Association between Atherosclerotic Index of Plasma and Long-term Aorta-related Adverse Events in TEVAR-treated Type B Aortic Dissection Patients

Association between Atherosclerotic Index of Plasma and Long-term Aorta-related Adverse Events in TEVAR-treated Type B Aortic Dissection Patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between Atherosclerotic Index of Plasma and Long-term Aorta-related Adverse Events in TEVAR-treated Type B Aortic Dissection Patients Shuangshuang Li, Wen Li, Jiahe Zhang, Zhichen Ding, Kaiwen Zhao, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7984351/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Mar, 2026 Read the published version in Cardiovascular Diabetology → Version 1 posted 13 You are reading this latest preprint version Abstract Background Previous research identifies the atherosclerotic index of plasma (AIP) as a key marker for cardiovascular risk, but its role in predicting aorta-related adverse events (ARAEs) in type B aortic dissection (TBAD) patients post-thoracic endovascular aortic repair (TEVAR) is uncertain. This study investigates the link between AIP and ARAEs at 1-year and 5-year intervals in TBAD patients after TEVAR, suggesting a new prognostic indicator for TBAD outcomes. Methods This retrospective cohort study involved 1,335 TBAD patients who underwent TEVAR, with clinical data extracted from electronic records. AIP was calculated as log (triglycerides/high-density lipoprotein cholesterol [TG/HDL-C]), and patients were categorized into three AIP tertiles. The primary endpoints were ARAEs at 1 and 5 years post-TEVAR. Cox regression identified variables linked to endpoints and assessed AIP's independent impact on ARAEs. Kaplan-Meier curves and log-rank tests compared ARAE incidence across groups. RCS models examined the AIP-ARAEs dose-response relationship, while subgroup analyses confirmed the association's stability. Time-dependent ROC curves evaluated AIP's predictive power for ARAEs over five years. Results Kaplan-Meier analysis showed higher incidence of ARAEs in AIP High group compared to Low group (1 year: 19.51% vs. 5.81%; 5 years: 24.39% vs. 10.32%; both P < 0.05). Cox analysis showed AIP High group had higher ARAE risk (1-year HR = 4.63, 95% CI: 2.57–8.34; 5-year HR = 2.59, 95% CI: 1.78–3.78; all P < 0.001). Furthermore, RCS analysis indicated a continuous positive linear relationship between AIP and ARAEs. Time-dependent ROC showed the area under the curve (AUC) surpassing 80% throughout the five-year duration. Subgroup analysis revealed higher AIP-related ARAEs risk in subacute surgery patients (1 year: HR = 6.78; 5 years: HR = 2.96) and lower risk in chronic surgery patients (1 year: HR = 0.02; 5 years: HR = 0.24). In the 5-year subgroup analysis, patients without chronic kidney disease (CKD) had a notably higher risk of AIP compared to those with CKD (HR = 2.09 vs. HR = 0.41) (P-interaction = 0.082). Conclusion AIP serves as an independent influencing factor for both the short term (1 year) and the long term (5 years) following TEVAR in patients with TBAD, demonstrating a linear relationship between the two timeframes. These findings highlight the significance of AIP as a crucial risk biomarker, providing a simple yet effective method for identifying the risk of ARAEs in this patient population. type B aortic dissection thoracic endovascular aortic repair atherosclerotic index of plasma aortic-related adverse events biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Aortic dissection (AD) represents one of the most devastating cardiovascular diseases (CVD), with an annual incidence rate of 35 cases per 100,000 individuals in this population [ 1 ]. Based on the dissection's location, AD is classified into Stanford type A aortic dissection (TAAD) and Stanford type B aortic dissection (TBAD). TBAD typically originates from the distal end of the left subclavian artery (LSA) [ 2 ]. Treatment options for patients with TBAD primarily include open surgical intervention, pharmacotherapy, and TEVAR [ 3 ]. TEVAR effectively stabilizes and reshapes the aorta, serving as the principal treatment modality for TBAD [ 4 ]. Nonetheless, ARAEs such as endoleak, retrograde type A dissection, and new hairpin layer formation may arise following TEVAR [ 5 , 6 , 7 ]. These complications can hinder vascular remodeling and contribute to a poor prognosis [ 8 , 9 ]. Consequently, the early prediction and prevention of ARAEs after TEVAR have garnered significant attention. There is an urgent need to identify prognostic markers with predictive value to accurately assess the risk of adverse outcomes in patients and facilitate timely intervention. Atherosclerotic index of plasma (AIP) is an emerging biomarker that reflects disorders in lipid metabolism and is considered a more reliable predictor of CVD than traditional lipid indicators, including total cholesterol, low-density lipoprotein cholesterol, and triglycerides [ 10 , 11 ]. AIP indicates the imbalance of circulating lipids and acts as an independent predictor of rapid plaque progression [ 12 , 13 , 14 ]. Increasing evidence suggests that elevated AIP correlates with a heightened risk of cardiovascular events, renal issues, metabolic syndrome, diabetes, hypertension, stroke, chronic coronary syndrome, and other metabolic disorders [ 15 , 16 , 17 , 18 , 19 ]. AIP may influence the clinical outcomes of patients with atherosclerotic cardiovascular disease through mechanisms such as endothelial dysfunction and heightened inflammation [ 20 ]. Furthermore, abnormal lipid metabolism is intricately linked to the pathogenesis of aortic dissection. A study found that among 439 identified lipids, 278 exhibit significant alterations when compared to the normal control group in the aortic dissection patient cohort [ 21 ]. There is a paucity of convenient metabolism-related indicators for assessing the risk of long-term ARAEs in patients with TBAD after TEVAR. While AIP has demonstrated predictive value for cardiovascular diseases, its relationship with long-term ARAEs in TBAD patients remains uncertain. This study aims to examine the association between AIP and the incidence of ARAEs at both 1-year and 5-year intervals in patients with TBAD after TEVAR, thereby proposing a novel prognostic indicator for TBAD outcomes. Materials and Methods Research cohort and design This study adopted a retrospective cohort design, encompassing 1,650 patients diagnosed with TBAD who received TEVAR at the First Affiliated Hospital of Naval Medical University in Shanghai, China, from August 2011 to June 2024. The exclusion criteria were as follows: (1) participants with traumatic aortic injury and iatrogenic aortic dissection (n = 11); (2) participants with Turner syndrome, Marfan syndrome, Ehlers-Danlos syndrome, two-lobular aortic valve, giant cell arteritis, ankylosing spondylitis, Behçet's disease, or Takayasu arteritis (n = 46); (3) participants with a history of aortic surgery (n = 24); (4) participants with a documented history of malignant tumors (n = 48); (5) participants lacking perioperative serum data (n = 116); and (6) individuals missing high-density lipoprotein cholesterol and triglyceride data (n = 70). Ultimately, a total of 1,335 patients were included in this study (Fig. 1 ). The research protocol received approval from the Ethics Committee of Shanghai Changhai Hospital (CHEC-Y2020-042). Given the retrospective nature of this study, the requirement for informed consent was waived. Data collection and definition AIP is calculated based on the following formula: log (triglycerides/high-density lipoprotein cholesterol [TG/HDL-C]). Participants were divided into three different groups [group1/Low (AIP < 0.27,n = 688), group 2/Middle (0.27 ≤ AIP < 0.52, n = 319), group 3/High (AIP ≥ 0.52, n = 328)] based on the terquartiles of AIP. The primary outcome was aortic-related adverse events after endovascular treatment of TBAD. Including retrograde aortic dissection, aortic rupture, aortic dilation, poor perfusion, and type I or type III endoleak [ 22 ]. Demographic baseline data, symptoms, history of comorbidities, and laboratory test results of the research subjects were obtained through the electronic medical record system. The laboratory test data included D-dimer (mg/L), creatinine (Cr, µmol/L), fasting blood glucose (FPG, mg/dL), prothrombin time (PT, s), activated partial thromboplastin time (APTT, s), thrombin time (TT, s), fibrin degradation products (FDP, mg/L), uric acid (UA, µmol/L), total cholesterol (TC, mg/dL), triglycerides (TG, mg/dL), low-density lipoprotein cholesterol (LDL-C, mg/dL), high-density lipoprotein cholesterol (HDL-C, mg/dL), and so on. Hematological indicators included white blood cell count (WBC, ×10⁹/L), lymphocyte count (LYM, ×10⁹/L), monocyte count (MO, ×10⁹/L), neutrophil count (NE, ×10⁹/L), hemoglobin (Hb, g/L), and platelet count (PLT, ×10⁹/L). All tests were conducted by the Laboratory Department of Shanghai Changhai Hospital using standard biochemical methods. Additionally, derivative indicators were calculated to assess systemic inflammation and metabolic disorders. These indicators include the neutrophil-to-lymphocyte ratio (NLR = NE/LYM), lymphocyte-to-monocyte ratio (LMR = LYM/MO), monocyte-to-lymphocyte ratio (MLR = MO/LYM), systemic immune inflammation index (SII = PLT × NE/LYM), systemic inflammation aggregation index (AISI = NE × MO × PLT/LYM), platelet-to-lymphocyte ratio (PLR = PLT/LYM), systemic inflammatory response index (SIRI = NE × MO/LYM), uric acid to HDL ratio (UHR = UA/HDL), and triglyceride-glucose index (TyG = ln [TG × FBG/2])[ 23 ]. For both non-emergency and emergency surgeries, blood samples are collected, and weight data are recorded while the patient is fasting in the morning prior to the operation. In the case of emergency surgeries, these measurements are obtained in the emergency room. Based on the duration of clinical onset, the clinical manifestations of TBAD are categorized into three groups: acute (≤ 14 days), subacute (15–90 days), and chronic (≥ 91 days) [ 24 ]. Variables exhibiting a missing data rate greater than 20% were excluded during the initial data collection and screening phase to mitigate potential bias arising from excessive missing values. For the remaining variables with minimal missing data, simple imputation techniques were employed. Specifically, continuous variables were imputed using the mean value of the respective variable, whereas categorical variables were imputed using the mode, or most frequently occurring category, of the respective variable. Follow-up and endpoints The objectives of this study were categorized by time frame: short-term outcomes, which encompass ARAEs occurring within one year, and long-term outcomes, which include ARAEs at five years. In instances of multiple adverse events, only the first occurrence will be analyzed. This research was conducted by qualified investigators utilizing medical records, and telephone interviews. Furthermore, adverse events were assessed through a thorough review of clinical records that necessitated readjustment or evaluation during outpatient visits. Two independent physicians, possessing expertise in the diagnosis and management of TBAD, evaluated the adverse events and endpoints while remaining blinded to the patients' clinical information. Statistical analysis The statistical analysis was performed using the mean ± standard deviation (SD) or the median (quartile), depending on the distribution of the data. For inter-group comparisons, the Mann-Whitney test or analysis of variance was employed for continuous variables, while categorical variables were analyzed using the chi-square test or Fisher's exact test. The cumulative survival curve was generated using the Kaplan-Meier (KM) method, and differences among groups were assessed with the log-rank test. The Cox proportional hazards regression model was utilized to examine the relationship between preoperative AIP and 1-year and 5-year ARAEs, calculating the hazard ratio (HR) and 95% confidence interval (CI). Initially, a univariate analysis was conducted, and variables with P < 0.05 were incorporated into the multivariate Cox model. The potential association between AIP and outcomes was further evaluated using restricted cubic splines (RCS). Subgroup analysis was conducted to investigate the robustness of the association between AIP and ARAEs in patients with TBAD. The time-dependent Receiver Operating Characteristic (ROC) curve is employed to assess the predictive efficacy of AIP for ARAEs across various time points within a five-year period. Statistical analyses were carried out using R (version 4.4.0) and SPSS software version 27.0. In this study, P value < 0.05 was deemed statistically significant. Result Baseline characteristics of the participants The baseline characteristics of the included patients are presented in Table 1 . The average age of the 1,335 patients included in this study was 59.0 ± 13.2 years (Low vs. Middle vs. High = 57.2 ± 12.5 vs. 60.4 ± 13.5 vs. 59.2 ± 13.4, P = 0.004). Among the participants, 1,099 were male (82.32%), with the highest proportion of males in the High group (285, 86.89%). Significant statistical differences in BMI were observed among the three groups, with the High group exhibiting a higher BMI than the other two groups (Low vs. Middle vs. High = 24.0 ± 3.6 vs. 24.5 ± 3.6 vs. 25.0 ± 3.2, P < 0.001). No statistically significant differences were found in systolic blood pressure (P = 0.155) or diastolic blood pressure (P = 0.409) among the groups at admission. Additionally, the distribution of hypertension (HBP) (P = 0.166) and chronic kidney disease (CKD) (P = 0.728) also showed no statistically significant differences among the three groups. However, significant differences were noted in the distribution of smoking, alcohol consumption,diabetes mellitus, and chronic obstructive pulmonary disease (COPD) among the groups (P < 0.001, P = 0.020, P = 0.009, P = 0.045). Compared to the other two groups, the white blood cell (WBC) count in the High AIP group was higher (P = 0.020), while fasting plasma glucose (FPG) (P = 0.023) and platelet count (PLT) (P < 0.001) were also significantly higher. Statistically significant differences were observed in total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) among the three groups (P = 0.001, P < 0.001, P < 0.001, P 0.05). Table 1 Baseline characteristics of the study participants. Variable AIP P-value Total Low Middle High N 1335 688 319 328 Age(years) 59.0 ± 13.2 57.2 ± 12.5 60.4 ± 13.5 59.2 ± 13.4 0.004 Male 1099 (82.32) 558 (81.10) 256 (80.25) 285 (86.89) 0.042 BMI,kg/m2 24.4 ± 3.5 24.0 ± 3.6 24.5 ± 3.6 25.0 ± 3.2 < 0.001 SBP at admission, mmHg 137.8 ± 21.7 139.0 ± 22.3 136.4 ± 21.3 136.8 ± 20.7 0.155 DBP at admission, mmHg 82.1 ± 11.3 82.6 ± 11.4 81.3 ± 11.1 81.9 ± 11.3 0.409 Smoking 636 (47.64) 277 (40.26) 170 (53.29) 189 (57.62) < 0.001 Alcohol consumption 233 (17.45) 104 (15.12) 56 (17.55) 73 (22.26) 0.020 Timing of operation 0.247 Acute 975 (73.03) 505 (73.40) 240 (75.24) 230 (70.12) Sub-acute 226 (16.93) 107 (15.55) 52 (16.30) 67 (20.43) Chronic 134 (10.04) 76 (11.05) 27 (8.46) 31 (9.45) Comorbidities Pericardial effusion 88 (6.59) 44 (6.40) 27 (8.46) 17 (5.18) 0.233 Pleural effusion 437 (32.73) 225 (32.70) 116 (36.36) 96 (29.27) 0.157 Ischemia of major arteries 84 (6.29) 36 (5.23) 22 (6.90) 26 (7.93) 0.224 Hypertension 1002 (75.06) 509 (73.98) 234 (73.35) 259 (78.96) 0.166 Diabetes mellitus 114 (8.54) 44 (6.40) 31 (9.72) 39 (11.89) 0.009 Stroke 74 (5.54) 29 (4.22) 25 (7.84) 20 (6.10) 0.057 COPD 146 (10.94) 67 (9.74) 47 (14.73) 32 (9.76) 0.045 CKD 74 (5.54) 35 (5.09) 20 (6.27) 19 (5.79) 0.728 Laboratory tests WBC, ×10 9 /L 8.6 ± 3.4 8.4 ± 3.3 8.5 ± 3.3 8.8 ± 3.5 0.020 LYM, ×10 9 /L 1.4 ± 0.6 1.3 ± 0.5 1.4 ± 0.6 1.5 ± 0.7 < 0.001 MO, ×10 9 /L 0.6 ± 0.3 0.6 ± 0.3 0.6 ± 0.3 0.6 ± 0.3 0.102 NE, ×10 9 /L 6.5 ± 3.4 6.3 ± 3.3 6.7 ± 3.5 6.1 ± 3.2 0.001 Hb, g/L 127.9 ± 18.1 127.2 ± 17.3 128.2 ± 19.2 128.9 ± 18.7 0.205 D-dimer, mg/L 3.5 ± 4.3 3.6 ± 4.3 3.3 ± 4.0 3.6 ± 4.5 0.177 Cr, µmol/L 101.3 ± 103.8 102.8 ± 107.2 103.9 ± 115.6 95.7 ± 82.2 0.952 PT, s 13.8 ± 1.3 13.8 ± 1.2 13.8 ± 1 13.7 ± 1.6 0.309 FPG, mg/dL 6.5 ± 2.4 6.4 ± 1.5 6.7 ± 3.8 6.7 ± 1.9 0.023 APTT, s 39.8 ± 11.6 40.2 ± 14.0 40.0 ± 9.5 38.7 ± 6.9 0.033 TT, s 17.5 ± 12.6 17.9 ± 15.6 17.1 ± 9.9 16.8 ± 5.7 0.796 Fbg, g/L 4.4 ± 1.6 4.3 ± 1.5 4.5 ± 1.7 4.4 ± 1.5 0.165 FDP, mg/L 13.0 ± 17.6 13.1 ± 17.1 13.3 ± 19.7 12.4 ± 16.3 0.128 PLT, ×10 9 /L 203.3 ± 74.4 195.9 ± 68.0 209.9 ± 80.8 212.3 ± 79.0 < 0.001 TC, mg/dL 170.2 ± 37.3 166.3 ± 24.4 169.1 ± 36.2 179.3 ± 55.1 0.001 TG, mg/dL 120.4 ± 102.4 88.8 ± 23.3 108.1 ± 24.4 198.6 ± 180.8 < 0.001 LDL-C, mg/dL 41.7 ± 136.5 27.2 ± 31.1 55.3 ± 195.1 58.8 ± 189.7 < 0.001 HDL-C, mg/dL 44.2 ± 11.5 48.5 ± 11.5 43.1 ± 9.1 36.0 ± 8.8 < 0.001 NLR 6.2 ± 6.0 5.7 ± 5.3 6.8 ± 6.4 5.5 ± 5.6 < 0.001 LMR 2.7 ± 1.9 2.6 ± 2.1 2.5 ± 1.3 2.9 ± 1.6 < 0.001 MLR 0.5 ± 0.3 0.5 ± 0.3 0.5 ± 0.3 0.5 ± 0.3 < 0.001 SII 1195.6 ± 1237.6 1231.7 ± 1188 1176.7 ± 1311.0 1138.5 ± 1267.3 0.013 AISI 770.5 ± 1031.7 765.7 ± 907.6 796.3 ± 1158.2 755.6 ± 1143.1 0.026 PLR 174.2 ± 106.1 175.0 ± 98.7 173.8 ± 107.7 173.1 ± 118.9 0.456 SIRI 3.9 ± 4.9 4.2 ± 5.1 3.8 ± 4.5 3.4 ± 5.0 < 0.001 UHR 3.6 ± 49.5 1.9 ± 0.5 2.2 ± 0.9 8.4 ± 99.8 < 0.001 UA, µmol/L 99.4 ± 1091.9 92.8 ± 915.9 55.6 ± 20.4 155.7 ± 1759.4 < 0.001 TyG 8.7 ± 0.5 8.5 ± 0.4 8.7 ± 0.4 9.2 ± 0.6 < 0.001 Values are presented as n (%), mean ± SD. Categorical variables were presented as n(%). BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; COPD, Chronic obstructive pulmonary disease; CKD, Chronic kidney disease. WBC, white blood cell; LYM, lymphocyte; MO, monocyte; NE, neutrophil; Hb, hemoglobin; Cr, creatinine; PT, prothrombin time; FPG, fasting plasma glucose; APTT, activated partial thromboplastin time; TT, thrombin time; Fbg, fasting blood glucose; FDP, fibrinogen degradation products; PLT, platelet; TC, total