c/h2\u003e\n \u003cp\u003eA total of 284 study subjects were included in this study, with 90-day mRS scores of 0\u0026ndash;2 categorized as good prognosis group and 3\u0026ndash;6 as poor prognosis group.Diabetes mellitus was higher in the poor prognosis group compared to the good prognosis group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05); the admission NIHSS score, admission THRIVE score, TyG index, FBG, TC indicators were significantly higher (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and HDL-C was lower.Gender, atrial fibrillation, previous stroke, hyperlipidemia, coronary heart disease, smoking history, drinking history, Hcy, UA, LDL-C, systolic blood pressure, diastolic blood pressure and other baseline data were not statistically different (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline characteristics of 2 groups\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGood prognosis group ( n\u0026thinsp;=\u0026thinsp;206)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePoor prognosis group ( n\u0026thinsp;=\u0026thinsp;78)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60 (29.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27 (34.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.370\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge/years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.31\u0026thinsp;\u0026plusmn;\u0026thinsp;11.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71.08\u0026thinsp;\u0026plusmn;\u0026thinsp;10.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e137 (66.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57 (73.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57 (27.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35 (44.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHyperlipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38 (18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoronary disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAtrial fibrillation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 (18.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16 (20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrevious stroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98 (47.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36 (46.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.831\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrinking history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66 (32.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23 (29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.679\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHcy (umol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.98 (12.90,22.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.08 (13.00,24.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUA (umol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e340 (281.75, 397.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e351.5 (239.25, 419.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.776\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.32 (3.61, 5.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.40 (3.40, 5.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.914\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22 (0.88, 1.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.69 (1.09, 2.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.48 (1.96, 3.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.44 (1.80, 3.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.639\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFBG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.77 (5.08, 7.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.79 (6.63, 9.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTyG index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSystolic blood pressure (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152.16\u0026thinsp;\u0026plusmn;\u0026thinsp;22.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e156.95\u0026thinsp;\u0026plusmn;\u0026thinsp;21.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiastolic blood pressure (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.26\u0026thinsp;\u0026plusmn;\u0026thinsp;14.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88.39\u0026thinsp;\u0026plusmn;\u0026thinsp;18.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.378\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdmission NIHSS score/s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (2, 7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.5 (6, 14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdmission THRIVE score/s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (1, 3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3 (2, 4.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\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 \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eData are expressed as IQR, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, or n (%)\u003c/p\u003e\n \u003cp\u003eHcy,homocysteine;UA,uric acid;TC,total cholesterol;TG,total glyceride;HDL-C,high density lipoprotein cholesterol;LDL-C,low density lipoprotein cholesterol;FBG,fasting blood glucose;TyG,triglyceride-glucose Index; NIHSS, National Institute of Health Stroke Scale;THRIVE score, Totaled Heath Risks in Vascular Events Score.