Risk factors and prediction model for sarcopenia in patients with cirrhosis undergoing transjugular intrahepatic portosystemic shunt

Risk factors and prediction model for sarcopenia in patients with cirrhosis undergoing transjugular intrahepatic portosystemic shunt | 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 Risk factors and prediction model for sarcopenia in patients with cirrhosis undergoing transjugular intrahepatic portosystemic shunt Shuyue Tuo, Jia Yuan, Ying Liu, Zhang Wen, Qiuju Ran, Yong Li, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7311054/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Oct, 2025 Read the published version in Internal and Emergency Medicine → Version 1 posted 4 You are reading this latest preprint version Abstract Background and aims: Sarcopenia is highly prevalent and predicts poor outcomes in patients with cirrhosis undergoing transjugular intrahepatic portosystemic shunt (TIPS). However, the risk factors for the development of sarcopenia in this population remain unclear. In addition, validated predictive models based on easily accessible clinical variables have not been developed. This study aimed to address these issues in patients with cirrhosis undergoing TIPS. Methods: All patients with cirrhosis undergoing TIPS at the Second Affiliated Hospital of Xi’an Jiaotong University from January 2018 to March 2024 were screened. The risk factors for sarcopenia were identified using Least Absolute Shrinkage and Selection Operator regression and multivariate logistic regression. A new model and nomogram were developed based on the selected variables. The model was evaluated and compared using discrimination, calibration, and decision curve analysis. Results: A total of 202 patients with cirrhosis undergoing TIPS were included in the study. Six independent risk factors for sarcopenia were identified: age; gender; body mass index (BMI); etiology; hepatic encephalopathy (HE); and right psoas muscle thickness (RPMT). A new model and nomogram for predicting sarcopenia were developed based on these risk factors. The model exhibited good overall performance, discrimination, calibration, and clinical utility, offering advantages over existing non-dedicated sarcopenia models. Patients were stratified into high-risk and low-risk groups based on the optimal cutoff value of 0.385, with a sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for distinguishing sarcopenia of 78.5%, 85.4%, 71.8%, 89.3%, and 83.2%, respectively. Conclusions: In patients with cirrhosis undergoing TIPS, age, male sex, BMI, etiology, HE, and RPMT were identified as independent risk factors for sarcopenia. Based on these risk factors, the newly developed sarcopenia model showed good predictive performance and holds potential as a practical screening tool in clinical settings. Sarcopenia Cirrhosis TIPS Risk factor Prediction model Figures Figure 1 Figure 2 Figure 3 1. Introduction Cirrhosis results from inflammation and fibrosis of the liver and is the final disease stage from multiple chronic liver diseases. It has a high prevalence worldwide and is a consequence of several etiologies such as hepatitis B or C virus infection, alcohol consumption, metabolic dysfunction-associated steatotic liver disease, and autoimmune diseases[ 1 ]. As the disease progresses, the formation of pseudolobules and regenerative nodules leads to increased hepatic vascular resistance and subsequent portal hypertension[ 2 ]. The development of portal hypertension-related complications, including ascites, hepatic hydrothorax, and variceal bleeding, signifies the transition from the compensated to the decompensated stage[ 3 , 4 ]. In advanced patients who develop recurrent or refractory ascites and variceal rebleeding, an effective strategy for reducing portal pressure and improving survival is transjugular intrahepatic portosystemic shunt (TIPS) placement, which creates an intrahepatic shunt between the hepatic and portal veins[ 5 , 6 ]. Sarcopenia is a syndrome characterized by progressive loss of muscle mass and function[ 7 ]. In patients with cirrhosis, insufficient dietary intake, malabsorption, physical inactivity, and hyperammonemia can lead to the development of sarcopenia[ 8 , 9 ]. Recent studies reported a sarcopenia prevalence of 37.5–40.1% among patients with cirrhosis[ 10 , 11 ], with rates exceeding 50% in those undergoing TIPS placement[ 12 ]. Sarcopenia is a significant predictor of adverse outcomes in patients with cirrhosis undergoing TIPS. Adverse outcomes include hepatic encephalopathy (HE), cardiac decompensation, acute-on-chronic liver failure, and mortality[ 13 , 14 ]. Furthermore, incorporating sarcopenia into prognostic models, such as the Model for End-Stage Liver Disease (MELD)-sarcopenia score, enhances predictive accuracy for post-TIPS mortality compared with traditional models[ 15 ]. Importantly, improvement or reversal of sarcopenia following TIPS could reduce the risks of both HE and mortality[ 16 ]. Despite the critical role sarcopenia plays in the prognosis of cirrhosis, it is often overlooked in clinical practice because it is time-consuming and labor-intensive to assess[ 17 ]. Although several methods exist for diagnosing sarcopenia, cross-sectional CT imaging is the gold standard for assessing sarcopenia in patients with cirrhosis[ 18 ]. However, this method requires specific software and trained personnel for evaluation, limiting its clinical applicability[ 14 ]. Therefore, it is essential to develop specialized predictive models using simple and readily available clinical variables to facilitate early recognition of sarcopenia in patients with cirrhosis undergoing TIPS. This study aimed to identify risk factors associated with sarcopenia and to construct a novel risk prediction model based on these factors for this patient population. 2. Methods 2.1 Study Population This cross-sectional study was conducted and reported in accordance with the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines[ 19 ]. All consecutive patients with cirrhosis indicated for TIPS at the Second Affiliated Hospital of Xi’an Jiaotong University from January 2018 to March 2024 were retrospectively screened. The inclusion criteria were: (1) Patients with cirrhosis due to various causes that was confirmed by clinical data combined with imaging or liver biopsy; (2) Patients indicated for TIPS based on reasonable reasons (variceal bleeding, refractory ascites, etc.); and (3) Patients with an abdominal CT scan within 1 month prior to TIPS to evaluate muscle mass. The exclusion criteria were: (1) Patients younger than 18 years, (2) Patients who failed to complete the TIPS procedure or were unable to tolerate the TIPS procedure due to rapidly progressive acute liver failure, severe HE, right heart failure, severe pulmonary hypertension, or uncontrolled sepsis, (3) Patients who underwent TIPS for other reasons, such as Budd-Chiari syndrome or idiopathic portal hypertension, (4) Patients with low-quality or incomplete abdominal CT images, and (5) Patients with incomplete demographic or clinical data. This study was approved by the institutional review board of our hospital (2022032), and informed consent was waived due to the retrospective nature of the study. 2.2 Data Collection The baseline characteristics of the patients before undergoing TIPS were extracted by reviewing the medical records of included patients. Demographic variables included age, sex, height, and weight. Clinical variables included smoking, drinking, etiology of cirrhosis, ascites, HE, spontaneous bacterial peritonitis, portal vein thrombosis, portal hypertensive gastropathy, hepatocellular carcinoma (HCC), comorbidity (hypertension, diabetes, and coronary heart disease), and previous treatment history (endoscopic treatment and oral nonselective β-blockers). Laboratory parameters included white blood cell, hemoglobin, platelet, total bilirubin, alanine aminotransferase, aspartate transaminase, albumin, total protein, prealbumin, gamma-glutamyl transferase, alkaline phosphatase, serum sodium, serum potassium, urea nitrogen, creatinine, cystatin C, total cholesterol, triglyceride, high-density lipoprotein, low-density lipoprotein, international normalized ratio, and blood ammonia. The neutrophil-lymphocyte ratio was calculated to assess systemic inflammation. Liver function was evaluated using the Child-Pugh score and MELD. 2.3 Skeletal muscle assessment Transverse CT images at the level of the third lumbar vertebra were independently assessed and analyzed using SliceOmatic software (Version 5.0, Tomovision, Montreal, Quebec, Canada) by two researchers specializing in hepatology imaging (J.Y. and S.D.). As previously described, standard Hounsfield Unit (HU) thresholds ranging from − 29 to 150 HU for skeletal muscle were applied to estimate the cross-sectional area of muscle mass[ 20 ]. The regions of interest were adjusted manually to match the actual muscle boundaries. The total skeletal muscles, including the transversus abdominis, external and internal obliques, rectus abdominis, psoas, erector spinae, and quadratus lumborum were delineated. The cross-sectional areas were computed by summing tissue pixels and multiplying by pixel surface area. The total skeletal muscle area was used to calculate the skeletal muscle index (SMI), a robust indicator of whole-body muscle mass. SMI was calculated by dividing the skeletal muscle area by height squared (m²) and was used to define sarcopenia. According to previous Chinese criteria, sarcopenia was defined by the following cutoffs: < 44.8 cm²/m² for males and < 32.5 cm²/m² for females[ 21 ]. Bilateral axial psoas muscle thickness (APMT) and transversal psoas muscle thickness (TPMT) were evaluated as clinically accessible muscle indices that do not require specialized software. APMT was defined as the largest axial diameter of the psoas muscle, while TPMT was measured as the transverse diameter perpendicular to the APMT[ 22 , 23 ]. 2.4 Statistical analyses When developing prediction models for binary outcomes, the sample size can be estimated using the events per variable method, which recommends at least 10 events per variable[ 24 ]. In this study six predictive factors were ultimately included, indicating a minimum requirement of 60 patients with sarcopenia. Our cohort met this requirement. Categorical variables were presented as frequency (percentage), while continuous variables were expressed as mean ± standard deviation or median (interquartile range) depending on the distribution of the data. Differences between the sarcopenia and non-sarcopenia group were analyzed by the chi-square or Fisher’s exact tests for categorical variables and Student’s t -test or Mann-Whitney U test for continuous variables as appropriate. The initial variable selection for sarcopenia was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression and the 10-fold cross-validation method to eliminate the potential effects of multicollinearity among variables[ 25 ]. To prevent overfitting, λ = λ_1SE was selected as the optimal regularization parameter, where λ_1SE is the largest value of λ such that the corresponding cross-validated error is within one standard error of the minimum cross-validated error. Subsequently, multivariate logistic regression analysis was performed using the variables selected by LASSO regression, including age, gender, drinking, body mass index (BMI), etiology, HE, and right psoas muscle thickness (RPMT). The results were presented as odds ratios (OR) with corresponding 95% confidence intervals (CI). Finally, a nomogram was developed based on the six independent variables with drinking excluded. The overall performance of the nomogram was evaluated using the Brier score and Nagelkerke R², which measure the accuracy of probabilistic predictions and the proportion of variance explained by the model, respectively. The receiver operating characteristic (ROC) curve and the corresponding area under the curve (AUC) were used to evaluate the discriminative performance of the nomogram, in comparison with the Hospital Italiano de Buenos Aires (HIBA) score[ 26 ], ABCS score[ 27 ], psoas muscle thickness standardized for height (PMTH) score[ 22 ], and sarcopenia index[ 28 ]. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated based on the maximum Youden index. The Calibration curve and Hosmer-Lemeshow goodness-of-fit test were used to assess the calibration performance of the predictive model. To ensure the robustness of the results, internal validation was performed using bootstrap resampling with 1000 iterations. Additionally, sensitivity analysis was performed by excluding patients with HCC. Decision curve analysis (DCA) was performed to assess the clinical utility of the prediction models. All statistical analyses were performed using R software (version 4.1.3; R Foundation for Statistical Computing, Vienna, Austria). Two-tailed p < 0.05 was considered statistically significant. 