VExUS Ultrasound-Based Noninvasive Risk Stratification Model for Esophageal Variceal Bleeding in Cirrhosis

VExUS Ultrasound-Based Noninvasive Risk Stratification Model for Esophageal Variceal Bleeding in Cirrhosis | 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 VExUS Ultrasound-Based Noninvasive Risk Stratification Model for Esophageal Variceal Bleeding in Cirrhosis Fei Wang, Zhen Tian, Long Chen, Yuan-Yuan Duan, Ya-Juan He, Mi Ke, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7334535/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Esophageal variceal bleeding (EVB) is a life-threatening complication of cirrhosis, necessitating accurate risk stratification. Current non-invasive methods lack sensitivity to dynamic hemodynamic changes. This study aimed to develop a VExUS ultrasound-based model integrating renal venous congestion and superior mesenteric artery (SMA) hemodynamics for noninvasive EVB risk prediction. Materials and Methods This retrospective study enrolled 161 patients with liver cirrhosis. Based on endoscopic findings, participants were stratified into two groups: those receiving endoscopic band ligation (EBL) (±pharmacotherapy) (n=78, 48.4%) and those managed conservatively (n=83, 51.6%). VExUS parameters, including renal vein Doppler patterns and superior mesenteric artery (SMA) peak systolic velocity (PSV), were analyzed alongside liver function markers. Using endoscopic results as the reference standard, we developed a predictive model for high-risk esophageal varices.The study protocol was approved by the Institutional Ethics Committee (No. XJTU1AF2024LSYY-339) and complied with the Declaration of Helsinki. Results The combination model (renal venous congestion + SMA-PSV + ALT) achieved an AUC of 0.931 (95% CI: 0.8916–0.9703), significantly outperforming individual parameters (p<0.01). Renal venous congestion alone showed an AUC of 0.841 (95% CI: 0.7745–0.9066). Conclusion The VExUS-based model provides a noninvasive, accurate tool for EVB risk stratification, potentially reducing reliance on endoscopy. Cirrhosis Esophageal varices VExUS Portal hypertension Hemodynamics Non-invasive assessment Figures Figure 1 Figure 2 Figure 3 Background Global Burden and Complications of Cirrhosis Cirrhosis ranks as the 11th leading cause of death worldwide, accounting for approximately 1 million annual fatalities, with a disproportionate impact on individuals aged 45–64 years where it is the third leading cause of mortality[1]. Among its life-threatening complications, esophageal varices (EV) develop in 50–60% of cirrhotic patients, escalating to 85% in those with compensated disease[2]. Esophagogastric variceal bleeding (EVB) remains a critical contributor to cirrhosis-related deaths, with 6-week mortality rates exceeding 20% despite advances in management[3]. Pathophysiology and Diagnostic Challenges of Portal Hypertension Portal hypertension, characterized by elevated portal venous pressure due to increased intrahepatic resistance and splanchnic hyperemia[4], manifests through splenomegaly, ascites, and collateral circulation formation (e.g., esophageal varices, caput medusae). While hepatic venous pressure gradient (HVPG) measurement and endoscopy remain gold standards for assessing clinically significant portal hypertension (CSPH) and variceal risk[2,5], their limitations are profound: HVPG is invasive and unsuitable for serial monitoring, whereas endoscopy requires repeated procedures for diagnosis and treatment[6]. Although non-selective β-blockers (NSBBs) and endoscopic variceal ligation (EVL) reduce bleeding risk, they neither reverse variceal progression nor eliminate the need for invasive surveillance[7]. Limitations of Current Non-Invasive Approaches Non-invasive tests (NITs)—including platelet count, liver stiffness measurement (LSM), and spleen diameter—have emerged as alternatives to predict CSPH and variceal bleeding risk[8]. However, these parameters exhibit suboptimal accuracy (AUROC: 0.75–0.85) and fail to capture dynamic hemodynamic changes, particularly venous congestion—a hallmark of progressive portal hypertension[9]. For instance, LSM reflects hepatic fibrosis but not real-time portal flow alterations, while spleen size correlates poorly with acute hemodynamic decompensation[10]. VExUS Ultrasound: A Novel Paradigm for Venous Congestion Assessment Venous Excess Ultrasound (VExUS), initially developed to quantify systemic venous congestion in cardiac failure[11], integrates inferior vena cava (IVC) diameter with Doppler evaluation of hepatic, portal, and renal veins to provide real-time hemodynamic profiling. While VExUS excels in detecting right heart failure-induced congestion[12], its adaptation to portal hypertension remains unexplored. Crucially, portal hypertension-driven venous congestion differs mechanistically: hepatic resistance elevates splanchnic venous pressure without proportional IVC dilation due to ascites-mediated compression[13]. This pathophysiological distinction necessitates redefining VExUS criteria for cirrhotic populations. Study Rationale and Objectives This study pioneers the application of VExUS ultrasound in cirrhosis by developing a multiparameter model that combines renal venous congestion, superior mesenteric artery hemodynamics, and liver function markers. We hypothesize that this approach will outperform existing NITs by directly addressing venous congestion—the missing link in current risk stratification tools. Our objectives are twofold:1.To validate VExUS parameters (renal vein Doppler patterns) as portal hypertension-specific markers;2.To establish a non-invasive predictive model for EVB risk, reducing reliance on endoscopy. Materials and Methods Study Design and Population This single-center retrospective cohort study enrolled 215 patients with cirrhosis at the First Affiliated Hospital of Xi’an Jiaotong University, Shaanxi, China, between January 2023 and December 2024.Among the 215 consecutive patients with cirrhosis initially screened, 54 cases were excluded according to the exclusion criteria(Figure 1). The main reasons were a history of previous gastrointestinal bleeding or previous endoscopic intervention (n = 15, 27.8%), transjugular intrahepatic portosystemic shunt (TIPS) placement (n = 9, 16.7%), Hepatic/splenic surgery(n = 4, 7.4%), hepatocellular carcinoma (n = 3, 5.6%), Right heart failure(n = 3, 5.6%);and refusal to undergo the examination or incomplete data (n = 20, 37.0%). The final cohort comprised 161 patients, with 78 (48.4%) requiring endoscopic band ligation (EBL) (± pharmacotherapy) and 83 (51.6%) managed conservatively (Table 1).Among the 161 patients, the etiological distribution was as follows: hepatitis virus-related cirrhosis (n=84, banding group: 40 vs. non-banding group: 44), alcoholic cirrhosis (n=3, banding group: 2 vs. non-banding group: 1), non-alcoholic fatty liver disease-related cirrhosis (n=2, banding group: 1 vs. non-banding group: 1), autoimmune cirrhosis (n=28, banding group: 11 vs. non-banding group: 17), and cirrhosis due to other causes (n=44, banding group: 24 vs. non-banding group: 20).No significant differences in etiology distribution were observed between groups (p=0.73).The study protocol was approved by the Research Ethics Committee of the First Affiliated Hospital of Xi’an Jiaotong University (No. XJTU1AF2024LSYY-339) , and complied with the Declaration of Helsinki.Written informed consent was waived due to the retrospective nature of the study. A total of 215 consecutive patients with liver cirrhosis were initially screened. According to the predefined exclusion criteria, 54 cases (25.1%) were excluded for the following reasons:History of gastrointestinal bleeding or prior endoscopic intervention (n=15, 27.8%);Transjugular intrahepatic portosystemic shunt (TIPS) placement (n=9, 16.7%);Hepatic or splenic surgery (n=4, 7.4%);Hepatocellular carcinoma (n=3, 5.6%);Right heart failure (n=3, 5.6%);Refusal to participate or incomplete data (n=20, 37.0%).The final analysis cohort comprised 161 patients. Inclusion Criteria: Age ≥18 years; Clinical or histopathological diagnosis of cirrhosis according to AASLD criteria[1]; Completion of both endoscopic and VExUS ultrasound examinations within the study period. Exclusion Criteria: History of gastrointestinal bleeding or prior endoscopic intervention; Transjugular intrahepatic portosystemic shunt (TIPS) placement; Hepatic/splenic surgery; Right heart failure[12]; Active HCC [14]; Refusal of examinations or incomplete data. Clinical Data Collection Demographic (age, sex) and laboratory parameters were extracted from electronic health records, including:Liver function: ALT, total bilirubin (TBil), INR, albumin;Renal function: creatinine;Hemostatic profile: platelet count.Child-Pugh and MELD scores were calculated using standard formulae[15]:MELD= 3.8 × ln[TBil (μmol/L) ÷ 17.1] + 11.2 × ln(INR) + 9.6 × ln[Scr (μmol/L) ÷ 88.4] + 6.4(Values <1 were set to 1 to avoid negative scores). Endoscopic Protocol All patients underwent esophagogastroduodenoscopy (EGD) within one month of ultrasound examination, following the 2023 Chinese Guidelines for Gastroesophageal Varices Management[16]. Varices were classified using the LDRf system:Rf0/Rf1 (Low-risk): No treatment or medical management;Rf2 (High-risk): Endoscopic variceal ligation (EVL) performed by expert endoscopists (≥5 years’ experience). VExUS Ultrasound Protocol Examinations were conducted using a Mindray M10 portable ultrasound system with linear (4–15 MHz) and convex (2–5 MHz) probes, following standardized VExUS protocols[17]:Preparation: 6–8 hour fasting, supine position;B-mode Imaging:Liver/spleen dimensions;Portal vein diameter (mid-extrahepatic segment);Inferior vena cava (IVC) diameter (subxiphoid view during quiet respiration).Doppler Assessment:Hepatic artery: Resistance index (RI) at the porta hepatis;Superior mesenteric artery (SMA): Peak systolic velocity (PSV) measured 1 cm distal to the aortic origin;Renal vein: Interlobar vein Doppler waveforms (continuous/discontinuous/monophasic) graded according to VExUS criteria.HV Doppler:Normal Hepatic Vein Doppler:S>D;Mild Hepatic Vein Abnormality: S<D;Severe Hepatic VeinAbnormality:S Reversal;PV Doppler:The portal vein pulsatility fraction (PVPF) is calculated using the formula:(Vmax − Vmin) / Vmax × 100%,Normal PVPF values are <30%,PVPF exceeding 30% but remaining below 50% suggests mild venous congestion,PVPF ≥50% indicates severe venous congestion;IRV Doppler:Normal physiology: Persistent continuous monophasic flow;Moderate venous congestion: Progresses to discontinuous biphasic pattern;Severe venous congestion: Becomes monophasic pulsatile waveform(Figure 2). All measurements were performed by two sonographers blinded to endoscopic results, with inter-observer variability assessed via intraclass correlation coefficient (ICC >0.85). Statistical Analysis Data were analyzed using SPSSAU (v24.0) and R (v4.2.1)[18]. Continuous variables are expressed as mean±SD (normal distribution) or median[IQR] (non-normal), compared via independent t-tests or Mann-Whitney U tests. Categorical variables were analyzed using χ² or Fisher’s exact tests. Variables with p<0.10 in univariate analysis were entered into backward stepwise logistic regression (retention threshold: p<0.05). Model performance was evaluated by ROC analysis (DeLong’s method for AUC comparison). A two-tailed p<0.05 indicated statistical significance. Results Baseline Characteristics Overall, a total of 161 patients with liver cirrhosis were enrolled in this study. Based on endoscopic findings, they were divided into two groups: 78 patients (48.4%) who required EBL and 83 patients (51.6%) who received conservative management (Table 1). Table 1. Baseline Characteristics of the Study Population Variable N=161 Grouping p Non-EBL Group(n=83) EBL Group(n=78) Age（year） 56.89±12.56 58.24±12.19 55.46±12.86 0.161 Gender 0.834 Female 75(46.58) 38(45.78) 37(47.44) Male 86(53.42) 45(54.22) 41(52.56) Platelets（×10⁹/L） 96.65±62.63 108.55±68.68 83.97±53.03 0.012* INR 1.28±0.28 1.24±0.24 1.32±0.31 0.072 ALT（U/L） 47.59±66.38 64.19±84.41 29.94±30.92 0.001** AST（U/L） 53.06±82.27 64.25±72.79 41.14±90.23 0.077 Serum Bilirubin（mg/dL） 34.68±45.59 40.75±38.67 28.21±51.43 0.081 Serum Albumin（g/L） 35.67±9.29 35.50±10.37 35.86±8.05 0.807 PT（s） 15.04±5.46 14.40±2.64 