Construction of a diagnostic model for early-stage postoperative complications after liver transplantation and molecular mechanism research

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Methods This study analysed 99 patients who underwent their first orthotopic liver transplantation, stratified by Clavien-Dindo grade ≥III complications into complication (n=21) and noncomplication (n=78) groups, with intergroup comparisons of body composition and clinically relevant parameters. Univariate and multivariate logistic regression identified risk factors for posttransplant complications, culminating in the development of a predictive model for postoperative complications following LT. The discriminative ability and calibration of the model were assessed via receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and the Hosmer-Lemeshow test, while its clinical utility was evaluated via decision curve analysis (DCA). Gene expression profiles were analysed via array datasets obtained from the Gene Expression Omnibus (GEO) database. Results Multivariate logistic regression analysis revealed that the SMI, CONUT score, and MELD score were independent risk factors for Grade III or higher early postoperative complications following LT. A predictive model incorporating the SMI, CONUT score, and MELD score demonstrated excellent discriminative ability, with an area under the ROC curve (AUC) of 0.856. The Hosmer-Lemeshow test confirmed good model fit ( P =0.339). DCA demonstrated superior net benefit compared with default strategies across clinically relevant threshold probabilities. Transcriptomic and immune profiling revealed pro-inflammatory pathway activation and distinct immune cell alterations in hepatic ischemia-reperfusion injury (HIRI) following LT. Conclusion The predictive model incorporating the SMI, CONUT score, and MELD score as independent risk factors for Grade III or higher early post-LT complications showed high clinical utility, while molecular insights highlighted potential therapeutic targets for mitigating post-LT complications. Liver transplantation Early-stage postoperative complications Diagnostic model Molecular mechanism Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Compared with palliative alternatives, liver transplantation (LT) represented the definitive therapeutic intervention for patients with end-stage liver disease (ESLD) or acute liver failure and selected those with hepatic malignancies, resulting in significantly improved survival and quality of life[ 1 ]. Despite advancements in surgical techniques, immunosuppressive therapies, and perioperative care, early postoperative complications—ranging from biliary strictures and vascular thrombosis to infections and graft dysfunction—remained a formidable challenge, occurring in 30–50% of recipients within the first 90 days posttransplantation[ 2 ]. These complications not only increase healthcare costs but also contribute to graft loss and increased mortality, underscoring the need for robust predictive tools to identify high-risk patients and guide preemptive interventions. Current risk stratification strategies predominantly ‌relied on clinical scoring systems (e.g., MELD, Child-Pugh) and donor-recipient matching criteria, which, while valuable, often overlooked critical physiological determinants of postoperative resilience, such as body composition and nutritional status[3]. Emerging evidence highlighted the prognostic significance of sarcopenia in LT outcomes, as these conditions exacerbate metabolic dysregulation, impair immune function, and delay wound healing[4]. Computed tomography (CT)-derived metrics such as the skeletal muscle index (SMI) offered objective, quantifiable measures of these parameters, yet their integration into preoperative risk assessment remained underutilized[5].Similarly, malnutrition—a pervasive yet frequently underdiagnosed condition in ESLD patients—had been linked to heightened postoperative morbidity. The controlling nutritional status (CONUT) score, a composite index incorporating albumin, cholesterol, and lymphocyte counts, provided a holistic assessment of nutritional reserve and immune competence, outperforming isolated biochemical markers in predicting infectious and noninfectious complications[6]. However, its synergy with body composition metrics and established liver disease severity scores (e.g., MELD) had been rarely evaluated in the context of LT. LT was inevitably accompanied by ischemia-reperfusion injury (IRI). Recent advances in bioinformatics have enabled systematic dissection of molecular pathways underlying post-LT complications. Hepatic ischemia-reperfusion injury (HIRI), a key contributor to early graft dysfunction, triggered cascades involving oxidative stress, immune activation, and aberrant repair mechanisms[ 7 ]. However, the interplay between these molecular events and clinically actionable risk factors (e.g., sarcopenia, malnutrition) remained unexplored. In this study, we developed a risk prediction model for early post-LT complications by integrating preoperative body composition parameters quantified by abdominal CT with conventional clinical indicators. Furthermore, we performed array-based gene expression profiling to identify HIRI-associated differentially expressed genes (DEGs), followed by comprehensive functional enrichment analysis and immune infiltration analysis to systematically investigate the molecular pathways involved in the pathogenesis of complications. Our findings not only provide a novel predictive tool combining physiological and molecular characteristics but also offer valuable insights for developing targeted therapeutic strategies. Materials and methods Patient population A retrospective cohort study was conducted utilizing clinical data from 99 patients who underwent first-time orthotopic allogeneic liver transplantation at the Department of Transplantation, The Second Affiliated Hospital of Nanchang University, between January 1, 2019 and December 31, 2024. The inclusion criteria for patients were as follows: (1) aged between 18 and 75 years; (2) died from donors meeting the criteria for donation after a citizen’s death; (3) underwent preoperative abdominal computed tomography (CT) encompassing the third lumbar vertebra (L3) within 1 month; and (4) had serum total cholesterol levels measured within 1 week before transplantation. The exclusion criteria for patients were as follows: (1) repeat, multivisceral, or salvage liver transplantation; (2) use of marginal donor livers; (3) combined multiorgan transplantation; (4) preoperative cardiovascular or cerebrovascular diseases and severe pneumonia; (5) poor-quality CT images (e.g., presence of artifacts); and (6) presence of severe ascites or anasarca. Clinical data collection Demographic characteristics, admission laboratory data, and medical history data, including gender, age, height, weight, body mass index (BMI), CONUT score, model for end-stage liver disease (MELD) score, alanine aminotransferase (ALT), aspartate aminotransferase (AST), direct bilirubin (D-BIL), albumin (ALB), total cholesterol (TC), total lymphocyte count (TLC), white blood cell (WBC), platelet (PLT), prothrombin time (PT), serum creatinine (Scr), international normalized ratio (INR), serum sodium (Na+), history of previous abdominal surgery, diabetes, radiofrequency ablation (RFA) for hepatocellular carcinoma (HCC), transcatheter arterial chemoembolization (TACE) for HCC, uncontrollable variceal bleeding, hepatic encephalopathy, and infection before liver transplantation, were retrieved from clinical records. The CONUT score was calculated on the basis of the recipient's most recent preoperative laboratory results, including ALB, TC, and