3. Results 3.1 Baseline characteristics of included patients A total of 262 patients with cirrhosis were initially indicated for TIPS, with 60 excluded for ineligibility. In total, 202 patients were included in the final analysis ( Supplementary Fig. 1 ). Among them 65 patients (32.2%) were diagnosed with sarcopenia. Baseline characteristics of included patients are shown in Table 1 . The median age of the included patients was 56 years, and 109 patients (54.0%) were male. Viral hepatitis was the most common cause (61.4%) of liver disease. Among the patients, 98.5% underwent TIPS due to variceal bleeding, 61.9% had concurrent ascites, 34.7% had portal vein thrombosis, 4.0% had HCC, and 3.5% had HE. Additionally, 12.4% of patients had comorbid hypertension, 17.4% had type 2 diabetes, and 3.0% had coronary artery disease. The majority of patients had liver function classified as Child-Pugh grade A (37.1%) or B (58.4%), with a median MELD score of 10. Compared with the non-sarcopenia group, the sarcopenia group had a lower proportion of viral hepatitis, a higher proportion of drinking, and a higher proportion of HE or coronary artery disease. Additionally, the sarcopenia group exhibited lower values for BMI, SMI, left psoas muscle length, left psoas muscle thickness, RPMT, while cystatin C levels were significantly higher. Table 1 Comparison of baseline characteristics according to sarcopenia status in patients with cirrhosis undergoing transjugular intrahepatic portosystemic shunt. Characteristic Total (n = 202) Sarcopenia (n = 65) Non-sarcopenia (n = 137) P Value Age, median(IQR), years 56 (50, 64) 58 (52, 67) 56 (49, 64) 0.093 Gender, n (%) Male 109 (54.0) 39 (60.0) 70 (51.1) 0.301 Female 93 (46.0) 26 (40.0) 67 (48.9) BMI, median (IQR), kg/m2 22.5 (20.4, 24.8) 20.40 (18.8, 23.1) 23.0 (21.2, 25.8) < 0.001 Smoking, n (%) No 154 (76.2) 44 (67.7) 110 (80.3) 0.074 Yes 48 (23.8) 21 (32.3) 27 (19.7) Drinking, n (%) No 166 (82.2) 47 (72.3) 119 (86.9) 0.020 Yes 36 (17.8) 18 (27.7) 18 (13.1) Etiology, n (%) Viral 124 (61.4) 29 (44.6) 95 (69.3) 0.003 Alcohol 15 ( 7.4) 8 (12.3) 7 ( 5.1) Autoimmune 23 (11.4) 13 (20.0) 10 ( 7.3) Others 40 (19.8) 15 (23.1) 25 (18.2) Complications Ascites, n (%) No 77 (38.1) 20 (30.8) 57 (41.6) 0.312 Yes 125 (61.9) 45 (69.1) 81 (58.4) HE, n (%) No 195 (96.5) 60 (92.3) 135 (98.5) 0.046 Yes 7 ( 3.5) 5 ( 7.7) 2 ( 1.5) SBP, n (%) No 199 (98.5) 64 (98.5) 135 (98.5) 1 Yes 3 ( 1.5) 1 ( 1.5) 2 ( 1.5) PVT, n (%) No 132 (65.3) 43 (66.2) 89 (65.0) 0.994 Yes 70 (34.7) 22 (33.8) 48 (35.0) PHG, n (%) No 148 (73.3) 48 (73.8) 100 (73.0) 1 Yes 54 (26.7) 17 (26.2) 37 (27.0) Lower extremity edema, n (%) No 180 (89.1) 61 (93.8) 119 (86.9) 0.319 Yes 22( 9.9) 4 ( 6.2) 18 ( 13.1) HCC, n (%) No 194 (96.0) 63 (96.9) 131 (95.6) 0.954 Yes 8 ( 4.0) 2 ( 3.1) 6 ( 4.4) Comorbidity Hypertension, n (%) No 177 (87.6) 55 (84.6) 122 (89.1) 0.506 Yes 25 (12.4) 10 (15.4) 15 (10.9) Diabetes, n (%) No 167 (82.7) 51 (78.5) 116 (84.7) 0.373 Yes 35 (17.3) 14 (21.5) 21 (15.3) Coronary heart disease, n (%) No 196 (97.0) 60 (92.3) 136 (99.3) 0.023 Yes 6 ( 3.0) 5 ( 7.7) 1 ( 0.7) CT at third lumbar vertebra level LPMT, median (IQR) 23.1 (18.8, 27.1) 21.3 (16.6, 23.7) 24.4 (19.8, 28.2) < 0.001 LPML, median (IQR) 45.0 (40.2, 50.2) 43.8 (38.8, 48.7) 45.9 (40.7, 51.0) 0.049 RPMT, median (IQR) 23.5 (19.4, 28.1) 21.1 (17.0, 23.8) 25.0 (20.4, 30.4) < 0.001 RPML, median (IQR) 43.4 (38.8, 48.5) 42.1 (39.0, 46.9) 44.4 (38.4, 49.6) 0.443 SMI, median (IQR) 42.1 (36.1, 48.2) 36.3 (30.4, 42.1) 45.7 (38.5, 51.1) < 0.001 Laboratory parameters White blood cell, median (IQR),10⁹/L 3.5 (2.4, 5.9) 3.8 (2.1, 6.5) 3.2 (2.4, 5.2) 0.379 Hemoglobin, median (IQR), g/L 79.0 (68.3, 92.0) 79.0 (65.0, 91.0) 79.0 (71.0, 93.0) 0.580 Platelet, median (IQR), 10⁹/L 70.0 (46.0, 97.5) 71.0 (47.0, 101.0) 69.0 (45.0, 95.0) 0.303 NLR, median (IQR) 3.4 (2.2, 5.4) 3.4 (2.3, 7.1) 3.4 (2.1, 5.1) 0.262 Serum sodium, median (IQR), mmol/L 138.4 (136.5, 140.6) 137.8 (135.8, 140.1) 138.6 (137.0, 140.9) 0.082 Serum potassium, median (IQR), mmol/L 4.0 (3.7, 4.3) 3.9 (3.7, 4.3) 4.0 (3.7, 4.3) 0.924 TBIL, median (IQR), umol/L 25.0 (16.5, 32.3) 25.7 (16.4, 32.6) 24.6 (16.6, 32.2) 0.776 ALT, median (IQR), IU/L 19.0 (14.3, 31.8) 21.0 (16.0, 39.0) 19.0 (14.0, 30.0) 0.157 AST, median (IQR), IU/L 30.0 (22.0, 43.0) 31.0 (24.0, 56.0) 30.0 (22.0, 39.0) 0.064 Total protein, median (IQR), g/L 58.9 (53.0, 66.2) 59.2 (52.8, 66.3) 58.7 (53.5, 66.1) 0.991 Albumin, median (IQR), g/L 32.1 (29.1, 36.6) 32.1 (29.3, 36.9) 32.1 (29.1, 35.9) 0.931 Prealbumin, median (IQR), mg/L 103.0 (81.8, 139.3) 108.0 (87.5, 150.3) 101.5 (80.8, 135.5) 0.295 GGT, median (IQR), U/L 25.0 (16.0, 49.0) 29.0 (16.0, 67.0) 24.5 (15.8, 40.0) 0.148 ALP, median (IQR), IU/L 79.0 (65.0, 108.0) 79.0 (65.0, 110.0) 79.0 (64.5, 108.0) 0.467 Urea Nitrogen, median (IQR), mmol/L 5.9 (4.1, 7.8) 6.5 (4.5, 9.0) 5.7 (4.1, 7.4) 0.056 Creatinine, median (IQR), umol/L 54.7 (44.3, 70.4) 58.0 (45.8, 76.7) 54.1 (44.2, 66.9) 0.204 Cystatin C, median (IQR), mg/L 1.0 (0.9, 1.3) 1.1 (0.9, 1.4) 1.0 (0.8, 1.2) 0.002 Total cholesterol, median (IQR), mmol/L 2.8 (2.4, 3.4) 2.7 (2.4, 3.3) 2.8 (2.4, 3.5) 0.299 Triglyceride, median (IQR), mmol/L 0.9 (0.7, 1.2) 1.0 (0.8, 1.3) 0.9 (0.7, 1.2) 0.199 HDL, median (IQR), mmol/L 0.8 (0.7, 1.0) 0.8 (0.7, 1.0) 0.8 (0.7, 1.0) 0.542 LDL, median (IQR), mmol/L 1.8 (1.4, 2.2) 1.7 (1.4, 2.0) 1.8 (1.4, 2.3) 0.138 INR, median (IQR) 1.2 (1.1, 1.3) 1.2 (1.2, 1.3) 1.2 (1.1, 1.3) 0.933 Blood ammonia, median (IQR), umol/L 36.5 (0.0, 56.8) 40.0 (0.0, 57.0) 35.0 (0.0, 56.0) 0.422 Child-Pugh class, n (%) A 75 (37.1) 23 (35.4) 52 (38.0) 0.306 B 118 (58.4) 37 (56.9) 81 (59.1) C 9 ( 4.5) 5 ( 7.7) 4 ( 2.9) MELD score, median (IQR) 10.0 (8.0, 12.0) 10.0 (8.0, 12.0) 10.0 (8.0, 12.0) 0.892 Previous treatments Endoscopic therapy, n (%) No 110 (54.5) 31 (47.7) 79 (57.7) 0.239 Yes 92 (45.5) 34 (52.3) 58 (42.3) Non-selective β-blockers, n (%) No 193 (95.5) 62 (95.4) 131 (95.6) 1.000 Yes 9 ( 4.5) 3 ( 4.6) 6 ( 4.4) BMI, body mass index; HE, hepatic encephalopathy; SBP, spontaneous bacterial peritonitis; PVT, portal vein thrombosis; PHG, portal hypertensive gastropathy; HCC, hepatocellular carcinoma; LPML, left psoas muscle length; LPMT, left psoas muscle thickness; RPML, right psoas muscle length; RPMT, right psoas muscle thickness; SMI, skeletal muscle index; CT, computed tomography; NLR, neutrophil to lymphocyte ratio; TBIL, total bilirubin; ALT, alanine aminotransferase; AST, aspartate transaminase; GGT, gamma-glutamyltransferase; ALP, alkaline phosphatase; HDL, high density lipoprotein; LDL, low density lipoprotein; PT, prothrombin time; INR, international normalized ratio; MELD, Model for End Stage Liver Disease. 3.2 Identification of the independent risk factors Twenty-nine candidate variables were included in the LASSO regression. After shrinkage, seven variables with nonzero coefficients were associated with sarcopenia, including age, sex, drinking, BMI, etiology, HE, and RPMT ( Supplementary Fig. 2 ). Then, these seven variables were included in a multivariate logistic regression analysis for further selection. In the multivariate analysis, six variables were independently associated with sarcopenia: Age (OR = 1.05; 95%CI: 1.01–1.10; p = 0.017); gender (female vs male: OR = 0.03; 95%CI: 0.01–0.11; p < 0.001); BMI (OR = 0.80; 95%CI: 0.70–0.91; p < 0.001); etiology (autoimmune vs viral: OR = 8.22; 95%CI: 2.26–29.83; p = 0.001), HE (OR = 31.33; 95%CI: 1.98-494.51; p = 0.014); and RPMT (OR = 0.77; 95%CI: 0.69–0.85; p < 0.001) (Table 2 ). Drinking was not independently associated with sarcopenia (OR = 1.41; 95%CI: 0.43–4.60; p = 0.569). A new model based on the six independent variables was developed, and the corresponding nomogram was drawn to predict the probability of sarcopenia in patients with cirrhosis undergoing TIPS (Fig. 1 ). Table 2 Multivariate logistic regression assessing independent risk factors for sarcopenia in patients with cirrhosis undergoing transjugular intrahepatic portosystemic shunt. Variable Adjusted OR (95% CI) P value Age 1.05 (1.01–1.10) 0.017 Gender Male Reference Female 0.03 (0.01–0.11) < 0.001 Drink No Reference Yes 1.41 (0.43–4.60) 0.569 BMI 0.80 (0.70–0.91) < 0.001 Etiology Viral Reference Alcohol 4.70 (1.17–18.93) 0.030 Autoimmune 8.22 (2.26–29.83) 0.001 Others 2.08 (0.71–6.11) 0.182 HE No Reference Yes 31.33(1.98-494.51) 0.014 RPMT 0.77 (0.69–0.85) < 0.001 BMI, body mass index; HE, hepatic encephalopathy; RPMT, right psoas muscle thickness. 3.3 Predictive value of the new model The new model demonstrated good overall predictive performance in estimating the risk of sarcopenia, with a Nagelkerke R² of 0.473 and a Brier score of 0.139 (Table 3 ). The new model demonstrated excellent discriminative performance, with an AUC (95%CI) of 0.857 (0.803–0.910) (Fig. 2 ; Supplementary Fig. 3 ). The optimal cutoff value was 0.385 and stratified patients into high-risk (> 0.385) and low-risk (≤ 0.385) groups. At this threshold, the sensitivity, specificity, PPV, NPV, and accuracy for identifying sarcopenia were 78.5%, 85.4%, 71.8%, 89.3%, and 83.2%, respectively (Table 3 ). Moreover, the calibration curve and Hosmer-Lemeshow test indicated good agreement between the predicted and observed probabilities of sarcopenia (Table 3 ; Supplementary Fig. 4 ). Sensitivity analysis excluding patients with HCC yielded comparable predictive performance, supporting the robustness of the model (Table 3 ; Supplementary Fig. 5 ). Table 3 Comparative analysis of predictive models for sarcopenia in patients with cirrhosis undergoing transjugular intrahepatic portosystemic shunt. New model New model excluding HCC HIBA score ABCS score PMTH score Sarcopenia index Discrimi- nation AUC (95%CI) 0.857(0.803–0.910) 0.867(0.816–0.921) 0.728(0.653–0.803) 0.712(0.634–0.790) 0.749(0.680–0.818) 0.579(0.493–0.664) P value for AUC reference 0.769 < 0.001 < 0.001 < 0.001 < 0.001 Youden index 0.639 0.656 0.384 0.353 0.457 0.190 Cut off 0.385 0.385 0.204 0.476 14.250 45.146 Sensitivity (95%CI) 0.785(0.685–0.885) 0.778(0.675–0.880) 0.785(0.685–0.885) 0.754(0.649–0.859) 0.642(0.562–0.723) 0.759(0.688–0.831) Specificity (95%CI) 0.854(0.795–0.913) 0.878(0.822–0.934) 0.599(0.516–0.681) 0.599(0.516–0.681) 0.815(0.721–0.910) 0.431(0.310–0.551) PPV (95%CI) 0.718(0.614–0.823) 0.754(0.649–0.859) 0.481(0.386–0.576) 0.471(0.375–0.567) 0.880(0.816–0.944) 0.738(0.665–0.810) NPV (95%CI) 0.893(0.840–0.946) 0.891(0.838–0.945) 0.854(0.784–0.925) 0.837(0.764–0.910) 0.520(0.423–0.617) 0.459(0.334–0.581) Accuracy (95%CI) 0.832(0.830–0.833) 0.845(0.844–0.847) 0.658(0.656–0.661) 0.649(0.646–0.651) 0.698(0.696-0.700) 0.653(0.651–0.656) Calibration Hosmer-Lemeshow p value 0.855 0.905 0.878 0.848 0.902 0.998 Overall R2 0.473 0.499 0.181 0.162 0.224 0.020 Brier score 0.139 0.133 0.187 0.190 0.184 0.215 HCC, hepatocellular carcinoma; HIBA, hospital italiano de buenos aires; PMTH, psoas muscle thickness standardized for height; SI, sarcopenia index; AUC, area under the curve; PPV, positive predictive value; NPV, negative predictive value. We compared the predictive performance of the new model with the HIBA score, ABCS score, PMTH score, and sarcopenia index for identifying sarcopenia in patients with cirrhosis undergoing TIPS. The new model demonstrated superior overall performance as evidenced by a lower Brier score and higher Nagelkerke R² (Table 3 ). The AUC of the new model was significantly greater than those of the comparator scores or index (Fig. 2 ; Table 3 ). The calibration curves and the Hosmer-Lemeshow test indicated good agreement between the predicted and observed sarcopenia risk across all models (Table 3 ; Supplementary Fig. 6 ). Decision curve analysis showed that the new model provided a greater net benefit than the alternative models when the predicted probability thresholds ranged from 6–40% (Fig. 3 ) 4. Discussion The development of dedicated predictive models for sarcopenia in patients with cirrhosis undergoing TIPS remains an unmet clinical need. In this study, we identified sarcopenia-associated predictors and developed a novel prediction model in this specific population. Through LASSO regression and subsequent multivariate logistic regression analysis, six clinically accessible independent predictors were identified: Age; male sex; BMI; etiology; HE; and RPMT. A risk prediction model incorporating these variables demonstrated superior performance in discrimination, calibration, and clinical utility compared with existing non-specialized models. A sensitivity analysis excluding patients with HCC yielded consistent results. The optimal cutoff value (0.385) achieved exceptional diagnostic performance for sarcopenia exclusion, with a sensitivity of 78.5% and NPV of 89.3%. This high NPV suggests that 89.3% of patients classified as low-risk by the model are truly sarcopenia-free, potentially obviating the need for complex confirmatory testing in this subgroup and streamlining clinical workflows. Our study identified advanced age as an independent predictor of sarcopenia in patients with cirrhosis. It is well-established that significant changes in muscle mass and function occur during the aging process, with muscle mass decreasing at an annual rate of 1%-2% after 50 years of age. The decline in muscle strength is even more