15.73±7.32 0.121 Scr（mg/dL） 59.24±18.91 59.48±16.74 58.99±21.07 0.869 Hepatic Encephalopathy 0.070 None 152(94.41) 81(97.59) 71(91.03) Present 9(5.59) 2(2.41) 7(8.97) Child-Pugh Score 7.28±2.21 7.29±2.40 7.27±2.00 0.954 MELD Score 5.24±5.36 5.93±5.60 4.50±5.02 0.091 Child-Pugh Classification 0.019* A 73(45.34) 41(49.40) 32(41.03) B 62(38.51) 24(28.92) 38(48.72) C 26(16.15) 18(21.69) 8(10.26) MELD Classification 0.386 Low 4(2.48) 2(2.41) 2(2.56) Medium 155(96.27) 79(95.18) 76(97.44) High 2(1.24) 2(2.41) 0(0.00) SMA PSV（cm/s） 116.47±36.18 135.98±27.74 95.71±32.45 0.000** SMA EDV（cm/s） 21.62±9.85 26.31±9.63 16.62±7.33 0.000** SMA RI 0.80±0.10 0.81±0.05 0.80±0.14 0.762 HA PSV（cm/s） 65.37±20.39 67.19±17.38 63.42±23.00 0.245 HA EDV（cm/s） 19.47±7.17 21.34±6.88 17.49±6.97 0.001** HA RI 0.70±0.07 0.68±0.07 0.71±0.06 0.000** PV Diameter（mm） 13.10±3.15 12.35±1.95 13.90±3.92 0.002** PV Flow Velocity（cm/s） 25.52±9.52 25.90±6.85 25.12±11.74 0.607 PV PI 0.35±0.13 0.32±0.11 0.37±0.14 0.020* Spleen Length（mm） 140.03±50.64 125.70±28.86 155.09±63.01 0.000** Spleen Thickness（mm） 46.18±16.77 41.27±11.31 51.35±19.83 0.000** SV Diameter（mm） 9.12±3.52 8.39±2.89 9.89±3.95 0.007** SV Flow Velocity（cm/s） 16.33±6.58 15.93±5.63 16.74±7.46 0.434 IVC Variability（%） 34.47±12.90 38.65±14.64 30.03±8.87 0.000** Inferior Vena Cava（mm） 15.82±3.25 16.06±3.18 15.56±3.32 0.335 PV Doppler 0.005** Normal 66(40.99) 43(51.81) 23(29.49) Mild Abnormality 73(45.34) 34(40.96) 39(50.00) Severe Abnormality 22(13.66) 6(7.23) 16(20.51) HV Doppler 0.009** Normal 112(69.57) 64(77.11) 48(61.54) Mild Abnormality 7(4.35) 0(0.00) 7(8.97) Severe Abnormality 42(26.09) 19(22.89) 23(29.49) IRV Doppler 0.000** Normal 98(60.87) 78(93.98) 20(25.64) Mild Abnormality 3(1.86) 0(0.00) 3(3.85) Severe Abnormality 60(37.27) 5(6.02) 55(70.51) VExUS Grade 0.045* Grade 0 140(86.96) 73(87.95) 67(85.90) Grade 1 9(5.59) 7(8.43) 2(2.56) Grade 2 7(4.35) 3(3.61) 4(5.13) Grade 3 5(3.11) 0(0.00) 5(6.41) UV Reopening 0.003** None 141(87.58) 79(95.18) 62(79.49) Present 20(12.42) 4(4.82) 16(20.51) * p<0.05 ** p<0.01.International Normalized Ratio(INR);Alanine Transaminase (ALT);Aspartate Transaminase (AST);Prothrombin Time(PT);Serum Creatinine(Scr);Superior Mesenteric Artery(SMA);Peak Systolic Velocity (PSV);End-Diastolic Velocity (EDV);Resistance Index (RI)RI;Hepatic Artery(HA);Portal Vein（PV）;Splenic Vein（SV）;Inferior Vena Cava（IVC）IVC; Hepatic Veins(HV);Intra-rena Venous(IRV);Venous Excess Utrasound (VExUS);Umbilical Venous（UV）. Key demographic and clinical comparisons revealed: Laboratory Markers: The EBL group exhibited significantly lower platelet counts (84.0±53.0 vs. 108.6±68.7×10⁹/L, p=0.012) and ALT levels (29.9±30.9 vs. 64.2±84.4 U/L, p=0.001). No differences in INR, bilirubin, or MELD scores were observed (all p>0.05). Disease Severity: Higher proportions of Child-Pugh class B/C were noted in the EBL group (59.0% vs. 50.6%, p=0.019). Hemodynamic Profiles: Reduced splanchnic perfusion: Banding patients demonstrated lower SMA peak systolic velocity (95.7±32.5 vs. 136.0±27.7 cm/s, p<0.001) and higher portal vein diameter (13.9±3.9 vs. 12.4±2.0 mm, p=0.002). Venous congestion markers: Severe intra-renal venous Doppler abnormalities were predominant in the EBL group (70.5% vs. 6.0%, p<0.001). Univariate analysis Univariate analysis results showed that there were no significant statistical differences between the banding and non-banding groups in terms of age, gender, Child score, MELD score, and other general data. However, there were statistical differences between the two groups in platelet count, alanine aminotransferase (ALT), Child classification, peak systolic velocity of the superior mesenteric artery, diastolic velocity of the superior mesenteric artery, diastolic velocity of the hepatic artery, hepatic artery resistance index (RI), portal vein diameter, portal vein pulsatility index (PI), spleen length, spleen thickness, splenic vein diameter, inferior vena cava variation rate, degree of portal vein congestion, degree of hepatic vein congestion, degree of renal vein congestion, ultrasound venous congestion (VExUS), and recanalization of the umbilical vein, with P < 0.05.（Table 2） Table 2.Summary of Binary Logit Regression Analysis Results Variable B SE z Waldχ2 p OR 95% CI for OR Platelets -0.007 0.006 -1.079 1.165 0.280 0.993 0.981 ~ 1.005 ALT -0.015 0.008 -1.855 3.440 0.064 0.985 0.969 ~ 1.001 Child-Pugh Classification -0.786 0.492 -1.597 2.552 0.110 0.455 0.174 ~ 1.195 SMA PSV -0.036 0.014 -2.494 6.221 0.013 0.965 0.938 ~ 0.992 SMA EDV -0.059 0.049 -1.197 1.433 0.231 0.943 0.857 ~ 1.038 PV Doppler 0.240 0.429 0.559 0.313 0.576 1.271 0.549 ~ 2.945 HA PSV -0.027 0.057 -0.474 0.225 0.635 0.974 0.871 ~ 1.088 HV Doppler 0.871 0.635 1.370 1.877 0.171 2.388 0.687 ~ 8.298 PV PI -5.209 2.758 -1.889 3.567 0.059 0.005 0.000 ~ 1.217 UV Reopening 1.649 1.028 1.604 2.572 0.109 5.202 0.693 ~ 39.02 SV Diameter -0.286 0.188 -1.521 2.313 0.128 0.751 0.519 ~ 1.086 HA RI 3.149 5.378 0.586 0.343 0.558 23.32 0.001 ~ 882318.2 Spleen Length 0.024 0.021 1.177 1.386 0.239 1.024 0.984 ~ 1.067 Spleen Thickness 0.031 0.067 0.460 0.212 0.645 1.031 0.904 ~ 1.176 IRV Doppler -1.656 0.366 -4.523 20.46 0.000 0.191 0.093 ~ 0.391 PV Diameter -0.061 0.092 -0.655 0.430 0.512 0.941 0.785 ~ 1.128 IVC Variability -0.032 0.029 -1.098 1.206 0.272 0.969 0.915 ~ 1.025 intercept 7.340 5.512 1.332 1.773 0.183 1541.308 0.031 ~ 75815288.664 McFadden R 2 = 0.622 Binary logistic regression analysis We included the indicators with statistical differences from the univariate analysis between the two groups into the binary logistic regression analysis. The results showed that the overall model was significant (P < 0.05, R2 = 0.638), with peak systolic velocity of the superior mesenteric artery and degree of renal vein congestion being independent factors affecting the need for banding treatment. When we separately modeled the peak systolic velocity of the superior mesenteric artery and degree of renal vein congestion, the model had an R2 = 0.487. Stepwise regression analysis indicated that alanine aminotransferase (ALT), peak systolic velocity of the superior mesenteric artery, and degree of left renal vein congestion had a significant negative impact on the banding group, with the model R2 = 0.509（Table 3）.The regression coefficient for the degree of renal vein congestion is 1.670, z = 5.669, p = 0.000 < 0.01, OR = 5.314.The regression coefficient for the peak systolic velocity of the superior mesenteric artery is -0（Table 3）.033, z = -4.046, p < 0.01, OR = 0.968.The model formula is:ln(p / (1 - p)) = 8.126 - 0.033 * peak systolic velocity of the superior mesenteric artery - 1.670 * degree of left renal vein congestion - 0.012 * alanine aminotransferase. Table 3.Results of Binary Logit Regression Analysis Variable B SE z  Wald χ2  p OR 95% CI for OR SMA PSV -0.033 0.008 -4.046 16.369 0.001 0.968 0.953 ~ 0.983 IRV Doppler -1.670 0.295 -5.669 32.138 0.001 0.188 0.106 ~ 0.335 ALT -0.012 0.006 -1.935 3.744 0.053 0.988 0.976 ~ 1.000 intercept 8.126 1.281 6.343 40.238 0.001 3381.956 274.620 ~ 41648.942 McFadden R 2 = 0.509 ROC Curve Analysis To evaluate the diagnostic performance of renal vein congestion, superior mesenteric artery peak systolic velocity, and liver function indicators in predicting the need for endoscopic treatment of esophageal varices, receiver operating characteristic (ROC) curve analysis was performed. The area under the curve (AUC) for renal vein congestion alone was 0.841(95% CI:0.775~0.907) , with a sensitivity of 74.4% and specificity of 94% at the optimal cutoff value of Intra-renal Venous Doppler Mild Abnormality. When renal vein congestion was combined with superior mesenteric artery peak systolic velocity and liver function indicators of ALT , the AUC increased to 0.931 (95% CI:0.892–0.970), with a sensitivity of 89.7% and specificity of 89.2% at the optimal cutoff value of 0.789. The combination model demonstrated significantly higher diagnostic accuracy compared to renal vein congestion alone (p < 0.01, DeLong’s test). Figure 3 shows the ROC curves for renal vein congestion alone and the combination model. Discussion This study developed a non-invasive model combining VExUS ultrasound parameters (renal vein congestion) and superior mesenteric artery peak systolic velocity with liver function indicators to predict the risk of esophageal varices bleeding in cirrhotic patients. The combination model demonstrated superior diagnostic accuracy compared to individual parameters alone. Renal venous congestion, a hallmark of systemic venous congestion in portal hypertension, was strongly associated with endoscopic treatment eligibility for esophageal varices. This aligns with Matsumoto et al(2018)[ 19 ], who linked left renal vein dilation to venous stasis and adverse outcomes in cirrhosis. Our study expands on this by incorporating spectral Doppler analysis of interlobar renal veins—a component of the VExUS protocol. Normal interlobar veins exhibit continuous waveforms (VExUS Grade 1), while progressive congestion leads to discontinuous (Grade 2) or monophasic patterns (Grade 3). We propose that portal hypertension-induced ascites and splanchnic venous engorgement elevate intra-abdominal pressure, compressing the IVC and transmitting pressure retrograde to the renal veins. This mechanism mirrors the pathophysiology of esophageal varices, where portal hypertension drives collateral circulation. Cirrhosis induces portal hypertension, which leads to esophageal varices. Meanwhile, it causes visceral vasodilation and insufficient effective circulating blood volume, activating the neurohumoral system and resulting in renal vasoconstriction and reduced renal perfusion. Additionally, the rupture and bleeding of esophageal varices will exacerbate neurohumoral disorders and inflammatory responses, thereby inducing or worsening nephrotic syndrome[ 20 ].By integrating renal venous congestion (reflecting systemic venous overload) with superior mesenteric artery hemodynamics (local splanchnic resistance), our model captures both systemic and localized manifestations of portal hypertension, offering a holistic risk assessment tool. SMA-PSV as a Hemodynamic Marker of Portal Hypertension.The significantly reduced SMA-PSV in the banding group suggests its potential role in reflecting splanchnic hemodynamic alterations secondary to portal hypertension. Consistent with the forward flow theory of portal hypertension, which posits that neurohormonal activation (e.g., angiotensin II) and vascular remodeling increase splanchnic arterial resistance[ 21 ], our findings of reduced SMA-PSV in the banding group align with prior observations of its inverse correlation with HVPG. This compensatory mechanism to limit portal inflow may coexist with renal venous congestion, reflecting the complex interplay between arterial and venous hemodynamics in cirrhosis[ 22 ].Altered mesenteric hemodynamics in cirrhosis were further evidenced by Sekimoto et al., who reported prolonged SMA-SMV microbubble transit time and impaired postprandial flow modulation using contrast-enhanced ultrasound[ 23 ].These findings complement our observations of reduced SMA-PSV, which may reflect compensatory vasoconstriction to mitigate portal overflow. Notably, the prognostic significance of SMA hemodynamics extends beyond portal hypertension, as low SMA-PSV has been independently associated with mortality in sepsis[ 24 ].Furthermore, severe intra-abdominal venous congestion and ascites may physically compress the SMA, exacerbating its diastolic dysfunction. Notably, while cirrhotic patients typically exhibit hyperdynamic circulation, the observed SMA-PSV reduction in our cohort might also indicate compromised cardiac output in advanced disease, possibly due to cirrhotic cardiomyopathy or sepsis-induced myocardial depression[ 25 , 26 ].The integration of SMA-PSV into our multiparameter model highlights its complementary value: it captures the “forward” component of portal hypertension (increased arterial resistance), whereas renal venous congestion reflects the “backward” consequences (venous overload). This dual assessment provides a more comprehensive risk stratification tool, as evidenced by the model’s superior AUC (0.931 vs. 0.841 for individual parameters). The absence of predictive value for the composite VExUS