TLC[ 8 ]. The scoring criteria were as follows: A) ALB (g/L): ≥35.0 (0 points), 30.0–34.9 (2 points), 25.0–29.9 (4 points), < 25.0 (6 points); B) TC (mg/dL): ≥180 (0 points), 140–179 (1 point), 100–139 (2 points), < 100 (3 points); and C) TLC (×10 9 /L): ≥1.60 (0 points), 1.20–1.59 (1 point), 0.80–1.19 (2 points), < 0.80 (3 points). The composite CONUT score ranged from 0 to 12, with higher scores indicating worse nutritional status. The MELD score, which was derived from total bilirubin (T-BIL), Scr and INR, provided a quantitative assessment of liver disease severity and reliably predicted short-term prognosis[ 9 ]. The MELD score was calculated via the following equation: MELD score = 3.78×ln(T-BIL [mg/dL]) + 9.57×ln(Scr [mg/dL]) + 11.2×ln(INR) + 6.43×(etiology: 0 for cholestatic or alcoholic cirrhosis, 1 for other causes). The MELD score typically ranged from 6 to 40, with higher values indicating greater severity of liver disease and higher mortality risk. CT scanning parameters and image acquisition Abdominal nonenhanced CT images were obtained from two CT scanners: a 128-slice scanner (Siemens Definition Flash AS) and a 256-slice scanner (Philips Brilliance iCT), with acquisition parameters including a 120 kVp tube voltage, 250–350 mA tube current (with automatic modulation), 0.6–1.25 pitch, 512×512 image matrix, and 1 mm slice thickness. The digital imaging and communications in medicine (DICOM) data of axial CT images at the level of the L3 maximal cross-sectional area were exported from the Philips radiology workstation as raw imaging data and subsequently imported into sliceOmatic software (version 5.0) for further analysis. CT image processing and grouping criteria Muscle and adipose tissue segmentation was performed via SliceOmatic software. All segmentations were independently completed by an abdominal radiologist (Dr. Zhou, with 9 years of experience) and subsequently validated by a second abdominal radiologist (Dr. Zhao, with 11 years of experience). Both radiologists were blinded to the participants' clinical data and outcomes. The skeletal muscle area (SMA) included the psoas, erector spinae, quadratus lumborum, transversus abdominis, external oblique, internal oblique, and rectus abdominis muscles, which were identified and quantified via threshold attenuation values (–30 to 150 Hounsfield units, HU). The visceral adipose tissue (VAT) area was quantified within the range from − 150 to − 50 HU. For subcutaneous adipose tissue (SAT) identification, attenuation values ranging from − 190 to − 30 HU were applied. The SliceOmatic software automatically quantified the following parameters: SMA, visceral adipose tissue area (VATA), subcutaneous adipose tissue area (SATA), and skeletal muscle density (SMD). Body muscle mass was strongly correlated with height. To account for this confounding effect and accurately assess skeletal muscle content, the SMI (cm 2 /m 2 ) was calculated by normalizing the skeletal muscle area (SMA, cm 2 ) to height squared (m 2 ). This study employed the internationally recognized Clavien-Dindo classification system to grade the severity of postoperative complications in liver transplant recipients[ 10 ]. The complications were categorized into five grades, with only Grade III complications and above being included in the statistical analysis. The recipients were followed up for postoperative complications within 90 days and were divided into a complication group and a noncomplication group on the basis of the occurrence of Grade III or higher complications. Biological function analysis The Gene Expression Omnibus (GEO) database was queried to retrieve datasets related to LT in patients with IRI (GSE14951). To evaluate the effect of IRI on the gene expression profile, gene expression levels in transplanted-reperfused livers (n = 5) were compared with basal values in donor livers (n = 5). Gene expression profiling was performed via array analysis. Following data normalization, differential expression analysis was conducted with thresholds ( P .adjust 0.58) to identify statistically significant DEGs[ 11 ]. The selected DEGs were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The immune cell infiltration profiles in control and HIRI groups were evaluated and analyzed using Cibersort. Statistical analysis Statistical analyses were performed via R software (version 4.4.2). Normality and homogeneity of variance were tested via the Kolmogorov-Smirnov and Levene tests, respectively. Normally distributed data were expressed as the mean ± SD and compared via t-tests; nonnormally distributed data were presented as medians (Q1–Q3) and analysed via Mann-Whitney U tests. Categorical variables were compared via the χ 2 test or Fisher's exact test. Univariate and multivariate logistic regression identified risk factors for posttransplant complications. Bioinformatics analyses included DEGs screening from the GEO database, GO/KEGG enrichment, and immune infiltration analysis. P or P .adjust < 0.05 was considered significant. Ethics The retrospective study was conducted in accordance with the ethical principles of the Declaration of Helsinki and approved by the Biomedical Research Ethics Committee of the Second Affiliated Hospital of Nanchang University(No. V1.0×20250104). The committee waived the requirement for informed consent due to the retrospective nature of the research. Results General information The study cohort comprised 99 patients (85 males [85.86%], 14 females [14.14%]) with a mean age of 50.30 ± 9.95 years, including 78 (78.79%) in the noncomplication group and 21 (21.21%) in the complication group. The etiological distribution was as follows: hepatitis B virus (HBV)-related cirrhosis (n = 65; including 29 with hepatocellular carcinoma [HCC], 1 with combined hepatocellular-cholangiocarcinoma, 14 with liver failure, and 2 with concurrent schistosomiasis cirrhosis and liver failure); hepatitis C virus (HCV)-related cirrhosis (n = 4; including 1 with HBV coinfection and 1 with HCC); alcoholic cirrhosis (n = 12; including 2 with HCC, 1 with HBV coinfection, 1 with both HBV coinfection and HCC, and 1 with autoimmune hepatitis-related cirrhosis); primary biliary cholangitis (PBC, n = 5; including 2 with liver failure); Wilson's disease with cirrhosis (n = 4); Budd-Chiari syndrome with cirrhosis (n = 1); primary cholangiocarcinoma (n = 2); autoimmune hepatitis-related cirrhosis (n = 1); isolated liver failure (n = 2); pancreatic neuroendocrine carcinoma with liver metastases (n = 1); and polycystic liver disease (n = 2). Differential analysis and multivariate logistic regression Differential analysis revealed several variables significantly associated with posttransplantation complications, including the serum ALB concentration, serum sodium concentration, CONUT score, MELD score, SMD, SMA, and SMI (all P 0.05) were found in demographic characteristics (BMI,age, height, weight, gender), laboratory parameters (D-BIL, TC, PT, INR, ALT, AST, TLC, WBC, PLT, Scr), body composition measures (VATA, SATA), and clinical history variables (previous abdominal surgery, complicated with diabetes, RFA, TACE, uncontrollable variceal bleeding, hepatic encephalopathy, infection before live transplantation) ( Table 1 ) . The heatmap further visualized these associations, with color intensity indicating correlation strength ( Fig. 1 ) . Multivariate logistic regression analysis revealed that the SMI, CONUT score, and MELD score were independent risk factors for early posttransplant complications (all P < 0.05) ( Table 2 ) . To demonstrate the clinical relevance of the identified independent risk factors, Fig. 2 presented two representative case studies of post-LT patients. Table 1 Baseline characteristics