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e\u003cstrong\u003e2.2\u003c/strong\u003e Secondary outcomes, stroke site and typing of 2 groups\u003c/h2\u003e\n \u003cp\u003eThe incidence of the secondary outcomes including short-term neurological deterioration (END), hemorrhagic transformation and death was significantly greater in the poor prognosis group, as well as the type of reperfusion therapy(p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).Patients receiving IA thrombectomy obtained poorer prognosis.And there was no statistically significant difference in the other types including TOAST typing, responsible vascularization and OCSP typing(p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) ( (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of secondary outcome,site of lesion and stroke types\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGood prognosis group ( n\u0026thinsp;=\u0026thinsp;166)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePoor prognosis group ( n\u0026thinsp;=\u0026thinsp;118)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary outcomes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShort-term neurological deterioration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30 (38.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (zero)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5 (6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHemorrhagic transformation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTOAST classification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90 (43.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSVO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77 (37.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19 (24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19 (24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResponsible vascularization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003einternal carotid artery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11 (14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.449\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebasilar artery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003evertebral artery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eanterior cerebral artery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiddle cerebral artery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e121 (58.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51 (65.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther artery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReperfusion therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIA thrombectomy only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21 (26.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV tPA only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e165 (80.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49 (62.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV tPA\u0026thinsp;+\u0026thinsp;IA thrombectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8 (10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOCSP typing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTACI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePACI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104 (50.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43 (55.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePOCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52 (25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLACI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19 (24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eData are expressed as IQR, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, or n (%)\u003c/p\u003e\n \u003cp\u003eTOAST,Trial of. Org 10172 in Acute Stroke Treatment;LAA, large artery Atherosclerosis;SVO, small vessel occlusion;CE, cardioembolism; IA, intra-arterial;IV tPA, intravenous tissue plasminogen activator; OCSP,Oxfordshire community stroke project;TACI,total anterior circulation infarction;PACI,partial anterior circulation infarction;POCI,posterior circulation infarction;POCI,postertior circulation infarction;LACI,lacunar infarction.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e\u003cstrong\u003e2.3\u003c/strong\u003e Baseline for TYG groups\u003c/h2\u003e\n \u003cp\u003eThe baseline characteristics of four groups are significantly different in all parameters (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Diabetes, hyperlipidemia, bleeding conversion and proportion of adverse outcomes were significantly increased in patients with elevated TyG index (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). As TyG index elevates, several physiological parameters such as total cholesterol (TC), triglycerides (TG), fasting blood glucose (FBG), systolic blood pressure (SBP), and diastolic blood pressure (DBP) increase while age and age and incidence of atrial fibrillation decrease. (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of baseline data for different TYG levels\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ1(n\u0026thinsp;=\u0026thinsp;71)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ2(n\u0026thinsp;=\u0026thinsp;71)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ3(n\u0026thinsp;=\u0026thinsp;71)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ4(n\u0026thinsp;=\u0026thinsp;71)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM/F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51/20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48/23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49/22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49/22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.957\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge/years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.03\u0026thinsp;\u0026plusmn;\u0026thinsp;11.937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.97\u0026thinsp;\u0026plusmn;\u0026thinsp;12.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.18\u0026thinsp;\u0026plusmn;\u0026thinsp;10.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.38\u0026thinsp;\u0026plusmn;\u0026thinsp;11.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45(23.