cholesterol; TG, triglycerides; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; NLR, neutrophil-to-lymphocyte ratio; LMR, lymphocyte-to-monocyte ratio; MLR, monocyte-to-lymphocyte ratio; SII, systemic immune-inflammation index; AISI, advanced inflammation-based score index; PLR, platelet-to-lymphocyte ratio; SIRI, systemic inflammatory response index; UHR, uric acid to high-density lipoprotein cholesterol ratio; UA, uric acid; TyG, triglyceride-glucose index. One-year and five-year outcomes The cumulative incidences of ARAEs, MACCEs, and all-cause mortality at 1 year and 5 years are presented in Table 2 . During the one-year follow-up, we identified 131 cases of ARAEs, 43 fatalities, and 25 instances of MACCEs. Over the 5-year follow-up, there were 199 cases of ARAEs, 114 fatalities, and 145 cases of MACCEs. Notably, the cumulative incidence of ARAEs in the High group was the highest at both 1 year (19.51%) and 5 years (24.39%), while the Low group exhibited the lowest incidences at 1 year (5.81%) and 5 years (10.32%). The P values for these comparisons were all less than 0.001 (Table 2 ; Figs. 2 and 3 ). However, the analysis indicated no statistically significant differences in all-cause mortality or the cumulative incidence of MACCEs among the three groups at either 1 year (P = 0.420, P = 0.407) or 5 years (P = 0.336, P = 0.135) (Table 2 ; Supplementary Figure S1). Table 2 Outcomes of TBAD patients receiving TEVAR grouped according to AIP. Variable Total Low Middle High P-value 1-year outcomes Cumulative incidence of 1-year ARAEs, n(%) 131 (9.81%) 40 (5.81%) 27 (8.46%) 64 (19.51%) < 0.001 Cumulative incidence of 1-year all-cause death, n(%) 43 (3.22%) 21 (3.05%) 8 (2.51%) 14 (4.27%) 0.420 Cumulative incidence of 1-year MACCEs, n(%) 25 (1.87%) 11 (1.60%) 5 (1.57%) 9 (2.74%) 0.407 5-year outcomes Cumulative incidence of 5-year ARAEs, n(%) 199 (14.91%) 71 (10.32%) 48 (15.05%) 80 (24.39%) < 0.001 Cumulative incidence of 5-yearall-cause death, n(%) 114 (8.54%) 57 (8.28%) 23 (7.21%) 34 (10.37%) 0.336 Cumulative incidence of 5-year MACCEs, n(%) 145 (10.86%) 71 (10.32%) 29 (9.09%) 45 (13.72%) 0.135 Values are presented as n (%). TBAD, type B aortic dissection; TEVAR, thoracic endovascular aortic repair; AIP, atherogenic index of plasma; ARAEs, aortic-related adverse events; MACCEs, major adverse cardiovascular and cerebrovascular events. Univariate and multivariate Cox analysis The results of the Cox proportional hazards regression analysis for 1-year ARAEs are shown in Table 3 . The results indicated that gender, AD stage at admission, pericardial effusion, ischemia of major arteries, and several ratios including NLR, MLR, SIRI, TyG, and categorical AIP—were significantly associated (P < 0.05) (Table 3 ). A multivariate Cox proportional hazards analysis was subsequently performed on these variables. The results revealed that AD stage (P = 0.022), pericardial effusion (P = 0.008), ischemia of major arteries (P = 0.001), NLR (P = 0.025), and MLR (P = 0.013) remained significantly correlated with the cumulative incidence of ARAEs at 1 year. After adjusting for factors such as AD stage at admission, pericardial effusion, renal artery ischemia, ischemia of major arteries, NLR, and MLR, a statistically significant association was observed between AIP (categorical) and the cumulative incidence of ARAEs at 1 year. Compared to the Low group, the risk of 1-year ARAEs was significantly increased in the High group (HR = 4.63, 95% CI: 2.57–8.34; P < 0.001). Table 3 Univariate and Multivariate Cox proportional hazard modeling analysis for 1-year ARAEs. Variable Univariate analysis Multivariate analysis HR 95% CI P -value HR 95% CI P -value Age 1.01 (0.99,1.02) 0.388 Male 1.96 (1.02,3.77) 0.045 1.71 (0.88,3.32) 0.111 BMI 1.01 (0.95,1.06) 0.810 SBP at admission 1.00 (0.99,1.01) 0.562 DBP at admission 1.00 (0.98,1.02) 0.872 AD stage at admission 0.72 (0.56,0.92) 0.009 0.73 (0.56,0.96) 0.022 Pericardial effusion 2.63 (1.52,4.57) 0.001 2.18 (1.26,3.88) 0.008 Pleural effusion 1.12 (0.74,1.71) 0.586 Ischemia of major arteries 3.15 (1.86,5.32) < 0.001 2.51 (1.45,4.33) 0.001 Hypertension 1.06 (0.66,1.70) 0.807 Diabetes mellitus 1.38 (0.73,2.58) 0.320 Stroke 1.931 0.972–3.837 0.06 COPD 0.76 (0.38,1.51) 0.435 CKD 1.59 (0.80,3.15) 0.188 WBC 1.04 (1.00,1.10) 0.075 Hb 0.99 (0.98,1.00) 0.223 D-dimer 1.02 (0.97,1.06) 0.486 Cr 1.00 (1.00,1.00) 0.359 FDP 1.01 (1.00,1.02) 0.307 NLR 1.03 (1.01,1.05) 0.001 1.05 (1.01,1.09) 0.025 LMR 0.85 (0.74,0.99) 0.039 MLR 2.25 (1.46,3.47) < 0.001 2.96 (1.26,6.95) 0.013 SII 1.00 (1.00,1.00) 0.767 AISI 1.00 (1.00,1.00) 0.782 PLR 1.00 (1.00,1.00) 0.440 SIRI 1.03 (1.01,1.06) 0.007 0.93 (0.87,1.00) 0.051 UHR 1.00 (0.99,1.01) 0.870 TyG 1.47 (1.06,2.03) 0.019 0.71 (0.46,1.09) 0.115 AIP groups Low Reference Reference Middle 1.41 (0.80,2.49) 0.239 1.56 (0.86,2.81) 0.141 High 3.30 (2.08,5.26) < 0.001 4.63 (2.57,8.34) < 0.001 Covariates for the multivariable model include gender, AD stage at admission, pericardial effusion, ischemia of major arteries, and several ratios including NLR, MLR, SIRI, TyG, and categorical AIP. Variables with a P value < 0.05 in univariable analysis were entered in the multivariable models. ARAEs, aortic-related adverse events; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; AD, aortic dissection; COPD, Chronic obstructive pulmonary disease; CKD, Chronic kidney disease; WBC, white blood cell; Hb, hemoglobin; Cr, creatinine; FDP, fibrinogen degradation products; NLR, neutrophil-to-lymphocyte ratio; LMR, lymphocyte-to-monocyte ratio; MLR, monocyte-to-lymphocyte ratio; SII, systemic immune-inflammation index; AISI, advanced inflammation-based score index; PLR, platelet-to-lymphocyte ratio; SIRI, systemic inflammatory response index; UHR, uric acid to high-density lipoprotein cholesterol ratio; TyG, triglyceride-glucose index; AIP, atherogenic index of plasma; HR, hazard ratio; CI, confidence interval. The univariate Cox proportional hazards analysis for 5-year ARAEs indicated that pericardial effusion, ischemia of major arteries, stroke, NLR, MLR, TyG, and AIP (categorical) were significantly associated with the cumulative incidence of 5-year ARAEs (P < 0.05) (Table 4 ). A subsequent multivariate Cox proportional hazards analysis was performed on these variables. Pericardial effusion (P < 0.001), NLR (P = 0.003) and MLR (P = .014) remained significantly correlated with the cumulative incidence of 5-year ARAEs. A statistically significant association between categorical AIP and the cumulative incidence of 5-year ARAEs was observed. Compared to the Low group, the risk of 5-year ARAEs was significantly higher in the High group (HR = 2.59, 95% CI: 1.78–3.78; P < 0.001) (Table 4 ). Table 4 Univariate and Multivariate Cox proportional hazard modeling analysis for 5-year ARAEs. Variable Univariate analysis Multivariate analysis HR 95% CI P -value HR 95% CI P -value Age 1.01 (1.00,1.02) 0.148 Male 1.58 (0.98,2.56) 0.062 BMI 0.99 (0.94,1.03) 0.550 SBP at admission 1.00 (0.99,1.01) 0.946 DBP at admission 1.00 (0.98,1.01) 0.601 AD stage at admission 0.93 (0.77,1.11) 0.403 Pericardial effusion 2.56 (1.62,4.07) < 0.001 2.48 (1.55,3.96) < 0.001 Pleural effusion 1.15 (0.82,1.60) 0.420 Ischemia of major arteries 2.11 (1.31,3.42) 0.002 1.63 (0.98,2.69) 0.058 Hypertension 1.21 (0.82,1.77) 0.344 Diabetes mellitus 1.58 (0.98,2.55) 0.063 Stroke 1.98 (1.22,3.22) 0.006 1.65 (1.00,2.77) 0.056 COPD 1.19 (0.75,1.91) 0.461 CKD 1.47 (0.83,2.60) 0.184 WBC 1.03 (0.99,1.07) 0.190 Hb 0.99 (0.99,1.00) 0.162 D-dimer 1.02 (0.99,1.05) 0.226 Cr 1.00 (1.00,1.00) 0.207 FDP 1.00 (1.00,1.01) 0.296 NLR 1.03 (1.01,1.04) 0.004 1.03 (1.01,1.05) 0.003 LMR 0.95 (0.86,1.04) 0.270 MLR 1.73 (1.18,2.55) 0.005 1.62 (1.10,2.38) 0.014 SII 1.00 (1.00,1.00) 0.945 AISI 1.00 (1.00,1.00) 0.694 PLR 1.00 (1.00,1.00) 0.964 SIRI 1.02 (1.00,1.05) 0.064 UHR 1.00 (0.99,1.01) 0.870 TyG 1.35 (1.01,1.79) 0.041 1.31 (0.99,1.74) 0.059 AIP groups Low Reference Reference Middle 1.47 (0.97,2.24) 0.071 1.47 (0.96,2.21) 0.078 High 2.46 (1.70,3.56) < 0.001 2.59 (1.78,3.78) < 0.001 Covariates for the multivariable model include pericardial effusion, ischemia of major arteries, stroke and several ratios including NLR, MLR, TyG, and categorical AIP. Variables with a P value < 0.05 in univariable analysis were entered in the multivariable models. ARAEs, aortic-related adverse events; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; AD, aortic dissection; COPD, Chronic obstructive pulmonary disease; CKD, Chronic kidney disease; WBC, white blood cell; Hb, hemoglobin; Cr, creatinine; FDP, fibrinogen degradation products; NLR, neutrophil-to-lymphocyte ratio; LMR, lymphocyte-to-monocyte ratio; MLR, monocyte-to-lymphocyte ratio; SII, systemic immune-inflammation index; AISI, advanced inflammation-based score index; PLR, platelet-to-lymphocyte ratio; SIRI, systemic inflammatory response index; UHR, uric acid to high-density lipoprotein cholesterol ratio; TyG, triglyceride-glucose index; AIP, atherogenic index of plasma; HR, hazard ratio; CI, confidence interval. The results of the univariate Cox analysis for MACCEs at 1 year and 5 years, as well as the univariate Cox analysis for all-cause mortality at 1 year and 5 years, are presented in Supplementary Tables 1 and 2. Significant risk factors for long-term (5-year) MACCEs and all-cause mortality include diabetes mellitus, CKD, pericardial effusion, ischemia of major arteries, Hb, NLR and MLR(all P < 0.05). Notably, for 5-year MACCEs, the risk associations for diabetes mellitus (HR = 2.48, 95% CI = 1.64–3.77), CKD (HR = 2.32, 95% CI = 1.40–3.85), and pericardial effusion (HR = 2.33, 95% CI = 1.44–3.77) were particularly pronounced (all P < 0.01). In the context of 5-year all-cause mortality, several factors demonstrated significant associations: diabetes mellitus (HR = 2.42, 95%CI = 1.51–3.88), pericardial effusion (HR = 2.57, 95%CI = 1.52–4.36), and MLR (HR = 2.00, 95%CI = 1.32–3.03) (all P values < 0.01). For short-term (1-year) outcomes, the only significant predictors of 1-year MACCEs were age (HR = 1.04, 95%CI = 1.01–1.07, P = 0.024), T2DM (HR = 4.09, 95%CI = 1.71–9.79, P = 0.002), and CKD (HR = 3.18, 95%CI = 1.09–9.27, P = 0.034). All-cause mortality within one year was primarily attributed to pericardial effusion (HR = 3.89, 95%CI = 1.87–8.12, P < 0.01), ischemia of major arteries (HR = 2.99, 95%CI = 1.33–6.73, P = 0.008), and CKD (HR = 3.44, 95%CI = 1.53–7.72, P = 0.003). Additionally, smoking (HR = 1.86, 95%CI = 1.00-3.45, P = 0.049) and HBP (HR = 2.56, 95%CI = 1.01–6.50, P = 0.049) were identified as significant influencing factors. Furthermore, AIP did not show statistical significance in relation to 1-year and 5-year MACCEs or all-cause mortality (all P > 0.05). Discussion on Linear/Nonlinear Relationship Restricted cubic spline (RCS) analyses were performed to investigate the nonlinear relationship between AIP levels and ARAEs in TBAD patients at one and five year ARAEs (Figs. 4 – 5 ). Notably, when AIP is treated as a continuous variable, it exhibits a linear relationship with ARAEs, demonstrating a positive correlation. This suggests that variations in AIP values may result in a continuous and stable linear change in the risk of ARAEs at one year (P = 0.014). Similarly, the association between AIP and the risk of ARAEs at five years also adheres to a linear pattern, further affirming the reliability of AIP as a linear risk predictor for ARAEs(P = 0.006). Sensitivity analysis In the sensitivity analysis, we initially excluded individuals who had died within the two years preceding the follow-up. Additionally, due to the presence of missing data for high-density lipoprotein cholesterol (HDL-C) and triglycerides (TG) used in the calculation of the Atherogenic Index of Plasma (AIP), we employed the multiple imputation method to address these missing values. We then repeated the primary analysis using the imputed data. The results from this sensitivity analysis align with those of the main analysis, thereby reinforcing the robustness and credibility of the conclusions drawn in this study. To further assess the robustness of our findings, we performed subgroup analyses based on age, gender, systolic blood pressure (SBP), diastolic blood pressure (DBP), surgical timing, pericardial effusion, ischemia of major arteries, hypertension, diabetes mellitus, stroke, COPD, and CKD (Fig. 6 – 7 ). In the overall population, AIP was associated with both 1-year ARAEs (HR = 2.33, 95% CI 1.19–4.57) and 5-year ARAEs (HR = 1.75, 95% CI 1.00-3.09). Subgroup interaction analysis revealed that only the operation timing subgroup exhibited a statistically significant interaction with 1-year ARAEs (P = 0.008). The association between subacute AIP and 1-year ARAEs (HR = 6.78, 95% CI 1.48–31.16) was notably stronger than that observed in the chronic stage (HR = 0.02, 95% CI 0.01–0.67). The interaction between CKD subgroup and 1-year (P = 0.121) and 5-year ARAEs (P = 0.082) approached significance. The association between AIP and ARAEs in CKD patients was weaker (1-year HR = 0.44, 5-year HR = 0.41) compared to non-CKD patients (1-year HR = 2.76, 5-year HR = 2.09). The interaction between the operation timing subgroup and 5-year ARAEs was also nearly significant (P = 0.081). A trend was observed in the association between AIP and 5-year ARAEs in patients in the subacute stage (HR = 2.96, 95% CI 0.72–12.16) and the acute stage (HR = 2.00, 95% CI 1.04–3.83), while patients in the chronic phase showed no association (HR = 0.24, 95% CI 0.03–1.92). Interactions among subgroups such as age, gender, SBP, pericardial effusion, ischemia of major arteries, hypertension, diabetes mellitus, stroke, and COPD were not statistically significant (all P > 0.05), indicating that these factors did not influence the association between AIP and ARAEs following TEVAR. Evaluate the predictive effectiveness Additionally, we employed a time-dependent AUC plot to assess predictive efficacy over the 5-year follow-up period (Fig. 8 ). The AUC point estimates were close to 80% at each predicted time point within five years (0.87, 0.86, 0.81, 0.80, and 0.76, respectively), demonstrating effective prediction of outcome events (Supplementary Table 3). The minimal differences observed between the 1 to 5-year follow-up periods suggest that these indicators possess strong long-term prognostic value. Discussion Atherosclerosis serves as the fundamental pathological basis for the onset and progression of cardiovascular diseases, with lipid metabolism disorders acting as a critical factor in the development of atherosclerosis. Although traditional lipid indicators, such as total cholesterol, low-density lipoprotein cholesterol, and triglycerides, have been widely utilized in cardiovascular risk assessment, they often fail to comprehensively capture the detrimental effects of lipid metabolism imbalances on the vascular wall [ 25 – 28 ]. The plasma atherosclerotic index (AIP), a novel metric for assessing lipid metabolism, offers the advantage of integrating the dual atherosclerotic impacts of elevated triglycerides and reduced high-density lipoprotein cholesterol [ 29 ]. In recent years, AIP has emerged as a focal point of research in the domain of cardiovascular diseases. Prior studies have established that elevated AIP is significantly associated with the risk of adverse cardiovascular events, such as myocardial infarction and vascular restenosis, in populations with coronary heart disease, ischemic stroke, and diabetic vascular complications. Notably, its predictive value surpasses that of traditional lipid indicators [ 30 , 31 ]. However, the clinical significance of AIP in aortic dissection, a unique and high-risk aortic condition, remains inadequately understood. While the incidence of aortic dissection is directly linked to hypertension and abnormal aortic wall structure, the role of atherosclerosis—an important contributor to degenerative changes in the aortic wall—on postoperative repair, long-term vascular stability, and adverse events in patients with dissection warrants further investigation [ 32 ]. Consequently, this study aims to examine the association between AIP and one-year and five-year ARAEs in patients with aortic dissection. This focus not only aligns with current research trends in lipid metabolism and cardiovascular prognosis but also addresses a critical gap in understanding the long-term prognostic implications of AIP in aortic dissection. The baseline characteristics of this study indicate that patients with TBAD in the AIP High group exhibit significant baseline risk factors. Notably, the prevalence of higher BMI (25.0 ± 3.2 vs 24.0 ± 3.6 in the Low group), diabetes (P = 0.009), and chronic obstructive pulmonary disease (P = 0.045) was greater in this group. Furthermore, lipid metabolism disorders were more pronounced, as evidenced by elevated triglycerides and decreased high-density lipoprotein cholesterol (both P < 0.001). These findings suggest a close relationship between elevated AIP and the burden of underlying diseases, as well as the extent of metabolic abnormalities in TBAD patients. In clinical practice, it is essential to prioritize the optimization of basic management strategies—such as weight control, blood sugar regulation, and lipid adjustment—for patients in the High group to mitigate the risk of ongoing aortic wall injury. Follow-up results at 1 year and 5 years revealed that the cumulative incidence of ARAEs in the AIP High group was significantly higher than that in the Low group (1 year: 19.51% vs 5.81%; 5 years: 24.39% vs 10.32%, both P 0.05). The clinical significance of this core discovery is underscored by the clear identification of AIP as primarily associated with adverse aortic events, including endoleak, retrograde type A dissection, new hairpin layers, and poor visceral perfusion, in patients with TBAD, rather than with general cardiovascular events or mortality. In clinical follow-up, for TBAD patients exhibiting elevated AIP, it is advisable to enhance regular monitoring of aortic