stratified by complication status following LT Variables Noncomplications (n = 78) Complications a (n = 21) Statistic value P value Age 49.73 ± 10.22 52.43 ± 8.76 t=–1.10 0.272 Height 169.23 ± 7.91 169.71 ± 8.23 t=–0.25 0.806 Weight 68.20 ± 13.10 69.01 ± 20.55 t=–0.22 0.827 BMI 23.73 ± 3.92 23.78 ± 6.08 t=–0.05 0.962 ALT 45.71 ± 64.71 256.30 ± 806.12 t=–1.20 0.246 AST 63.27 ± 84.53 293.86 ± 755.38 t=–1.40 0.178 D-BIL 75.10 ± 110.72 127.78 ± 132.91 t=–1.85 0.067 ALB 35.89 ± 5.29 32.34 ± 6.52 t = 2.59 0.011* TC 3.49 ± 1.20 3.12 ± 1.14 t = 1.27 0.209 TLC 0.89 ± 0.59 0.75 ± 0.66 t = 0.98 0.328 WBC 5.00 ± 3.75 4.76 ± 2.77 t = 0.28 0.780 PLT 88.99 ± 63.33 82.81 ± 56.92 t = 0.40 0.686 PT 17.32 ± 6.27 19.68 ± 8.49 t=–1.41 0.161 INR 1.54 ± 0.57 1.87 ± 1.25 t=–1.76 0.081 Scr 77.66 ± 32.01 111.26 ± 114.95 t=–1.33 0.199 serum sodium 137.38 ± 4.59 134.92 ± 4.56 t = 2.18 0.032* CONUT score 4.86 ± 2.50 7.00 ± 1.87 t=–3.65 < 0.001* MELD score 14.83 ± 7.55 21.19 ± 8.86 t=–3.30 0.001* SMD 41.48 ± 7.03 35.85 ± 8.26 t = 3.14 0.002* SMA 127.04 ± 30.46 104.96 ± 23.73 t = 3.08 0.003* VATA 90.65 ± 55.82 88.13 ± 42.56 t = 0.19 0.848 SATA 104.05 ± 54.77 99.25 ± 75.54 t = 0.33 0.744 SMI 44.06 ± 9.15 36.34 ± 7.33 t = 3.57 < 0.001* Gender χ 2 = 0.00 1.000 Male 67 (85.90) 18 (85.71) Female 11 (14.10) 3 (14.29) Previous abdominal surgery χ 2 = 0.34 0.561 Without 39 (50.00) 12 (57.14) With 39 (50.00) 9 (42.86) Complicated with diabetes χ 2 = 0.52 0.472 Without 70 (89.74) 17 (80.95) With 8 (10.26) 4 (19.05) RFA χ 2 = 1.17 0.280 Without 70 (89.74) 21 (100.00) With 8 (10.26) 0 (0.00) TACE χ 2 = 0.34 0.560 Without 66 (84.62) 16 (76.19) With 12 (15.38) 5 (23.81) Uncontrollable variceal bleeding χ 2 = 0.91 0.339 Without 61 (78.21) 19 (90.48) With 17 (21.79) 2 (9.52) Hepatic encephalopathy χ 2 = 0.83 0.361 Without 71 (91.03) 17 (80.95) With 7 (8.97) 4 (19.05) Infection before live transplantation χ 2 = 1.38 0.240 Without 59 (75.64) 19 (90.48) With 19 (24.36) 2 (9.52) t: t-test, χ 2 : Chi-square test. a Complications classified as Grade IIIa, including biliary leakage (n = 5), bile duct stricture (n = 3), peritoneal hemorrhage requiring drainage (n = 7), symptomatic peritoneal effusion necessitating drainage (n = 2), hemothorax (n = 1), pleural effusion requiring drainage (n = 2), and portal vein stenosis (n = 1). Table 2 Multivariate analysis of risk factors for early postoperative complications after LT Risk factor β value OR (95%CI) P value SMI –0.28 0.76 (0.60–0.95) 0.018 CONUT score 0.50 1.66 (1.10–2.51) 0.017 MELD score 0.10 1.10 (1.02–1.19) 0.016 (A, B) A 53-year-old male with decompensated cirrhosis developed posttransplant biliary leakage complications (SMI: 36.22; CONUT score: 8; MELD score: 28). (C, D) A 44-year-old male with uncomplicated posttransplant (SMI: 59.49; CONUT score: 1; MELD score: 8). Construction of the prediction model The predictive model exhibited excellent diagnostic performance, with an AUC of 0.856 (95% CI: 0.777–0.938), an accuracy of 0.808, a sensitivity of 0.905, and a specificity of 0.782. The optimal cut-off value was 0.194, corresponding to a Youden index of 0.687 ( Fig. 3 ) . The results demonstrated that the prediction model exhibited good discriminative ability. The Hosmer-Lemeshow goodness-of-fit test yielded a nonsignificant p value of 0.339 ( p > 0.05), indicating no statistically significant deviation between the predicted and observed values, thus demonstrating adequate model calibration ( Fig. 4 ) . Decision curve analysis showed that most patients derived high net benefit from the predictive model, suggesting the model's clinical utility in both optimizing resource allocation and improving treatment outcomes and prognosis ( Fig. 5 ) . The nomogram was constructed to visually represent the predictive model outcomes, demonstrating the individual contributions of independent risk factors and generating a composite score for predicting the probability of early postoperative complications following LT, as shown in Fig. 6 . Biologic functions associated with IRI following LT Bioinformatic analysis of the GEO dataset identified 606 significant DEGs ( Fig. 7 A, B ) . GO and KEGG enrichment analyses revealed significant enrichment of DEGs related to negative regulation of protein modification process, nuclear speck, DNA-binding transcription factor binding, TNF signaling pathway, NF-kappa B signaling pathway and IL-17 signaling pathway ( Fig. 7 C, D ) . Immune infiltration analysis revealed distinct cellular profiles between groups: Control samples showed higher proportions of CD8⁺ T cells, regulatory T cells (Tregs), resting mast cells, monocytes, and plasma cells, whereas the HIRI group exhibited predominant infiltration of activated mast cells, eosinophils, resting CD4⁺ memory T cells, M0 macrophages, and naive B cells ( Fig. 7 E ) . Compared with controls, the HIRI group exhibited significantly decreased proportions of infiltrating CD8⁺ T cells ( p = 0.010), regulatory T cells (Tregs; p = 0.045), and resting mast cells ( p = 0.010), whereas the proportions of activated mast cells ( p = 0.010) and eosinophils ( p = 0.031) were markedly increased ( Fig. 7 F ) . Discussion This study successfully developed and validated a predictive model for early postoperative complications following LT by integrating preoperative body composition parameters with established clinical risk scores. Our findings demonstrated that the SMI, CONUT score, and MELD score were independent risk factors for Clavien-Dindo grade ≥ III complications, with the combined model exhibiting excellent discriminative ability (AUC: 0.856) and clinical utility. These results provided complementary evidence for current risk assessment strategies by incorporating objective CT-derived body composition metrics, which were frequently overlooked in preoperative evaluations. The predictive performance of the SMI aligned with emerging evidence on the role of sarcopenia in post-LT outcomes[ 12 ]. Skeletal muscle mass, reflected by the SMI, played a crucial role in the body's physiological functions. A lower SMI indicated sarcopenia, which could compromise wound healing and immune function through mechanisms such as chronic inflammation, metabolic dysregulation, and impaired ammonia metabolism[ 13 – 15 ]. Recipients with low SMI demonstrated a significantly increased risk of Grade ≥ III postoperative complications. This association was possibly attributed to reduced skeletal muscle mass leading to diminished amino acid reserves, which subsequently impairs the supply of adequate amino acids for cellular repair. Consequently, this deficiency likely contributed to vascular and biliary complications, as well as impaired wound healing[ 16 – 18 ]. Furthermore, skeletal muscle released glutamine to activate lymphocytes, thereby enhancing immune function. Reduced muscle mass would compromise this immunomodulatory effect, leading to immunosuppression in transplant recipients. Additionally, decreased respiratory muscle mass could impair postoperative cough efficiency, potentially contributing to pulmonary complications[ 19 – 20 ]. Our CT-based quantification provided a more precise assessment than traditional anthropometric measures such as BMI, which failed to show significance in our cohort. This finding supported recent recommendations by Shafaat et al[ 12 ] to incorporate body composition analysis in transplant candidate selection. The association between low SMD and complications in the univariate analysis further suggested that myosteatosis might contribute to adverse outcomes, although it did not retain independence in the multivariate modelling. The CONUT score's predictive value underscored the impact of malnutrition on post-LT recovery. Unlike isolated serum albumin