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44(22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45(23.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60(30.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15(21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20(31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46(59.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHyperlipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(23.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19(26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoronary disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.969\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAtrial fibrillation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19(26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18(25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrevious stroke\u003c/p\u003e\n \u003cp\u003eHemorrhage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14(9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26(36.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33(46.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38(53.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37(52.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrinking history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18(25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20(28.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22(31.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29(40.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHcy (umol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.14(11.44,19.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.32(13.34,22.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.26(13.23,26.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.57(14.13,21.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUA (umol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e335.41\u0026thinsp;\u0026plusmn;\u0026thinsp;104.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e343.04\u0026thinsp;\u0026plusmn;\u0026thinsp;98.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e343.48\u0026thinsp;\u0026plusmn;\u0026thinsp;108.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e367.72\u0026thinsp;\u0026plusmn;\u0026thinsp;116.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.321\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.79(3.27,4.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.88(3.27,4.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.58(3.595,5.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.69(4.165,5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78(0.6,0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12(0.905,1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.62(1.42,1.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.8(1.965,3.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0941\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.3915\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.7328\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.743\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFBG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.36(4.61,5.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.94(5.24,7.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.1(5.53,7.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.3(7.02,11.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSystolic blood pressure (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e149.99\u0026thinsp;\u0026plusmn;\u0026thinsp;24.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e148.92\u0026thinsp;\u0026plusmn;\u0026thinsp;18.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155.51\u0026thinsp;\u0026plusmn;\u0026thinsp;22.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e159.48\u0026thinsp;\u0026plusmn;\u0026thinsp;22.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiastolic blood pressure (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89(75,100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86(78,98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91(85,100.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90(83.5,104)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdmission NIHSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(2,9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(2,10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(2,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(3,9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdmission THRIVE score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(1,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(1,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(1,3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(2,3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary endpoints\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(0,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(0,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(1,3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(1,3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoor prognosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22(34.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39(50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary endpoints\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShort-term neurological deterioration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19(26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15(21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBleeding Conversion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.894\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eData are expressed as IQR, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, or n (%)\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eCHD,Coronary heart disease;Hcy,homocysteine;UA,uric acid;TC,total cholesterol;TG,total glyceride; HDL-C,high density lipoprotein cholesterol; LDL-C,low density lipoprotein cholesterol;FBG,fasting blood glucose;TyG,triglyceride-glucose Index; NIHSS, National Institute of Health Stroke Scale;THRIVE, Totaled Heath Risks in Vascular Events; mRS,modified Rankin score.