score in our model highlights the need to reinterpret venous congestion assessment in cirrhotic populations. While VExUS was originally designed for cardiac-related systemic congestion, its criteria may not translate directly to portal hypertension[ 27 ]. For instance, IVC dilation (≥ 2 cm)—a cornerstone of VExUS grading—is rare in cirrhosis due to competing effects of ascites-induced compression and reduced venous return. Similarly, hepatic vein waveform flattening in cirrhosis reflects intrahepatic obstruction rather than right atrial pressure transmission, rendering traditional VExUS HV grading less discriminative[ 28 ]. In contrast, renal venous congestion emerges as a portal hypertension-specific marker. By focusing on interlobar renal vein Doppler patterns, we bypassed the confounding effects of IVC/HV parameters, directly linking renal venous hemodynamics to variceal severity. Future iterations of VExUS should consider cirrhosis-specific thresholds and parameters (e.g., portal vein flow pulsatility) to improve applicability. This study has several limitations that should be acknowledged. First, the single-center retrospective design and moderate sample size (n = 161) may restrict the generalizability of findings and limit statistical power for detecting subtle associations in subgroup analyses. Second, operator-dependent variability in VExUS ultrasound measurements—particularly in Doppler waveform interpretation (e.g., distinguishing continuous vs. discontinuous patterns)—could introduce measurement bias and affect reproducibility. While standardized protocols were implemented, subjective differences in spectral analysis remain a challenge for ultrasound-based assessments.Future multicenter prospective studies with larger cohorts are required to validate these findings. Additionally, the development of automated Doppler analysis algorithms and AI-enhanced measurement tools could minimize inter-observer variability, thereby improving the reliability of VExUS grading in clinical practice. Our model integrates renal venous congestion, superior mesenteric artery velocity, and liver function markers to non-invasively predict esophageal variceal bleeding risk in cirrhosis. This approach may reduce endoscopy frequency, especially in resource-limited settings, while enhancing patient comfort. Future studies should validate its utility across diverse populations and integrate it into clinical practice for personalized care. Conclusion Our non-invasive model (AUC = 0.931), combining VExUS-based renal venous congestion, SMA velocity, and liver function markers, effectively predicts esophageal variceal bleeding risk in cirrhosis. It reduces reliance on endoscopy and enables dynamic risk stratification. Future efforts should validate this model across etiologies, automate measurements via AI, and integrate it into clinical workflows to transform portal hypertension management. Abbreviations Esophageal variceal bleeding(EVB);Superior mesenteric artery(SMA);Endoscopic band ligation(EBL);Peak systolic velocity(PSV);Esophageal varices (EV);Hepatic venous pressure gradient(HVPG);Clinically significant portal hypertension(CSPH);Venous Excess Ultrasound(VExUS);Esophagogastroduodenoscopy(EGD);Inferior vena cava (IVC);Resistance index (RI);Portal vein pulsatility fraction(PVPF);Receiver operating characteristic(ROC);Area under the curve(AUC);International Normalized Ratio(INR);Alanine Transaminase (ALT);Aspartate Transaminase(AST);Prothrombin Time(PT);Serum Creatinine(Scr);Superior Mesenteric Artery(SMA);End-Diastolic Velocity(EDV);Hepatic Artery(HA);Portal Vein(PV);Splenic Vein(SV);Inferior Vena Cava(IVC);Hepatic Veins(HV);Intra-rena Venous(IRV). Declarations Acknowledgements We sincerely thank our families for their unwavering encouragement and support throughout this work. We are also grateful to the anonymous reviewers for their insightful comments and valuable suggestions, which have significantly improved the quality of this manuscript. Authors' contributions Fei Wang : Conceptualization, Methodology, Writing – Original Draft Zhen Tian : Methodology, Writing – Original Draft, Data Curation Yuan-Yuan Duan : Formal Analysis, Visualization Long Chen : Investigation, Data Curation (Ultrasound Examination and Data Collection) Ya-Juan He : Data Curation, Investigation Mi Ke : Data Curation, Project Administration Yu-Xin Liu : Investigation (Patient Follow-up) Chen-Peng Mi : Data Curation, Investigation Jie Zhang : Data Curation, Project Administration Tian-Yan Chen : Conceptualization, Methodology, Supervision, Project Administration, Writing – Review & Editing, Corresponding Author All authors read and approved the final manuscript. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Availability of data and materials The datasets generated and/or analyzed during the current retrospective study are available from the corresponding author on reasonable request. All materials used are described within the manuscript. Ethics approval and consent to participate The study protocol was approved by the Institutional Ethics Committee (No. XJTU1AF2024LSYY-339) and complied with the Declaration of Helsinki. As this was a retrospective analysis of anonymized data, the requirement for informed consent from individual participants was waived by the approving ethics committee. Consent for publication Not applicable. This manuscript does not contain any individual person's data in any form (including individual details, images, or videos). Competing interests The authors declare that they have no competing interests relevant to the content of this manuscript. Author details 1 Ultrasound Department-Hepatobiliary and Gastrointestinal Ultrasound Section, The First Affiliated Hospital of Xi’an Jiaotong University,Xi’an City, Shaanxi province 710061, China. 2 Department of Infectious Diseases, The First Affiliated Hospital of Xi’an Jiaotong University,Xi’an City, Shaanxi province 710061, China. 3 Department of Ultrasound Medicine, Baoji High-Tech Hospital,Baoji City, Shaanxi province 710000, China. 4 Department of Viral Disease Laboratory,Xi'an Center for Disease Control and Prevention,,Xi’an City, Shaanxi province 710061, China. 5 Department of Medical Imaging,Xi'an Chang'an District Hospital,Xi’an City, Shaanxi province 710061, China. References GBD 2017 Cirrhosis Collaborators. The global, regional, and national burden of cirrhosis by cause in 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Gastroenterol Hepatol 2020;5(3):245-266. doi:10.1016/S2468-1253(19)30349-8. De Franchis R, Bosch J, Garcia-Tsao G, Reiberger T, Ripoll C; Baveno VII Faculty. Baveno VII - Renewing consensus in portal hypertension [published correction appears in J Hepatol. 2022 Jul;77(1):271. doi: 10.1016/j.jhep.2022.03.024]. J Hepatol 2022;76(4):959-974. doi:10.1016/j.jhep.2021.12.022. Garcia-Tsao G, Abraldes JG, Berzigotti A, Bosch J. Portal hypertensive bleeding in cirrhosis: Risk stratification, diagnosis, and management: 2016 practice guidance by the American Association for the study of liver diseases [published correction appears in Hepatology. 2017 Jul;66(1):304. doi: 10.1002/hep.29169]. Hepatology 2017;65(1):310-335. doi:10.1002/hep.28906. Iwakiri Y. Pathophysiology of portal hypertension. Clin Liver Dis 2014;18(2):281-291. doi:10.1016/j.cld.2013.12.001. Liu, C, Li, J, He, R, et al. A Combined Model of Spleen Stiffness and Baveno VII Criteria for Clinically Significant Portal Hypertension in Compensated Advanced Chronic Liver Disease: An International Multicenter Study Portal Hypertension &amp; Cirrhosis 2023; doi: 10.1002/poh2.70004. Berzigotti A, Bosch J, Boyer TD. Use of noninvasive markers of portal hypertension and timing of screening endoscopy for gastroesophageal varices in patients with chronic liver disease. Hepatology 2014;59(2):729-731. doi:10.1002/hep.26652. Tripathi D, Stanley AJ, Hayes PC, et al. U.K. guidelines on the management of variceal haemorrhage in cirrhotic patients. Gut 2015;64(11):1680-1704. doi:10.1136/gutjnl-2015-309262 Blachier M, Leleu H, Peck-Radosavljevic M, Valla DC, Roudot-Thoraval F. The burden of liver disease in Europe: a review of available epidemiological data. J Hepatol 2013;58(3):593-608. doi:10.1016/j.jhep.2012.12.005. European Association for Study of Liver; Asociacion Latinoamer-icana para el Estudio del Higado. EASL-ALEH Clinical Practice Guidelines: Non-invasive tests for evaluation of liver disease severity and prognosis. J Hepatol 2015;63(1):237–264. Abraldes JG, Bureau C, Stefanescu H, et al. Noninvasive tools and risk of clinically significant portal hypertension and varices in compensated cirrhosis: The "Anticipate" study [published correction appears in Hepatology. 2017 Jul;66(1):304-305. doi: 10.1002/hep.29201.]. Hepatology 2016;64(6):2173-2184. doi:10.1002/hep.28824. Colecchia A, Ravaioli F, Marasco G, et al. A combined model based on spleen stiffness measurement and Baveno VI criteria to rule out high-risk varices in advanced chronic liver disease. J Hepatol 2018;69(2):308-317. doi:10.1016/j.jhep.2018.04.023. Longino AA, Martin KC, Leyba KR, et al. Reliability and reproducibility of the venous excess ultrasound (VExUS) score, a multi-site prospective study: validating a novel ultrasound technique for comprehensive assessment of venous congestion. Crit Care 2024;28(1):197. Published 2024 Jun 11. doi:10.1186/s13054-024-04961-9. Anastasiou, V, Peteinidou, E, Moysidis, D, et al. Multiorgan congestion assessment by venous excess ultrasound score in acute heart failure .EUR HEART J 2025; 45. doi: 10.1093/eurheartj/ehae666.1001. Zhou, J, Sun, H, Wang, Z, et al. Guidelines for the Diagnosis and Treatment of Hepatocellular Carcinoma (2019 Edition). LIVER CANCER 2020; 9 (6): 682-720. doi: 10.1159/000509424. Gralnek, IM, Camus Duboc, M, Garcia-Pagan, JC, et al. Endoscopic diagnosis and management of esophagogastric variceal hemorrhage: European Society of Gastrointestinal Endoscopy (ESGE) Guideline. ENDOSCOPY 2022; 54 ENDOSCOPY. doi: 10.1055/a-1939-4887 Chinese Society of Hepatology, Chinese Society of Gastroenterology, and Chinese Society of Digestive Endoscopology of Chinese Medical Association.Guidelines on the management of esophagogastric variceal bleeding in cirrhotic portal hypertension. Chin J Hepatol October 2022, 30(10): 1029-1043.DOI：10.3760/cma.j.cn501113-20220824-00436. Assavapokee T , Rola P , Assavapokee N ,et al.Decoding VExUS: a practical guide for excelling in point-of-care ultrasound assessment of venous congestion.The Ultrasound Journal 2024, 16(1).DOI:10.1186/s13089-024-00396-z. The SPSSAU project (2024). SPSSAU. (Version 24.0) [Online Application Software]. Retrieved from https://www.spssau.com. Matsumoto, N, Ogawa, M, Kumagawa, M, et al. Renal vein dilation predicts poor outcome in patients with refractory cirrhotic ascites. HEPATOL RES 2018; 48 (3): E117-E125. doi: 10.1111/hepr.12935. Nevens, F, Bittencourt, PL, Coenraad, MJ, et al. Recommendations on the Diagnosis and Initial Management of Acute Variceal Bleeding and Hepatorenal Syndrome in Patients with Cirrhosis. DIGEST DIS SCI 2019; 64 (6): 1419-1431. doi: 10.1007/s10620-018-5448-y. Iwakiri, Y, Groszmann, RJ. The hyperdynamic circulation of chronic liver diseases: from the patient to the molecule. HEPATOLOGY 2006; 43 (2 Suppl 1): S121-31. doi: 10.1002/hep.20993. Sastre, E, Caracuel, L, Prieto, I, et al. Decompensated liver cirrhosis and neural regulation of mesenteric vascular tone in rats: role of sympathetic, nitrergic and sensory innervations. Sci Rep 2016; 6 31076. doi: 10.1038/srep31076. Sekimoto, T, Maruyama, H, Kondo, T, et al. Potential stagnation in the splanchnic hemodynamics demonstrated by the dynamic microbubbles in chronic liver disease. J GASTROEN HEPATOL 2015; 30 (6): 1001-8. doi: 10.1111/jgh.12875. Gai, X, Wang, Y, Gao, D, et al. Risk factors for the prognosis of patients with sepsis in intensive care units. PLoS One 2022; 17 (9): e0273377. doi: 10.1371/journal.pone.0273377. Wu, G, Chen, M, Fan, Q, et al. Transcriptome analysis of mesenteric arterioles changes and its mechanisms in cirrhotic rats with portal hypertension. BMC Genomics 2023; 24 (1): 20. doi: 10.1186/s12864-023-09125-7. Danielsen KV, Wiese S, Busk T, et al. Cardiovascular Mapping in Cirrhosis From the Compensated Stage to Hepatorenal Syndrome: A Magnetic Resonance Study. Am J Gastroenterol 2022;117(8):1269-1278. doi:10.14309/ajg.0000000000001847. Beaubien-Souligny, W, Rola, P, Haycock, K, et al. Quantifying systemic congestion with Point-Of-Care ultrasound: development of the venous excess ultrasound grading system. Ultrasound J 2020; 12 (1): 16. doi: 10.1186/s13089-020-00163-w. Koizumi, Y, Hirooka, M, Tanaka, T, et al. Noninvasive ultrasound technique for assessment of liver fibrosis and cardiac function in Fontan-associated liver disease: diagnosis based on elastography and hepatic vein waveform type. J MED ULTRASON 2021; 48 (2): 235-244. doi: 10.1007/s10396-020-01078-8. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 03 Jan, 2026 Reviewers agreed at journal 10 Dec, 2025 Reviewers invited by journal 05 Dec, 2025 Editor invited by journal 10 Nov, 2025 Editor assigned by journal 28 Aug, 2025 Submission checks completed at journal 28 Aug, 2025 First submitted to journal 28 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":37319,"visible":true,"origin":"","legend":"\u003cp\u003ePatient screening and enrollment flowchart\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7334535/v1/805d591da38bb2a7e8845d44.png"},{"id":97832006,"identity":"422733fd-fcc7-4c46-b347-7b9bc215514a","added_by":"auto","created_at":"2025-12-10 00:37:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":674775,"visible":true,"origin":"","legend":"\u003cp\u003eVExUS Ultrasound Protocol\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7334535/v1/4bdcdb78673ea9048c6de32e.png"},{"id":97832011,"identity":"f0f1dd36-95fb-4d49-92c6-cb2fb47d9f99","added_by":"auto","created_at":"2025-12-10 00:37:34","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":117013,"visible":true,"origin":"","legend":"\u003cp\u003eillustrates the receiver operating characteristic (ROC) curves for predicting the need of endoscopic treatment in cirrhosis patients.Left panel: ROC curve of renal vein congestion alone\u003c/p\u003e\n\u003cp\u003eRight panel: Comparison of ROC curves between the renal vein congestion model and the combination model.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7334535/v1/ded5489be69268610110a52e.jpeg"},{"id":97903249,"identity":"9af623b4-41fd-48e4-91a6-3c334918d9f1","added_by":"auto","created_at":"2025-12-10 15:54:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1540638,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7334535/v1/5f38e7ab-d1cc-4619-90ac-21e0fde2afda.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"VExUS Ultrasound-Based Noninvasive Risk Stratification Model for Esophageal Variceal Bleeding in Cirrhosis","fulltext":[{"header":"Background","content":"\u003cp\u003eGlobal Burden and Complications of Cirrhosis\u003c/p\u003e\n\u003cp\u003eCirrhosis ranks as the 11th leading cause of death worldwide, accounting for approximately 1 million annual fatalities, with a disproportionate impact on individuals aged 45\u0026ndash;64 years where it is the third leading cause of mortality[1]. Among its life-threatening complications, esophageal varices (EV) develop in 50\u0026ndash;60% of cirrhotic patients, escalating to 85% in those with compensated disease[2]. Esophagogastric variceal bleeding (EVB) remains a critical contributor to cirrhosis-related deaths, with 6-week mortality rates exceeding 20% despite advances in management[3].\u003c/p\u003e\n\u003cp\u003ePathophysiology and Diagnostic Challenges of Portal Hypertension\u003c/p\u003e\n\u003cp\u003ePortal hypertension, characterized by elevated portal venous pressure due to increased intrahepatic resistance and splanchnic hyperemia[4], manifests through splenomegaly, ascites, and collateral circulation formation (e.g., esophageal varices, caput medusae). While hepatic venous pressure gradient (HVPG) measurement and endoscopy remain gold standards for assessing clinically significant portal hypertension (CSPH) and variceal risk[2,5], their limitations are profound: HVPG is invasive and unsuitable for serial monitoring, whereas endoscopy requires repeated procedures for diagnosis and treatment[6]. Although non-selective\u0026nbsp;\u0026beta;-blockers (NSBBs) and endoscopic variceal ligation (EVL) reduce bleeding risk, they neither reverse variceal progression nor eliminate the need for invasive surveillance[7].\u003c/p\u003e\n\u003cp\u003eLimitations of Current Non-Invasive Approaches\u003c/p\u003e\n\u003cp\u003eNon-invasive tests (NITs)\u0026mdash;including platelet count, liver stiffness measurement (LSM), and spleen diameter\u0026mdash;have emerged as alternatives to predict CSPH and variceal bleeding risk[8]. However, these parameters exhibit suboptimal accuracy (AUROC: 0.75\u0026ndash;0.85) and fail to capture dynamic hemodynamic changes, particularly venous congestion\u0026mdash;a hallmark of progressive portal hypertension[9]. For instance, LSM reflects hepatic fibrosis but not real-time portal flow alterations, while spleen size correlates poorly with acute hemodynamic decompensation[10].\u003c/p\u003e\n\u003cp\u003eVExUS Ultrasound: A Novel Paradigm for Venous Congestion Assessment\u003c/p\u003e\n\u003cp\u003eVenous Excess Ultrasound (VExUS), initially developed to quantify systemic venous congestion in cardiac failure[11], integrates inferior vena cava (IVC) diameter with Doppler evaluation of hepatic, portal, and renal veins to provide real-time hemodynamic profiling. While VExUS excels in detecting right heart failure-induced congestion[12], its adaptation to portal hypertension remains unexplored. Crucially, portal hypertension-driven venous congestion differs mechanistically: hepatic resistance elevates splanchnic venous pressure without proportional IVC dilation due to ascites-mediated compression[13]. This pathophysiological distinction necessitates redefining VExUS criteria for cirrhotic populations.\u003c/p\u003e\n\u003cp\u003eStudy Rationale and Objectives\u003c/p\u003e\n\u003cp\u003eThis study pioneers the application of VExUS ultrasound in cirrhosis by developing a multiparameter model that combines renal venous congestion, superior mesenteric artery hemodynamics, and liver function markers. We hypothesize that this approach will outperform existing NITs by directly addressing venous congestion\u0026mdash;the missing link in current risk stratification tools. Our objectives are twofold:1.To validate VExUS parameters (renal vein Doppler patterns) as portal hypertension-specific markers;2.To establish a non-invasive predictive model for EVB risk, reducing reliance on endoscopy.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eStudy Design and Population\u003c/p\u003e\n\u003cp\u003eThis single-center retrospective cohort study enrolled 215 patients with cirrhosis at the First Affiliated Hospital of Xi\u0026rsquo;an Jiaotong University, Shaanxi, China, between January 2023 and December 2024.Among the 215 consecutive patients with cirrhosis initially screened, 54 cases were excluded according to the exclusion criteria(Figure 1). The main reasons were a history of previous gastrointestinal bleeding or previous endoscopic intervention (n = 15, 27.8%), transjugular intrahepatic portosystemic shunt (TIPS) placement (n = 9, 16.7%), Hepatic/splenic surgery(n = 4, 7.4%), hepatocellular carcinoma (n = 3, 5.6%), Right heart failure(n = 3, 5.6%);and refusal to undergo the examination or incomplete data (n = 20, 37.0%). The final cohort comprised 161 patients, with 78 (48.4%) requiring endoscopic band ligation (EBL) (\u0026plusmn; pharmacotherapy) and 83 (51.6%) managed conservatively (Table 1).Among the 161 patients, the etiological distribution was as follows: hepatitis virus-related cirrhosis (n=84, banding group: 40 vs. non-banding group: 44), alcoholic cirrhosis (n=3, banding group: 2 vs. non-banding group: 1), non-alcoholic fatty liver disease-related cirrhosis (n=2, banding group: 1 vs. non-banding group: 1), autoimmune cirrhosis (n=28, banding group: 11 vs. non-banding group: 17), and cirrhosis due to other causes (n=44, banding group: 24 vs. non-banding group: 20).No significant differences in etiology distribution were observed between groups (p=0.73).The study protocol was approved by the Research Ethics Committee of the First Affiliated Hospital of Xi\u0026rsquo;an Jiaotong University (No. XJTU1AF2024LSYY-339) , and complied with the Declaration of Helsinki.Written informed consent was waived due to the retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003eA total of 215 consecutive patients with liver cirrhosis were initially screened. According to the predefined exclusion criteria, 54 cases (25.1%) were excluded for the following reasons:History of gastrointestinal bleeding or prior endoscopic intervention (n=15, 27.8%);Transjugular intrahepatic portosystemic shunt (TIPS) placement (n=9, 16.7%);Hepatic or splenic surgery (n=4, 7.4%);Hepatocellular carcinoma (n=3, 5.6%);Right heart failure (n=3, 5.6%);Refusal to participate or incomplete data (n=20, 37.0%).The final analysis cohort comprised 161 patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInclusion Criteria:\u003c/p\u003e\n\u003cp\u003eAge \u0026ge;18 years;\u003c/p\u003e\n\u003cp\u003eClinical or histopathological diagnosis of cirrhosis according to AASLD criteria[1];\u003c/p\u003e\n\u003cp\u003eCompletion of both endoscopic and VExUS ultrasound examinations within the study period.\u003c/p\u003e\n\u003cp\u003eExclusion Criteria:\u003c/p\u003e\n\u003cp\u003eHistory of gastrointestinal bleeding or prior endoscopic intervention;\u003c/p\u003e\n\u003cp\u003eTransjugular intrahepatic portosystemic shunt (TIPS) placement;\u003c/p\u003e\n\u003cp\u003eHepatic/splenic surgery;\u003c/p\u003e\n\u003cp\u003eRight heart failure[12];\u003c/p\u003e\n\u003cp\u003eActive HCC [14];\u003c/p\u003e\n\u003cp\u003eRefusal of examinations or incomplete data.\u003c/p\u003e\n\u003cp\u003eClinical Data Collection\u003c/p\u003e\n\u003cp\u003eDemographic (age, sex) and laboratory parameters were extracted from electronic health records, including:Liver function: ALT, total bilirubin (TBil), INR, albumin;Renal function: creatinine;Hemostatic profile: platelet count.Child-Pugh and MELD scores were calculated using standard formulae[15]:MELD= 3.8 \u0026times; ln[TBil (\u0026mu;mol/L) \u0026divide; 17.1] + 11.2 \u0026times; ln(INR) + 9.6 \u0026times; ln[Scr (\u0026mu;mol/L) \u0026divide; 88.4] + 6.4(Values \u0026lt;1 were set to 1 to avoid negative scores).\u003c/p\u003e\n\u003cp\u003eEndoscopic Protocol\u003c/p\u003e\n\u003cp\u003eAll patients underwent esophagogastroduodenoscopy (EGD) within one month of ultrasound examination, following the 2023 Chinese Guidelines for Gastroesophageal Varices Management[16]. Varices were classified using the LDRf system:Rf0/Rf1 (Low-risk): No treatment or medical management;Rf2 (High-risk): Endoscopic variceal ligation (EVL) performed by expert endoscopists (\u0026ge;5 years\u0026rsquo; experience).\u003c/p\u003e\n\u003cp\u003eVExUS Ultrasound Protocol\u003c/p\u003e\n\u003cp\u003eExaminations were conducted using a Mindray M10 portable ultrasound system with linear (4\u0026ndash;15 MHz) and convex (2\u0026ndash;5 MHz) probes, following standardized VExUS protocols[17]:Preparation: 6\u0026ndash;8 hour fasting, supine position;B-mode Imaging:Liver/spleen dimensions;Portal vein diameter (mid-extrahepatic segment);Inferior vena cava (IVC) diameter (subxiphoid view during quiet respiration).Doppler Assessment:Hepatic artery: Resistance index (RI) at the porta hepatis;Superior mesenteric artery (SMA): Peak systolic velocity (PSV) measured 1 cm distal to the aortic origin;Renal vein: Interlobar vein Doppler waveforms (continuous/discontinuous/monophasic) graded according to VExUS criteria.HV Doppler:Normal Hepatic Vein Doppler:S\u0026gt;D;Mild Hepatic Vein Abnormality: S\u0026lt;D;Severe Hepatic VeinAbnormality:S Reversal;PV Doppler:The portal vein pulsatility fraction (PVPF) is calculated using the formula:(Vmax \u0026minus; Vmin) / Vmax \u0026times; 100%,Normal PVPF values are \u0026lt;30%,PVPF exceeding 30% but remaining below 50% suggests mild venous congestion,PVPF \u0026ge;50% indicates severe venous congestion;IRV Doppler:Normal physiology: Persistent continuous monophasic flow;Moderate venous congestion: Progresses to discontinuous biphasic pattern;Severe venous congestion: Becomes monophasic pulsatile waveform(Figure 2).