levels, this composite metric integrated nutritional reserve (total cholesterol) and immune competence (lymphocytes), reflecting multiple pathways through which malnutrition exacerbated surgical risk[ 21 – 22 ]. The CONUT score was calculated on the basis of three clinically relevant parameters, all of which were routinely monitored in standard patient care. This made data acquisition timely and convenient, while the scoring system itself remained simple to implement. The score's association with complications persisted after adjusting for liver disease severity, suggesting that nutritional interventions pretransplant could mitigate risk. Our model's incorporation of the MELD score confirmed its enduring relevance in predicting technical and physiological challenges during early post-LT recovery. In patients with end-stage liver disease, impaired hepatic synthetic function reduced cholesterol production by hepatocytes. As cholesterol was an essential component of cell membranes and played a critical role in maintaining cellular structure and function, its deficiency might compromise cellular repair mechanisms, potentially contributing to postoperative complications[ 23 – 24 ]. However, the modest effect size (OR: 1.10, 95% CI: 1.02–1.19) highlighted its limitations as a standalone predictor, emphasizing the added value of body composition and nutritional metrics. Notably, conventional liver function tests (ALT, AST, bilirubin) showed no significant associations, reinforcing the need for multidimensional risk assessment. Methodologically, our study advanced prior work through rigorous CT protocol standardization and blinded radiologist analyses, minimizing measurement variability. The model's strong calibration (Hosmer-Lemeshow P = 0.339) and net benefit across decision curve thresholds supported its clinical applicability for identifying high-risk patients who could benefit from enhanced monitoring or prehabilitation. Vascular complications, such as portal vein stenosis and hepatic artery thrombosis, were partly driven by endothelial dysfunction due to IRI. Our bioinformatic analysis identified 606 DEGs in IRI-affected grafts, with TNF/NF-kappaB signaling as the most enriched pathway. This aligns with prior work showing TNF-α drived hepatocyte apoptosis during reperfusion [ 25 ], while NF-kappaB activation amplifies pro-inflammatory cytokine release[ 26 ]. And the upregulation of C5AR1 promotes neutrophil infiltration and microthrombosis[ 27 ].Additionally, biliary complications were a major concern post-LT, with IRI damaging cholangiocytes via oxidative stress and immune activation. The upregulation of IL1RN suggested a counter-regulatory response to IL-1β-driven inflammation, which was pivotal in biliary injury[ 28 – 29 ]. The IL-17 pathway, identified in our analysis, was known to promote fibrotic remodeling in bile ducts, contributing to cholangiopathy[ 29 ]. Our Cibersort analysis revealed that the HIRI group exhibited a pro-inflammatory immune landscape, with elevated activated mast cells and eosinophils. Mast cells release histamine and vascular endothelial growth factor, promoting vascular leakage[ 30 ], while eosinophil-derived major basic protein could directly damage biliary epithelia leading to immune microenvironment shifts[ 31 ]. Conversely, depleted CD8⁺ T cells and Tregs suggest compromised immune regulation, permitting unchecked inflammation. This study had several limitations that should be acknowledged. First, its single-center retrospective design might introduce selection bias and limit the generalizability of our findings. External validation in multicenter prospective cohorts was necessary to confirm the model's robustness. Second, postoperative management protocols (e.g., immunosuppression regimens, surgical techniques) were not adjusted for in the analysis, which could influence complication rates. Third, while bioinformatic analysis implicated HIRI in LT complications, further functional experiments are needed to validate these molecular mechanisms. Finally, the small sample size would affect the model's long-term predictive performance. Future studies should incorporate larger, multiethnic cohorts and assess whether preoperative physical prehabilitation can mitigate the risk of complications. Conclusion This study established a novel, clinically practical model integrating the SMI, CONUT, and MELD scores to predict early post-LT complications. By bridging body composition analysis with established risk metrics, our approach enabled more comprehensive preoperative risk stratification. These findings advocated for routine CT-based body composition assessment in transplant candidacy evaluations and underscored malnutrition and sarcopenia as modifiable risk factors deserving targeted interventions. Bioinformatic findings implicated HIRI in LT complications. Clinical implementation of CT-based assessment and mechanistic investigations will be warranted. Abbreviations LT liver transplantation SMI skeletal muscle index CONUT controlling nutritional status MELD model for end-stage liver disease HIRI hepatic ischemia-reperfusion injury IRI ischemia-reperfusion injury ROC receiver operating characteristic DCA decision curve analysis GEO Gene Expression Omnibus GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes DEGs differentially expressed genes Declarations Acknowledgements Not applicable. Author contributions Manuscript drafting and manuscript revision for important intellectual content, J.H.Z.; literature research, J.H.Z. and Z.L.Y.; statistical analysis, J.H.Z. and Y.J.Z.; manuscript editing, J.H.Z., Y.J.Z., H.X.J. and J.Q.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; approval of final version of submitted manuscript, all authors; agrees to ensure any questions related to the work are appropriately resolved, all authors. Funding Science and Technology Program of Jiangxi Provincial Health Commission (Grant NO.202510320). Availability of data and materials The datasets used in this study are not publicly available due to regulatory restrictions but can be obtained from the corresponding author upon reasonable request following institutional approval. Ethics approval and consent to participate The retrospective study was conducted in accordance with the ethical principles of the Declaration of Helsinki and approved by the Biomedical Research Ethics Committee of the Second Affiliated Hospital of Nanchang University(No. V1.0×20250104). The committee waived the requirement for informed consent due to the retrospective nature of the research. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Adam R, Karam V, Delvart V, et al. Evolution of indications and results of liver transplantation in Europe. 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Sarcopenia As a Predictor of Survival and Complications of Patients With Cirrhosis After Liver Transplantation: A Systematic Review and Meta-Analysis. Clin Transpl. 2025;39(2):e70088. 10.1111/ctr.70088 . Dasarathy S. Myostatin and beyond in cirrhosis: all roads lead to sarcopenia. J Cachexia Sarcopenia Muscle. 2017;8(6):864–9. 10.1002/jcsm.12262 . Fodor M, Zelger P, Pallua JD, et al. Prediction of Biliary Complications After Human Liver Transplantation Using Hyperspectral Imaging and Convolutional Neural Networks: A Proof-of-concept Study. Transplantation. 2024;108(2):506–15. 10.1097/TP.0000000000004757 . Tandon P, Low G, Mourtzakis M et al. A Model to Identify Sarcopenia in Patients With Cirrhosis [published correction appears in Clin Gastroenterol Hepatol. 2022;20(6):1423. 10.1016/j.cgh.2022.03 .027.]. Clin Gastroenterol Hepatol. 