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e\u003cstrong\u003e2.4\u003c/strong\u003e Spearman correlation analysis\u003c/h2\u003e\n \u003cp\u003eBoth TyG index and admission THRIVE score were positively correlated with mRS score after 90d in AIS patients after reperfusion therapy (r\u0026thinsp;=\u0026thinsp;0.360, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that TyG index and THRIVE score are both correlated with the prognosis of AIS patients firmly.\u003c/p\u003e\n \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n \u003ch2\u003e\u003cstrong\u003e2.5.1\u003c/strong\u003e Multifactor Logistic Regression Analysis\u003c/h2\u003e\n \u003cp\u003eIn the univariate analysis for 90 days mRS, the differences in age, diabetes, admission NIHSS, admission THRIVE, TyG index, FBG, TG, and type of reperfusion therapy were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Because of the covariance between TG and TyG index, all single factors with significant differences were included in the Logistic regressive analysis except for TG. And it showed that age, THRIVE score, TyG index, NIHSS score, and diabetes were all independent risk factors for poor prognosis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as shown in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMultifactorial Logistic Regression Analysis of Factors Influencing Prognosis in Ischemic Stroke Patients\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eunadjusted\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eadjusted\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.047 (1.021, 1.073)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09 (1.044, 1.138)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTHRIVE score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.646 (1.368, 1.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.635 (0.427, 0.944)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTyG index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.531 (1.728, 3.707)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.211 (0.944, 6.081)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNIHSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.188 (1.125, 1.255)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.292 (1.162, 1.438)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.128 (1.239, 3.653)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.462 (1.382, 8.674)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eNote: TG was not included due to covariance with the study index TyG index\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e\u003cstrong\u003e2.5.2\u003c/strong\u003e Subgroup analysis\u003c/h2\u003e\n \u003cp\u003ePatients were stratified according to THRIVE score (\u0026le;\u0026thinsp;2, \u0026gt;\u0026thinsp;2), stroke severity (NHISS score 0\u0026ndash;4 for mild stroke, 5\u0026ndash;42 for moderate-to-severe stroke), diabetes, age (\u0026lt;\u0026thinsp;65, \u0026ge;\u0026thinsp;65), and type of reperfusion therapy(intravenous thrombolysis, non-intravenous thrombolysis), and adjusted for age, diabetes, admission NIHSS score, admission THRIVE score, TyG index, FBG, and reperfusion therapy, a subgroup analysis of TyG index and the risk of poor 90-day prognosis at discharge in patients with AIS was performed, and the TyG index was associated with an increased risk of poor 90-day prognosis at discharge in patients with AIS in all stratified populations except for mild stroke and diabetes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (e.g., Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.5.3\u003c/strong\u003e Construction of multivariate logistic regression THRIVE-c model, modified THRIVE-c model and TyG index THRIVE joint equation\u003c/p\u003e\n \u003cp\u003eThrive-c multivariate logistic regression model was constructed with age, admission NIHSS score, diabetes, atrial fibrillation, hypertension as independent variables to generate predictive probabilities and joint equations with the outcome of \u0026quot;Prognosis of patients with AIS after reperfusion therapy\u0026quot;: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{p}（\\text{T}\\text{H}\\text{R}\\text{I}\\text{V}\\text{E}-\\text{c} \\text{m}\\text{o}\\text{d}\\text{e}\\text{l}）=\\frac{1}{1+{\\text{e}}^{-（-5.741+0.045\\times \\text{a}\\text{g}\\text{e}+0.211\\times \\text{N}\\text{I}\\text{H}\\text{S}\\text{S} \\text{s}\\text{c}\\text{o}\\text{r}\\text{e}-1.21\\times \\text{d}\\text{i}\\text{a}\\text{b}\\text{e}\\text{t}\\text{e}\\text{s}+1.129\\times \\text{a}\\text{t}\\text{r}\\text{i}\\text{a}\\text{l} \\text{f}\\text{i}\\text{b}\\text{r}\\text{i}\\text{l}\\text{l}\\text{a}\\text{t}\\text{i}\\text{o}\\text{n}-0.092\\times \\text{h}\\text{y}\\text{p}\\text{e}\\text{r}\\text{t}\\text{e}\\text{n}\\text{s}\\text{i}\\text{o}\\text{n}）}}\\)\u003c/span\u003e\u003c/span\u003e ;\u003c/p\u003e\n \u003cp\u003eWe construct a modified Thrive-c model with age, admission NIHSS score, TyG index, atrial fibrillation and hypertension as independent variables:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\text{p}（\\text{M}\\text{o}\\text{d}\\text{i}\\text{f}\\text{i}\\text{e}\\text{d} \\text{T}\\text{H}\\text{R}\\text{I}\\text{V}\\text{E}-\\text{c}\\text{m}\\text{o}\\text{d}\\text{e}\\text{l}）=\\frac{1}{1+{\\text{e}}^{-（-18.859+0.063\\times \\text{a}\\text{g}\\text{e}+0.192\\times \\text{N}\\text{I}\\text{H}\\text{S}\\text{S} \\text{s}\\text{c}\\text{o}\\text{r}\\text{e}+1.265\\times \\text{T}\\text{y}\\text{G} \\text{i}\\text{n}\\text{d}\\text{e}\\text{x}+1.029\\times \\text{a}\\text{t}\\text{r}\\text{i}\\text{a}\\text{l} \\text{f}\\text{i}\\text{b}\\text{r}\\text{i}\\text{l}\\text{l}\\text{a}\\text{t}\\text{i}\\text{o}\\text{n}-0.11\\times \\text{h}\\text{y}\\text{p}\\text{e}\\text{r}\\text{t}\\text{e}\\text{n}\\text{s}\\text{i}\\text{o}\\text{n}）}}$$\u003c/div\u003e\n \u003c/div\u003e;\u003cp\u003eBecause age, diabetes, and NHISS score are all components of the THRIVE score, they were not included in the joint equation, and a joint predictive equation was constructed for TyG index and THRIVE score[\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{p}（\\text{T}\\text{y}\\text{G} \\text{i}\\text{n}\\text{d}\\text{e}\\text{x} \\text{c}\\text{o}\\text{m}\\text{b}\\text{i}\\text{n}\\text{e}\\text{d} \\text{w}\\text{i}\\text{t}\\text{h} \\text{T}\\text{H}\\text{R}\\text{I}\\text{V}\\text{E} \\text{s}\\text{c}\\text{o}\\text{r}\\text{e}）=\\frac{1}{1+{\\text{e}}^{\\begin{array}{c}-（-11.667+0.546\\times THRIVE score+1.02\\times TyG index\\\\ ）\\end{array}}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e, and the results of the regression coefficients for each model are shown in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of regression coefficients for each model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModified THRIVE-c model\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTHRIVE-c model\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTyG index combiend with THRIVE score\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003evariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegression coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003evariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegression coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003evariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegression coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTyG index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNIHSS score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNIHSS score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTHRIVE score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTyG index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ediabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eatrial fibrillation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eatrial fibrillation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehigh blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehigh blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eintercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-18.859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-11.667\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eNote: The AF/Hypertension/Diabetes categorical variable is assigned as the assignment: no\u0026thinsp;=\u0026thinsp;0 and yes\u0026thinsp;=\u0026thinsp;1;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.6.1\u003c/strong\u003e Efficacy of TyG index, THRIVE score, the combination, THRIVE-c model, and modified THRIVE-c model on prognosis of AIS patients\u003c/p\u003e\n \u003cp\u003eROC curve analysis showed that TyG index, THRIVE score, combination of both, THRIVE-c model, and modified THRIVE-c model had the applied value of predicting the prognosis of patients with AIS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Delong analysis was used to analyze whether there was a statistical difference in the AUC of each index of appeal, and it was found that there was no statistically significant difference between the AUCs of the other metrics, except for TyG index - modified THRIVE-c model, THRIVE score - TyG index combined with THRIVE score, THRIVE score - THRIVE-c model, THRIVE score - modified THRIVE-c model, TyG index combined THRIVE score - modified THRIVE-c model, and THRIVE-c model - modified THRIVE-c model were found to be statistically significant (|z| \u0026gt; 1.96 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This suggests that the modified THRIVE-c model, THRIVE-c model, and TyG index combined with THRIVE score predictive efficacy were all superior to single THRIVE score and TyG index indicators, and that the predictive efficacy of the modified THRIVE-c model was highest while the predictive efficacy of the TyG index combined with THRIVE score was lowest (Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of ROC curve analysis for each indicator and prediction model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ethreshold value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003esensitivity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003especificity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTyG index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66 to 0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTHRIVE score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.643to 0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTHRIVE score combined with TyG index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.729 to 0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTHRIVE-c model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.755 to 0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModified THRIVE-c model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.801 to 0.894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDelong analysis parameters\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003esports event\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ez\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAUC Variance\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTyG Index - THRIVE Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTyG Index - TyG Index United THRIVE Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTyG index - THRIVE-c model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTyG Index - Modified THRIVE-c Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTHRIVE Score - TyG Index Joint THRIVE Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTHRIVE scores - THRIVE-c modeling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTHRIVE Scoring - Modified THRIVE-c Modeling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTyG Index Joint THRIVE Score - THRIVE-c Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTyG index United THRIVE score - modified THRIVE-c model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTHRIVE-c Model - Modified THRIVE-c Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.6.3\u003c/strong\u003e Analysis of the results of the predictive value of each indicator of different reperfusion therapies on the prognosis of patients with AIS\u003c/p\u003e\n \u003cp\u003ePatients were divided into intravenous thrombolysis group and non-intravenous thrombolysis group (mechanical thrombolysis and both) and the ROC curve was established to test the predictive value of each index on the prognosis of AIS patients. The TyG index, the THRIVE score, the combination of both, the THRIVE-c model and the modified THRIVE-c model in the intravenous thrombolysis group all had predicting value for prognosis of AIS patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).The TyG index and modified THRIVE-c model had stronger predictive efficacy and the THRIVE score was slightly weaker in the non-intravenous thrombolysis group.(p\u0026thinsp;\u0026gt;\u0026thinsp;0.05, AUC\u0026thinsp;=\u0026thinsp;0.637) (Table \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eParameters of ROC curves for different reperfusion therapies\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eIntravenous thrombolysis group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eNon-intravenous thrombolysis group\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTyG index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.621\u0026ndash;0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.697\u0026ndash;0.913\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdmission THRIVE score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.623\u0026ndash;0.791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.507\u0026ndash;0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTHRIVE score\u0026thinsp;+\u0026thinsp;TyG index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.695\u0026ndash;0.837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.732\u0026ndash;0.922\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTHRIVE-c model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.736\u0026ndash;0.869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.713\u0026ndash;0.915\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModified THRIVE-c model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.778\u0026ndash;0.894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.783\u0026ndash;0.955\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eIntravenous thrombolysis group, Non-intravenous thrombolysis group\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eInsulin resistance (IR) is a new risk factor for stroke in more than 50% of non-diabetic patients with acute ischemic stroke (AIS) or transient ischemic attack (TIA)[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].Initial research had indicated that IR might be a trigger for AIS, by promoting atherosclerosis and inducing a prothrombotic condition, which led to poor outcomes[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Recently, the link between IR and cerebrovascular disease has been revealed. IR activated inflammatory genes and hindered insulin signal[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], which resulted in different levels of chronic inflammation, oxidative stress and endothelial dysfunction.These conditions could impair vascular function, thereby leading to cerebrovascular diseases[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e][\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Additionally, endothelial cells released more nitric oxide ( NO) and coagulant factors, which promoted platelet aggregation and thrombosis[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]], both led to cerebrovascular diseases[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e][\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Also IR triggered endoplasmic reticulum stress and macrophage apoptosis, which increased the development of vulnerable plaques, plaque necrosis and atherosclerosis[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Furthermore, IR was associated with adverse reactions in AIS patients with intravenous (IV) thrombolytic therapy[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e][\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The triglyceride-glucose (TyG) index, derived from fasting plasma glucose (FPG) and triglycerides (TG), was recognized as an accessible marker of insulin resistance[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].The TyG index was associated with neurological severity and early recurrence in AIS[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e][\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A higher TyG index was linked with an increased risk of early neurological deterioration and a decreased early neurological improvement in AIS patients with IV thrombolytic therapy. Particularly, the TyG index was closely correlated with early neurological deterioration in patients with subcortical infarcts near the middle cerebral artery[30]. It was reported that higher TyG index was related to increased mortality and poorer functional outcomes in both diabetic and non-diabetic patients[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e31\u003c/span\u003e], which confirmed our observation