imaging, rather than solely concentrating on MACCE endpoints such as death, myocardial infarction, or stroke, to prevent overlooking local aortic risks. Furthermore, through univariate and multivariate analyses, after adjusting for variables such as AD stage at admission, pericardial effusion, ischemia of critical arteries, NLR, and MLR, the AIP High group demonstrated a significantly increased risk of ARAEs (1-year HR = 4.63, 95% CI: 2.57–8.34; 5-year HR = 2.59, 95% CI: 1.78–3.78; all P < 0.001). This finding indicates that AIP can function as an independent predictor of long-term ARAEs in patients with TBAD. Additionally, it suggests that routine lipid data calculation suffices, eliminating the need for further testing, thereby facilitating broad clinical application and aiding in the accurate identification of high-risk groups. We will now further investigate the relationship between AIP and ARAEs. Can we identify a specific threshold or range? RCS analysis demonstrated a continuous positive linear correlation between AIP and ARAEs, devoid of inflection points or plateau phases. This finding indicates that higher AIP levels correspond to an increased risk of ARAEs. Consequently, there is no necessity to establish a single critical value in clinical practice; instead, dynamic monitoring of AIP fluctuations can effectively assess risk trends. The 5-year time-dependent AUC consistently exceeded 80%, signifying that AIP reliably maintained strong predictive efficacy throughout the 5-year follow-up and could effectively forecast outcome events in a relatively stable manner. After excluding early mortality and multiple interpolation missing values, the results remained consistent, affirming the robustness of the association between AIP and ARAEs. This conclusion can be generalized to the majority of the TBAD population. Subgroup analysis revealed significant stratification insights: Firstly, patients undergoing subacute surgery exhibited the highest risk of AIP-related ARAEs (HR = 2.96), whereas those undergoing chronic surgery demonstrated the lowest risk (HR = 0.24) (P = 0.081); Secondly, patients without CKD faced a more pronounced risk of elevated AIP (HR = 2.09, HR = 0.41 for CKD patients) (P = 0.082). These findings suggest that clinical practice should prioritize more intensive follow-up and intervention strategies for the subgroups of patients characterized by "subacute surgery + high AIP" and "no CKD + high AIP." We propose two primary reasons for the observed results regarding surgical timing in the subgroup analysis: Firstly, differences in pathophysiological processes. In patients with subacute aortic disease, the lesions are in a progressive stage, characterized by active destruction of the vascular wall structure. This anatomical complexity and the heightened intraoperative risks significantly elevate the likelihood of adverse events. Conversely, in patients with chronic-stage lesions, the conditions are relatively stable, and the false lumen may have partially thrombosed. Consequently, the "driving force" behind lesion progression diminishes, thereby reducing the risks associated with surgical intervention. Secondly, the interactive influence between treatment timing and intervention effects. The subacute stage represents a critical window for the progression of aortic lesions. While surgery is a necessary intervention during this period, the active state of the lesion, combined with the impact of surgical trauma, may exacerbate the risk of adverse events. In contrast, the stability of lesions in the chronic stage shifts the benefit-risk ratio of surgical intervention favorably, resulting in a significant difference in risk. Therefore, the timing of surgery holds substantial clinical significance for prognosis. Based on the research findings, the core clinical significance of this study encompasses two primary aspects. First, the long-term management of postoperative requirements in patients with aortic dissection necessitates the accurate identification of high-risk groups. Currently, the prognosis derived from commonly used clinical assessment tools, such as Stanford type, the number of aortic dissections upon admission, and complications, primarily depends on anatomical or baseline disease states [ 33 ]. There is a notable absence of indicators that can dynamically reflect lipid metabolism and chronic damage to the vascular wall. This study establishes that the Atherogenic Index of Plasma (AIP) can serve as an independent predictor of long-term adverse renal and aortic events (ARAE) in patients with aortic dissection. Its advantages include: Firsty. convenience of detection, as it can be directly calculated from routine lipid test data without incurring additional detection costs; Secondly, dynamic monitoring capabilities, allowing for the evaluation of lipid intervention effects and trends in vascular risk through follow-up assessments of AIP changes; and Thirdly, high specificity, as it effectively reflects the risk of aortic wall injury related to atherosclerosis, thereby addressing the limitations of traditional assessment indicators. Second, AIP can be incorporated into the long-term risk stratification model for patients with aortic dissection. For patients exhibiting significantly elevated AIP (AIP ≥ 0.52), it is advisable to increase the frequency of long-term follow-up. This should include re-evaluating aortic imaging, blood lipid levels, and AIP every three to six months. Concurrently, it is essential to enhance interventions targeting lipid metabolism to mitigate the ongoing damage of atherosclerosis to the stability of the aortic wall. In contrast, for patients with normal or low AIP, the intensity of intervention may be appropriately reduced while maintaining regular follow-up, thereby facilitating precise management and preventing unnecessary medical treatment. Regarding the association between AIP and long-term ARAEs in patients with TBAD observed in this study, we proposed several possible mechanisms to elucidate the correlation between AIP and AD: Firstly, An increase in AIP may result from enhanced stability of aortic atherosclerotic plaques. The presence of small, dense low-density lipoprotein (sdLDL) at a high ratio reflects the intricate interplay of lipoprotein metabolism and aids in predicting plasma atherosclerosis. Due to its smaller size, sdLDL is more susceptible to oxidative stress, which facilitates its conversion to oxidized low-density lipoprotein. This process ultimately triggers an inflammatory response within the blood vessels, enhances binding to endothelial proteoglycans, and promotes the formation of foam cells [ 34 ], thereby accelerating lipid deposition in the aortic wall. Elevated AIP may also suggest that adipocytes are storing excess triglycerides (TG) as fat, which increases the accumulation of cholesterol crystals in the intima of atherosclerotic arteries. This accumulation can lead to lumen narrowing and obstruction, ultimately resulting in atherosclerosis [ 35 ]. In patients with aortic dissection, even if the acute phase laceration is repaired, persistent elevation of AIP may continue to compromise the aortic wall's repair capacity due to ongoing atherosclerotic processes. Secondly, Elevated AIP levels create a vulnerable aortic wall through lipid metabolism imbalance, thereby reducing tolerance to hypertension.Hypertension represents the most critical risk factor for the development of AD. Furthermore, an increase in AIP markedly exacerbates the detrimental impact of hypertension on the aorta by intensifying damage to the vascular wall structure. The primary pathological basis of AD is cystic necrosis in the middle layer of the aorta, characterized by the rupture of elastic fibers, a reduction in collagen fibers, and the apoptosis of smooth muscle cells. Elevated AIP exacerbates this degenerative process by promoting atherosclerosis. Specifically, the abnormal AIP, coupled with decreased high-density lipoprotein cholesterol (HDL-C), diminishes its cholesterol reverse transport function. This deficiency leads to cholesterol accumulation in the smooth muscle cells of the aortic middle layer, resulting in lipid-induced toxic damage and subsequent cell apoptosis. Furthermore, oxidized very-low-density lipoprotein (ox-VLDL) recruits monocytes that differentiate into macrophages, which in turn release matrix metalloproteinases (MMPs) to degrade the elastic and collagen fibers in the middle layer [ 36 ]. These alterations contribute to the atrophy of the aortic middle layer. Under the persistent mechanical strain of hypertension, interlaminar tearing is highly likely to occur. The vascular wall's tolerance to the "pulsed mechanical shear force" induced by hypertension is significantly diminished [ 37 ]. A retrospective cohort study involving 6,540 individuals confirmed that AIP serves as an independent risk factor for hypertension development. In participants with normal blood pressure, the adjusted odds ratio was 1.84 (95% CI: 1.41–2.39, p < 0.001), while in those with elevated blood pressure, it was 1.88 (95% CI: 1.40–2.52, p < 0.001). Furthermore, a nonlinear relationship exists between AIP and the incidence of hypertension. Degang Mo et al. noted that AIP demonstrates superior predictive capability for hypertension occurrence compared to individual indicators of triglycerides and HDL-C [ 38 ]. 3. AIP can better reflect hidden risks In patients with aortic dissection, some may exhibit normal traditional lipid indicators while presenting with an elevated AIP, characterized by slightly elevated triglycerides and reduced high-density lipoprotein cholesterol. The risk of aortic wall injury in these patients is frequently underestimated. This study establishes the independent predictive value of AIP, indicating that even when traditional lipid indicators remain within normal limits, an increase in AIP necessitates vigilance regarding the risk of long-term ARAEs. This finding offers a novel foundation for identifying latent lipid-related risks. The findings of this study align with previous research on Atherogenic Index of Plasma (AIP) in various cardiovascular diseases, while also extending the investigation to the population affected by aortic dissection. A study utilizing data from the China Health and Retirement Longitudinal Survey (CHARLS) [ 39 ] demonstrated that inadequate control of AIP levels correlates with an elevated risk of cardiovascular events among individuals with stage 0–3 cardiovascular kidney-metabolic syndrome (CKM). Furthermore, another investigation conducted by the same research team using the National Health and Nutrition Examination Survey (NHANES) in the United States [ 40 ] revealed that elevated AIP levels in patients with CKM syndrome are significantly associated with an increased risk of mortality, particularly all-cause mortality in advanced cases and cardiovascular disease mortality in both advanced and non-advanced patients. Ramin et al. [ 41 ] demonstrate that elevated AIP is significantly linked to an increased risk, greater severity, and poorer prognosis of coronary heart disease, regardless of whether an individual has a confirmed diagnosis. Additionally, a study utilizing a large population dataset from the National Health Insurance Service - National Health Screening Cohort (NHIS-HEALS) in South Korea suggested that AIP may serve as an effective screening tool for identifying patients at high risk of cardiovascular events [ 42 ]. Furthermore, research employing machine learning to assess the predictive value of AIP on the length of hospital stay (LOS) for patients with critical atherosclerotic cardiovascular disease (ASCVD) revealed that a higher AIP was independently associated with extended stays in both the intensive care unit (ICU) and the hospital [ 43 ]. This study exhibits notable distinctions from other research, particularly the Bogalusa Heart Study, a seminal longitudinal epidemiological investigation that first demonstrated a direct correlation between the life-course cumulative burden of AIP and arteriosclerosis, as well as arterial wall thickening, in a cohort of 900 subjects [ 44 ]. The differences in research orientation, primary objectives, and clinical implications are significant. The Bogalusa study focused on healthy individuals or the general population in the subclinical stages of atherosclerosis, aiming to identify early risk factors associated with abnormal arterial wall structure. In contrast, the present study targets patients diagnosed with TBAD, with the objective of assessing the influence of AIP on the long-term clinical outcomes of these patients. It is important to note that these differences are not mutually exclusive but rather complementary. The Bogalusa study established a causal link between AIP and arterial wall injury from an epidemiological standpoint. Conversely, this study advances the investigation of AIP within the specific high-risk population of TBAD, transitioning the focus from subclinical structural changes to endpoint events, thereby rendering the findings clinically actionable. This research addresses the gap in understanding the role of AIP in the long-term prognosis of patients with aortic dissection. Limitations This study presents several limitations warranting attention in future research: First, as a single-center retrospective investigation constrained by a sample size of 1,335 patients, it may be susceptible to selection bias. These findings necessitate validation via multi-center, large-sample prospective studies. Secondly, lifestyle interventions and lipid-lowering medication adjustments during patient follow-up were not included. These factors may indirectly influence prognosis by affecting AIP levels, and further control is required in subsequent studies. Third, the interaction between AIP and other AD biomarkers, such as matrix metalloproteinases and microRNAs, remains unexplored. Consequently, it is unclear whether AIP impacts prognosis by modulating other pathways or through synergistic effects. Conclusion This study demonstrates that elevated AIP markedly increases the risk of long-term ARAEs in patients with TBAD. Furthermore, AIP serves as an independent predictor of long-term ARAEs in this population, exhibiting strong predictive efficacy. These findings underscore the importance of AIP as a valuable risk biomarker, offering a straightforward and effective means of identifying the risk of ARAEs in patients with AD. Abbreviations AD Aortic dissection TBAD Type B aortic dissection CVD Cardiovascular disease CKD Chronic kidney disease CKM Chronic kidney disease complicated with cardiovascular diseases CAD Coronary artery disease TEVAR Thoracic endovascular aortic repair COPD Chronic obstructive pulmonary disease AIP Atherogenic index of plasma ARAEs Aortic-related adverse events MACCEs Major adverse cardiovascular and cerebrovascular events TC Total cholesterol TG Triglyceride HDL-C High-density lipoprotein cholesterol LDL-C Low-density lipoprotein cholesterol HBP Hypertension T2DM Type 2 diabetes mellitus BMI Body mass index SBP Systolic blood pressure DBP Diastolic blood pressure WBC White blood cell count FDP Fibrin degradation products Hb Hemoglobin Cr Creatinine NLR Neutrophil-to-lymphocyte ratio LMR Lymphocyte-to-monocyte ratio MLR Monocyte-to-lymphocyte ratio SII Systemic immune-inflammation index AISI Advanced inflammation-based score index PLR Platelet-to-lymphocyte ratio; SIRI Systemic inflammatory response index UHR Uric acid to high-density lipoprotein cholesterol ratio TyG Triglyceride-glucose index sdLDL Small and dense low-density lipoprotein ox-VLDL Oxidized modified very low-density lipoprotein MMPs Matrix metalloproteinases ASCVD Atherosclerotic cardiovascular disease LOS Predicts the length of hospital stay ICU Intensive care unit SMA The superior mesenteric artery RA Renal artery K-M Kaplan-Meier curve RCS Restricted cubic spline AUC The area under the time-of-administration curve HR Hazard ratio CI Confidence interval Declarations Acknowledgements The authors express their gratitude to the Department of Vascular Surgery of the First Affiliated Hospital of Naval Medical University for its data contribution. CRediT authorship contribution statement Shuangshuang Li, Wen Li, and Jiahe Zhang contributed equally to this research. Shuangshuang Li was involved in research design, data collection, data analysis, and manuscript preparation. Wen Li and Jiahe Zhang contributed to data collection, data analysis, and manuscript writing. Kaiwen Zhao and Zhichen Ding assisted in revising the manuscript. Jianli Ren, Wenping Hu, Qingsheng Lu and Jian Zhou participated in the research design. All authors reviewed and approved the final manuscript. Ethics approval and consent to participate The requirement for consent was waived, and the Clinical Research Ethics Committee of Changhai Hospital, Naval Medical University, approved the research protocol. The protocol number is CHEC-Y2020-042, and the ratification date is August 21, 2020. Funding The study was funded by the National Natural Science Foundation of China [82570560], Scientific Research Cultivation Project of the Third Affiliated Hospital of Naval Medical University (2025QN04), Clinical Research Special Scientific Research Project funded by the Science and Technology and Economy Commission of Yangpu District (YPM202545). Competing interests The authors declare no competing interests. References Nienaber CA, Clough RE, Sakalihasan N, et al. Aortic dissection. Nat Rev Dis Primers. 2016;2. 10.1038/nrdp.2016.53 . :16053. Published 2016 Jul 21. Tadros RO, Tang GHL, Barnes HJ, et al. Optimal Treatment of Uncomplicated Type B Aortic Dissection: JACC Review Topic of the Week. J Am Coll Cardiol. 2019;74(11):1494–504. 10.1016/j.jacc.2019.07.063 . Tanaka A, Hebert AM, Smith-Washington A, et al. Knowledge gaps in surgical management for aortic dissection. Semin Vasc Surg. 2022;35(1):35–42. 10.1053/j.semvascsurg.2022.02.009 . 