pronounced[ 29 ]. Multiple factors, including immobility, malnutrition, systemic inflammation, hormonal and metabolic changes, and neuromuscular aging, are considered potential mechanisms underlying age-related sarcopenia[ 29 ]. Advanced age as a risk factor for sarcopenia has been well-documented in the general elderly population as well as in chronic disease populations, including those with cirrhosis[ 11 , 30 ]. Sarcopenia in patients with cirrhosis exhibits significant sex-specific differences and being male was identified as an independent risk factor. This association may be mediated through the critical role of testosterone in muscle anabolism. Testosterone promotes skeletal muscle growth by inhibiting myostatin and activating the insulin-like growth factor-1 and mammalian target of rapamycin signaling pathways, both of which are predominant in males[ 31 ]. Progressive testosterone deficiency is often seen as liver disease severity increases in males. Up to 90% of male patients with cirrhosis exhibit hypogonadism. In contrast, female patients with cirrhosis demonstrate preserved testosterone homeostasis[ 31 ]. A cross-sectional analysis of 82 females with cirrhosis revealed no significant difference in testosterone levels between those with and without sarcopenia[ 32 ]. These findings align with earlier observations that showed comparable testosterone levels in healthy controls and 18 females with cirrhosis[ 33 ]. The predominance of males in sarcopenia persists across all stages of progressive liver disease including compensated cirrhosis, decompensated cirrhosis, HCC, and patients with cirrhosis both before and after liver transplantation[ 34 ]. A 2024 meta-analysis including 55 studies corroborated our findings, demonstrating that being male is a significant predictor for sarcopenia in patients with cirrhosis[ 11 ]. BMI serves as an indicator of obesity as well as nutritional status, with low BMI suggesting inadequate nutritional intake or absorption. Consequently, BMI is a key component in malnutrition risk assessment[ 35 ]. The association between BMI and sarcopenia may stem from its reflection of whole-body lean mass, as evidenced by the significant positive correlation between BMI and SMI[ 36 , 37 ]. Low BMI is associated with a higher prevalence of sarcopenia in patients with both diabetes and cirrhosis[ 36 , 37 ]. Consistent with our findings, recent meta-analyses have shown that low BMI is an independent predictor of sarcopenia in cirrhosis[ 11 ]. The predictive value of BMI for sarcopenia in cirrhosis might theoretically be confounded by ascites or edema. This has led to proposals for 'dry BMI.' This modified approach requires imaging-based assessment of ascites severity and subjective clinical evaluation of lower-extremity edema[ 38 ]. Notably, growing evidence suggests that fluid retention minimally impacts the predictive capacity of BMI for sarcopenia, as both standard BMI and dry BMI remain independently associated with sarcopenia[ 36 , 38 ]. Additionally, BMI demonstrates independent predictive value not only in general cirrhotic populations but also in advanced patients with acute-on-chronic liver failure[ 36 , 39 ]. Our analysis confirmed the independent clinical predictive value of standard BMI in sarcopenia prediction, significantly enhancing the practicality of the implementation of the new model. Excessive alcohol consumption induces alcohol-related myopathy, with alcoholic liver disease (ALD) demonstrating accelerated muscle loss progression compared with other etiological factors[ 40 ]. Ethanol and its metabolites elevate systemic levels of inflammatory cytokines and endotoxins while impairing hepatic ureagenesis and mitochondrial function. These effects are compounded by increased autophagy, elevated muscle ammonia concentrations, and upregulation of myostatin, which is a potent negative regulator of skeletal muscle growth. Collectively, these pathological changes disrupt muscle protein homeostasis and ultimate result in accelerated muscle catabolism[ 40 ]. This phenomenon was corroborated by a cohort study demonstrating that ALD significantly increased the risk of accelerated muscle loss in patients with cirrhosis compared with viral hepatitis[ 40 ]. Our previous meta-analyses revealed a significantly higher sarcopenia prevalence in patients with ALD compared with other etiologies[ 10 ], confirming that ALD is an independent predictor of sarcopenia[ 11 ]. While the current study observed a higher prevalence of sarcopenia in patients with ALD (53.3%) than in patients with viral hepatitis (23.4%), this association did not reach statistical significance due to the limited ALD sample size. Notably, our study identified autoimmune liver diseases as an independent risk factor for sarcopenia compared with viral hepatitis. Although evidence demonstrating the relationship between sarcopenia and autoimmune cirrhosis is scarce, one study suggested that patients with primary biliary cholangitis exhibit a higher sarcopenia prevalence than other etiologies[ 41 ]. This predisposition may stem from persistent chronic inflammation, which is a well-established driver of sarcopenia pathogenesis[ 42 ]. A previous study demonstrated that patients with autoimmune diseases exhibited an independently elevated risk of sarcopenia[ 42 ]. The association between sarcopenia and overt HE has been well-established, with sarcopenia identified as an independent predictor of HE development in patients with cirrhosis[ 43 ]. Conversely, patients with cirrhosis and HE exhibited an elevated risk of sarcopenia, as confirmed by our study. This association is primarily driven by hyperammonemia. Elevated ammonia levels are a key mediator of sarcopenia pathogenesis through myostatin-dependent pathways[ 43 ]. RPMT is the largest transverse diameter of the right psoas muscle at the L3 vertebral level. It offers a simplified alternative to the widely accepted L3-SMI measurement. Unlike SMI quantification requiring specialized software for muscle area segmentation, RPMT enables rapid assessment without complex analytical tools. A prior study demonstrated a moderate correlation between RPMT and SMI[ 44 ], supporting its utility as a pragmatic surrogate. However, the diagnostic value of RPMT for sarcopenia remains suboptimal. Earlier investigation reported an AUC of 0.70 for RPMT in detecting SMI-defined sarcopenia[ 45 ], closely aligning with our findings (AUC = 0.74). This underscores the need to enhance diagnostic accuracy by integrating RPMT with complementary variables. Current predictive models for sarcopenia in patients with cirrhosis, particularly those requiring TIPS placement, remain underdeveloped. Given the prognostic significance of sarcopenia before TIPS in predicting postprocedural overt HE and mortality, the identification of this high-risk population is critical for optimizing clinical decision-making. Targeted strategies, including TIPS stent diameter selection, tailored postprocedural surveillance, and interventions such as nutritional support and supervised exercise, may improve outcomes in these patients. To address this gap, we developed a novel prediction model incorporating six readily accessible clinical variables to identify sarcopenia in patients with cirrhosis undergoing TIPS. This model demonstrated superior predictive value compared with existing non-specialized tools. Notably, it exhibited exceptional diagnostic utility for sarcopenia exclusion at the defined cutoff value, achieving an NPV of 89.3%. Furthermore, we constructed a user-friendly nomogram to facilitate clinical implementation. However, several limitations of this study should be acknowledged. First, the single-center retrospective design, despite internal validation through bootstrap resampling, necessitates external validation in multicenter cohorts to confirm generalizability. Second, our cohort predominantly comprised patients undergoing TIPS due to variceal bleeding. This population is distinct from those with refractory ascites who exhibit higher sarcopenia prevalence due to exacerbated malnutrition risks[ 11 ]. Therefore, the performance of this novel model in patients with cirrhosis undergoing TIPS for refractory ascites or hydrothorax warrants further investigation. In conclusion, this study identified age, male sex, BMI, etiology, overt HE, and RPMT as independent predictors of sarcopenia in patients with cirrhosis undergoing TIPS. Based on these predictors, we developed a novel nomogram model that demonstrated superior clinical utility compared with existing non-specialized tools. This model enhances the identification of sarcopenia before TIPS, enabling optimized perioperative management in this patient population. Abbreviations ALD alcoholic liver disease APMT axial psoas muscle thickness AUC area under the curve BMI body mass index CI confidence intervals DCA decision curve analysis EPV events per variable HCC hepatocellular carcinoma HE hepatic encephalopathy HU hounsfield unit LASSO least absolute shrinkage and selection operator LPML left psoas muscle length LPMT left psoas muscle thickness MELD model for End-Stage liver disease NLR neutrophil–lymphocyte ratio NPV negative predictive value OR odds ratios PMTH psoas muscle thickness standardized for height PPV positive predictive value ROC receiver operating characteristic RPMT right psoas muscle thickness SMA skeletal muscle area SMI skeletal muscle index TIPS transjugular intrahepatic portosystemic shunt TPMT transversal psoas muscle thickness TRIPOD Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis. Declarations Ethics approval and consent to participate The study protocol received official approval from the Ethical Committee of our hospital (No. 2022032), and individual patient consent was waived due to the retrospective nature of the analysis. Consent for publication Not applicable. Data availability and sources Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. Competing interests All authors do not have conflict of interest. Funding This study was supported by grants from Xi'an Science and Technology Plan Project(22YXYJ0122); IIT Clinical Research Fund of The Second Affiliated Hospital of Xi’an Jiaotong University (M074); Science and Technology Research Project for Social Development of Shaanxi Province(2015SF145). Authors’ contributions X.T., S.D., and L.L.: conception and design. L.L., S.D. and J.W.: administrative support. S.T., Z.W., Q.R., and Y.L.: acquisition of data. X.T., S.T., J.Y., and Q.Y.: data preparation. X.T., S.T., and Q.R.: statistical analyses and consulting. X.T., Y.L. and C.L.: interpretation of the data. X.T., S.T. and J.Y.: drafting the manuscript. X.T., L.L., S.D. and J.W.: critical review of manuscript. All authors critically reviewed the manuscript. Acknowledgements None declared. References Ginès P, Krag A, Abraldes JG, Solà E, Fabrellas N, Kamath PS (2021) Liver cirrhosis. Lancet 398(10308):1359–1376. https://doi.org/10.1016/s0140-6736(21)01374-x D'Amico G, Morabito A, D'Amico M, Pasta L, Malizia G, Rebora P, Valsecchi MG (2018) Clinical states of cirrhosis and competing risks. J Hepatol 68(3):563–576. https://doi.org/10.1016/j.jhep.2017.10.020 Guixé-Muntet S, Quesada-Vázquez S, Gracia-Sancho J (2024) Pathophysiology and therapeutic options for cirrhotic portal hypertension. 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14:59:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7311054/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7311054/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11739-025-04173-1","type":"published","date":"2025-10-31T15:58:06+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89979377,"identity":"3bb3aa9d-e106-4027-9f95-2dadf9c023d1","added_by":"auto","created_at":"2025-08-27 06:18:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":163823,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting sarcopenia in patients with cirrhosis undergoing transjugular intrahepatic portosystemic shunt. BMI, body mass index; HE, hepatic encephalopathy; RPMT, right psoas muscle thickness.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7311054/v1/f9fe4f614a9a2056b42a7ca7.png"},{"id":89979379,"identity":"edde5747-25cb-490f-a7ac-5bf7bbf63c89","added_by":"auto","created_at":"2025-08-27 06:18:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":331760,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of receiver operating characteristic curves of different models for predicting sarcopenia in patients with cirrhosis undergoing transjugular intrahepatic portosystemic shunt. HIBA, Hospital Italiano de Buenos Aires; PMTH, psoas muscle thickness standardized for height.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7311054/v1/ee49dc35a263cc16b57bdd00.png"},{"id":89981665,"identity":"4fbe4cdf-9bdb-4ccb-a05e-ce2cf481ecfe","added_by":"auto","created_at":"2025-08-27 06:26:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":395697,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the decision curve analysis of different models for predicting sarcopenia in patients with cirrhosis undergoing transjugular intrahepatic portosystemic shunt. HIBA, Hospital Italiano de Buenos Aires; PMTH, psoas muscle thickness standardized for height.