in this study. We found that more early neurological deterioration and hemorrhagic transformation and higher mortality in the high-TyG-index group. Our analysis also showed that both the THRIVE score and TyG index could independently predict 90-day outcomes for AIS patients with reperfusion therapy after adjusting factors such as diabetes, hypertension, age, NIHSS scores, the THRIVE scores at admission, TyG index, FBG, and types of reperfusion therapy.In general, the TyG index was a significant prognostic marker in AIS[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe THRIVE score had been verified to evaluate long-term outcomes in AIS patients with endovascular treatment and rt-PA IV thrombolysis [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Higher THRIVE score was correlated with poorer outcomes, higher mortality and more hemorrhagic transformation [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. A retrospective research suggested that the THRIVE score could predict outcomes in Chinese AIS patients with or without IV thrombolytic treatment including those with cardioembolic strokes [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e35\u003c/span\u003e][\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e36\u003c/span\u003e]].Further research had found that the THRIVE score could accurately predict long-term outcomes in AIS patients, regardless of whether they received thrombolytic or endovascular treatments [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In our research, it was determined that the admission THRIVE score and TyG index were both independent risk factors for long-term prognosis by multivariate regression analysis.AUC of the ROC curves for both indicators was above 0.7. And AUC of the ROC curves for the combination was 0.784, showing higher sensitivity (83.3%) and specificity (66%). The THRIVE score was composed of clinical metrics which were easily accessed but was not so precise. Therefore, Flint et al. created THRIVE-c model based on the THRIVE score, which was more precise. Our study modified THRIVE-c model by replacing diabetes with Tyg index, yielding the highest predictive precision (AUC\u0026thinsp;=\u0026thinsp;0.847), sensitivity (83.3%), and specificity (74.3%).\u003c/p\u003e \u003cp\u003eAccording to our observation, individuals with moderate to severe stroke demonstrated poorer outcomes. And patients without IV thrombolysis therapy showed particularly worse outcomes, which was likely attributable to more severe neurological impairments at admission. The TyG index was not significantly different in various THRIVE score groups which meant the TyG index was not affected by the THRIVE score. Furthermore, the TyG index appeared to be more pronounced in individuals under 65 or who had no history of diabetes, owing to the prevalent use of hypoglycemic agents and antihyperlipidemic drugs among the older, which could potentially influence the TyG index. These observations required further investigation to validate.\u003c/p\u003e \u003cp\u003eIn conclusion, for AIS patients receiving reperfusion therapy, both the TyG index and the THRIVE score measured at admission were independent predictors for short-term prognosis. Since the THRIVE score did not include IR\u0026mdash;which was an important part in AIS development and also a significant predictor.The modified THRIVE-c model with the combination of both enhanced the predictive capability for short-term outcomes of AIS and was practical in clinical use. However, merging the TyG index with the THRIVE score, or modified THRIVE-c model lacked medical imaging and other essential laboratory markers, which may undermine the predictive performance and introduce constraints in our study.Our study was retrospective with limited population and partial clinical data.So, future research should build a more accurate model by more prospective, multicenter researches with large population and combine modified THRIVE-c model with medical imaging and other markers.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eIR Insulin resistance\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTyG index Triglyceride\u0026ndash;glucose index\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAIS\u0026nbsp;acute ischemic stroke\u0026nbsp;\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSBP Systolic blood pressure\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDBP Diastolic blood pressure \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFPG Fasting plasma glucose\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTC Total cholesterol\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTG Triglycerides\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHDL-C High-density lipoprotein cholesterol\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLDL-C Low-density lipoprotein cholesterol\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHcy Homocysteine\u0026nbsp;\u003c/p\u003e\n\u003cp\u003emRS\u0026nbsp;modified Rankin Scale scores\u003c/p\u003e\n\u003cp\u003eTPA tissue-type plasminogen activator\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIA intra-arterial\u003c/p\u003e\n\u003cp\u003eOCSP\u0026nbsp;Oxfordshire community stroke project (OCSP)\u003c/p\u003e\n\u003cp\u003eTACI\u0026nbsp;total anterior circulation infarction\u003c/p\u003e\n\u003cp\u003ePACI\u0026nbsp;partial anterior circulation infarction\u003c/p\u003e\n\u003cp\u003ePOCI\u0026nbsp;posterior circulation infarction\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLACL\u0026nbsp;lacunar infarction\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUA uric acid\u003c/p\u003e\n\u003cp\u003eEND short-term neurological deterioration end\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all the\u0026nbsp;investigators and subjects who\u0026nbsp;participated\u0026nbsp;in this\u0026nbsp;project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLC conceived and designed the experiments and wrote the manuscript.QY and LJ organized the data and LC performed the analyses.LJ and YS contributed to the quality control of the data and the finalization of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received the support of \u0026nbsp;from the National Natural Science Foundation of China (Research Grant #81871088) \u0026nbsp;and The construction and practice of a regionalized health management consortium with the integration of medical treatment and prevention（Research Grant WY22A08）.