\u003c/p\u003e\n\u003cp\u003eAll measurements were performed by two sonographers blinded to endoscopic results, with inter-observer variability assessed via intraclass correlation coefficient (ICC \u0026gt;0.85).\u003c/p\u003e\n\u003cp\u003eStatistical Analysis\u003c/p\u003e\n\u003cp\u003eData were analyzed using SPSSAU (v24.0) and R (v4.2.1)[18]. Continuous variables are expressed as mean\u0026plusmn;SD (normal distribution) or median[IQR] (non-normal), compared via independent t-tests or Mann-Whitney U tests. Categorical variables were analyzed using \u0026chi;\u0026sup2; or Fisher\u0026rsquo;s exact tests. Variables with p\u0026lt;0.10 in univariate analysis were entered into backward stepwise logistic regression (retention threshold: p\u0026lt;0.05). Model performance was evaluated by ROC analysis (DeLong\u0026rsquo;s method for AUC comparison). A two-tailed p\u0026lt;0.05 indicated statistical significance.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eBaseline Characteristics\u003c/p\u003e\n\u003cp\u003eOverall, a total of 161 patients with liver cirrhosis were enrolled in this study. Based on endoscopic findings, they were divided into two groups: 78 patients (48.4%) who required EBL and 83 patients (51.6%) who received conservative management (Table 1).\u003c/p\u003e\n\u003cp\u003eTable 1. Baseline Characteristics of the Study Population\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 165px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 114px;\"\u003e\n \u003cp\u003eN=161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 204px;\"\u003e\n \u003cp\u003eGrouping\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 68px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eNon-EBL Group(n=83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eEBL\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Group(n=78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eAge（year）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e56.89\u0026plusmn;12.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e58.24\u0026plusmn;12.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e55.46\u0026plusmn;12.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.834\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e75(46.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e38(45.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e37(47.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e86(53.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e45(54.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e41(52.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003ePlatelets（\u0026times;10⁹/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e96.65\u0026plusmn;62.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e108.55\u0026plusmn;68.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e83.97\u0026plusmn;53.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.012*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eINR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1.28\u0026plusmn;0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e1.24\u0026plusmn;0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e1.32\u0026plusmn;0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eALT（U/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e47.59\u0026plusmn;66.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e64.19\u0026plusmn;84.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e29.94\u0026plusmn;30.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eAST（U/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e53.06\u0026plusmn;82.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e64.25\u0026plusmn;72.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e41.14\u0026plusmn;90.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eSerum Bilirubin（mg/dL）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e34.68\u0026plusmn;45.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e40.75\u0026plusmn;38.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e28.21\u0026plusmn;51.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eSerum Albumin（g/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e35.67\u0026plusmn;9.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e35.50\u0026plusmn;10.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e35.86\u0026plusmn;8.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.807\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003ePT（s）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e15.04\u0026plusmn;5.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e14.40\u0026plusmn;2.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e15.73\u0026plusmn;7.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eScr（mg/dL）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e59.24\u0026plusmn;18.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e59.48\u0026plusmn;16.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e58.99\u0026plusmn;21.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.869\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eHepatic Encephalopathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e152(94.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e81(97.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e71(91.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e9(5.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e2(2.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e7(8.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eChild-Pugh Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e7.28\u0026plusmn;2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e7.29\u0026plusmn;2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e7.27\u0026plusmn;2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eMELD Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e5.24\u0026plusmn;5.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e5.93\u0026plusmn;5.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e4.50\u0026plusmn;5.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eChild-Pugh Classification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.019*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e73(45.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e41(49.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e32(41.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e62(38.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e24(28.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e38(48.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e26(16.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e18(21.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e8(10.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eMELD Classification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.386\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e4(2.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e2(2.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e2(2.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e155(96.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e79(95.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e76(97.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e2(1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e2(2.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0(0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eSMA PSV（cm/s）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e116.47\u0026plusmn;36.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e135.98\u0026plusmn;27.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e95.71\u0026plusmn;32.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eSMA EDV（cm/s）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e21.62\u0026plusmn;9.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e26.31\u0026plusmn;9.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e16.62\u0026plusmn;7.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eSMA RI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.80\u0026plusmn;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.81\u0026plusmn;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.80\u0026plusmn;0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eHA PSV（cm/s）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e65.37\u0026plusmn;20.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e67.19\u0026plusmn;17.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e63.42\u0026plusmn;23.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eHA EDV（cm/s）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e19.47\u0026plusmn;7.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e21.34\u0026plusmn;6.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e17.49\u0026plusmn;6.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eHA RI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.70\u0026plusmn;0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.68\u0026plusmn;0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.71\u0026plusmn;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003ePV Diameter（mm）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e13.10\u0026plusmn;3.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e12.35\u0026plusmn;1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e13.90\u0026plusmn;3.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.002**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003ePV Flow Velocity（cm/s）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e25.52\u0026plusmn;9.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e25.90\u0026plusmn;6.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e25.12\u0026plusmn;11.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003ePV PI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.35\u0026plusmn;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.32\u0026plusmn;0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.37\u0026plusmn;0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.020*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eSpleen Length（mm）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e140.03\u0026plusmn;50.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e125.70\u0026plusmn;28.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e155.09\u0026plusmn;63.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eSpleen Thickness（mm）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e46.18\u0026plusmn;16.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e41.27\u0026plusmn;11.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e51.35\u0026plusmn;19.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eSV Diameter（mm）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e9.12\u0026plusmn;3.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e8.39\u0026plusmn;2.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e9.89\u0026plusmn;3.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.007**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eSV Flow Velocity（cm/s）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e16.33\u0026plusmn;6.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e15.93\u0026plusmn;5.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e16.74\u0026plusmn;7.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eIVC Variability（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e34.47\u0026plusmn;12.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e38.65\u0026plusmn;14.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e30.03\u0026plusmn;8.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eInferior Vena Cava（mm）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e15.82\u0026plusmn;3.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e16.06\u0026plusmn;3.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e15.56\u0026plusmn;3.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003ePV Doppler\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.005**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e66(40.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e43(51.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e23(29.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eMild Abnormality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e73(45.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e34(40.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e39(50.