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16:39:09","extension":"html","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":256750,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7984351/v1/cf434116991760d3b8b990fa.html"},{"id":95663602,"identity":"4c1c1523-cf32-4374-89ed-4fafc7cb8ec8","added_by":"auto","created_at":"2025-11-11 16:39:08","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":85940,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the patient selection process.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7984351/v1/16a0d485379833ba3c670ad5.jpg"},{"id":95663620,"identity":"b8d72648-188d-4780-9983-cd24463d3e0f","added_by":"auto","created_at":"2025-11-11 16:39:11","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":61479,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival of 1-year outcomes for TBAD patients undergoing TEVAR. The 1-year overall survival probability.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7984351/v1/9112ea0143d9f5ba649ee312.jpg"},{"id":95663614,"identity":"6d1ec7da-fcbf-4fab-b4ff-3f8976f3e82e","added_by":"auto","created_at":"2025-11-11 16:39:10","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":63506,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival of 5-year outcomes for TBAD patients undergoing TEVAR.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7984351/v1/c4bb57cf8a5bfdf313783a89.jpg"},{"id":95663760,"identity":"6b6dc68d-2392-49ad-b5ec-8117503ebf1b","added_by":"auto","created_at":"2025-11-11 16:39:18","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":40158,"visible":true,"origin":"","legend":"\u003cp\u003eRCS for the relationship between AIP and the incidence of 1-year ARAEs in TBAD patients after TEVAR. Red shadows and lines represent the 95% CI%. RCS, restricted cubic spline; AIP, atherogenic index of plasma; ARAEs, aortic-related adverse events; TBAD, type B aortic dissection; TEVAR, thoracic endovascular aortic repair; HR, hazard ratio; CI, confidence interval.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7984351/v1/47b0ba306b6e7867a814390d.jpg"},{"id":95663579,"identity":"27f57f34-9042-4ac8-9d0a-417c0e3c7104","added_by":"auto","created_at":"2025-11-11 16:39:07","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":43888,"visible":true,"origin":"","legend":"\u003cp\u003eRCS for the relationship between AIP and the incidence of 5-year ARAEs in TBAD patients after TEVAR. Red shadows and lines represent the 95% CI%. RCS, restricted cubic spline; AIP, atherogenic index of plasma; ARAEs, aortic-related adverse events; TBAD, type B aortic dissection; TEVAR, thoracic endovascular aortic repair; HR, hazard ratio; CI, confidence interval.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7984351/v1/0366fc0124d6fc03a87d4071.jpg"},{"id":95663586,"identity":"38dbad54-0507-479e-a887-f0da541a219d","added_by":"auto","created_at":"2025-11-11 16:39:07","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":97409,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis for association between AIP and 1-year ARAEs in TBAD patients after TEVAR. AIP, atherogenic index of plasma; ARAEs, aortic-related adverse events; SBP, systolic blood pressure; DBP, diastolic blood pressure; COPD, chronic obstructive pulmonary disease; CKD, chronic kidney disease; HR, hazards ratio; Cl, confidence interval.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7984351/v1/9b310b00c5ee537b0f57fa7b.jpg"},{"id":95663596,"identity":"fc2d6146-b52c-45c8-a49c-a2c0e319a365","added_by":"auto","created_at":"2025-11-11 16:39:08","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":112441,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis for association between AIP and 5-year ARAEs in TBAD patients after TEVAR. AIP, atherogenic index of plasma; ARAEs, aortic-related adverse events; SBP, systolic blood pressure; DBP, diastolic blood pressure; COPD, chronic obstructive pulmonary disease; CKD, chronic kidney disease; HR, hazards ratio; Cl, confidence interval.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7984351/v1/8eb380e8ad5f30c98eb3cd69.jpg"},{"id":95663666,"identity":"6025fddb-d673-43fa-b904-b1acff8dc649","added_by":"auto","created_at":"2025-11-11 16:39:13","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":51282,"visible":true,"origin":"","legend":"\u003cp\u003eThe time-dependent ROC curve for the relationship between AIP and the incidence of ARAEs over five years. ROC, receiver operating characteristic; AIP, atherogenic index of plasma; ARAEs, aortic-related adverse events. AUC, area under the curve.\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7984351/v1/7acc09dc2776ce5f5216f54b.jpg"},{"id":104251467,"identity":"16b42a1c-b02c-4aa2-9973-dee9da80752a","added_by":"auto","created_at":"2026-03-09 16:13:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2072476,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7984351/v1/3013da5f-a87d-4eea-b07f-27f981f7974e.pdf"},{"id":95663581,"identity":"030717ca-8f29-448d-9d00-6222c6a098e4","added_by":"auto","created_at":"2025-11-11 16:39:07","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":153487,"visible":true,"origin":"","legend":"","description":"","filename":"s1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7984351/v1/2b1370bc8a35bdfdadde9d93.docx"},{"id":95663591,"identity":"9d77ecd3-717f-44da-ab9c-05d12ae4659d","added_by":"auto","created_at":"2025-11-11 16:39:08","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":28275,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7984351/v1/8dba56fe8724a1a1f7103469.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between Atherosclerotic Index of Plasma and Long-term Aorta-related Adverse Events in TEVAR-treated Type B Aortic Dissection Patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAortic dissection (AD) represents one of the most devastating cardiovascular diseases (CVD), with an annual incidence rate of 35 cases per 100,000 individuals in this population [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Based on the dissection's location, AD is classified into Stanford type A aortic dissection (TAAD) and Stanford type B aortic dissection (TBAD). TBAD typically originates from the distal end of the left subclavian artery (LSA) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Treatment options for patients with TBAD primarily include open surgical intervention, pharmacotherapy, and TEVAR [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. TEVAR effectively stabilizes and reshapes the aorta, serving as the principal treatment modality for TBAD [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Nonetheless, ARAEs such as endoleak, retrograde type A dissection, and new hairpin layer formation may arise following TEVAR [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These complications can hinder vascular remodeling and contribute to a poor prognosis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Consequently, the early prediction and prevention of ARAEs after TEVAR have garnered significant attention. There is an urgent need to identify prognostic markers with predictive value to accurately assess the risk of adverse outcomes in patients and facilitate timely intervention.\u003c/p\u003e\u003cp\u003eAtherosclerotic index of plasma (AIP) is an emerging biomarker that reflects disorders in lipid metabolism and is considered a more reliable predictor of CVD than traditional lipid indicators, including total cholesterol, low-density lipoprotein cholesterol, and triglycerides [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. AIP indicates the imbalance of circulating lipids and acts as an independent predictor of rapid plaque progression [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Increasing evidence suggests that elevated AIP correlates with a heightened risk of cardiovascular events, renal issues, metabolic syndrome, diabetes, hypertension, stroke, chronic coronary syndrome, and other metabolic disorders [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. AIP may influence the clinical outcomes of patients with atherosclerotic cardiovascular disease through mechanisms such as endothelial dysfunction and heightened inflammation [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Furthermore, abnormal lipid metabolism is intricately linked to the pathogenesis of aortic dissection. A study found that among 439 identified lipids, 278 exhibit significant alterations when compared to the normal control group in the aortic dissection patient cohort [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThere is a paucity of convenient metabolism-related indicators for assessing the risk of long-term ARAEs in patients with TBAD after TEVAR. While AIP has demonstrated predictive value for cardiovascular diseases, its relationship with long-term ARAEs in TBAD patients remains uncertain. This study aims to examine the association between AIP and the incidence of ARAEs at both 1-year and 5-year intervals in patients with TBAD after TEVAR, thereby proposing a novel prognostic indicator for TBAD outcomes.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eResearch cohort and design\u003c/h2\u003e\u003cp\u003eThis study adopted a retrospective cohort design, encompassing 1,650 patients diagnosed with TBAD who received TEVAR at the First Affiliated Hospital of Naval Medical University in Shanghai, China, from August 2011 to June 2024. The exclusion criteria were as follows: (1) participants with traumatic aortic injury and iatrogenic aortic dissection (n\u0026thinsp;=\u0026thinsp;11); (2) participants with Turner syndrome, Marfan syndrome, Ehlers-Danlos syndrome, two-lobular aortic valve, giant cell arteritis, ankylosing spondylitis, Beh\u0026ccedil;et's disease, or Takayasu arteritis (n\u0026thinsp;=\u0026thinsp;46); (3) participants with a history of aortic surgery (n\u0026thinsp;=\u0026thinsp;24); (4) participants with a documented history of malignant tumors (n\u0026thinsp;=\u0026thinsp;48); (5) participants lacking perioperative serum data (n\u0026thinsp;=\u0026thinsp;116); and (6) individuals missing high-density lipoprotein cholesterol and triglyceride data (n\u0026thinsp;=\u0026thinsp;70). Ultimately, a total of 1,335 patients were included in this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The research protocol received approval from the Ethics Committee of Shanghai Changhai Hospital (CHEC-Y2020-042). Given the retrospective nature of this study, the requirement for informed consent was waived.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData collection and definition\u003c/h3\u003e\n\u003cp\u003eAIP is calculated based on the following formula: log (triglycerides/high-density lipoprotein cholesterol [TG/HDL-C]). Participants were divided into three different groups [group1/Low (AIP\u0026thinsp;\u0026lt;\u0026thinsp;0.27,n\u0026thinsp;=\u0026thinsp;688), group 2/Middle (0.27\u0026thinsp;\u0026le;\u0026thinsp;AIP\u0026thinsp;\u0026lt;\u0026thinsp;0.52, n\u0026thinsp;=\u0026thinsp;319), group 3/High (AIP\u0026thinsp;\u0026ge;\u0026thinsp;0.52, n\u0026thinsp;=\u0026thinsp;328)] based on the terquartiles of AIP.\u003c/p\u003e\u003cp\u003eThe primary outcome was aortic-related adverse events after endovascular treatment of TBAD. Including retrograde aortic dissection, aortic rupture, aortic dilation, poor perfusion, and type I or type III endoleak [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDemographic baseline data, symptoms, history of comorbidities, and laboratory test results of the research subjects were obtained through the electronic medical record system. The laboratory test data included D-dimer (mg/L), creatinine (Cr, \u0026micro;mol/L), fasting blood glucose (FPG, mg/dL), prothrombin time (PT, s), activated partial thromboplastin time (APTT, s), thrombin time (TT, s), fibrin degradation products (FDP, mg/L), uric acid (UA, \u0026micro;mol/L), total cholesterol (TC, mg/dL), triglycerides (TG, mg/dL), low-density lipoprotein cholesterol (LDL-C, mg/dL), high-density lipoprotein cholesterol (HDL-C, mg/dL), and so on. Hematological indicators included white blood cell count (WBC, \u0026times;10⁹/L), lymphocyte count (LYM, \u0026times;10⁹/L), monocyte count (MO, \u0026times;10⁹/L), neutrophil count (NE, \u0026times;10⁹/L), hemoglobin (Hb, g/L), and platelet count (PLT, \u0026times;10⁹/L). All tests were conducted by the Laboratory Department of Shanghai Changhai Hospital using standard biochemical methods.\u003c/p\u003e\u003cp\u003eAdditionally, derivative indicators were calculated to assess systemic inflammation and metabolic disorders. These indicators include the neutrophil-to-lymphocyte ratio (NLR\u0026thinsp;=\u0026thinsp;NE/LYM), lymphocyte-to-monocyte ratio (LMR\u0026thinsp;=\u0026thinsp;LYM/MO), monocyte-to-lymphocyte ratio (MLR\u0026thinsp;=\u0026thinsp;MO/LYM), systemic immune inflammation index (SII\u0026thinsp;=\u0026thinsp;PLT \u0026times; NE/LYM), systemic inflammation aggregation index (AISI\u0026thinsp;=\u0026thinsp;NE \u0026times; MO \u0026times; PLT/LYM), platelet-to-lymphocyte ratio (PLR\u0026thinsp;=\u0026thinsp;PLT/LYM), systemic inflammatory response index (SIRI\u0026thinsp;=\u0026thinsp;NE \u0026times; MO/LYM), uric acid to HDL ratio (UHR\u0026thinsp;=\u0026thinsp;UA/HDL), and triglyceride-glucose index (TyG\u0026thinsp;=\u0026thinsp;ln [TG \u0026times; FBG/2])[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. For both non-emergency and emergency surgeries, blood samples are collected, and weight data are recorded while the patient is fasting in the morning prior to the operation. In the case of emergency surgeries, these measurements are obtained in the emergency room.\u003c/p\u003e\u003cp\u003eBased on the duration of clinical onset, the clinical manifestations of TBAD are categorized into three groups: acute (\u0026le;\u0026thinsp;14 days), subacute (15\u0026ndash;90 days), and chronic (\u0026ge;\u0026thinsp;91 days) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Variables exhibiting a missing data rate greater than 20% were excluded during the initial data collection and screening phase to mitigate potential bias arising from excessive missing values. For the remaining variables with minimal missing data, simple imputation techniques were employed. Specifically, continuous variables were imputed using the mean value of the respective variable, whereas categorical variables were imputed using the mode, or most frequently occurring category, of the respective variable.\u003c/p\u003e\n\u003ch3\u003eFollow-up and endpoints\u003c/h3\u003e\n\u003cp\u003eThe objectives of this study were categorized by time frame: short-term outcomes, which encompass ARAEs occurring within one year, and long-term outcomes, which include ARAEs at five years. In instances of multiple adverse events, only the first occurrence will be analyzed. This research was conducted by qualified investigators utilizing medical records, and telephone interviews. Furthermore, adverse events were assessed through a thorough review of clinical records that necessitated readjustment or evaluation during outpatient visits. Two independent physicians, possessing expertise in the diagnosis and management of TBAD, evaluated the adverse events and endpoints while remaining blinded to the patients' clinical information.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eThe statistical analysis was performed using the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or the median (quartile), depending on the distribution of the data. For inter-group comparisons, the Mann-Whitney test or analysis of variance was employed for continuous variables, while categorical variables were analyzed using the chi-square test or Fisher's exact test. The cumulative survival curve was generated using the Kaplan-Meier (KM) method, and differences among groups were assessed with the log-rank test. The Cox proportional hazards regression model was utilized to examine the relationship between preoperative AIP and 1-year and 5-year ARAEs, calculating the hazard ratio (HR) and 95% confidence interval (CI). Initially, a univariate analysis was conducted, and variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were incorporated into the multivariate Cox model. The potential association between AIP and outcomes was further evaluated using restricted cubic splines (RCS). Subgroup analysis was conducted to investigate the robustness of the association between AIP and ARAEs in patients with TBAD. The time-dependent Receiver Operating Characteristic (ROC) curve is employed to assess the predictive efficacy of AIP for ARAEs across various time points within a five-year period. Statistical analyses were carried out using R (version 4.4.0) and SPSS software version 27.0. In this study, P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was deemed statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eBaseline characteristics of the participants\u003c/h2\u003e\u003cp\u003eThe baseline characteristics of the included patients are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The average age of the 1,335 patients included in this study was 59.0\u0026thinsp;\u0026plusmn;\u0026thinsp;13.2 years (Low vs. Middle vs. High\u0026thinsp;=\u0026thinsp;57.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5 vs. 60.4\u0026thinsp;\u0026plusmn;\u0026thinsp;13.5 vs. 59.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4, P\u0026thinsp;=\u0026thinsp;0.004). Among the participants, 1,099 were male (82.32%), with the highest proportion of males in the High group (285, 86.89%). Significant statistical differences in BMI were observed among the three groups, with the High group exhibiting a higher BMI than the other two groups (Low vs. Middle vs. High\u0026thinsp;=\u0026thinsp;24.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6 vs. 24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6 vs. 25.