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7311054/v1/9a7cc642c555e66693868dd8.png"},{"id":95039991,"identity":"3aea93a2-bc49-48d3-88bb-d39724668b6c","added_by":"auto","created_at":"2025-11-03 16:07:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2130802,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7311054/v1/91e651ce-d3f0-4919-8e23-5d2345d37b1c.pdf"},{"id":89981666,"identity":"4d6119d3-d90c-48bf-8e6c-422aa925a141","added_by":"auto","created_at":"2025-08-27 06:26:08","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":630188,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7311054/v1/ef374991b04e5dfe8c89d631.docx"}],"financialInterests":"","formattedTitle":"Risk factors and prediction model for sarcopenia in patients with cirrhosis undergoing transjugular intrahepatic portosystemic shunt","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCirrhosis results from inflammation and fibrosis of the liver and is the final disease stage from multiple chronic liver diseases. It has a high prevalence worldwide and is a consequence of several etiologies such as hepatitis B or C virus infection, alcohol consumption, metabolic dysfunction-associated steatotic liver disease, and autoimmune diseases[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As the disease progresses, the formation of pseudolobules and regenerative nodules leads to increased hepatic vascular resistance and subsequent portal hypertension[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The development of portal hypertension-related complications, including ascites, hepatic hydrothorax, and variceal bleeding, signifies the transition from the compensated to the decompensated stage[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In advanced patients who develop recurrent or refractory ascites and variceal rebleeding, an effective strategy for reducing portal pressure and improving survival is transjugular intrahepatic portosystemic shunt (TIPS) placement, which creates an intrahepatic shunt between the hepatic and portal veins[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSarcopenia is a syndrome characterized by progressive loss of muscle mass and function[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In patients with cirrhosis, insufficient dietary intake, malabsorption, physical inactivity, and hyperammonemia can lead to the development of sarcopenia[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Recent studies reported a sarcopenia prevalence of 37.5\u0026ndash;40.1% among patients with cirrhosis[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], with rates exceeding 50% in those undergoing TIPS placement[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Sarcopenia is a significant predictor of adverse outcomes in patients with cirrhosis undergoing TIPS. Adverse outcomes include hepatic encephalopathy (HE), cardiac decompensation, acute-on-chronic liver failure, and mortality[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Furthermore, incorporating sarcopenia into prognostic models, such as the Model for End-Stage Liver Disease (MELD)-sarcopenia score, enhances predictive accuracy for post-TIPS mortality compared with traditional models[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Importantly, improvement or reversal of sarcopenia following TIPS could reduce the risks of both HE and mortality[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite the critical role sarcopenia plays in the prognosis of cirrhosis, it is often overlooked in clinical practice because it is time-consuming and labor-intensive to assess[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Although several methods exist for diagnosing sarcopenia, cross-sectional CT imaging is the gold standard for assessing sarcopenia in patients with cirrhosis[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, this method requires specific software and trained personnel for evaluation, limiting its clinical applicability[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTherefore, it is essential to develop specialized predictive models using simple and readily available clinical variables to facilitate early recognition of sarcopenia in patients with cirrhosis undergoing TIPS. This study aimed to identify risk factors associated with sarcopenia and to construct a novel risk prediction model based on these factors for this patient population.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Population\u003c/h2\u003e\u003cp\u003eThis cross-sectional study was conducted and reported in accordance with the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. All consecutive patients with cirrhosis indicated for TIPS at the Second Affiliated Hospital of Xi\u0026rsquo;an Jiaotong University from January 2018 to March 2024 were retrospectively screened. The inclusion criteria were: (1) Patients with cirrhosis due to various causes that was confirmed by clinical data combined with imaging or liver biopsy; (2) Patients indicated for TIPS based on reasonable reasons (variceal bleeding, refractory ascites, etc.); and (3) Patients with an abdominal CT scan within 1 month prior to TIPS to evaluate muscle mass. The exclusion criteria were: (1) Patients younger than 18 years, (2) Patients who failed to complete the TIPS procedure or were unable to tolerate the TIPS procedure due to rapidly progressive acute liver failure, severe HE, right heart failure, severe pulmonary hypertension, or uncontrolled sepsis, (3) Patients who underwent TIPS for other reasons, such as Budd-Chiari syndrome or idiopathic portal hypertension, (4) Patients with low-quality or incomplete abdominal CT images, and (5) Patients with incomplete demographic or clinical data. This study was approved by the institutional review board of our hospital (2022032), and informed consent was waived due to the retrospective nature of the study.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data Collection\u003c/h2\u003e\u003cp\u003eThe baseline characteristics of the patients before undergoing TIPS were extracted by reviewing the medical records of included patients. Demographic variables included age, sex, height, and weight. Clinical variables included smoking, drinking, etiology of cirrhosis, ascites, HE, spontaneous bacterial peritonitis, portal vein thrombosis, portal hypertensive gastropathy, hepatocellular carcinoma (HCC), comorbidity (hypertension, diabetes, and coronary heart disease), and previous treatment history (endoscopic treatment and oral nonselective β-blockers). Laboratory parameters included white blood cell, hemoglobin, platelet, total bilirubin, alanine aminotransferase, aspartate transaminase, albumin, total protein, prealbumin, gamma-glutamyl transferase, alkaline phosphatase, serum sodium, serum potassium, urea nitrogen, creatinine, cystatin C, total cholesterol, triglyceride, high-density lipoprotein, low-density lipoprotein, international normalized ratio, and blood ammonia. The neutrophil-lymphocyte ratio was calculated to assess systemic inflammation. Liver function was evaluated using the Child-Pugh score and MELD.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Skeletal muscle assessment\u003c/h2\u003e\u003cp\u003eTransverse CT images at the level of the third lumbar vertebra were independently assessed and analyzed using SliceOmatic software (Version 5.0, Tomovision, Montreal, Quebec, Canada) by two researchers specializing in hepatology imaging (J.Y. and S.D.). As previously described, standard Hounsfield Unit (HU) thresholds ranging from \u0026minus;\u0026thinsp;29 to 150 HU for skeletal muscle were applied to estimate the cross-sectional area of muscle mass[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The regions of interest were adjusted manually to match the actual muscle boundaries. The total skeletal muscles, including the transversus abdominis, external and internal obliques, rectus abdominis, psoas, erector spinae, and quadratus lumborum were delineated. The cross-sectional areas were computed by summing tissue pixels and multiplying by pixel surface area. The total skeletal muscle area was used to calculate the skeletal muscle index (SMI), a robust indicator of whole-body muscle mass. SMI was calculated by dividing the skeletal muscle area by height squared (m\u0026sup2;) and was used to define sarcopenia. According to previous Chinese criteria, sarcopenia was defined by the following cutoffs: \u0026lt; 44.8 cm\u0026sup2;/m\u0026sup2; for males and \u0026lt;\u0026thinsp;32.5 cm\u0026sup2;/m\u0026sup2; for females[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Bilateral axial psoas muscle thickness (APMT) and transversal psoas muscle thickness (TPMT) were evaluated as clinically accessible muscle indices that do not require specialized software. APMT was defined as the largest axial diameter of the psoas muscle, while TPMT was measured as the transverse diameter perpendicular to the APMT[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Statistical analyses\u003c/h2\u003e\u003cp\u003eWhen developing prediction models for binary outcomes, the sample size can be estimated using the events per variable method, which recommends at least 10 events per variable[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In this study six predictive factors were ultimately included, indicating a minimum requirement of 60 patients with sarcopenia. Our cohort met this requirement.\u003c/p\u003e\u003cp\u003eCategorical variables were presented as frequency (percentage), while continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (interquartile range) depending on the distribution of the data. Differences between the sarcopenia and non-sarcopenia group were analyzed by the chi-square or Fisher\u0026rsquo;s exact tests for categorical variables and Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test or Mann-Whitney \u003cem\u003eU\u003c/em\u003e test for continuous variables as appropriate.\u003c/p\u003e\u003cp\u003eThe initial variable selection for sarcopenia was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression and the 10-fold cross-validation method to eliminate the potential effects of multicollinearity among variables[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. To prevent overfitting, λ\u0026thinsp;=\u0026thinsp;λ_1SE was selected as the optimal regularization parameter, where λ_1SE is the largest value of λ such that the corresponding cross-validated error is within one standard error of the minimum cross-validated error. Subsequently, multivariate logistic regression analysis was performed using the variables selected by LASSO regression, including age, gender, drinking, body mass index (BMI), etiology, HE, and right psoas muscle thickness (RPMT). The results were presented as odds ratios (OR) with corresponding 95% confidence intervals (CI). Finally, a nomogram was developed based on the six independent variables with drinking excluded.\u003c/p\u003e\u003cp\u003eThe overall performance of the nomogram was evaluated using the Brier score and Nagelkerke R\u0026sup2;, which measure the accuracy of probabilistic predictions and the proportion of variance explained by the model, respectively.\u003c/p\u003e\u003cp\u003eThe receiver operating characteristic (ROC) curve and the corresponding area under the curve (AUC) were used to evaluate the discriminative performance of the nomogram, in comparison with the Hospital Italiano de Buenos Aires (HIBA) score[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], ABCS score[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], psoas muscle thickness standardized for height (PMTH) score[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and sarcopenia index[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated based on the maximum Youden index. The Calibration curve and Hosmer-Lemeshow goodness-of-fit test were used to assess the calibration performance of the predictive model. To ensure the robustness of the results, internal validation was performed using bootstrap resampling with 1000 iterations. Additionally, sensitivity analysis was performed by excluding patients with HCC. Decision curve analysis (DCA) was performed to assess the clinical utility of the prediction models. All statistical analyses were performed using R software (version 4.1.3; R Foundation for Statistical Computing, Vienna, Austria). Two-tailed \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Baseline characteristics of included patients\u003c/h2\u003e\u003cp\u003eA total of 262 patients with cirrhosis were initially indicated for TIPS, with 60 excluded for ineligibility. In total, 202 patients were included in the final analysis (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e). Among them 65 patients (32.2%) were diagnosed with sarcopenia. Baseline characteristics of included patients are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The median age of the included patients was 56 years, and 109 patients (54.0%) were male. Viral hepatitis was the most common cause (61.4%) of liver disease. Among the patients, 98.5% underwent TIPS due to variceal bleeding, 61.9% had concurrent ascites, 34.7% had portal vein thrombosis, 4.0% had HCC, and 3.5% had HE. Additionally, 12.4% of patients had comorbid hypertension, 17.4% had type 2 diabetes, and 3.0% had coronary artery disease. The majority of patients had liver function classified as Child-Pugh grade A (37.1%) or B (58.4%), with a median MELD score of 10. Compared with the non-sarcopenia group, the sarcopenia group had a lower proportion of viral hepatitis, a higher proportion of drinking, and a higher proportion of HE or coronary artery disease. Additionally, the sarcopenia group exhibited lower values for BMI, SMI, left psoas muscle length, left psoas muscle thickness, RPMT, while cystatin C levels were significantly higher.