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ematerials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets\u0026nbsp;used and/or analyzed\u0026nbsp;in the study\u0026nbsp;are\u0026nbsp;available from\u0026nbsp;the\u0026nbsp;cor-\u0026nbsp;responding author\u0026nbsp;upon\u0026nbsp;reasonable\u0026nbsp;request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved\u0026nbsp;by the\u0026nbsp;medical ethics committee\u0026nbsp;of\u0026nbsp;Wuhan\u0026nbsp;Third\u0026nbsp; \u0026nbsp; \u0026nbsp; Hospital and all\u0026nbsp;methods were\u0026nbsp;performed\u0026nbsp;in accordance with the\u0026nbsp;applicable guidelines and\u0026nbsp;regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003ePluta R, Januszewski S, Czuczwar SJ. 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Sci Rep, 2022, 12(1):1596.\u003c/li\u003e\n \u003cli\u003eLee, M, Kim, CH, Kim, Y, et al. High Triglyceride Glucose Index Is Associated with Poor Outcomes in Ischemic Stroke Patients after Reperfusion Therapy. CEREBROVASC DIS. 2021; 50 (6): 691-699.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eFlint A C, Kamel H, Rao V A, et al. Validation of the Totaled Health Risks In Vascular Events ( THRIVE) score for outcome prediction in endovascular stroke treatment [J].Int J Stroke, 2014, 9( 1) : 32-39.\u003c/li\u003e\n \u003cli\u003eKamel H, Patel N, Rao V A, et al. The totaled health risks in vascular events ( THRIVE) score predicts ischemic stroke outcomes independent of thrombolytic therapy in the NINDS tPA trial[J]. J Stroke Cerebrovasc Dis, 2013, 22( 7) : 1111-1116.\u003c/li\u003e\n \u003cli\u003eLei C, Wu B, Liu M, et al. Totaled health risks in vascular events score predicts clinical outcomes in patients with cardioembolic and other subtypes of ischemic stroke［J］. Stroke, 2014, 45( 6):1689-1694.\u003c/li\u003e\n \u003cli\u003eYou Shoujiang, The predictive value of\u0026nbsp;THRIVE\u0026nbsp;score on the prognosis of AIS patients with atrial fibrillation [J]. Chinese Journal of Internal Medicine, 2014, 53(7) : 532-536.\u003c/li\u003e\n \u003cli\u003eKamel, H, Patel, N, Rao, VA, et al. The totaled health risks in vascular events (THRIVE) score predicts ischemic stroke outcomes independent of thrombolytic therapy in the NINDS tPA trial. j STROKE CEREBROVASC. 2012; 22 (7): 1111-6.\u0026nbsp;\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":"TyG index, THRIVE score, Acute ischemic stroke, Prognosis, IR","lastPublishedDoi":"10.21203/rs.3.rs-4479122/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4479122/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eTo explore the predictive value of combining the TyG index with the THRIVE score at admission for AIS patients following reperfusion therapy.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective study enrolled 284 AIS patients who had undergone reperfusion therapy. Patients were classified into good or poor prognosis groups based on their modified Rankin Scale (mRS) scores. We analyzed the relationship between the TyG index, the THRIVE score at admission, and the prognosis of AIS. We applied Spearman correlation analysis to investigate the correlation between the TyG index and the THRIVE score at admission against the 90-day mRS scores of AIS patients post-reperfusion. The study developed a logistic regression analysis model to establish a combined predictive formula. ROC curves were constructed to evaluate the predictive power of the TyG index, the admission THRIVE score, their combined use, the THRIVE-c model, and the modified THRIVE-c model for AIS prognosis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThere were significant differences observed between the groups with poor and good prognoses regarding diabetes, hypertension, atrial fibrillation, gender, NIHSS score at admission, admission THRIVE score, TyG index, triglycerides, and fasting blood glucose. Logistic regression analysis determined that both the TyG index and THRIVE score at admission serve as independent risk factors for poor 90-day prognosis in ischemic stroke patients. The combined predictive coefficient, the THRIVE-c model, and the modified THRIVE-c model yielded AUCs of 0.784, 0.81, and 0.847, respectively, indicating their reliable predictive efficacy.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eBoth the TyG index and the THRIVE score assessed at admission are significant independent predictors of adverse outcomes in AIS patients post-reperfusion therapy. The integration of these metrics, along with the THRIVE-c and modified THRIVE-c models, enhances the accuracy of prognosis prediction for AIS patients.\u003c/p\u003e","manuscriptTitle":"Predictive value of modified Thrive-c model in the prognosis of AIS patients after reperfusion therapy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-25 14:36:08","doi":"10.21203/rs.3.rs-4479122/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":"e1b95f25-1eb4-494e-b98a-cd160d2b5b56","owner":[],"postedDate":"June 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-31T00:38:26+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-25 14:36:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4479122","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4479122","identity":"rs-4479122","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}