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No statistically significant differences were found in systolic blood pressure (P\u0026thinsp;=\u0026thinsp;0.155) or diastolic blood pressure (P\u0026thinsp;=\u0026thinsp;0.409) among the groups at admission. Additionally, the distribution of hypertension (HBP) (P\u0026thinsp;=\u0026thinsp;0.166) and chronic kidney disease (CKD) (P\u0026thinsp;=\u0026thinsp;0.728) also showed no statistically significant differences among the three groups. However, significant differences were noted in the distribution of smoking, alcohol consumption,diabetes mellitus, and chronic obstructive pulmonary disease (COPD) among the groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, P\u0026thinsp;=\u0026thinsp;0.020, P\u0026thinsp;=\u0026thinsp;0.009, P\u0026thinsp;=\u0026thinsp;0.045). Compared to the other two groups, the white blood cell (WBC) count in the High AIP group was higher (P\u0026thinsp;=\u0026thinsp;0.020), while fasting plasma glucose (FPG) (P\u0026thinsp;=\u0026thinsp;0.023) and platelet count (PLT) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were also significantly higher. Statistically significant differences were observed in total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) among the three groups (P\u0026thinsp;=\u0026thinsp;0.001, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant differences were found in hemoglobin, D-dimer, creatinine, fibrinogen, and fibrin degradation products (FDP) (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of the study participants.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eAIP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1335\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e688\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge(years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59.0\u0026thinsp;\u0026plusmn;\u0026thinsp;13.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60.4\u0026thinsp;\u0026plusmn;\u0026thinsp;13.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e59.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1099 (82.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e558 (81.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e256 (80.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e285 (86.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI,kg/m2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP at admission, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e137.8\u0026thinsp;\u0026plusmn;\u0026thinsp;21.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e139.0\u0026thinsp;\u0026plusmn;\u0026thinsp;22.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e136.4\u0026thinsp;\u0026plusmn;\u0026thinsp;21.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e136.8\u0026thinsp;\u0026plusmn;\u0026thinsp;20.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.155\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP at admission, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82.1\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e81.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e81.9\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.409\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e636 (47.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e277 (40.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e170 (53.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e189 (57.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol consumption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e233 (17.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e104 (15.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56 (17.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e73 (22.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTiming of operation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.247\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcute\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e975 (73.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e505 (73.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e240 (75.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e230 (70.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSub-acute\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e226 (16.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e107 (15.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52 (16.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e67 (20.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e134 (10.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76 (11.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27 (8.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31 (9.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePericardial effusion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e88 (6.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44 (6.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27 (8.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17 (5.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.233\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePleural effusion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e437 (32.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e225 (32.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e116 (36.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e96 (29.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.157\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIschemia of major arteries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84 (6.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36 (5.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22 (6.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26 (7.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.224\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1002 (75.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e509 (73.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e234 (73.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e259 (78.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.166\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes mellitus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e114 (8.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44 (6.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31 (9.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39 (11.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStroke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74 (5.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29 (4.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25 (7.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20 (6.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOPD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e146 (10.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67 (9.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47 (14.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32 (9.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCKD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74 (5.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35 (5.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20 (6.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19 (5.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.728\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLaboratory tests\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLYM, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMO, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.102\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNE, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHb, g/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e127.9\u0026thinsp;\u0026plusmn;\u0026thinsp;18.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e127.2\u0026thinsp;\u0026plusmn;\u0026thinsp;17.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e128.2\u0026thinsp;\u0026plusmn;\u0026thinsp;19.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e128.9\u0026thinsp;\u0026plusmn;\u0026thinsp;18.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.205\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD-dimer, mg/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.177\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCr, \u0026micro;mol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e101.3\u0026thinsp;\u0026plusmn;\u0026thinsp;103.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102.8\u0026thinsp;\u0026plusmn;\u0026thinsp;107.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e103.9\u0026thinsp;\u0026plusmn;\u0026thinsp;115.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95.7\u0026thinsp;\u0026plusmn;\u0026thinsp;82.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.952\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePT, s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.309\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFPG, mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAPTT, s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.2\u0026thinsp;\u0026plusmn;\u0026thinsp;14.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e38.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTT, s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.9\u0026thinsp;\u0026plusmn;\u0026thinsp;15.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.796\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFbg, g/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.165\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFDP, mg/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.0\u0026thinsp;\u0026plusmn;\u0026thinsp;17.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.1\u0026thinsp;\u0026plusmn;\u0026thinsp;17.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.3\u0026thinsp;\u0026plusmn;\u0026thinsp;19.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.4\u0026thinsp;\u0026plusmn;\u0026thinsp;16.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.128\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLT, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e203.3\u0026thinsp;\u0026plusmn;\u0026thinsp;74.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e195.9\u0026thinsp;\u0026plusmn;\u0026thinsp;68.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e209.9\u0026thinsp;\u0026plusmn;\u0026thinsp;80.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e212.3\u0026thinsp;\u0026plusmn;\u0026thinsp;79.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC, mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e170.2\u0026thinsp;\u0026plusmn;\u0026thinsp;37.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e166.3\u0026thinsp;\u0026plusmn;\u0026thinsp;24.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e169.1\u0026thinsp;\u0026plusmn;\u0026thinsp;36.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e179.3\u0026thinsp;\u0026plusmn;\u0026thinsp;55.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG, mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e120.4\u0026thinsp;\u0026plusmn;\u0026thinsp;102.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88.8\u0026thinsp;\u0026plusmn;\u0026thinsp;23.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e108.1\u0026thinsp;\u0026plusmn;\u0026thinsp;24.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e198.6\u0026thinsp;\u0026plusmn;\u0026thinsp;180.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-C, mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41.7\u0026thinsp;\u0026plusmn;\u0026thinsp;136.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.2\u0026thinsp;\u0026plusmn;\u0026thinsp;31.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55.3\u0026thinsp;\u0026plusmn;\u0026thinsp;195.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58.8\u0026thinsp;\u0026plusmn;\u0026thinsp;189.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C, mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1195.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1237.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1231.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1176.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1311.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1138.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1267.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAISI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e770.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1031.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e765.7\u0026thinsp;\u0026plusmn;\u0026thinsp;907.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e796.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1158.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e755.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1143.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e174.2\u0026thinsp;\u0026plusmn;\u0026thinsp;106.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e175.0\u0026thinsp;\u0026plusmn;\u0026thinsp;98.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e173.8\u0026thinsp;\u0026plusmn;\u0026thinsp;107.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e173.1\u0026thinsp;\u0026plusmn;\u0026thinsp;118.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.456\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;49.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.4\u0026thinsp;\u0026plusmn;\u0026thinsp;99.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUA, \u0026micro;mol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e99.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1091.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e92.8\u0026thinsp;\u0026plusmn;\u0026thinsp;915.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55.6\u0026thinsp;\u0026plusmn;\u0026thinsp;20.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e155.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1759.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eValues are presented as n (%), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Categorical variables were presented as n(%). BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; COPD, Chronic obstructive pulmonary disease; CKD, Chronic kidney disease. WBC, white blood cell; LYM, lymphocyte; MO, monocyte; NE, neutrophil; Hb, hemoglobin; Cr, creatinine; PT, prothrombin time; FPG, fasting plasma glucose; APTT, activated partial thromboplastin time; TT, thrombin time; Fbg, fasting blood glucose; FDP, fibrinogen degradation products; PLT, platelet; TC, total cholesterol; TG, triglycerides; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; NLR, neutrophil-to-lymphocyte ratio; LMR, lymphocyte-to-monocyte ratio; MLR, monocyte-to-lymphocyte ratio; SII, systemic immune-inflammation index; AISI, advanced inflammation-based score index; PLR, platelet-to-lymphocyte ratio; SIRI, systemic inflammatory response index; UHR, uric acid to high-density lipoprotein cholesterol ratio; UA, uric acid; TyG, triglyceride-glucose index.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eOne-year and five-year outcomes\u003c/h3\u003e\n\u003cp\u003eThe cumulative incidences of ARAEs, MACCEs, and all-cause mortality at 1 year and 5 years are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. During the one-year follow-up, we identified 131 cases of ARAEs, 43 fatalities, and 25 instances of MACCEs. Over the 5-year follow-up, there were 199 cases of ARAEs, 114 fatalities, and 145 cases of MACCEs. Notably, the cumulative incidence of ARAEs in the High group was the highest at both 1 year (19.51%) and 5 years (24.39%), while the Low group exhibited the lowest incidences at 1 year (5.81%) and 5 years (10.32%). The P values for these comparisons were all less than 0.001 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, the analysis indicated no statistically significant differences in all-cause mortality or the cumulative incidence of MACCEs among the three groups at either 1 year (P\u0026thinsp;=\u0026thinsp;0.420, P\u0026thinsp;=\u0026thinsp;0.407) or 5 years (P\u0026thinsp;=\u0026thinsp;0.336, P\u0026thinsp;=\u0026thinsp;0.135) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Supplementary Figure S1).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOutcomes of TBAD patients receiving TEVAR grouped according to AIP.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1-year outcomes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCumulative incidence of 1-year ARAEs, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e131 (9.81%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40 (5.81%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27 (8.46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e64 (19.51%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCumulative incidence of 1-year all-cause death, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e43 (3.22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21 (3.05%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8 (2.51%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14 (4.27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.420\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCumulative incidence of 1-year MACCEs, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25 (1.87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11 (1.60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5 (1.57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9 (2.74%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.407\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e5-year outcomes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCumulative incidence of 5-year ARAEs, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e199 (14.91%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71 (10.32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48 (15.05%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e80 (24.39%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCumulative incidence of 5-yearall-cause death, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e114 (8.54%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57 (8.28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23 (7.21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e34 (10.37%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.336\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCumulative incidence of 5-year MACCEs, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e145 (10.86%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71 (10.32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29 (9.