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of baseline characteristics according to sarcopenia status in patients with cirrhosis undergoing transjugular intrahepatic portosystemic shunt.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;202)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSarcopenia (n\u0026thinsp;=\u0026thinsp;65)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNon-sarcopenia (n\u0026thinsp;=\u0026thinsp;137)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, median(IQR), years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56 (50, 64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58 (52, 67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56 (49, 64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e109 (54.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39 (60.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70 (51.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.301\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93 (46.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (40.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67 (48.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI, median (IQR), kg/m2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.5 (20.4, 24.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.40 (18.8, 23.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.0 (21.2, 25.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e154 (76.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44 (67.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e110 (80.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48 (23.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (32.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27 (19.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrinking, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e166 (82.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47 (72.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e119 (86.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36 (17.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (27.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18 (13.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEtiology, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eViral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e124 (61.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29 (44.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95 (69.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15 ( 7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (12.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 ( 5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAutoimmune\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23 (11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 ( 7.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40 (19.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (23.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25 (18.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComplications\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAscites, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e77 (38.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (30.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57 (41.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.312\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e125 (61.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45 (69.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e81 (58.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHE, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e195 (96.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60 (92.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e135 (98.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 ( 3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 ( 7.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 ( 1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e199 (98.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64 (98.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e135 (98.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 ( 1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 ( 1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 ( 1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePVT, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e132 (65.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (66.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89 (65.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.994\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70 (34.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (33.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48 (35.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePHG, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e148 (73.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48 (73.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100 (73.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54 (26.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (26.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37 (27.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLower extremity edema, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e180 (89.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61 (93.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e119 (86.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.319\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22( 9.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 ( 6.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18 ( 13.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHCC, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e194 (96.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63 (96.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e131 (95.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.954\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 ( 4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 ( 3.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 ( 4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComorbidity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e177 (87.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55 (84.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e122 (89.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.506\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25 (12.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (15.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (10.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e167 (82.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51 (78.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e116 (84.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.373\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35 (17.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (21.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21 (15.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoronary heart disease, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e196 (97.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60 (92.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e136 (99.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 ( 3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 ( 7.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 ( 0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCT at third lumbar vertebra level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLPMT, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.1 (18.8, 27.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.3 (16.6, 23.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.4 (19.8, 28.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLPML, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45.0 (40.2, 50.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43.8 (38.8, 48.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45.9 (40.7, 51.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRPMT, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.5 (19.4, 28.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.1 (17.0, 23.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.0 (20.4, 30.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRPML, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43.4 (38.8, 48.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42.1 (39.0, 46.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44.4 (38.4, 49.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.443\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSMI, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42.1 (36.1, 48.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.3 (30.4, 42.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45.7 (38.5, 51.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLaboratory parameters\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite blood cell, median (IQR),10⁹/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.5 (2.4, 5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.8 (2.1, 6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.2 (2.4, 5.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.379\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin, median (IQR), g/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79.0 (68.3, 92.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79.0 (65.0, 91.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e79.0 (71.0, 93.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.580\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet, median (IQR), 10⁹/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70.0 (46.0, 97.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71.0 (47.0, 101.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69.0 (45.0, 95.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.303\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.4 (2.2, 5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.4 (2.3, 7.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.4 (2.1, 5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.262\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum sodium, median (IQR), mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e138.4 (136.5, 140.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e137.8 (135.8, 140.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e138.6 (137.0, 140.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.082\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum potassium, median (IQR), mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.0 (3.7, 4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.9 (3.7, 4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.0 (3.7, 4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.924\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTBIL, median (IQR), umol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.0 (16.5, 32.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.7 (16.4, 32.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.6 (16.6, 32.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.776\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT, median (IQR), IU/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.0 (14.3, 31.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.0 (16.0, 39.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.0 (14.0, 30.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.157\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST, median (IQR), IU/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.0 (22.0, 43.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.0 (24.0, 56.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.0 (22.0, 39.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal protein, median (IQR), g/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58.9 (53.0, 66.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59.2 (52.8, 66.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58.7 (53.5, 66.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.991\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin, median (IQR), g/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.1 (29.1, 36.