2016;14(10):1473–1480.e3. doi:10.1016/j.cgh.2016.04.040. Bhanji RA, Moctezuma-Velazquez C, Duarte-Rojo A, et al. Myosteatosis and sarcopenia are associated with hepatic encephalopathy in patients with cirrhosis. Hepatol Int. 2018;12(4):377–86. 10.1007/s12072-018-9875-9 . Newsholme P. Why is L-glutamine metabolism important to cells of the immune system in health, postinjury, surgery or infection? J Nutr. 2001;131(9 Suppl):S2515–4. 10.1093/jn/131.9.2515S . Okazaki T, Suzukamo Y, Miyatake M, et al. Respiratory Muscle Weakness as a Risk Factor for Pneumonia in Older People. Gerontology. 2021;67(5):581–90. 10.1159/000514007 . Spoletini G, Ferri F, Mauro A, et al. CONUT Score Predicts Early Morbidity After Liver Transplantation: A Collaborative Study. Front Nutr. 2022;8:793885. 10.3389/fnut.2021.793885 . Published 2022 Jan 7. Takagi K, Domagala P, Polak WG, Buettner S, Ijzermans JNM. Prognostic significance of the controlling nutritional status (CONUT) score in patients undergoing hepatectomy for hepatocellular carcinoma: a systematic review and meta-analysis. BMC Gastroenterol. 2019;19(1):211. 10.1186/s12876-019-1126-6 . Published 2019 Dec 9. Radulovic M, Wenzel EM, Gilani S, et al. Cholesterol transfer via endoplasmic reticulum contacts mediates lysosome damage repair. EMBO J. 2022;41(24):e112677. 10.15252/embj.2022112677 . Tan JX, Finkel T. A phosphoinositide signalling pathway mediates rapid lysosomal repair. Nature. 2022;609(7928):815–21. 10.1038/s41586-022-05164-4 . Schwabe RF, Brenner DA. Mechanisms of Liver Injury. I. TNF-alpha-induced liver injury: role of IKK, JNK, and ROS pathways. Am J Physiol Gastrointest Liver Physiol. 2006;290(4):G583–9. 10.1152/ajpgi.00422.2005 . Lawrence T. The nuclear factor NF-kappaB pathway in inflammation. Cold Spring Harb Perspect Biol. 2009;1(6):a001651. 10.1101/cshperspect.a001651 . Liu X, Hu Y, Yu X, et al. Differential contributions of the C5b-9 and C5a/C5aR pathways to microvascular and macrovascular thrombosis in complement-mediated thrombotic microangiopathy patients. Clin Immunol. 2024;259:109871. 10.1016/j.clim.2023.109871 . Margaryan S, Kriegova E, Fillerova R, Smotkova Kraiczova V, Manukyan G. Hypomethylation of IL1RN and NFKB1 genes is linked to the dysbalance in IL1β/IL-1Ra axis in female patients with type 2 diabetes mellitus. PLoS ONE. 2020;15(5):e0233737. 10.1371/journal.pone.0233737 . Published 2020 May 29. Arsenijevic A, Milovanovic J, Stojanovic B, et al. Gal-3 Deficiency Suppresses Novosphyngobium aromaticivorans Inflammasome Activation and IL-17 Driven Autoimmune Cholangitis in Mice. Front Immunol. 2019;10:1309. 10.3389/fimmu.2019.01309 . Published 2019 Jun 7. Nguyen SMT, Rupprecht CP, Haque A, Pattanaik D, Yusin J, Krishnaswamy G. Mechanisms Governing Anaphylaxis: Inflammatory Cells, Mediators, Endothelial Gap Junctions and Beyond. Int J Mol Sci. 2021;22(15):7785. Published 2021 Jul 21. 10.3390/ijms22157785 Yamazaki K, Gleich GJ, Kita H. Bile acids induce eosinophil degranulation by two different mechanisms. Hepatology. 2001;33(3):582–90. 10.1053/jhep.2001.22168 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 19 Jun, 2025 Reviewers agreed at journal 13 Jun, 2025 Reviewers invited by journal 30 May, 2025 Editor assigned by journal 26 May, 2025 Editor invited by journal 09 May, 2025 Submission checks completed at journal 06 May, 2025 First submitted to journal 06 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6533495","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":465000567,"identity":"5efa29ab-756d-4e9b-8bc5-cf6ab9d4acc6","order_by":0,"name":"Jinhong Zhao","email":"","orcid":"","institution":"Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006","correspondingAuthor":false,"prefix":"","firstName":"Jinhong","middleName":"","lastName":"Zhao","suffix":""},{"id":465000568,"identity":"68a0331c-b79a-4878-885d-2c2382b9c67b","order_by":1,"name":"Yongjie Zhou","email":"","orcid":"","institution":"Department of Radiology, Jiangxi Cancer Hospital, Nanchang, Jiangxi 330029","correspondingAuthor":false,"prefix":"","firstName":"Yongjie","middleName":"","lastName":"Zhou","suffix":""},{"id":465000569,"identity":"eeb61681-9557-42f2-9b30-72a3bbb0abfc","order_by":2,"name":"Fei Zou","email":"","orcid":"","institution":"Department of Radiology, Jiangxi Cancer Hospital, Nanchang, Jiangxi 330029","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Zou","suffix":""},{"id":465000570,"identity":"7cd00297-7b85-4393-a7a6-410ce58f815c","order_by":3,"name":"Zhili Yang","email":"","orcid":"","institution":"Department of Ultrasound, Jiangxi Maternal and Child Health Hospital, Nanchang, Jiangxi 330038","correspondingAuthor":false,"prefix":"","firstName":"Zhili","middleName":"","lastName":"Yang","suffix":""},{"id":465000571,"identity":"fa401200-b4cf-49f0-90b8-ebad4999d322","order_by":4,"name":"Linhua Zhong","email":"","orcid":"","institution":"Department of Radiology, Jiangxi Cancer Hospital, Nanchang, Jiangxi 330029","correspondingAuthor":false,"prefix":"","firstName":"Linhua","middleName":"","lastName":"Zhong","suffix":""},{"id":465000572,"identity":"6207504e-081b-4c4f-ba10-fc708a2e6784","order_by":5,"name":"Yinquan Ye","email":"","orcid":"","institution":"Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006","correspondingAuthor":false,"prefix":"","firstName":"Yinquan","middleName":"","lastName":"Ye","suffix":""},{"id":465000573,"identity":"a2bbf7f2-c9f4-4cae-8cf0-a37772291e75","order_by":6,"name":"Shiguo Xu","email":"","orcid":"","institution":"Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006","correspondingAuthor":false,"prefix":"","firstName":"Shiguo","middleName":"","lastName":"Xu","suffix":""},{"id":465000574,"identity":"ee60d192-164f-4c3f-ac39-8417c8e76a24","order_by":7,"name":"Guangsheng Zha","email":"","orcid":"","institution":"Department of Radiology, Wuyuan County People's Hospital, Wuyuan, Jiangxi 333200","correspondingAuthor":false,"prefix":"","firstName":"Guangsheng","middleName":"","lastName":"Zha","suffix":""},{"id":465000575,"identity":"e122fd3f-ea91-432b-9616-6abd6ba64035","order_by":8,"name":"Minjing Zuo","email":"","orcid":"","institution":"Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006","correspondingAuthor":false,"prefix":"","firstName":"Minjing","middleName":"","lastName":"Zuo","suffix":""},{"id":465000576,"identity":"027364b1-4b14-44f4-9688-bd26b0420fce","order_by":9,"name":"Xijian Dai","email":"","orcid":"","institution":"Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006","correspondingAuthor":false,"prefix":"","firstName":"Xijian","middleName":"","lastName":"Dai","suffix":""},{"id":465000577,"identity":"2045bfe9-cc32-4ec1-b3d3-8b8f3897f174","order_by":10,"name":"Lianggeng Gong","email":"","orcid":"","institution":"Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006","correspondingAuthor":false,"prefix":"","firstName":"Lianggeng","middleName":"","lastName":"Gong","suffix":""},{"id":465000578,"identity":"76c16dbc-482b-4fce-a1b5-3df230ddf66f","order_by":11,"name":"Huaxiang Jiang","email":"","orcid":"","institution":"Department of Stomatology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006","correspondingAuthor":false,"prefix":"","firstName":"Huaxiang","middleName":"","lastName":"Jiang","suffix":""},{"id":465000579,"identity":"69c3eb1a-d565-4f00-a281-4a4bc3f5f483","order_by":12,"name":"Jia Qiu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYBACefnnx39+MLCRY2NvIFKLYUNOgrRERZoxP88BYq05kGAgwXPmcOLMGQlE6mBsAOqRbEtj3HDz8cYbDDU20QS1sDM2HkgobLNhNridVmzBcCwtt4GgLc0MCQeAtrAZ3M4xk2BsOExYC8MxYAjwth3mMbh5hlgtZxiMGYDel5CcwUOkFsMZPGnMwEA24OcB+iWBGL/IS7AfYwRGZX0b++GNNz7U2BDhMCRgIJFAinKIFlJ1jIJRMApGwcgAAEVdP3hVvtQtAAAAAElFTkSuQmCC","orcid":"","institution":"Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006","correspondingAuthor":true,"prefix":"","firstName":"Jia","middleName":"","lastName":"Qiu","suffix":""}],"badges":[],"createdAt":"2025-04-26 07:53:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6533495/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6533495/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83901127,"identity":"bde44b1a-69f2-445d-b2d0-f441ab90f4b4","added_by":"auto","created_at":"2025-06-04 09:29:05","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1183398,"visible":true,"origin":"","legend":"\u003cp\u003eSpearman's correlation heatmap of predictors for early postoperative complications after LT.