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e45 (13.72%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.135\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eValues are presented as n (%). TBAD, type B aortic dissection; TEVAR, thoracic endovascular aortic repair; AIP, atherogenic index of plasma; ARAEs, aortic-related adverse events; MACCEs, major adverse cardiovascular and cerebrovascular events.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eUnivariate and multivariate Cox analysis\u003c/h3\u003e\n\u003cp\u003eThe results of the Cox proportional hazards regression analysis for 1-year ARAEs are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The results indicated that gender, AD stage at admission, pericardial effusion, ischemia of major arteries, and several ratios including NLR, MLR, SIRI, TyG, and categorical AIP\u0026mdash;were significantly associated (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). A multivariate Cox proportional hazards analysis was subsequently performed on these variables. The results revealed that AD stage (P\u0026thinsp;=\u0026thinsp;0.022), pericardial effusion (P\u0026thinsp;=\u0026thinsp;0.008), ischemia of major arteries (P\u0026thinsp;=\u0026thinsp;0.001), NLR (P\u0026thinsp;=\u0026thinsp;0.025), and MLR (P\u0026thinsp;=\u0026thinsp;0.013) remained significantly correlated with the cumulative incidence of ARAEs at 1 year. After adjusting for factors such as AD stage at admission, pericardial effusion, renal artery ischemia, ischemia of major arteries, NLR, and MLR, a statistically significant association was observed between AIP (categorical) and the cumulative incidence of ARAEs at 1 year. Compared to the Low group, the risk of 1-year ARAEs was significantly increased in the High group (HR\u0026thinsp;=\u0026thinsp;4.63, 95% CI: 2.57\u0026ndash;8.34; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate and Multivariate Cox proportional hazard modeling analysis for 1-year ARAEs.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eUnivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eMultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.99,1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.388\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.02,3.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e(0.88,3.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.111\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.95,1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.810\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP at admission\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.99,1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP at admission\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.98,1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.872\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAD stage at admission\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.56,0.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e(0.56,0.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePericardial effusion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.52,4.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e(1.26,3.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePleural effusion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.74,1.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.586\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIschemia of major arteries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.86,5.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e(1.45,4.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.66,1.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.807\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes mellitus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.73,2.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStroke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.931\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.972\u0026ndash;3.837\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOPD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.38,1.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCKD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.80,3.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.00,1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.98,1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD-dimer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.97,1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.486\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.00,1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.359\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.00,1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.01,1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e(1.01,1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.74,0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.46,3.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e(1.26,6.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.00,1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.767\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAISI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.00,1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.782\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.00,1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.440\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.01,1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e(0.87,1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.99,1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.870\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.06,2.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e(0.46,1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.115\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAIP groups\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.80,2.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e(0.86,2.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.141\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(2.08,5.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e(2.57,8.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eCovariates for the multivariable model include gender, AD stage at admission, pericardial effusion, ischemia of major arteries, and several ratios including NLR, MLR, SIRI, TyG, and categorical AIP. Variables with a P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariable analysis were entered in the multivariable models. ARAEs, aortic-related adverse events; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; AD, aortic dissection; COPD, Chronic obstructive pulmonary disease; CKD, Chronic kidney disease; WBC, white blood cell; Hb, hemoglobin; Cr, creatinine; FDP, fibrinogen degradation products; NLR, neutrophil-to-lymphocyte ratio; LMR, lymphocyte-to-monocyte ratio; MLR, monocyte-to-lymphocyte ratio; SII, systemic immune-inflammation index; AISI, advanced inflammation-based score index; PLR, platelet-to-lymphocyte ratio; SIRI, systemic inflammatory response index; UHR, uric acid to high-density lipoprotein cholesterol ratio; TyG, triglyceride-glucose index; AIP, atherogenic index of plasma; HR, hazard ratio; CI, confidence interval.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe univariate Cox proportional hazards analysis for 5-year ARAEs indicated that pericardial effusion, ischemia of major arteries, stroke, NLR, MLR, TyG, and AIP (categorical) were significantly associated with the cumulative incidence of 5-year ARAEs (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A subsequent multivariate Cox proportional hazards analysis was performed on these variables. Pericardial effusion (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), NLR (P\u0026thinsp;=\u0026thinsp;0.003) and MLR (P\u0026thinsp;=\u0026thinsp;.014) remained significantly correlated with the cumulative incidence of 5-year ARAEs. A statistically significant association between categorical AIP and the cumulative incidence of 5-year ARAEs was observed. Compared to the Low group, the risk of 5-year ARAEs was significantly higher in the High group (HR\u0026thinsp;=\u0026thinsp;2.59, 95% CI: 1.78\u0026ndash;3.78; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate and Multivariate Cox proportional hazard modeling analysis for 5-year ARAEs.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eUnivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eMultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.00,1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.98,2.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.94,1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.550\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP at admission\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.99,1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.946\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP at admission\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.98,1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAD stage at admission\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.77,1.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePericardial effusion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.62,4.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e(1.55,3.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePleural effusion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.82,1.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIschemia of major arteries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.31,3.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e(0.98,2.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.82,1.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes mellitus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.98,2.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStroke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.22,3.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e(1.00,2.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOPD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.75,1.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCKD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.83,2.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.99,1.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.99,1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD-dimer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.99,1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.00,1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.00,1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.01,1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e(1.01,1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.86,1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.18,2.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e(1.10,2.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.00,1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.945\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAISI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.00,1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.694\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.00,1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.00,1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.99,1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.870\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.01,1.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e(0.99,1.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAIP groups\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.97,2.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e(0.96,2.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.70,3.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e(1.78,3.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eCovariates for the multivariable model include pericardial effusion, ischemia of major arteries, stroke and several ratios including NLR, MLR, TyG, and categorical AIP. Variables with a P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariable analysis were entered in the multivariable models. ARAEs, aortic-related adverse events; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; AD, aortic dissection; COPD, Chronic obstructive pulmonary disease; CKD, Chronic kidney disease; WBC, white blood cell; Hb, hemoglobin; Cr, creatinine; FDP, fibrinogen degradation products; NLR, neutrophil-to-lymphocyte ratio; LMR, lymphocyte-to-monocyte ratio; MLR, monocyte-to-lymphocyte ratio; SII, systemic immune-inflammation index; AISI, advanced inflammation-based score index; PLR, platelet-to-lymphocyte ratio; SIRI, systemic inflammatory response index; UHR, uric acid to high-density lipoprotein cholesterol ratio; TyG, triglyceride-glucose index; AIP, atherogenic index of plasma; HR, hazard ratio; CI, confidence interval.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe results of the univariate Cox analysis for MACCEs at 1 year and 5 years, as well as the univariate Cox analysis for all-cause mortality at 1 year and 5 years, are presented in Supplementary Tables\u0026nbsp;1 and 2. Significant risk factors for long-term (5-year) MACCEs and all-cause mortality include diabetes mellitus, CKD, pericardial effusion, ischemia of major arteries, Hb, NLR and MLR(all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Notably, for 5-year MACCEs, the risk associations for diabetes mellitus (HR\u0026thinsp;=\u0026thinsp;2.48, 95% CI\u0026thinsp;=\u0026thinsp;1.64\u0026ndash;3.77), CKD (HR\u0026thinsp;=\u0026thinsp;2.32, 95% CI\u0026thinsp;=\u0026thinsp;1.40\u0026ndash;3.85), and pericardial effusion (HR\u0026thinsp;=\u0026thinsp;2.33, 95% CI\u0026thinsp;=\u0026thinsp;1.44\u0026ndash;3.77) were particularly pronounced (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In the context of 5-year all-cause mortality, several factors demonstrated significant associations: diabetes mellitus (HR\u0026thinsp;=\u0026thinsp;2.42, 95%CI\u0026thinsp;=\u0026thinsp;1.51\u0026ndash;3.88), pericardial effusion (HR\u0026thinsp;=\u0026thinsp;2.57, 95%CI\u0026thinsp;=\u0026thinsp;1.52\u0026ndash;4.36), and MLR (HR\u0026thinsp;=\u0026thinsp;2.00, 95%CI\u0026thinsp;=\u0026thinsp;1.32\u0026ndash;3.03) (all P values\u0026thinsp;\u0026lt;\u0026thinsp;0.01). For short-term (1-year) outcomes, the only significant predictors of 1-year MACCEs were age (HR\u0026thinsp;=\u0026thinsp;1.04, 95%CI\u0026thinsp;=\u0026thinsp;1.01\u0026ndash;1.07, P\u0026thinsp;=\u0026thinsp;0.024), T2DM (HR\u0026thinsp;=\u0026thinsp;4.09, 95%CI\u0026thinsp;=\u0026thinsp;1.71\u0026ndash;9.79, P\u0026thinsp;=\u0026thinsp;0.002), and CKD (HR\u0026thinsp;=\u0026thinsp;3.18, 95%CI\u0026thinsp;=\u0026thinsp;1.09\u0026ndash;9.27, P\u0026thinsp;=\u0026thinsp;0.034). All-cause mortality within one year was primarily attributed to pericardial effusion (HR\u0026thinsp;=\u0026thinsp;3.89, 95%CI\u0026thinsp;=\u0026thinsp;1.87\u0026ndash;8.12, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), ischemia of major arteries (HR\u0026thinsp;=\u0026thinsp;2.99, 95%CI\u0026thinsp;=\u0026thinsp;1.33\u0026ndash;6.73, P\u0026thinsp;=\u0026thinsp;0.008), and CKD (HR\u0026thinsp;=\u0026thinsp;3.44, 95%CI\u0026thinsp;=\u0026thinsp;1.53\u0026ndash;7.72, P\u0026thinsp;=\u0026thinsp;0.003). Additionally, smoking (HR\u0026thinsp;=\u0026thinsp;1.86, 95%CI\u0026thinsp;=\u0026thinsp;1.00-3.45, P\u0026thinsp;=\u0026thinsp;0.049) and HBP (HR\u0026thinsp;=\u0026thinsp;2.56, 95%CI\u0026thinsp;=\u0026thinsp;1.01\u0026ndash;6.50, P\u0026thinsp;=\u0026thinsp;0.049) were identified as significant influencing factors. Furthermore, AIP did not show statistical significance in relation to 1-year and 5-year MACCEs or all-cause mortality (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eDiscussion on Linear/Nonlinear Relationship\u003c/h2\u003e\u003cp\u003eRestricted cubic spline (RCS) analyses were performed to investigate the nonlinear relationship between AIP levels and ARAEs in TBAD patients at one and five year ARAEs (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Notably, when AIP is treated as a continuous variable, it exhibits a linear relationship with ARAEs, demonstrating a positive correlation. This suggests that variations in AIP values may result in a continuous and stable linear change in the risk of ARAEs at one year (P\u0026thinsp;=\u0026thinsp;0.014). Similarly, the association between AIP and the risk of ARAEs at five years also adheres to a linear pattern, further affirming the reliability of AIP as a linear risk predictor for ARAEs(P\u0026thinsp;=\u0026thinsp;0.006).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eSensitivity analysis\u003c/h2\u003e\u003cp\u003eIn the sensitivity analysis, we initially excluded individuals who had died within the two years preceding the follow-up. Additionally, due to the presence of missing data for high-density lipoprotein cholesterol (HDL-C) and triglycerides (TG) used in the calculation of the Atherogenic Index of Plasma (AIP), we employed the multiple imputation method to address these missing values. We then repeated the primary analysis using the imputed data. The results from this sensitivity analysis align with those of the main analysis, thereby reinforcing the robustness and credibility of the conclusions drawn in this study.