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eSevere Abnormality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e22(13.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e6(7.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e16(20.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eHV Doppler\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.009**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e112(69.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e64(77.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e48(61.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eMild Abnormality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e7(4.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0(0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e7(8.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eSevere Abnormality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e42(26.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e19(22.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e23(29.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eIRV Doppler\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e98(60.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e78(93.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e20(25.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eMild Abnormality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e3(1.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0(0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e3(3.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eSevere Abnormality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e60(37.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e5(6.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e55(70.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eVExUS Grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.045*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eGrade 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e140(86.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e73(87.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e67(85.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eGrade 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e9(5.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e7(8.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e2(2.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eGrade 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e7(4.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e3(3.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e4(5.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eGrade 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e5(3.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0(0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e5(6.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eUV Reopening\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.003**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e141(87.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e79(95.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e62(79.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e20(12.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e4(4.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e16(20.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* p\u0026lt;0.05 ** p\u0026lt;0.01.International Normalized Ratio(INR);Alanine Transaminase (ALT);Aspartate Transaminase (AST);Prothrombin Time(PT);Serum Creatinine(Scr);Superior Mesenteric Artery(SMA);Peak Systolic Velocity (PSV);End-Diastolic Velocity (EDV);Resistance Index (RI)RI;Hepatic Artery(HA);Portal Vein（PV）;Splenic Vein（SV）;Inferior Vena Cava（IVC）IVC; Hepatic Veins(HV);Intra-rena Venous(IRV);Venous Excess Utrasound (VExUS);Umbilical Venous（UV）.\u003c/p\u003e\n\u003cp\u003eKey demographic and clinical comparisons revealed:\u003c/p\u003e\n\u003cp\u003eLaboratory Markers:\u003c/p\u003e\n\u003cp\u003eThe EBL group exhibited significantly lower platelet counts (84.0\u0026plusmn;53.0 vs. 108.6\u0026plusmn;68.7\u0026times;10⁹/L, p=0.012) and ALT levels (29.9\u0026plusmn;30.9 vs. 64.2\u0026plusmn;84.4 U/L, p=0.001).\u003c/p\u003e\n\u003cp\u003eNo differences in INR, bilirubin, or MELD scores were observed (all p\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003eDisease Severity:\u003c/p\u003e\n\u003cp\u003eHigher proportions of Child-Pugh class B/C were noted in the EBL group (59.0% vs. 50.6%, p=0.019).\u003c/p\u003e\n\u003cp\u003eHemodynamic Profiles:\u003c/p\u003e\n\u003cp\u003eReduced splanchnic perfusion: Banding patients demonstrated lower SMA peak systolic velocity (95.7\u0026plusmn;32.5 vs. 136.0\u0026plusmn;27.7 cm/s, p\u0026lt;0.001) and higher portal vein diameter (13.9\u0026plusmn;3.9 vs. 12.4\u0026plusmn;2.0 mm, p=0.002).\u003c/p\u003e\n\u003cp\u003eVenous congestion markers: Severe intra-renal venous Doppler abnormalities were predominant in the EBL group (70.5% vs. 6.0%, p\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003eUnivariate analysis\u003c/p\u003e\n\u003cp\u003eUnivariate analysis results showed that there were no significant statistical differences between the banding and non-banding groups in terms of age, gender, Child score, MELD score, and other general data. However, there were statistical differences between the two groups in platelet count, alanine aminotransferase (ALT), Child classification, peak systolic velocity of the superior mesenteric artery, diastolic velocity of the superior mesenteric artery, diastolic velocity of the hepatic artery, hepatic artery resistance index (RI), portal vein diameter, portal vein pulsatility index (PI), spleen length, spleen thickness, splenic vein diameter, inferior vena cava variation rate, degree of portal vein congestion, degree of hepatic vein congestion, degree of renal vein congestion, ultrasound venous congestion (VExUS), and recanalization of the umbilical vein, with P \u0026lt; 0.05.（Table 2）\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" style=\"width: 100px;\"\u003e\n \u003cp\u003eTable 2.Summary of Binary Logit Regression Analysis Results\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003ez \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eWald\u0026chi;2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003ep\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e95% CI for OR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ePlatelets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e-1.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.981 ~ 1.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eALT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e-1.855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e3.440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.969 ~ 1.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eChild-Pugh\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eClassification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e-1.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e2.552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.174 ~ 1.195\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eSMA PSV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e-2.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e6.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.938 ~ 0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eSMA EDV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e-1.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.857 ~ 1.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ePV Doppler\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.549 ~ 2.945\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eHA PSV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.871 ~ 1.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eHV Doppler\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e2.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.687 ~ 8.298\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ePV PI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e-5.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e2.758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e-1.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e3.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.000 ~ 1.217\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eUV Reopening\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e1.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e2.572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e5.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.693 ~ 39.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eSV Diameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e-1.521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e2.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.519 ~ 1.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eHA RI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e5.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e23.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.001 ~ 882318.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eSpleen Length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.984 ~ 1.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eSpleen Thickness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.904 ~ 1.176\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eIRV Doppler\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e-1.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e-4.523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e20.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.093 ~ 0.391\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ePV Diameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.785 ~ 1.128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eIVC Variability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e-1.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.915 ~ 1.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;intercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e7.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e5.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1541.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.031 ~ 75815288.664\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" style=\"width: 99px;\"\u003e\n \u003cp\u003eMcFadden R \u003csup\u003e2\u003c/sup\u003e = 0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBinary logistic regression analysis \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe included the indicators with statistical differences from the univariate analysis between the two groups into the binary logistic regression analysis. The results showed that the overall model was significant (P \u0026lt; 0.05, R2 = 0.638), with peak systolic velocity of the superior mesenteric artery and degree of renal vein congestion being independent factors affecting the need for banding treatment. When we separately modeled the peak systolic velocity of the superior mesenteric artery and degree of renal vein congestion, the model had an R2 = 0.487. Stepwise regression analysis indicated that alanine aminotransferase (ALT), peak systolic velocity of the superior mesenteric artery, and degree of left renal vein congestion had a significant negative impact on the banding group, with the model R2 = 0.509（Table 3）.The regression coefficient for the degree of renal vein congestion is 1.670, z = 5.669, p = 0.000 \u0026lt; 0.01, OR = 5.314.The regression coefficient for the peak systolic velocity of the superior mesenteric artery is -0（Table 3）.033, z = -4.046, p \u0026lt; 0.01, OR = 0.968.The model formula is:ln(p / (1 - p)) = 8.126 - 0.033 * peak systolic velocity of the superior mesenteric artery - 1.670 * degree of left renal vein congestion - 0.012 * alanine aminotransferase.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\n \u003cp\u003eTable 3.Results of Binary Logit Regression Analysis \u0026emsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003ez \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eWald \u0026chi;2 \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003ep\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e95% CI for OR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSMA PSV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-4.