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.1 (29.3, 36.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.1 (29.1, 35.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.931\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrealbumin, median (IQR), mg/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e103.0 (81.8, 139.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e108.0 (87.5, 150.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e101.5 (80.8, 135.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.295\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGGT, median (IQR), U/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.0 (16.0, 49.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.0 (16.0, 67.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.5 (15.8, 40.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.148\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALP, median (IQR), IU/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79.0 (65.0, 108.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79.0 (65.0, 110.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e79.0 (64.5, 108.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.467\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrea Nitrogen, median (IQR), mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.9 (4.1, 7.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.5 (4.5, 9.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.7 (4.1, 7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine, median (IQR), umol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54.7 (44.3, 70.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.0 (45.8, 76.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54.1 (44.2, 66.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.204\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCystatin C, median (IQR), mg/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0 (0.9, 1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.1 (0.9, 1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0 (0.8, 1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal cholesterol, median (IQR), mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.8 (2.4, 3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.7 (2.4, 3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.8 (2.4, 3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.299\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriglyceride, median (IQR), mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9 (0.7, 1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0 (0.8, 1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9 (0.7, 1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.199\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL, median (IQR), mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.8 (0.7, 1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.8 (0.7, 1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.8 (0.7, 1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.542\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL, median (IQR), mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.8 (1.4, 2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.7 (1.4, 2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.8 (1.4, 2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.138\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINR, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.2 (1.1, 1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.2 (1.2, 1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.2 (1.1, 1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.933\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood ammonia, median (IQR), umol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.5 (0.0, 56.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.0 (0.0, 57.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.0 (0.0, 56.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.422\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild-Pugh class, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75 (37.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 (35.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52 (38.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.306\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e118 (58.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37 (56.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e81 (59.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 ( 4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 ( 7.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 ( 2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMELD score, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.0 (8.0, 12.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.0 (8.0, 12.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.0 (8.0, 12.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.892\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePrevious treatments\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEndoscopic therapy, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e110 (54.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31 (47.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e79 (57.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.239\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92 (45.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (52.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58 (42.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-selective β-blockers, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e193 (95.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62 (95.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e131 (95.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 ( 4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 ( 4.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 ( 4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eBMI, body mass index; HE, hepatic encephalopathy; SBP, spontaneous bacterial peritonitis; PVT, portal vein thrombosis; PHG, portal hypertensive gastropathy; HCC, hepatocellular carcinoma; LPML, left psoas muscle length; LPMT, left psoas muscle thickness; RPML, right psoas muscle length; RPMT, right psoas muscle thickness; SMI, skeletal muscle index; CT, computed tomography; NLR, neutrophil to lymphocyte ratio; TBIL, total bilirubin; ALT, alanine aminotransferase; AST, aspartate transaminase; GGT, gamma-glutamyltransferase; ALP, alkaline phosphatase; HDL, high density lipoprotein; LDL, low density lipoprotein; PT, prothrombin time; INR, international normalized ratio; MELD, Model for End Stage Liver Disease.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Identification of the independent risk factors\u003c/h2\u003e\u003cp\u003eTwenty-nine candidate variables were included in the LASSO regression. After shrinkage, seven variables with nonzero coefficients were associated with sarcopenia, including age, sex, drinking, BMI, etiology, HE, and RPMT (\u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e). Then, these seven variables were included in a multivariate logistic regression analysis for further selection. In the multivariate analysis, six variables were independently associated with sarcopenia: Age (OR\u0026thinsp;=\u0026thinsp;1.05; 95%CI: 1.01\u0026ndash;1.10; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017); gender (female \u003cem\u003evs\u003c/em\u003e male: OR\u0026thinsp;=\u0026thinsp;0.03; 95%CI: 0.01\u0026ndash;0.11; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); BMI (OR\u0026thinsp;=\u0026thinsp;0.80; 95%CI: 0.70\u0026ndash;0.91; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); etiology (autoimmune \u003cem\u003evs\u003c/em\u003e viral: OR\u0026thinsp;=\u0026thinsp;8.22; 95%CI: 2.26\u0026ndash;29.83; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), HE (OR\u0026thinsp;=\u0026thinsp;31.33; 95%CI: 1.98-494.51; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014); and RPMT (OR\u0026thinsp;=\u0026thinsp;0.77; 95%CI: 0.69\u0026ndash;0.85; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Drinking was not independently associated with sarcopenia (OR\u0026thinsp;=\u0026thinsp;1.41; 95%CI: 0.43\u0026ndash;4.60; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.569). A new model based on the six independent variables was developed, and the corresponding nomogram was drawn to predict the probability of sarcopenia in patients with cirrhosis undergoing TIPS (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate logistic regression assessing independent risk factors for sarcopenia in patients with cirrhosis undergoing transjugular intrahepatic portosystemic shunt.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.05 (1.01\u0026ndash;1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.03 (0.01\u0026ndash;0.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrink\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.41 (0.43\u0026ndash;4.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.569\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.80 (0.70\u0026ndash;0.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEtiology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eViral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.70 (1.17\u0026ndash;18.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAutoimmune\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.22 (2.26\u0026ndash;29.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.08 (0.71\u0026ndash;6.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.182\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31.33(1.98-494.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRPMT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.77 (0.69\u0026ndash;0.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eBMI, body mass index; HE, hepatic encephalopathy; RPMT, right psoas muscle thickness.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Predictive value of the new model\u003c/h2\u003e\u003cp\u003eThe new model demonstrated good overall predictive performance in estimating the risk of sarcopenia, with a Nagelkerke R\u0026sup2; of 0.473 and a Brier score of 0.139 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The new model demonstrated excellent discriminative performance, with an AUC (95%CI) of 0.857 (0.803\u0026ndash;0.910) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; \u003cb\u003eSupplementary Fig.\u0026nbsp;3\u003c/b\u003e). The optimal cutoff value was 0.385 and stratified patients into high-risk (\u0026gt;\u0026thinsp;0.385) and low-risk (\u0026le;\u0026thinsp;0.385) groups. At this threshold, the sensitivity, specificity, PPV, NPV, and accuracy for identifying sarcopenia were 78.5%, 85.4%, 71.8%, 89.3%, and 83.2%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Moreover, the calibration curve and Hosmer-Lemeshow test indicated good agreement between the predicted and observed probabilities of sarcopenia (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; \u003cb\u003eSupplementary Fig.\u0026nbsp;4\u003c/b\u003e). Sensitivity analysis excluding patients with HCC yielded comparable predictive performance, supporting the robustness of the model (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; \u003cb\u003eSupplementary Fig.\u0026nbsp;5\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparative analysis of predictive models for sarcopenia in patients with cirrhosis undergoing transjugular intrahepatic portosystemic shunt.