\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6533495/v1/78fd7ed4d0c708876161fb4b.jpeg"},{"id":83899772,"identity":"8f0d2c46-4a9d-41e1-bff7-50506b05530c","added_by":"auto","created_at":"2025-06-04 09:21:05","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1709810,"visible":true,"origin":"","legend":"\u003cp\u003eAxial nonenhanced abdominal CT at the third lumbar vertebra level in two patients with hepatocellular carcinoma secondary to hepatitis B virus, before and after body composition analysis via SliceOmatic segmentation software (version 5.0) (blue: subcutaneous adipose tissue; red: skeletal muscle; green: visceral adipose tissue).\u003c/p\u003e\n\u003cp\u003e(A, B) A 53-year-old male with decompensated cirrhosis developed posttransplant biliary leakage complications (SMI: 36.22; CONUT score: 8; MELD score: 28).\u003c/p\u003e\n\u003cp\u003e(C, D) A 44-year-old male with uncomplicated posttransplant (SMI: 59.49; CONUT score: 1; MELD score: 8).\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6533495/v1/685f1faf438f35aaa8cdcd85.jpeg"},{"id":83901130,"identity":"355f780c-4b2a-4c04-8ec4-720c2605312a","added_by":"auto","created_at":"2025-06-04 09:29:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":27736,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curve of the risk prediction model for early postoperative complications after LT.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6533495/v1/d46bbb5fd3e131517af11a88.png"},{"id":83899768,"identity":"2b8b1a34-7c3d-42bc-b322-de7cdb0531da","added_by":"auto","created_at":"2025-06-04 09:21:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":70628,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve of the risk prediction model for early postoperative LT complications.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6533495/v1/ad2e5f694aa4e608f5cf670b.png"},{"id":83901489,"identity":"9a42e5b4-cb57-47e4-ae87-667ad77dcec8","added_by":"auto","created_at":"2025-06-04 09:37:05","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":56642,"visible":true,"origin":"","legend":"\u003cp\u003eClinical decision curve analysis of the risk prediction model for early postoperative LT complications.\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6533495/v1/27c7931f50591948af0142ba.jpeg"},{"id":83899775,"identity":"4c0feeec-e8fe-4158-be4a-00f76e11bb35","added_by":"auto","created_at":"2025-06-04 09:21:05","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":79841,"visible":true,"origin":"","legend":"\u003cp\u003eCombining the SMI, MELD score, and CONUT score, a clinical-radiomic nomogram was developed to predict early postoperative complications after LT.\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6533495/v1/6733e6b23245f17918b9ea1e.jpeg"},{"id":83899777,"identity":"ca1ad127-24f0-40a3-9e7d-f33b5e3b1255","added_by":"auto","created_at":"2025-06-04 09:21:05","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":407998,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential gene expression and immune cell infiltration analysis in HIRIfollowing LT via array gene expression profiling data from the GEO database (n=10). \u003cstrong\u003e(A)\u003c/strong\u003eHeatmap of the top 30 DEGs. \u003cstrong\u003e(B)\u003c/strong\u003e Volcano plot illustrating DEGs. The red dots represented significantly upregulated genes, the blue dots indicated significantly downregulated genes, and the gray dots denoted nonsignificant genes. A functional enrichment analysis was performed on the DEGs. The results were presented as bubble plots for \u003cstrong\u003e(C)\u003c/strong\u003e GO and \u003cstrong\u003e(D)\u003c/strong\u003e KEGG pathway analyses. The gene ratio reflected the proportion of genes mapped to a specific pathway relative to the total number of genes analysed. The gene count corresponded to the number of genes associated with each pathway. Statistical significance was assessed via false discovery rate (FDR) adjusted \u003cem\u003ep\u003c/em\u003e values (\u003cem\u003eP\u003c/em\u003e.adjust). \u003cstrong\u003e(E)\u003c/strong\u003e Stacked bar plot showing the proportions of 22 immune cell types based on Cibersort. The left five columns represented the HIRI group, while the right five columns corresponded to the control group. \u003cstrong\u003e(F)\u003c/strong\u003eViolin plots comparing immune cell infiltration levels between control and HIRI groups.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-6533495/v1/24eb7a447286997d0025c15a.png"},{"id":83902668,"identity":"0ff764a9-7957-41ed-a5e9-6750490469bc","added_by":"auto","created_at":"2025-06-04 09:45:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4391425,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6533495/v1/ea6164df-af3e-4730-bf36-6e488d809d21.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Construction of a diagnostic model for early-stage postoperative complications after liver transplantation and molecular mechanism research","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCompared with palliative alternatives, liver transplantation (LT) represented the definitive therapeutic intervention for patients with end-stage liver disease (ESLD) or acute liver failure and selected those with hepatic malignancies, resulting in significantly improved survival and quality of life[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite advancements in surgical techniques, immunosuppressive therapies, and perioperative care, early postoperative complications\u0026mdash;ranging from biliary strictures and vascular thrombosis to infections and graft dysfunction\u0026mdash;remained a formidable challenge, occurring in 30\u0026ndash;50% of recipients within the first 90 days posttransplantation[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These complications not only increase healthcare costs but also contribute to graft loss and increased mortality, underscoring the need for robust predictive tools to identify high-risk patients and guide preemptive interventions.\u003c/p\u003e \u003cp\u003eCurrent risk stratification strategies predominantly \u0026zwnj;relied on clinical scoring systems (e.g., MELD, Child-Pugh) and donor-recipient matching criteria, which, while valuable, often overlooked critical physiological determinants of postoperative resilience, such as body composition and nutritional status[3]. Emerging evidence highlighted the prognostic significance of sarcopenia in LT outcomes, as these conditions exacerbate metabolic dysregulation, impair immune function, and delay wound healing[4]. Computed tomography (CT)-derived metrics such as the skeletal muscle index (SMI) offered objective, quantifiable measures of these parameters, yet their integration into preoperative risk assessment remained underutilized[5].Similarly, malnutrition\u0026mdash;a pervasive yet frequently underdiagnosed condition in ESLD patients\u0026mdash;had been linked to heightened postoperative morbidity. The controlling nutritional status (CONUT) score, a composite index incorporating albumin, cholesterol, and lymphocyte counts, provided a holistic assessment of nutritional reserve and immune competence, outperforming isolated biochemical markers in predicting infectious and noninfectious complications[6]. However, its synergy with body composition metrics and established liver disease severity scores (e.g., MELD) had been rarely evaluated in the context of LT.