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16.369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.953 ~ 0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIRV Doppler\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-5.669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.106 ~ 0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eALT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.976 ~ 1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eintercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e40.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3381.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e274.620 ~ 41648.942\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\n \u003cp\u003eMcFadden R 2 = 0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eROC Curve Analysis\u003c/p\u003e\n\u003cp\u003eTo evaluate the diagnostic performance of renal vein congestion, superior mesenteric artery peak systolic velocity, and liver function indicators in predicting the need for endoscopic treatment of esophageal varices, receiver operating characteristic (ROC) curve analysis was performed. The area under the curve (AUC) for renal vein congestion alone was 0.841(95% CI:0.775~0.907) , with a sensitivity of 74.4% and specificity of 94% at the optimal cutoff value of Intra-renal Venous Doppler Mild Abnormality. When renal vein congestion was combined with superior mesenteric artery peak systolic velocity and liver function indicators of ALT , the AUC increased to 0.931 (95% CI:0.892\u0026ndash;0.970), with a sensitivity of 89.7% and specificity of 89.2% at the optimal cutoff value of 0.789. The combination model demonstrated significantly higher diagnostic accuracy compared to renal vein congestion alone (p \u0026lt; 0.01, DeLong\u0026rsquo;s test). Figure 3 shows the ROC curves for renal vein congestion alone and the combination model.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study developed a non-invasive model combining VExUS ultrasound parameters (renal vein congestion) and superior mesenteric artery peak systolic velocity with liver function indicators to predict the risk of esophageal varices bleeding in cirrhotic patients. The combination model demonstrated superior diagnostic accuracy compared to individual parameters alone.\u003c/p\u003e\u003cp\u003eRenal venous congestion, a hallmark of systemic venous congestion in portal hypertension, was strongly associated with endoscopic treatment eligibility for esophageal varices. This aligns with Matsumoto et al(2018)[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], who linked left renal vein dilation to venous stasis and adverse outcomes in cirrhosis. Our study expands on this by incorporating spectral Doppler analysis of interlobar renal veins\u0026mdash;a component of the VExUS protocol. Normal interlobar veins exhibit continuous waveforms (VExUS Grade 1), while progressive congestion leads to discontinuous (Grade 2) or monophasic patterns (Grade 3). We propose that portal hypertension-induced ascites and splanchnic venous engorgement elevate intra-abdominal pressure, compressing the IVC and transmitting pressure retrograde to the renal veins. This mechanism mirrors the pathophysiology of esophageal varices, where portal hypertension drives collateral circulation. Cirrhosis induces portal hypertension, which leads to esophageal varices. Meanwhile, it causes visceral vasodilation and insufficient effective circulating blood volume, activating the neurohumoral system and resulting in renal vasoconstriction and reduced renal perfusion. Additionally, the rupture and bleeding of esophageal varices will exacerbate neurohumoral disorders and inflammatory responses, thereby inducing or worsening nephrotic syndrome[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].By integrating renal venous congestion (reflecting systemic venous overload) with superior mesenteric artery hemodynamics (local splanchnic resistance), our model captures both systemic and localized manifestations of portal hypertension, offering a holistic risk assessment tool.\u003c/p\u003e\u003cp\u003eSMA-PSV as a Hemodynamic Marker of Portal Hypertension.The significantly reduced SMA-PSV in the banding group suggests its potential role in reflecting splanchnic hemodynamic alterations secondary to portal hypertension. Consistent with the forward flow theory of portal hypertension, which posits that neurohormonal activation (e.g., angiotensin II) and vascular remodeling increase splanchnic arterial resistance[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], our findings of reduced SMA-PSV in the banding group align with prior observations of its inverse correlation with HVPG. This compensatory mechanism to limit portal inflow may coexist with renal venous congestion, reflecting the complex interplay between arterial and venous hemodynamics in cirrhosis[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].Altered mesenteric hemodynamics in cirrhosis were further evidenced by Sekimoto et al., who reported prolonged SMA-SMV microbubble transit time and impaired postprandial flow modulation using contrast-enhanced ultrasound[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].These findings complement our observations of reduced SMA-PSV, which may reflect compensatory vasoconstriction to mitigate portal overflow. Notably, the prognostic significance of SMA hemodynamics extends beyond portal hypertension, as low SMA-PSV has been independently associated with mortality in sepsis[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].Furthermore, severe intra-abdominal venous congestion and ascites may physically compress the SMA, exacerbating its diastolic dysfunction. Notably, while cirrhotic patients typically exhibit hyperdynamic circulation, the observed SMA-PSV reduction in our cohort might also indicate compromised cardiac output in advanced disease, possibly due to cirrhotic cardiomyopathy or sepsis-induced myocardial depression[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].The integration of SMA-PSV into our multiparameter model highlights its complementary value: it captures the \u0026ldquo;forward\u0026rdquo; component of portal hypertension (increased arterial resistance), whereas renal venous congestion reflects the \u0026ldquo;backward\u0026rdquo; consequences (venous overload). This dual assessment provides a more comprehensive risk stratification tool, as evidenced by the model\u0026rsquo;s superior AUC (0.931 vs. 0.841 for individual parameters).\u003c/p\u003e\u003cp\u003eThe absence of predictive value for the composite VExUS score in our model highlights the need to reinterpret venous congestion assessment in cirrhotic populations. While VExUS was originally designed for cardiac-related systemic congestion, its criteria may not translate directly to portal hypertension[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. For instance, IVC dilation (\u0026ge;\u0026thinsp;2 cm)\u0026mdash;a cornerstone of VExUS grading\u0026mdash;is rare in cirrhosis due to competing effects of ascites-induced compression and reduced venous return. Similarly, hepatic vein waveform flattening in cirrhosis reflects intrahepatic obstruction rather than right atrial pressure transmission, rendering traditional VExUS HV grading less discriminative[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In contrast, renal venous congestion emerges as a portal hypertension-specific marker. By focusing on interlobar renal vein Doppler patterns, we bypassed the confounding effects of IVC/HV parameters, directly linking renal venous hemodynamics to variceal severity. Future iterations of VExUS should consider cirrhosis-specific thresholds and parameters (e.g., portal vein flow pulsatility) to improve applicability.\u003c/p\u003e\u003cp\u003eThis study has several limitations that should be acknowledged. First, the single-center retrospective design and moderate sample size (n\u0026thinsp;=\u0026thinsp;161) may restrict the generalizability of findings and limit statistical power for detecting subtle associations in subgroup analyses. Second, operator-dependent variability in VExUS ultrasound measurements\u0026mdash;particularly in Doppler waveform interpretation (e.g., distinguishing continuous vs. discontinuous patterns)\u0026mdash;could introduce measurement bias and affect reproducibility. While standardized protocols were implemented, subjective differences in spectral analysis remain a challenge for ultrasound-based assessments.Future multicenter prospective studies with larger cohorts are required to validate these findings. Additionally, the development of automated Doppler analysis algorithms and AI-enhanced measurement tools could minimize inter-observer variability, thereby improving the reliability of VExUS grading in clinical practice.\u003c/p\u003e\u003cp\u003eOur model integrates renal venous congestion, superior mesenteric artery velocity, and liver function markers to non-invasively predict esophageal variceal bleeding risk in cirrhosis. This approach may reduce endoscopy frequency, especially in resource-limited settings, while enhancing patient comfort. Future studies should validate its utility across diverse populations and integrate it into clinical practice for personalized care.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur non-invasive model (AUC\u0026thinsp;=\u0026thinsp;0.931), combining VExUS-based renal venous congestion, SMA velocity, and liver function markers, effectively predicts esophageal variceal bleeding risk in cirrhosis. It reduces reliance on endoscopy and enables dynamic risk stratification. Future efforts should validate this model across etiologies, automate measurements via AI, and integrate it into clinical workflows to transform portal hypertension management.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eEsophageal variceal bleeding(EVB);Superior mesenteric artery(SMA);Endoscopic band ligation(EBL);Peak systolic velocity(PSV);Esophageal varices (EV);Hepatic venous pressure gradient(HVPG);Clinically significant portal hypertension(CSPH);Venous Excess Ultrasound(VExUS);Esophagogastroduodenoscopy(EGD);Inferior vena cava (IVC);Resistance index (RI);Portal vein pulsatility fraction(PVPF);Receiver operating characteristic(ROC);Area under the curve(AUC);International Normalized Ratio(INR);Alanine Transaminase (ALT);Aspartate Transaminase(AST);Prothrombin Time(PT);Serum Creatinine(Scr);Superior Mesenteric Artery(SMA);End-Diastolic Velocity(EDV);Hepatic Artery(HA);Portal Vein(PV);Splenic Vein(SV);Inferior Vena Cava(IVC);Hepatic Veins(HV);Intra-rena Venous(IRV).\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe sincerely thank our families for their unwavering encouragement and support throughout this work. We are also grateful to the anonymous reviewers for their insightful comments and valuable suggestions, which have significantly improved the quality of this manuscript.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eFei Wang : Conceptualization, Methodology, Writing \u0026ndash; Original Draft\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eZhen Tian : Methodology, Writing \u0026ndash; Original Draft, Data Curation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eYuan-Yuan Duan : Formal Analysis, Visualization\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLong Chen : Investigation, Data Curation (Ultrasound Examination and Data Collection)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eYa-Juan He : Data Curation, Investigation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMi Ke : Data Curation, Project Administration\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eYu-Xin Liu : Investigation (Patient Follow-up)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChen-Peng Mi : Data Curation, Investigation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJie Zhang : Data Curation, Project Administration\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTian-Yan Chen : Conceptualization, Methodology, Supervision, Project Administration, Writing \u0026ndash; Review \u0026amp; Editing, Corresponding Author\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current retrospective study are available from the corresponding author on reasonable request. All materials used are described within the manuscript.