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNew model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNew model excluding HCC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHIBA score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eABCS score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePMTH score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSarcopenia index\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e\u003cp\u003e\u003cb\u003eDiscrimi-\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003enation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eAUC (95%CI)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.857(0.803\u0026ndash;0.910)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.867(0.816\u0026ndash;0.921)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.728(0.653\u0026ndash;0.803)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.712(0.634\u0026ndash;0.790)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.749(0.680\u0026ndash;0.818)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.579(0.493\u0026ndash;0.664)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eP value for AUC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eYouden index\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.656\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.384\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.353\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.457\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.190\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eCut off\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e14.250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e45.146\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eSensitivity (95%CI)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.785(0.685\u0026ndash;0.885)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.778(0.675\u0026ndash;0.880)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.785(0.685\u0026ndash;0.885)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.754(0.649\u0026ndash;0.859)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.642(0.562\u0026ndash;0.723)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.759(0.688\u0026ndash;0.831)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eSpecificity (95%CI)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.854(0.795\u0026ndash;0.913)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.878(0.822\u0026ndash;0.934)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.599(0.516\u0026ndash;0.681)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.599(0.516\u0026ndash;0.681)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.815(0.721\u0026ndash;0.910)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.431(0.310\u0026ndash;0.551)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ePPV (95%CI)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.718(0.614\u0026ndash;0.823)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.754(0.649\u0026ndash;0.859)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.481(0.386\u0026ndash;0.576)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.471(0.375\u0026ndash;0.567)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.880(0.816\u0026ndash;0.944)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.738(0.665\u0026ndash;0.810)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eNPV (95%CI)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.893(0.840\u0026ndash;0.946)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.891(0.838\u0026ndash;0.945)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.854(0.784\u0026ndash;0.925)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.837(0.764\u0026ndash;0.910)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.520(0.423\u0026ndash;0.617)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.459(0.334\u0026ndash;0.581)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eAccuracy (95%CI)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.832(0.830\u0026ndash;0.833)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.845(0.844\u0026ndash;0.847)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.658(0.656\u0026ndash;0.661)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.649(0.646\u0026ndash;0.651)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.698(0.696-0.700)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.653(0.651\u0026ndash;0.656)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCalibration\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eHosmer-Lemeshow p value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.905\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.848\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.902\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.998\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eOverall\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eR2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eBrier score\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.215\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eHCC, hepatocellular carcinoma; HIBA, hospital italiano de buenos aires; PMTH, psoas muscle thickness standardized for height; SI, sarcopenia index; AUC, area under the curve; PPV, positive predictive value; NPV, negative predictive value.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe compared the predictive performance of the new model with the HIBA score, ABCS score, PMTH score, and sarcopenia index for identifying sarcopenia in patients with cirrhosis undergoing TIPS. The new model demonstrated superior overall performance as evidenced by a lower Brier score and higher Nagelkerke R\u0026sup2; (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The AUC of the new model was significantly greater than those of the comparator scores or index (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The calibration curves and the Hosmer-Lemeshow test indicated good agreement between the predicted and observed sarcopenia risk across all models (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; \u003cb\u003eSupplementary Fig.\u0026nbsp;6\u003c/b\u003e). Decision curve analysis showed that the new model provided a greater net benefit than the alternative models when the predicted probability thresholds ranged from 6\u0026ndash;40% (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe development of dedicated predictive models for sarcopenia in patients with cirrhosis undergoing TIPS remains an unmet clinical need. In this study, we identified sarcopenia-associated predictors and developed a novel prediction model in this specific population. Through LASSO regression and subsequent multivariate logistic regression analysis, six clinically accessible independent predictors were identified: Age; male sex; BMI; etiology; HE; and RPMT. A risk prediction model incorporating these variables demonstrated superior performance in discrimination, calibration, and clinical utility compared with existing non-specialized models. A sensitivity analysis excluding patients with HCC yielded consistent results. The optimal cutoff value (0.385) achieved exceptional diagnostic performance for sarcopenia exclusion, with a sensitivity of 78.5% and NPV of 89.3%. This high NPV suggests that 89.3% of patients classified as low-risk by the model are truly sarcopenia-free, potentially obviating the need for complex confirmatory testing in this subgroup and streamlining clinical workflows.\u003c/p\u003e\u003cp\u003eOur study identified advanced age as an independent predictor of sarcopenia in patients with cirrhosis. It is well-established that significant changes in muscle mass and function occur during the aging process, with muscle mass decreasing at an annual rate of 1%-2% after 50 years of age. The decline in muscle strength is even more pronounced[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Multiple factors, including immobility, malnutrition, systemic inflammation, hormonal and metabolic changes, and neuromuscular aging, are considered potential mechanisms underlying age-related sarcopenia[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Advanced age as a risk factor for sarcopenia has been well-documented in the general elderly population as well as in chronic disease populations, including those with cirrhosis[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSarcopenia in patients with cirrhosis exhibits significant sex-specific differences and being male was identified as an independent risk factor. This association may be mediated through the critical role of testosterone in muscle anabolism. Testosterone promotes skeletal muscle growth by inhibiting myostatin and activating the insulin-like growth factor-1 and mammalian target of rapamycin signaling pathways, both of which are predominant in males[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Progressive testosterone deficiency is often seen as liver disease severity increases in males. Up to 90% of male patients with cirrhosis exhibit hypogonadism. In contrast, female patients with cirrhosis demonstrate preserved testosterone homeostasis[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. A cross-sectional analysis of 82 females with cirrhosis revealed no significant difference in testosterone levels between those with and without sarcopenia[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. These findings align with earlier observations that showed comparable testosterone levels in healthy controls and 18 females with cirrhosis[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The predominance of males in sarcopenia persists across all stages of progressive liver disease including compensated cirrhosis, decompensated cirrhosis, HCC, and patients with cirrhosis both before and after liver transplantation[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. A 2024 meta-analysis including 55 studies corroborated our findings, demonstrating that being male is a significant predictor for sarcopenia in patients with cirrhosis[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBMI serves as an indicator of obesity as well as nutritional status, with low BMI suggesting inadequate nutritional intake or absorption. Consequently, BMI is a key component in malnutrition risk assessment[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The association between BMI and sarcopenia may stem from its reflection of whole-body lean mass, as evidenced by the significant positive correlation between BMI and SMI[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Low BMI is associated with a higher prevalence of sarcopenia in patients with both diabetes and cirrhosis[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Consistent with our findings, recent meta-analyses have shown that low BMI is an independent predictor of sarcopenia in cirrhosis[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The predictive value of BMI for sarcopenia in cirrhosis might theoretically be confounded by ascites or edema. This has led to proposals for 'dry BMI.' This modified approach requires imaging-based assessment of ascites severity and subjective clinical evaluation of lower-extremity edema[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Notably, growing evidence suggests that fluid retention minimally impacts the predictive capacity of BMI for sarcopenia, as both standard BMI and dry BMI remain independently associated with sarcopenia[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Additionally, BMI demonstrates independent predictive value not only in general cirrhotic populations but also in advanced patients with acute-on-chronic liver failure[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Our analysis confirmed the independent clinical predictive value of standard BMI in sarcopenia prediction, significantly enhancing the practicality of the implementation of the new model.\u003c/p\u003e\u003cp\u003eExcessive alcohol consumption induces alcohol-related myopathy, with alcoholic liver disease (ALD) demonstrating accelerated muscle loss progression compared with other etiological factors[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Ethanol and its metabolites elevate systemic levels of inflammatory cytokines and endotoxins while impairing hepatic ureagenesis and mitochondrial function. These effects are compounded by increased autophagy, elevated muscle ammonia concentrations, and upregulation of myostatin, which is a potent negative regulator of skeletal muscle growth. Collectively, these pathological changes disrupt muscle protein homeostasis and ultimate result in accelerated muscle catabolism[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This phenomenon was corroborated by a cohort study demonstrating that ALD significantly increased the risk of accelerated muscle loss in patients with cirrhosis compared with viral hepatitis[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Our previous meta-analyses revealed a significantly higher sarcopenia prevalence in patients with ALD compared with other etiologies[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], confirming that ALD is an independent predictor of sarcopenia[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. While the current study observed a higher prevalence of sarcopenia in patients with ALD (53.3%) than in patients with viral hepatitis (23.4%), this association did not reach statistical significance due to the limited ALD sample size. Notably, our study identified autoimmune liver diseases as an independent risk factor for sarcopenia compared with viral hepatitis. Although evidence demonstrating the relationship between sarcopenia and autoimmune cirrhosis is scarce, one study suggested that patients with primary biliary cholangitis exhibit a higher sarcopenia prevalence than other etiologies[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. This predisposition may stem from persistent chronic inflammation, which is a well-established driver of sarcopenia pathogenesis[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. A previous study demonstrated that patients with autoimmune diseases exhibited an independently elevated risk of sarcopenia[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe association between sarcopenia and overt HE has been well-established, with sarcopenia identified as an independent predictor of HE development in patients with cirrhosis[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Conversely, patients with cirrhosis and HE exhibited an elevated risk of sarcopenia, as confirmed by our study. This association is primarily driven by hyperammonemia. Elevated ammonia levels are a key mediator of sarcopenia pathogenesis through myostatin-dependent pathways[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRPMT is the largest transverse diameter of the right psoas muscle at the L3 vertebral level. It offers a simplified alternative to the widely accepted L3-SMI measurement. Unlike SMI quantification requiring specialized software for muscle area segmentation, RPMT enables rapid assessment without complex analytical tools. A prior study demonstrated a moderate correlation between RPMT and SMI[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], supporting its utility as a pragmatic surrogate. However, the diagnostic value of RPMT for sarcopenia remains suboptimal. Earlier investigation reported an AUC of 0.70 for RPMT in detecting SMI-defined sarcopenia[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], closely aligning with our findings (AUC\u0026thinsp;=\u0026thinsp;0.74). This underscores the need to enhance diagnostic accuracy by integrating RPMT with complementary variables.