\u003c/p\u003e \u003cp\u003eLT was inevitably accompanied by ischemia-reperfusion injury (IRI). Recent advances in bioinformatics have enabled systematic dissection of molecular pathways underlying post-LT complications. Hepatic ischemia-reperfusion injury (HIRI), a key contributor to early graft dysfunction, triggered cascades involving oxidative stress, immune activation, and aberrant repair mechanisms[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, the interplay between these molecular events and clinically actionable risk factors (e.g., sarcopenia, malnutrition) remained unexplored.\u003c/p\u003e \u003cp\u003eIn this study, we developed a risk prediction model for early post-LT complications by integrating preoperative body composition parameters quantified by abdominal CT with conventional clinical indicators. Furthermore, we performed array-based gene expression profiling to identify HIRI-associated differentially expressed genes (DEGs), followed by comprehensive functional enrichment analysis and immune infiltration analysis to systematically investigate the molecular pathways involved in the pathogenesis of complications. Our findings not only provide a novel predictive tool combining physiological and molecular characteristics but also offer valuable insights for developing targeted therapeutic strategies.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient population\u003c/h2\u003e \u003cp\u003eA retrospective cohort study was conducted utilizing clinical data from 99 patients who underwent first-time orthotopic allogeneic liver transplantation at the Department of Transplantation, The Second Affiliated Hospital of Nanchang University, between January 1, 2019 and December 31, 2024.\u003c/p\u003e \u003cp\u003eThe inclusion criteria for patients were as follows: (1) aged between 18 and 75 years; (2) died from donors meeting the criteria for donation after a citizen\u0026rsquo;s death; (3) underwent preoperative abdominal computed tomography (CT) encompassing the third lumbar vertebra (L3) within 1 month; and (4) had serum total cholesterol levels measured within 1 week before transplantation.\u003c/p\u003e \u003cp\u003eThe exclusion criteria for patients were as follows: (1) repeat, multivisceral, or salvage liver transplantation; (2) use of marginal donor livers; (3) combined multiorgan transplantation; (4) preoperative cardiovascular or cerebrovascular diseases and severe pneumonia; (5) poor-quality CT images (e.g., presence of artifacts); and (6) presence of severe ascites or anasarca.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClinical data collection\u003c/h3\u003e\n\u003cp\u003eDemographic characteristics, admission laboratory data, and medical history data, including gender, age, height, weight, body mass index (BMI), CONUT score, model for end-stage liver disease (MELD) score, alanine aminotransferase (ALT), aspartate aminotransferase (AST), direct bilirubin (D-BIL), albumin (ALB), total cholesterol (TC), total lymphocyte count (TLC), white blood cell (WBC), platelet (PLT), prothrombin time (PT), serum creatinine (Scr), international normalized ratio (INR), serum sodium (Na+), history of previous abdominal surgery, diabetes, radiofrequency ablation (RFA) for hepatocellular carcinoma (HCC), transcatheter arterial chemoembolization (TACE) for HCC, uncontrollable variceal bleeding, hepatic encephalopathy, and infection before liver transplantation, were retrieved from clinical records.\u003c/p\u003e \u003cp\u003eThe CONUT score was calculated on the basis of the recipient's most recent preoperative laboratory results, including ALB, TC, and TLC[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The scoring criteria were as follows: A) ALB (g/L): \u0026ge;35.0 (0 points), 30.0\u0026ndash;34.9 (2 points), 25.0\u0026ndash;29.9 (4 points), \u0026lt;\u0026thinsp;25.0 (6 points); B) TC (mg/dL): \u0026ge;180 (0 points), 140\u0026ndash;179 (1 point), 100\u0026ndash;139 (2 points), \u0026lt;\u0026thinsp;100 (3 points); and C) TLC (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L): \u0026ge;1.60 (0 points), 1.20\u0026ndash;1.59 (1 point), 0.80\u0026ndash;1.19 (2 points), \u0026lt;\u0026thinsp;0.80 (3 points). The composite CONUT score ranged from 0 to 12, with higher scores indicating worse nutritional status.\u003c/p\u003e \u003cp\u003eThe MELD score, which was derived from total bilirubin (T-BIL), Scr and INR, provided a quantitative assessment of liver disease severity and reliably predicted short-term prognosis[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The MELD score was calculated via the following equation: MELD score\u0026thinsp;=\u0026thinsp;3.78\u0026times;ln(T-BIL [mg/dL])\u0026thinsp;+\u0026thinsp;9.57\u0026times;ln(Scr [mg/dL])\u0026thinsp;+\u0026thinsp;11.2\u0026times;ln(INR)\u0026thinsp;+\u0026thinsp;6.43\u0026times;(etiology: 0 for cholestatic or alcoholic cirrhosis, 1 for other causes). The MELD score typically ranged from 6 to 40, with higher values indicating greater severity of liver disease and higher mortality risk.\u003c/p\u003e\n\u003ch3\u003eCT scanning parameters and image acquisition\u003c/h3\u003e\n\u003cp\u003eAbdominal nonenhanced CT images were obtained from two CT scanners: a 128-slice scanner (Siemens Definition Flash AS) and a 256-slice scanner (Philips Brilliance iCT), with acquisition parameters including a 120 kVp tube voltage, 250\u0026ndash;350 mA tube current (with automatic modulation), 0.6\u0026ndash;1.25 pitch, 512\u0026times;512 image matrix, and 1 mm slice thickness.\u003c/p\u003e \u003cp\u003eThe digital imaging and communications in medicine (DICOM) data of axial CT images at the level of the L3 maximal cross-sectional area were exported from the Philips radiology workstation as raw imaging data and subsequently imported into sliceOmatic software (version 5.0) for further analysis.\u003c/p\u003e\n\u003ch3\u003eCT image processing and grouping criteria\u003c/h3\u003e\n\u003cp\u003eMuscle and adipose tissue segmentation was performed via SliceOmatic software. All segmentations were independently completed by an abdominal radiologist (Dr. Zhou, with 9 years of experience) and subsequently validated by a second abdominal radiologist (Dr. Zhao, with 11 years of experience). Both radiologists were blinded to the participants' clinical data and outcomes.\u003c/p\u003e \u003cp\u003eThe skeletal muscle area (SMA) included the psoas, erector spinae, quadratus lumborum, transversus abdominis, external oblique, internal oblique, and rectus abdominis muscles, which were identified and quantified via threshold attenuation values (\u0026ndash;30 to 150 Hounsfield units, HU). The visceral adipose tissue (VAT) area was quantified within the range from \u0026minus;\u0026thinsp;150 to \u0026minus;\u0026thinsp;50 HU. For subcutaneous adipose tissue (SAT) identification, attenuation values ranging from \u0026minus;\u0026thinsp;190 to \u0026minus;\u0026thinsp;30 HU were applied. The SliceOmatic software automatically quantified the following parameters: SMA, visceral adipose tissue area (VATA), subcutaneous adipose tissue area (SATA), and skeletal muscle density (SMD). Body muscle mass was strongly correlated with height. To account for this confounding effect and accurately assess skeletal muscle content, the SMI (cm\u003csup\u003e2\u003c/sup\u003e/m\u003csup\u003e2\u003c/sup\u003e) was calculated by normalizing the skeletal muscle area (SMA, cm\u003csup\u003e2\u003c/sup\u003e) to height squared (m\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eThis study employed the internationally recognized Clavien-Dindo classification system to grade the severity of postoperative complications in liver transplant recipients[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The complications were categorized into five grades, with only Grade III complications and above being included in the statistical analysis. The recipients were followed up for postoperative complications within 90 days and were divided into a complication group and a noncomplication group on the basis of the occurrence of Grade III or higher complications.