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Institutional Ethics Committee (No. XJTU1AF2024LSYY-339) and complied with the Declaration of Helsinki. As this was a retrospective analysis of anonymized data, the requirement for informed consent from individual participants was waived by the approving ethics committee.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable. This manuscript does not contain any individual person\u0026apos;s data in any form (including individual details, images, or videos).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests relevant to the content of this manuscript.\u003c/p\u003e\n\u003cp\u003eAuthor details\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eUltrasound Department-Hepatobiliary and Gastrointestinal Ultrasound Section, The First Affiliated Hospital of Xi\u0026rsquo;an Jiaotong University,Xi\u0026rsquo;an City, Shaanxi province 710061, China.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eDepartment of Infectious Diseases, The First Affiliated Hospital of Xi\u0026rsquo;an Jiaotong University,Xi\u0026rsquo;an City, Shaanxi province 710061, China.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eDepartment of Ultrasound Medicine, Baoji High-Tech Hospital,Baoji City, Shaanxi province 710000, China.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e4\u003c/sup\u003eDepartment of Viral Disease Laboratory,Xi\u0026apos;an Center for Disease Control and Prevention,,Xi\u0026rsquo;an City, Shaanxi province 710061, China.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e5\u003c/sup\u003eDepartment of Medical Imaging,Xi\u0026apos;an Chang\u0026apos;an District Hospital,Xi\u0026rsquo;an City, Shaanxi province 710061, China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGBD 2017 Cirrhosis Collaborators. The global, regional, and national burden of cirrhosis by cause in 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Gastroenterol Hepatol 2020;5(3):245-266. doi:10.1016/S2468-1253(19)30349-8.\u003c/li\u003e\n\u003cli\u003eDe Franchis R, Bosch J, Garcia-Tsao G, Reiberger T, Ripoll C; Baveno VII Faculty. Baveno VII - Renewing consensus in portal hypertension [published correction appears in J Hepatol. 2022 Jul;77(1):271. doi: 10.1016/j.jhep.2022.03.024]. J Hepatol 2022;76(4):959-974. doi:10.1016/j.jhep.2021.12.022.\u003c/li\u003e\n\u003cli\u003eGarcia-Tsao G, Abraldes JG, Berzigotti A, Bosch J. Portal hypertensive bleeding in cirrhosis: Risk stratification, diagnosis, and management: 2016 practice guidance by the American Association for the study of liver diseases [published correction appears in Hepatology. 2017 Jul;66(1):304. doi: 10.1002/hep.29169]. Hepatology 2017;65(1):310-335. doi:10.1002/hep.28906.\u003c/li\u003e\n\u003cli\u003eIwakiri Y. Pathophysiology of portal hypertension. Clin Liver Dis 2014;18(2):281-291. doi:10.1016/j.cld.2013.12.001.\u003c/li\u003e\n\u003cli\u003eLiu, C, Li, J, He, R, et al. A Combined Model of Spleen Stiffness and Baveno VII Criteria for Clinically Significant Portal Hypertension in Compensated Advanced Chronic Liver Disease: An International Multicenter Study Portal Hypertension \u0026amp;amp;amp; Cirrhosis 2023; doi: 10.1002/poh2.70004.\u003c/li\u003e\n\u003cli\u003eBerzigotti A, Bosch J, Boyer TD. Use of noninvasive markers of portal hypertension and timing of screening endoscopy for gastroesophageal varices in patients with chronic liver disease. Hepatology 2014;59(2):729-731. doi:10.1002/hep.26652.\u003c/li\u003e\n\u003cli\u003eTripathi D, Stanley AJ, Hayes PC, et al. U.K. guidelines on the management of variceal haemorrhage in cirrhotic patients. Gut 2015;64(11):1680-1704. doi:10.1136/gutjnl-2015-309262\u003c/li\u003e\n\u003cli\u003eBlachier M, Leleu H, Peck-Radosavljevic M, Valla DC, Roudot-Thoraval F. The burden of liver disease in Europe: a review of available epidemiological data. J Hepatol 2013;58(3):593-608. doi:10.1016/j.jhep.2012.12.005.\u003c/li\u003e\n\u003cli\u003eEuropean Association for Study of Liver; Asociacion Latinoamer-icana para el Estudio del Higado. EASL-ALEH Clinical Practice Guidelines: Non-invasive tests for evaluation of liver disease severity and prognosis. J Hepatol 2015;63(1):237\u0026ndash;264.\u003c/li\u003e\n\u003cli\u003eAbraldes JG, Bureau C, Stefanescu H, et al. Noninvasive tools and risk of clinically significant portal hypertension and varices in compensated cirrhosis: The \u0026quot;Anticipate\u0026quot; study [published correction appears in Hepatology. 2017 Jul;66(1):304-305. doi: 10.1002/hep.29201.]. Hepatology 2016;64(6):2173-2184. doi:10.1002/hep.28824.\u003c/li\u003e\n\u003cli\u003eColecchia A, Ravaioli F, Marasco G, et al. A combined model based on spleen stiffness measurement and Baveno VI criteria to rule out high-risk varices in advanced chronic liver disease. J Hepatol 2018;69(2):308-317. doi:10.1016/j.jhep.2018.04.023.\u003c/li\u003e\n\u003cli\u003eLongino AA, Martin KC, Leyba KR, et al. Reliability and reproducibility of the venous excess ultrasound (VExUS) score, a multi-site prospective study: validating a novel ultrasound technique for comprehensive assessment of venous congestion. Crit Care 2024;28(1):197. Published 2024 Jun 11. doi:10.1186/s13054-024-04961-9.\u003c/li\u003e\n\u003cli\u003eAnastasiou, V, Peteinidou, E, Moysidis, D, et al. Multiorgan congestion assessment by venous excess ultrasound score in acute heart failure .EUR HEART J 2025; 45. doi: 10.1093/eurheartj/ehae666.1001.\u003c/li\u003e\n\u003cli\u003eZhou, J, Sun, H, Wang, Z, et al. Guidelines for the Diagnosis and Treatment of Hepatocellular Carcinoma (2019 Edition). LIVER CANCER 2020; 9 (6): 682-720. doi: 10.1159/000509424.\u003c/li\u003e\n\u003cli\u003eGralnek, IM, Camus Duboc, M, Garcia-Pagan, JC, et al. Endoscopic diagnosis and management of esophagogastric variceal hemorrhage: European Society of Gastrointestinal Endoscopy (ESGE) Guideline. ENDOSCOPY 2022; 54 ENDOSCOPY. doi: 10.1055/a-1939-4887\u003c/li\u003e\n\u003cli\u003eChinese Society of Hepatology, Chinese Society of Gastroenterology, and Chinese Society of Digestive Endoscopology of Chinese Medical Association.Guidelines on the management of esophagogastric variceal bleeding in cirrhotic portal hypertension. Chin J Hepatol October 2022, 30(10): 1029-1043.DOI：10.3760/cma.j.cn501113-20220824-00436.\u003c/li\u003e\n\u003cli\u003eAssavapokee T , Rola P , Assavapokee N ,et al.Decoding VExUS: a practical guide for excelling in point-of-care ultrasound assessment of venous congestion.The Ultrasound Journal 2024, 16(1).DOI:10.1186/s13089-024-00396-z.\u003c/li\u003e\n\u003cli\u003eThe SPSSAU project (2024). SPSSAU. (Version 24.0) [Online Application Software]. Retrieved from https://www.spssau.com.\u003c/li\u003e\n\u003cli\u003eMatsumoto, N, Ogawa, M, Kumagawa, M, et al. Renal vein dilation predicts poor outcome in patients with refractory cirrhotic ascites. HEPATOL RES 2018; 48 (3): E117-E125. doi: 10.1111/hepr.12935.\u003c/li\u003e\n\u003cli\u003eNevens, F, Bittencourt, PL, Coenraad, MJ, et al. Recommendations on the Diagnosis and Initial Management of Acute Variceal Bleeding and Hepatorenal Syndrome in Patients with Cirrhosis. DIGEST DIS SCI 2019; 64 (6): 1419-1431. doi: 10.1007/s10620-018-5448-y.\u003c/li\u003e\n\u003cli\u003eIwakiri, Y, Groszmann, RJ. The hyperdynamic circulation of chronic liver diseases: from the patient to the molecule. HEPATOLOGY 2006; 43 (2 Suppl 1): S121-31. doi: 10.1002/hep.20993.\u003c/li\u003e\n\u003cli\u003eSastre, E, Caracuel, L, Prieto, I, et al. Decompensated liver cirrhosis and neural regulation of mesenteric vascular tone in rats: role of sympathetic, nitrergic and sensory innervations. Sci Rep 2016; 6 31076. doi: 10.1038/srep31076.\u003c/li\u003e\n\u003cli\u003eSekimoto, T, Maruyama, H, Kondo, T, et al. Potential stagnation in the splanchnic hemodynamics demonstrated by the dynamic microbubbles in chronic liver disease. J GASTROEN HEPATOL 2015; 30 (6): 1001-8. doi: 10.1111/jgh.12875.\u003c/li\u003e\n\u003cli\u003eGai, X, Wang, Y, Gao, D, et al. Risk factors for the prognosis of patients with sepsis in intensive care units. PLoS One 2022; 17 (9): e0273377. doi: 10.1371/journal.pone.0273377.\u003c/li\u003e\n\u003cli\u003eWu, G, Chen, M, Fan, Q, et al. Transcriptome analysis of mesenteric arterioles changes and its mechanisms in cirrhotic rats with portal hypertension. BMC Genomics 2023; 24 (1): 20. doi: 10.1186/s12864-023-09125-7.\u003c/li\u003e\n\u003cli\u003eDanielsen KV, Wiese S, Busk T, et al. Cardiovascular Mapping in Cirrhosis From the Compensated Stage to Hepatorenal Syndrome: A Magnetic Resonance Study. Am J Gastroenterol 2022;117(8):1269-1278. doi:10.14309/ajg.0000000000001847.\u003c/li\u003e\n\u003cli\u003eBeaubien-Souligny, W, Rola, P, Haycock, K, et al. Quantifying systemic congestion with Point-Of-Care ultrasound: development of the venous excess ultrasound grading system. Ultrasound J 2020; 12 (1): 16. doi: 10.1186/s13089-020-00163-w.\u003c/li\u003e\n\u003cli\u003eKoizumi, Y, Hirooka, M, Tanaka, T, et al. Noninvasive ultrasound technique for assessment of liver fibrosis and cardiac function in Fontan-associated liver disease: diagnosis based on elastography and hepatic vein waveform type. J MED ULTRASON 2021; 48 (2): 235-244. doi: 10.1007/s10396-020-01078-8.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cirrhosis, Esophageal varices, VExUS, Portal hypertension, Hemodynamics, Non-invasive assessment","lastPublishedDoi":"10.21203/rs.3.rs-7334535/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7334535/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\u003eEsophageal variceal bleeding (EVB) is a life-threatening complication of cirrhosis, necessitating accurate risk stratification. Current non-invasive methods lack sensitivity to dynamic hemodynamic changes. This study aimed to develop a VExUS ultrasound-based model integrating renal venous congestion and superior mesenteric artery (SMA) hemodynamics for noninvasive EVB risk prediction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study enrolled 161 patients with liver cirrhosis. Based on endoscopic findings, participants were stratified into two groups: those receiving endoscopic band ligation (EBL) (±pharmacotherapy) (n=78, 48.4%) and those managed conservatively (n=83, 51.6%). VExUS parameters, including renal vein Doppler patterns and superior mesenteric artery (SMA) peak systolic velocity (PSV), were analyzed alongside liver function markers. Using endoscopic results as the reference standard, we developed a predictive model for high-risk esophageal varices.The study protocol was approved by the Institutional Ethics Committee (No. XJTU1AF2024LSYY-339) and complied with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe combination model (renal venous congestion + SMA-PSV + ALT) achieved an AUC of 0.931 (95% CI: 0.8916–0.9703), significantly outperforming individual parameters (p\u0026lt;0.01). Renal venous congestion alone showed an AUC of 0.841 (95% CI: 0.7745–0.9066).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe VExUS-based model provides a noninvasive, accurate tool for EVB risk stratification, potentially reducing reliance on endoscopy.\u003c/p\u003e","manuscriptTitle":"VExUS Ultrasound-Based Noninvasive Risk Stratification Model for Esophageal Variceal Bleeding in Cirrhosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-10 00:37:29","doi":"10.21203/rs.3.rs-7334535/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-01-03T13:11:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"189176519694922190775841306814822102897","date":"2025-12-10T12:55:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-05T09:33:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-10T05:17:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-28T17:07:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-28T16:21:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2025-08-28T16:18:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"22a86a91-a9e8-4b65-ae98-e549fe9f8415","owner":[],"postedDate":"December 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-10T00:37:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-10 00:37:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7334535","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7334535","identity":"rs-7334535","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}