\u003c/p\u003e\u003cp\u003eCurrent predictive models for sarcopenia in patients with cirrhosis, particularly those requiring TIPS placement, remain underdeveloped. Given the prognostic significance of sarcopenia before TIPS in predicting postprocedural overt HE and mortality, the identification of this high-risk population is critical for optimizing clinical decision-making. Targeted strategies, including TIPS stent diameter selection, tailored postprocedural surveillance, and interventions such as nutritional support and supervised exercise, may improve outcomes in these patients. To address this gap, we developed a novel prediction model incorporating six readily accessible clinical variables to identify sarcopenia in patients with cirrhosis undergoing TIPS. This model demonstrated superior predictive value compared with existing non-specialized tools. Notably, it exhibited exceptional diagnostic utility for sarcopenia exclusion at the defined cutoff value, achieving an NPV of 89.3%. Furthermore, we constructed a user-friendly nomogram to facilitate clinical implementation. However, several limitations of this study should be acknowledged. First, the single-center retrospective design, despite internal validation through bootstrap resampling, necessitates external validation in multicenter cohorts to confirm generalizability. Second, our cohort predominantly comprised patients undergoing TIPS due to variceal bleeding. This population is distinct from those with refractory ascites who exhibit higher sarcopenia prevalence due to exacerbated malnutrition risks[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, the performance of this novel model in patients with cirrhosis undergoing TIPS for refractory ascites or hydrothorax warrants further investigation.\u003c/p\u003e\u003cp\u003eIn conclusion, this study identified age, male sex, BMI, etiology, overt HE, and RPMT as independent predictors of sarcopenia in patients with cirrhosis undergoing TIPS. Based on these predictors, we developed a novel nomogram model that demonstrated superior clinical utility compared with existing non-specialized tools. This model enhances the identification of sarcopenia before TIPS, enabling optimized perioperative management in this patient population.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eALD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ealcoholic liver disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAPMT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eaxial psoas muscle thickness\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003earea under the curve\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ebody mass index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003econfidence intervals\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003edecision curve analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEPV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eevents per variable\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHCC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ehepatocellular carcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ehepatic encephalopathy\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHU\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ehounsfield unit\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLASSO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eleast absolute shrinkage and selection operator\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLPML\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eleft psoas muscle length\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLPMT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eleft psoas muscle thickness\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMELD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emodel for End-Stage liver disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNLR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eneutrophil\u0026ndash;lymphocyte ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNPV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003enegative predictive value\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eodds ratios\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePMTH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epsoas muscle thickness standardized for height\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePPV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epositive predictive value\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ereceiver operating characteristic\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRPMT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eright psoas muscle thickness\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSMA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eskeletal muscle area\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eskeletal muscle index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTIPS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003etransjugular intrahepatic portosystemic shunt\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTPMT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003etransversal psoas muscle thickness\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTRIPOD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTransparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol received official approval from the Ethical Committee of our hospital (No. 2022032), and individual patient consent was waived due to the retrospective nature of the analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability and sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSome or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors do not have conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by grants from Xi\u0026apos;an Science and Technology Plan Project(22YXYJ0122); IIT Clinical Research Fund of The Second Affiliated Hospital of Xi\u0026rsquo;an Jiaotong University (M074); Science and Technology Research Project for Social Development of Shaanxi Province(2015SF145).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eX.T., S.D., and L.L.: conception and design.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eL.L., S.D. and J.W.: administrative support.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eS.T., Z.W., Q.R., and Y.L.: acquisition of data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eX.T., S.T., J.Y., and Q.Y.: data preparation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eX.T., S.T., and Q.R.: statistical analyses and consulting.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eX.T., Y.L. and C.L.: interpretation of the data.\u003c/p\u003e\n\u003cp\u003eX.T., S.T. and J.Y.: drafting the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eX.T., L.L., S.D. and J.W.: critical review of manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors critically reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGin\u0026egrave;s P, Krag A, Abraldes JG, Sol\u0026agrave; E, Fabrellas N, Kamath PS (2021) Liver cirrhosis. 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Liver Int 39(12):2374\u0026ndash;2385. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/liv.14217\u003c/span\u003e\u003cspan address=\"10.1111/liv.14217\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"internal-and-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"iaem","sideBox":"Learn more about [Internal and Emergency Medicine](http://link.springer.com/journal/11739)","snPcode":"11739","submissionUrl":"https://www.editorialmanager.com/iaem/default.aspx","title":"Internal and Emergency Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Sarcopenia, Cirrhosis, TIPS, Risk factor, Prediction model","lastPublishedDoi":"10.21203/rs.3.rs-7311054/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7311054/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and aims:\u003c/h2\u003e\u003cp\u003eSarcopenia is highly prevalent and predicts poor outcomes in patients with cirrhosis undergoing transjugular intrahepatic portosystemic shunt (TIPS). However, the risk factors for the development of sarcopenia in this population remain unclear. In addition, validated predictive models based on easily accessible clinical variables have not been developed. This study aimed to address these issues in patients with cirrhosis undergoing TIPS.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eAll patients with cirrhosis undergoing TIPS at the Second Affiliated Hospital of Xi\u0026rsquo;an Jiaotong University from January 2018 to March 2024 were screened. The risk factors for sarcopenia were identified using Least Absolute Shrinkage and Selection Operator regression and multivariate logistic regression. A new model and nomogram were developed based on the selected variables. The model was evaluated and compared using discrimination, calibration, and decision curve analysis.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eA total of 202 patients with cirrhosis undergoing TIPS were included in the study. Six independent risk factors for sarcopenia were identified: age; gender; body mass index (BMI); etiology; hepatic encephalopathy (HE); and right psoas muscle thickness (RPMT). A new model and nomogram for predicting sarcopenia were developed based on these risk factors. The model exhibited good overall performance, discrimination, calibration, and clinical utility, offering advantages over existing non-dedicated sarcopenia models. Patients were stratified into high-risk and low-risk groups based on the optimal cutoff value of 0.385, with a sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for distinguishing sarcopenia of 78.5%, 85.4%, 71.8%, 89.3%, and 83.2%, respectively.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e\u003cp\u003eIn patients with cirrhosis undergoing TIPS, age, male sex, BMI, etiology, HE, and RPMT were identified as independent risk factors for sarcopenia. Based on these risk factors, the newly developed sarcopenia model showed good predictive performance and holds potential as a practical screening tool in clinical settings.\u003c/p\u003e","manuscriptTitle":"Risk factors and prediction model for sarcopenia in patients with cirrhosis undergoing transjugular intrahepatic portosystemic shunt","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-27 06:18:03","doi":"10.21203/rs.3.rs-7311054/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-08-17T05:25:14+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-16T22:39:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-14T07:08:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Internal and Emergency Medicine","date":"2025-08-13T21:25:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"internal-and-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"iaem","sideBox":"Learn more about [Internal and Emergency Medicine](http://link.springer.com/journal/11739)","snPcode":"11739","submissionUrl":"https://www.editorialmanager.com/iaem/default.aspx","title":"Internal and Emergency Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9afb208c-0f1b-4f19-9145-8bb894c74f9e","owner":[],"postedDate":"August 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-03T16:02:15+00:00","versionOfRecord":{"articleIdentity":"rs-7311054","link":"https://doi.org/10.1007/s11739-025-04173-1","journal":{"identity":"internal-and-emergency-medicine","isVorOnly":false,"title":"Internal and Emergency Medicine"},"publishedOn":"2025-10-31 15:58:06","publishedOnDateReadable":"October 31st, 2025"},"versionCreatedAt":"2025-08-27 06:18:03","video":"","vorDoi":"10.1007/s11739-025-04173-1","vorDoiUrl":"https://doi.org/10.1007/s11739-025-04173-1","workflowStages":[]},"version":"v1","identity":"rs-7311054","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7311054","identity":"rs-7311054","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}