\u003c/p\u003e\u003cp\u003eTo further assess the robustness of our findings, we performed subgroup analyses based on age, gender, systolic blood pressure (SBP), diastolic blood pressure (DBP), surgical timing, pericardial effusion, ischemia of major arteries, hypertension, diabetes mellitus, stroke, COPD, and CKD (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). In the overall population, AIP was associated with both 1-year ARAEs (HR\u0026thinsp;=\u0026thinsp;2.33, 95% CI 1.19\u0026ndash;4.57) and 5-year ARAEs (HR\u0026thinsp;=\u0026thinsp;1.75, 95% CI 1.00-3.09). Subgroup interaction analysis revealed that only the operation timing subgroup exhibited a statistically significant interaction with 1-year ARAEs (P\u0026thinsp;=\u0026thinsp;0.008). The association between subacute AIP and 1-year ARAEs (HR\u0026thinsp;=\u0026thinsp;6.78, 95% CI 1.48\u0026ndash;31.16) was notably stronger than that observed in the chronic stage (HR\u0026thinsp;=\u0026thinsp;0.02, 95% CI 0.01\u0026ndash;0.67). The interaction between CKD subgroup and 1-year (P\u0026thinsp;=\u0026thinsp;0.121) and 5-year ARAEs (P\u0026thinsp;=\u0026thinsp;0.082) approached significance. The association between AIP and ARAEs in CKD patients was weaker (1-year HR\u0026thinsp;=\u0026thinsp;0.44, 5-year HR\u0026thinsp;=\u0026thinsp;0.41) compared to non-CKD patients (1-year HR\u0026thinsp;=\u0026thinsp;2.76, 5-year HR\u0026thinsp;=\u0026thinsp;2.09). The interaction between the operation timing subgroup and 5-year ARAEs was also nearly significant (P\u0026thinsp;=\u0026thinsp;0.081). A trend was observed in the association between AIP and 5-year ARAEs in patients in the subacute stage (HR\u0026thinsp;=\u0026thinsp;2.96, 95% CI 0.72\u0026ndash;12.16) and the acute stage (HR\u0026thinsp;=\u0026thinsp;2.00, 95% CI 1.04\u0026ndash;3.83), while patients in the chronic phase showed no association (HR\u0026thinsp;=\u0026thinsp;0.24, 95% CI 0.03\u0026ndash;1.92). Interactions among subgroups such as age, gender, SBP, pericardial effusion, ischemia of major arteries, hypertension, diabetes mellitus, stroke, and COPD were not statistically significant (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating that these factors did not influence the association between AIP and ARAEs following TEVAR.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eEvaluate the predictive effectiveness\u003c/h2\u003e\u003cp\u003eAdditionally, we employed a time-dependent AUC plot to assess predictive efficacy over the 5-year follow-up period (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The AUC point estimates were close to 80% at each predicted time point within five years (0.87, 0.86, 0.81, 0.80, and 0.76, respectively), demonstrating effective prediction of outcome events (Supplementary Table\u0026nbsp;3). The minimal differences observed between the 1 to 5-year follow-up periods suggest that these indicators possess strong long-term prognostic value.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAtherosclerosis serves as the fundamental pathological basis for the onset and progression of cardiovascular diseases, with lipid metabolism disorders acting as a critical factor in the development of atherosclerosis. Although traditional lipid indicators, such as total cholesterol, low-density lipoprotein cholesterol, and triglycerides, have been widely utilized in cardiovascular risk assessment, they often fail to comprehensively capture the detrimental effects of lipid metabolism imbalances on the vascular wall [\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The plasma atherosclerotic index (AIP), a novel metric for assessing lipid metabolism, offers the advantage of integrating the dual atherosclerotic impacts of elevated triglycerides and reduced high-density lipoprotein cholesterol [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn recent years, AIP has emerged as a focal point of research in the domain of cardiovascular diseases. Prior studies have established that elevated AIP is significantly associated with the risk of adverse cardiovascular events, such as myocardial infarction and vascular restenosis, in populations with coronary heart disease, ischemic stroke, and diabetic vascular complications. Notably, its predictive value surpasses that of traditional lipid indicators [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. However, the clinical significance of AIP in aortic dissection, a unique and high-risk aortic condition, remains inadequately understood. While the incidence of aortic dissection is directly linked to hypertension and abnormal aortic wall structure, the role of atherosclerosis\u0026mdash;an important contributor to degenerative changes in the aortic wall\u0026mdash;on postoperative repair, long-term vascular stability, and adverse events in patients with dissection warrants further investigation [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Consequently, this study aims to examine the association between AIP and one-year and five-year ARAEs in patients with aortic dissection. This focus not only aligns with current research trends in lipid metabolism and cardiovascular prognosis but also addresses a critical gap in understanding the long-term prognostic implications of AIP in aortic dissection.\u003c/p\u003e\u003cp\u003eThe baseline characteristics of this study indicate that patients with TBAD in the AIP High group exhibit significant baseline risk factors. Notably, the prevalence of higher BMI (25.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2 vs 24.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6 in the Low group), diabetes (P\u0026thinsp;=\u0026thinsp;0.009), and chronic obstructive pulmonary disease (P\u0026thinsp;=\u0026thinsp;0.045) was greater in this group. Furthermore, lipid metabolism disorders were more pronounced, as evidenced by elevated triglycerides and decreased high-density lipoprotein cholesterol (both P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings suggest a close relationship between elevated AIP and the burden of underlying diseases, as well as the extent of metabolic abnormalities in TBAD patients. In clinical practice, it is essential to prioritize the optimization of basic management strategies\u0026mdash;such as weight control, blood sugar regulation, and lipid adjustment\u0026mdash;for patients in the High group to mitigate the risk of ongoing aortic wall injury. Follow-up results at 1 year and 5 years revealed that the cumulative incidence of ARAEs in the AIP High group was significantly higher than that in the Low group (1 year: 19.51% vs 5.81%; 5 years: 24.39% vs 10.32%, both P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, no statistically significant differences were observed in all-cause mortality and MACCEs among the three groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The clinical significance of this core discovery is underscored by the clear identification of AIP as primarily associated with adverse aortic events, including endoleak, retrograde type A dissection, new hairpin layers, and poor visceral perfusion, in patients with TBAD, rather than with general cardiovascular events or mortality. In clinical follow-up, for TBAD patients exhibiting elevated AIP, it is advisable to enhance regular monitoring of aortic imaging, rather than solely concentrating on MACCE endpoints such as death, myocardial infarction, or stroke, to prevent overlooking local aortic risks. Furthermore, through univariate and multivariate analyses, after adjusting for variables such as AD stage at admission, pericardial effusion, ischemia of critical arteries, NLR, and MLR, the AIP High group demonstrated a significantly increased risk of ARAEs (1-year HR\u0026thinsp;=\u0026thinsp;4.63, 95% CI: 2.57\u0026ndash;8.34; 5-year HR\u0026thinsp;=\u0026thinsp;2.59, 95% CI: 1.78\u0026ndash;3.78; all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This finding indicates that AIP can function as an independent predictor of long-term ARAEs in patients with TBAD. Additionally, it suggests that routine lipid data calculation suffices, eliminating the need for further testing, thereby facilitating broad clinical application and aiding in the accurate identification of high-risk groups.\u003c/p\u003e\u003cp\u003eWe will now further investigate the relationship between AIP and ARAEs. Can we identify a specific threshold or range? RCS analysis demonstrated a continuous positive linear correlation between AIP and ARAEs, devoid of inflection points or plateau phases. This finding indicates that higher AIP levels correspond to an increased risk of ARAEs. Consequently, there is no necessity to establish a single critical value in clinical practice; instead, dynamic monitoring of AIP fluctuations can effectively assess risk trends. The 5-year time-dependent AUC consistently exceeded 80%, signifying that AIP reliably maintained strong predictive efficacy throughout the 5-year follow-up and could effectively forecast outcome events in a relatively stable manner. After excluding early mortality and multiple interpolation missing values, the results remained consistent, affirming the robustness of the association between AIP and ARAEs. This conclusion can be generalized to the majority of the TBAD population. Subgroup analysis revealed significant stratification insights: Firstly, patients undergoing subacute surgery exhibited the highest risk of AIP-related ARAEs (HR\u0026thinsp;=\u0026thinsp;2.96), whereas those undergoing chronic surgery demonstrated the lowest risk (HR\u0026thinsp;=\u0026thinsp;0.24) (P\u0026thinsp;=\u0026thinsp;0.081); Secondly, patients without CKD faced a more pronounced risk of elevated AIP (HR\u0026thinsp;=\u0026thinsp;2.09, HR\u0026thinsp;=\u0026thinsp;0.41 for CKD patients) (P\u0026thinsp;=\u0026thinsp;0.082). These findings suggest that clinical practice should prioritize more intensive follow-up and intervention strategies for the subgroups of patients characterized by \"subacute surgery\u0026thinsp;+\u0026thinsp;high AIP\" and \"no CKD\u0026thinsp;+\u0026thinsp;high AIP.\"\u003c/p\u003e\u003cp\u003eWe propose two primary reasons for the observed results regarding surgical timing in the subgroup analysis: Firstly, differences in pathophysiological processes. In patients with subacute aortic disease, the lesions are in a progressive stage, characterized by active destruction of the vascular wall structure. This anatomical complexity and the heightened intraoperative risks significantly elevate the likelihood of adverse events. Conversely, in patients with chronic-stage lesions, the conditions are relatively stable, and the false lumen may have partially thrombosed. Consequently, the \"driving force\" behind lesion progression diminishes, thereby reducing the risks associated with surgical intervention. Secondly, the interactive influence between treatment timing and intervention effects. The subacute stage represents a critical window for the progression of aortic lesions. While surgery is a necessary intervention during this period, the active state of the lesion, combined with the impact of surgical trauma, may exacerbate the risk of adverse events. In contrast, the stability of lesions in the chronic stage shifts the benefit-risk ratio of surgical intervention favorably, resulting in a significant difference in risk. Therefore, the timing of surgery holds substantial clinical significance for prognosis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBased on the research findings, the core clinical significance of this study encompasses two primary aspects.\u003c/b\u003e First, the long-term management of postoperative requirements in patients with aortic dissection necessitates the accurate identification of high-risk groups. Currently, the prognosis derived from commonly used clinical assessment tools, such as Stanford type, the number of aortic dissections upon admission, and complications, primarily depends on anatomical or baseline disease states [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. There is a notable absence of indicators that can dynamically reflect lipid metabolism and chronic damage to the vascular wall. This study establishes that the Atherogenic Index of Plasma (AIP) can serve as an independent predictor of long-term adverse renal and aortic events (ARAE) in patients with aortic dissection. Its advantages include: Firsty. convenience of detection, as it can be directly calculated from routine lipid test data without incurring additional detection costs; Secondly, dynamic monitoring capabilities, allowing for the evaluation of lipid intervention effects and trends in vascular risk through follow-up assessments of AIP changes; and Thirdly, high specificity, as it effectively reflects the risk of aortic wall injury related to atherosclerosis, thereby addressing the limitations of traditional assessment indicators. Second, AIP can be incorporated into the long-term risk stratification model for patients with aortic dissection. For patients exhibiting significantly elevated AIP (AIP\u0026thinsp;\u0026ge;\u0026thinsp;0.52), it is advisable to increase the frequency of long-term follow-up. This should include re-evaluating aortic imaging, blood lipid levels, and AIP every three to six months. Concurrently, it is essential to enhance interventions targeting lipid metabolism to mitigate the ongoing damage of atherosclerosis to the stability of the aortic wall. In contrast, for patients with normal or low AIP, the intensity of intervention may be appropriately reduced while maintaining regular follow-up, thereby facilitating precise management and preventing unnecessary medical treatment.\u003c/p\u003e\u003cp\u003eRegarding the association between AIP and long-term ARAEs in patients with TBAD observed in this study, we proposed several possible mechanisms to elucidate the correlation between AIP and AD: Firstly, An increase in AIP may result from enhanced stability of aortic atherosclerotic plaques. The presence of small, dense low-density lipoprotein (sdLDL) at a high ratio reflects the intricate interplay of lipoprotein metabolism and aids in predicting plasma atherosclerosis. Due to its smaller size, sdLDL is more susceptible to oxidative stress, which facilitates its conversion to oxidized low-density lipoprotein. This process ultimately triggers an inflammatory response within the blood vessels, enhances binding to endothelial proteoglycans, and promotes the formation of foam cells [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], thereby accelerating lipid deposition in the aortic wall. Elevated AIP may also suggest that adipocytes are storing excess triglycerides (TG) as fat, which increases the accumulation of cholesterol crystals in the intima of atherosclerotic arteries. This accumulation can lead to lumen narrowing and obstruction, ultimately resulting in atherosclerosis [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In patients with aortic dissection, even if the acute phase laceration is repaired, persistent elevation of AIP may continue to compromise the aortic wall's repair capacity due to ongoing atherosclerotic processes.\u003c/p\u003e\u003cp\u003eSecondly, Elevated AIP levels create a vulnerable aortic wall through lipid metabolism imbalance, thereby reducing tolerance to hypertension.Hypertension represents the most critical risk factor for the development of AD. Furthermore, an increase in AIP markedly exacerbates the detrimental impact of hypertension on the aorta by intensifying damage to the vascular wall structure.\u003c/p\u003e\u003cp\u003eThe primary pathological basis of AD is cystic necrosis in the middle layer of the aorta, characterized by the rupture of elastic fibers, a reduction in collagen fibers, and the apoptosis of smooth muscle cells. Elevated AIP exacerbates this degenerative process by promoting atherosclerosis. Specifically, the abnormal AIP, coupled with decreased high-density lipoprotein cholesterol (HDL-C), diminishes its cholesterol reverse transport function. This deficiency leads to cholesterol accumulation in the smooth muscle cells of the aortic middle layer, resulting in lipid-induced toxic damage and subsequent cell apoptosis. Furthermore, oxidized very-low-density lipoprotein (ox-VLDL) recruits monocytes that differentiate into macrophages, which in turn release matrix metalloproteinases (MMPs) to degrade the elastic and collagen fibers in the middle layer [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. These alterations contribute to the atrophy of the aortic middle layer. Under the persistent mechanical strain of hypertension, interlaminar tearing is highly likely to occur. The vascular wall's tolerance to the \"pulsed mechanical shear force\" induced by hypertension is significantly diminished [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. A retrospective cohort study involving 6,540 individuals confirmed that AIP serves as an independent risk factor for hypertension development. In participants with normal blood pressure, the adjusted odds ratio was 1.84 (95% CI: 1.41\u0026ndash;2.39, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while in those with elevated blood pressure, it was 1.88 (95% CI: 1.40\u0026ndash;2.52, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, a nonlinear relationship exists between AIP and the incidence of hypertension. Degang Mo et al. noted that AIP demonstrates superior predictive capability for hypertension occurrence compared to individual indicators of triglycerides and HDL-C [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e3. AIP can better reflect hidden risks\u003c/p\u003e\u003cp\u003eIn patients with aortic dissection, some may exhibit normal traditional lipid indicators while presenting with an elevated AIP, characterized by slightly elevated triglycerides and reduced high-density lipoprotein cholesterol. The risk of aortic wall injury in these patients is frequently underestimated. This study establishes the independent predictive value of AIP, indicating that even when traditional lipid indicators remain within normal limits, an increase in AIP necessitates vigilance regarding the risk of long-term ARAEs. This finding offers a novel foundation for identifying latent lipid-related risks.\u003c/p\u003e\u003cp\u003eThe findings of this study align with previous research on Atherogenic Index of Plasma (AIP) in various cardiovascular diseases, while also extending the investigation to the population affected by aortic dissection. A study utilizing data from the China Health and Retirement Longitudinal Survey (CHARLS) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] demonstrated that inadequate control of AIP levels correlates with an elevated risk of cardiovascular events among individuals with stage 0\u0026ndash;3 cardiovascular kidney-metabolic syndrome (CKM). Furthermore, another investigation conducted by the same research team using the National Health and Nutrition Examination Survey (NHANES) in the United States [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] revealed that elevated AIP levels in patients with CKM syndrome are significantly associated with an increased risk of mortality, particularly all-cause mortality in advanced cases and cardiovascular disease mortality in both advanced and non-advanced patients. Ramin et al. [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] demonstrate that elevated AIP is significantly linked to an increased risk, greater severity, and poorer prognosis of coronary heart disease, regardless of whether an individual has a confirmed diagnosis. Additionally, a study utilizing a large population dataset from the National Health Insurance Service - National Health Screening Cohort (NHIS-HEALS) in South Korea suggested that AIP may serve as an effective screening tool for identifying patients at high risk of cardiovascular events [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Furthermore, research employing machine learning to assess the predictive value of AIP on the length of hospital stay (LOS) for patients with critical atherosclerotic cardiovascular disease (ASCVD) revealed that a higher AIP was independently associated with extended stays in both the intensive care unit (ICU) and the hospital [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study exhibits notable distinctions from other research, particularly the Bogalusa Heart Study, a seminal longitudinal epidemiological investigation that first demonstrated a direct correlation between the life-course cumulative burden of AIP and arteriosclerosis, as well as arterial wall thickening, in a cohort of 900 subjects [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The differences in research orientation, primary objectives, and clinical implications are significant. The Bogalusa study focused on healthy individuals or the general population in the subclinical stages of atherosclerosis, aiming to identify early risk factors associated with abnormal arterial wall structure. In contrast, the present study targets patients diagnosed with TBAD, with the objective of assessing the influence of AIP on the long-term clinical outcomes of these patients. It is important to note that these differences are not mutually exclusive but rather complementary. The Bogalusa study established a causal link between AIP and arterial wall injury from an epidemiological standpoint. Conversely, this study advances the investigation of AIP within the specific high-risk population of TBAD, transitioning the focus from subclinical structural changes to endpoint events, thereby rendering the findings clinically actionable. This research addresses the gap in understanding the role of AIP in the long-term prognosis of patients with aortic dissection.\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eThis study presents several limitations warranting attention in future research: First, as a single-center retrospective investigation constrained by a sample size of 1,335 patients, it may be susceptible to selection bias. These findings necessitate validation via multi-center, large-sample prospective studies. Secondly, lifestyle interventions and lipid-lowering medication adjustments during patient follow-up were not included. These factors may indirectly influence prognosis by affecting AIP levels, and further control is required in subsequent studies. Third, the interaction between AIP and other AD biomarkers, such as matrix metalloproteinases and microRNAs, remains unexplored. Consequently, it is unclear whether AIP impacts prognosis by modulating other pathways or through synergistic effects.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that elevated AIP markedly increases the risk of long-term ARAEs in patients with TBAD. Furthermore, AIP serves as an independent predictor of long-term ARAEs in this population, exhibiting strong predictive efficacy. These findings underscore the importance of AIP as a valuable risk biomarker, offering a straightforward and effective means of identifying the risk of ARAEs in patients with AD.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAD \u0026nbsp; \u0026nbsp; \u0026nbsp; Aortic dissection\u003c/p\u003e\n\u003cp\u003eTBAD \u0026nbsp; \u0026nbsp; Type B aortic dissection\u003c/p\u003e\n\u003cp\u003eCVD \u0026nbsp; \u0026nbsp; \u0026nbsp;Cardiovascular disease\u003c/p\u003e\n\u003cp\u003eCKD \u0026nbsp; \u0026nbsp; \u0026nbsp;Chronic kidney disease\u003c/p\u003e\n\u003cp\u003eCKM \u0026nbsp; \u0026nbsp; \u0026nbsp;Chronic kidney disease complicated with cardiovascular diseases\u003c/p\u003e\n\u003cp\u003eCAD \u0026nbsp; \u0026nbsp; \u0026nbsp;Coronary artery disease\u003c/p\u003e\n\u003cp\u003eTEVAR \u0026nbsp; \u0026nbsp;Thoracic endovascular aortic repair\u003c/p\u003e\n\u003cp\u003eCOPD \u0026nbsp; \u0026nbsp; Chronic obstructive pulmonary disease\u003c/p\u003e\n\u003cp\u003eAIP \u0026nbsp; \u0026nbsp; \u0026nbsp; Atherogenic index of plasma\u003c/p\u003e\n\u003cp\u003eARAEs \u0026nbsp; \u0026nbsp;Aortic-related adverse events\u003c/p\u003e\n\u003cp\u003eMACCEs \u0026nbsp; Major adverse cardiovascular and cerebrovascular events\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Total cholesterol\u003c/p\u003e\n\u003cp\u003eTG \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Triglyceride\u003c/p\u003e\n\u003cp\u003eHDL-C \u0026nbsp; \u0026nbsp; High-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eLDL-C \u0026nbsp; \u0026nbsp; Low-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eHBP \u0026nbsp; \u0026nbsp; \u0026nbsp; Hypertension\u003c/p\u003e\n\u003cp\u003eT2DM \u0026nbsp; \u0026nbsp; Type 2 diabetes mellitus\u003c/p\u003e\n\u003cp\u003eBMI \u0026nbsp; \u0026nbsp; \u0026nbsp; Body mass index\u003c/p\u003e\n\u003cp\u003eSBP \u0026nbsp; \u0026nbsp; \u0026nbsp; Systolic blood pressure\u003c/p\u003e\n\u003cp\u003eDBP \u0026nbsp; \u0026nbsp; \u0026nbsp; Diastolic blood pressure\u003c/p\u003e\n\u003cp\u003eWBC \u0026nbsp; \u0026nbsp; \u0026nbsp;White blood cell count\u003c/p\u003e\n\u003cp\u003eFDP \u0026nbsp; \u0026nbsp; \u0026nbsp; Fibrin degradation products\u003c/p\u003e\n\u003cp\u003eHb \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Hemoglobin\u003c/p\u003e\n\u003cp\u003eCr \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Creatinine\u003c/p\u003e\n\u003cp\u003eNLR \u0026nbsp; \u0026nbsp; \u0026nbsp;Neutrophil-to-lymphocyte ratio\u003c/p\u003e\n\u003cp\u003eLMR \u0026nbsp; \u0026nbsp; \u0026nbsp;Lymphocyte-to-monocyte ratio\u003c/p\u003e\n\u003cp\u003eMLR \u0026nbsp; \u0026nbsp; \u0026nbsp;Monocyte-to-lymphocyte ratio\u003c/p\u003e\n\u003cp\u003eSII \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Systemic immune-inflammation index\u003c/p\u003e\n\u003cp\u003eAISI \u0026nbsp; \u0026nbsp; \u0026nbsp; Advanced inflammation-based score index\u003c/p\u003e\n\u003cp\u003ePLR \u0026nbsp; \u0026nbsp; \u0026nbsp; Platelet-to-lymphocyte ratio;\u003c/p\u003e\n\u003cp\u003eSIRI \u0026nbsp; \u0026nbsp; \u0026nbsp; Systemic inflammatory response index\u003c/p\u003e\n\u003cp\u003eUHR \u0026nbsp; \u0026nbsp; \u0026nbsp;Uric acid to high-density lipoprotein cholesterol ratio\u003c/p\u003e\n\u003cp\u003eTyG \u0026nbsp; \u0026nbsp; \u0026nbsp; Triglyceride-glucose index\u003c/p\u003e\n\u003cp\u003esdLDL \u0026nbsp; \u0026nbsp; Small and dense low-density lipoprotein\u003c/p\u003e\n\u003cp\u003eox-VLDL \u0026nbsp; Oxidized modified very low-density lipoprotein\u003c/p\u003e\n\u003cp\u003eMMPs \u0026nbsp; \u0026nbsp; Matrix metalloproteinases\u003c/p\u003e\n\u003cp\u003eASCVD \u0026nbsp; \u0026nbsp;Atherosclerotic cardiovascular disease\u003c/p\u003e\n\u003cp\u003eLOS \u0026nbsp; \u0026nbsp; \u0026nbsp; Predicts the length of hospital stay\u003c/p\u003e\n\u003cp\u003eICU \u0026nbsp; \u0026nbsp; \u0026nbsp; Intensive care unit\u003c/p\u003e\n\u003cp\u003eSMA \u0026nbsp; \u0026nbsp; \u0026nbsp;The superior mesenteric artery\u003c/p\u003e\n\u003cp\u003eRA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Renal artery\u003c/p\u003e\n\u003cp\u003eK-M \u0026nbsp; \u0026nbsp; \u0026nbsp; Kaplan-Meier curve\u003c/p\u003e\n\u003cp\u003eRCS \u0026nbsp; \u0026nbsp; \u0026nbsp; Restricted cubic spline\u003c/p\u003e\n\u003cp\u003eAUC \u0026nbsp; \u0026nbsp; \u0026nbsp; The area under the time-of-administration curve\u003c/p\u003e\n\u003cp\u003eHR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Hazard ratio\u003c/p\u003e\n\u003cp\u003eCI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Confidence interval\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors express their gratitude to the Department of Vascular Surgery of the First Affiliated Hospital of Naval Medical University for its data contribution.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShuangshuang Li, Wen Li, and Jiahe Zhang contributed equally to this research. Shuangshuang Li was involved in research design, data collection, data analysis, and manuscript preparation. Wen Li and Jiahe Zhang contributed to data collection, data analysis, and manuscript writing. Kaiwen Zhao and Zhichen Ding assisted in revising the manuscript. Jianli Ren, Wenping Hu, Qingsheng Lu and Jian Zhou participated in the research design. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe requirement for consent was waived, and the Clinical Research Ethics Committee of Changhai Hospital, Naval Medical University, approved the research protocol. The protocol number is CHEC-Y2020-042, and the ratification date is August 21, 2020.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was funded by the National Natural Science Foundation of China [82570560], Scientific Research Cultivation Project of the Third Affiliated Hospital of Naval Medical University (2025QN04), Clinical Research Special Scientific Research Project funded by the Science and Technology and Economy Commission of Yangpu District (YPM202545).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNienaber CA, Clough RE, Sakalihasan N, et al. Aortic dissection. 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Published 2022 May 22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12933-022-01522-8\u003c/span\u003e\u003cspan address=\"10.1186/s12933-022-01522-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuo Y, Wang F, Ma S, et al. Relationship between atherogenic index of plasma and length of stay in critically ill patients with atherosclerotic cardiovascular disease: a retrospective cohort study and predictive modeling based on machine learning. Cardiovasc Diabetol. 2025;24(1):95. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12933-025-02654-3\u003c/span\u003e\u003cspan address=\"10.1186/s12933-025-02654-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Published 2025 Feb 28.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFan B, Zhang T, Li S, et al. Differential Roles of Life-Course Cumulative Burden of Cardiovascular Risk Factors in Arterial Stiffness and Thickness. Can J Cardiol. 2022;38(8):1253\u0026ndash;62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cjca.2022.03.009\u003c/span\u003e\u003cspan address=\"10.1016/j.cjca.2022.03.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cardiovascular-diabetology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cvdb","sideBox":"Learn more about [Cardiovascular Diabetology](http://cardiab.biomedcentral.com/)","snPcode":"12933","submissionUrl":"https://submission.nature.com/new-submission/12933/3","title":"Cardiovascular Diabetology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"type B aortic dissection, thoracic endovascular aortic repair, atherosclerotic index of plasma, aortic-related adverse events, biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-7984351/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7984351/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrevious research identifies the atherosclerotic index of plasma (AIP) as a key marker for cardiovascular risk, but its role in predicting aorta-related adverse events (ARAEs) in type B aortic dissection (TBAD) patients post-thoracic endovascular aortic repair (TEVAR) is uncertain. This study investigates the link between AIP and ARAEs at 1-year and 5-year intervals in TBAD patients after TEVAR, suggesting a new prognostic indicator for TBAD outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective cohort study involved 1,335 TBAD patients who underwent TEVAR, with clinical data extracted from electronic records. AIP was calculated as log (triglycerides/high-density lipoprotein cholesterol [TG/HDL-C]), and patients were categorized into three AIP tertiles. The primary endpoints were ARAEs at 1 and 5 years post-TEVAR. Cox regression identified variables linked to endpoints and assessed AIP's independent impact on ARAEs. Kaplan-Meier curves and log-rank tests compared ARAE incidence across groups. RCS models examined the AIP-ARAEs dose-response relationship, while subgroup analyses confirmed the association's stability. Time-dependent ROC curves evaluated AIP's predictive power for ARAEs over five years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan-Meier analysis showed higher incidence of ARAEs in AIP High group compared to Low group (1 year: 19.51% vs. 5.81%; 5 years: 24.39% vs. 10.32%; both P \u0026lt; 0.05). Cox analysis showed AIP High group had higher ARAE risk (1-year HR = 4.63, 95% CI: 2.57–8.34; 5-year HR = 2.59, 95% CI: 1.78–3.78; all P \u0026lt; 0.001). Furthermore, RCS analysis indicated a continuous positive linear relationship between AIP and ARAEs. Time-dependent ROC showed the area under the curve (AUC) surpassing 80% throughout the five-year duration. Subgroup analysis revealed higher AIP-related ARAEs risk in subacute surgery patients (1 year: HR = 6.78; 5 years: HR = 2.96) and lower risk in chronic surgery patients (1 year: HR = 0.02; 5 years: HR = 0.24). In the 5-year subgroup analysis, patients without chronic kidney disease (CKD) had a notably higher risk of AIP compared to those with CKD (HR = 2.09 vs. HR = 0.41) (P-interaction = 0.082).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAIP serves as an independent influencing factor for both the short term (1 year) and the long term (5 years) following TEVAR in patients with TBAD, demonstrating a linear relationship between the two timeframes. These findings highlight the significance of AIP as a crucial risk biomarker, providing a simple yet effective method for identifying the risk of ARAEs in this patient population.\u003c/p\u003e","manuscriptTitle":"Association between Atherosclerotic Index of Plasma and Long-term Aorta-related Adverse Events in TEVAR-treated Type B Aortic Dissection Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-11 16:34:19","doi":"10.21203/rs.3.rs-7984351/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-22T14:25:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-22T12:45:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-11T01:24:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"138695066999360247645389054304215737010","date":"2025-11-03T03:42:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"106737375200992459729402047939030280132","date":"2025-11-01T20:43:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"281120690169005976481450325491888944370","date":"2025-10-31T10:58:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-31T10:30:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"328455518796825214562316464993272971919","date":"2025-10-31T10:08:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"243126212419668426874479693190959151863","date":"2025-10-31T09:22:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-30T16:53:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-30T16:48:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-30T13:46:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cardiovascular Diabetology","date":"2025-10-30T02:41:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cardiovascular-diabetology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cvdb","sideBox":"Learn more about [Cardiovascular Diabetology](http://cardiab.biomedcentral.com/)","snPcode":"12933","submissionUrl":"https://submission.nature.com/new-submission/12933/3","title":"Cardiovascular Diabetology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"58eb7690-6661-465f-849a-06c84b7bc65f","owner":[],"postedDate":"November 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T16:08:59+00:00","versionOfRecord":{"articleIdentity":"rs-7984351","link":"https://doi.org/10.1186/s12933-026-03094-3","journal":{"identity":"cardiovascular-diabetology","isVorOnly":false,"title":"Cardiovascular Diabetology"},"publishedOn":"2026-03-05 15:59:11","publishedOnDateReadable":"March 5th, 2026"},"versionCreatedAt":"2025-11-11 16:34:19","video":"","vorDoi":"10.1186/s12933-026-03094-3","vorDoiUrl":"https://doi.org/10.1186/s12933-026-03094-3","workflowStages":[]},"version":"v1","identity":"rs-7984351","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7984351","identity":"rs-7984351","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}