\u003c/p\u003e\n\u003ch3\u003eBiological function analysis\u003c/h3\u003e\n\u003cp\u003eThe Gene Expression Omnibus (GEO) database was queried to retrieve datasets related to LT in patients with IRI (GSE14951). To evaluate the effect of IRI on the gene expression profile, gene expression levels in transplanted-reperfused livers (n\u0026thinsp;=\u0026thinsp;5) were compared with basal values in donor livers (n\u0026thinsp;=\u0026thinsp;5). Gene expression profiling was performed via array analysis. Following data normalization, differential expression analysis was conducted with thresholds (\u003cem\u003eP\u003c/em\u003e.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |logFC|\u0026gt;0.58) to identify statistically significant DEGs[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The selected DEGs were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The immune cell infiltration profiles in control and HIRI groups were evaluated and analyzed using Cibersort.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed via R software (version 4.4.2). Normality and homogeneity of variance were tested via the Kolmogorov-Smirnov and Levene tests, respectively. Normally distributed data were expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD and compared via t-tests; nonnormally distributed data were presented as medians (Q1\u0026ndash;Q3) and analysed via Mann-Whitney U tests. Categorical variables were compared via the χ\u003csup\u003e2\u003c/sup\u003e test or Fisher's exact test. Univariate and multivariate logistic regression identified risk factors for posttransplant complications. Bioinformatics analyses included DEGs screening from the GEO database, GO/KEGG enrichment, and immune infiltration analysis. \u003cem\u003eP\u003c/em\u003e or \u003cem\u003eP\u003c/em\u003e.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthics\u003c/h3\u003e\n\u003cp\u003e The retrospective study was conducted in accordance with the ethical principles of the Declaration of Helsinki and approved by the Biomedical Research Ethics Committee of the Second Affiliated Hospital of Nanchang University(No. V1.0\u0026times;20250104). The committee waived the requirement for informed consent due to the retrospective nature of the research.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGeneral information\u003c/h2\u003e \u003cp\u003eThe study cohort comprised 99 patients (85 males [85.86%], 14 females [14.14%]) with a mean age of 50.30\u0026thinsp;\u0026plusmn;\u0026thinsp;9.95 years, including 78 (78.79%) in the noncomplication group and 21 (21.21%) in the complication group. The etiological distribution was as follows: hepatitis B virus (HBV)-related cirrhosis (n\u0026thinsp;=\u0026thinsp;65; including 29 with hepatocellular carcinoma [HCC], 1 with combined hepatocellular-cholangiocarcinoma, 14 with liver failure, and 2 with concurrent schistosomiasis cirrhosis and liver failure); hepatitis C virus (HCV)-related cirrhosis (n\u0026thinsp;=\u0026thinsp;4; including 1 with HBV coinfection and 1 with HCC); alcoholic cirrhosis (n\u0026thinsp;=\u0026thinsp;12; including 2 with HCC, 1 with HBV coinfection, 1 with both HBV coinfection and HCC, and 1 with autoimmune hepatitis-related cirrhosis); primary biliary cholangitis (PBC, n\u0026thinsp;=\u0026thinsp;5; including 2 with liver failure); Wilson's disease with cirrhosis (n\u0026thinsp;=\u0026thinsp;4); Budd-Chiari syndrome with cirrhosis (n\u0026thinsp;=\u0026thinsp;1); primary cholangiocarcinoma (n\u0026thinsp;=\u0026thinsp;2); autoimmune hepatitis-related cirrhosis (n\u0026thinsp;=\u0026thinsp;1); isolated liver failure (n\u0026thinsp;=\u0026thinsp;2); pancreatic neuroendocrine carcinoma with liver metastases (n\u0026thinsp;=\u0026thinsp;1); and polycystic liver disease (n\u0026thinsp;=\u0026thinsp;2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDifferential analysis and multivariate logistic regression\u003c/h2\u003e \u003cp\u003eDifferential analysis revealed several variables significantly associated with posttransplantation complications, including the serum ALB concentration, serum sodium concentration, CONUT score, MELD score, SMD, SMA, and SMI (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No statistically significant differences (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) were found in demographic characteristics (BMI,age, height, weight, gender), laboratory parameters (D-BIL, TC, PT, INR, ALT, AST, TLC, WBC, PLT, Scr), body composition measures (VATA, SATA), and clinical history variables (previous abdominal surgery, complicated with diabetes, RFA, TACE, uncontrollable variceal bleeding, hepatic encephalopathy, infection before live transplantation) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The heatmap further visualized these associations, with color intensity indicating correlation strength \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eMultivariate logistic regression analysis revealed that the SMI, CONUT score, and MELD score were independent risk factors for early posttransplant complications (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. To demonstrate the clinical relevance of the identified independent risk factors, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presented two representative case studies of post-LT patients.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics stratified by complication status following LT\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNoncomplications\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;78)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComplications\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStatistic value\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\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.73\u0026thinsp;\u0026plusmn;\u0026thinsp;10.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.43\u0026thinsp;\u0026plusmn;\u0026thinsp;8.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et=\u0026ndash;1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e169.23\u0026thinsp;\u0026plusmn;\u0026thinsp;7.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e169.71\u0026thinsp;\u0026plusmn;\u0026thinsp;8.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et=\u0026ndash;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.20\u0026thinsp;\u0026plusmn;\u0026thinsp;13.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.01\u0026thinsp;\u0026plusmn;\u0026thinsp;20.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et=\u0026ndash;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.827\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\u003e23.73\u0026thinsp;\u0026plusmn;\u0026thinsp;3.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.78\u0026thinsp;\u0026plusmn;\u0026thinsp;6.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et=\u0026ndash;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.71\u0026thinsp;\u0026plusmn;\u0026thinsp;64.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e256.30\u0026thinsp;\u0026plusmn;\u0026thinsp;806.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et=\u0026ndash;1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.27\u0026thinsp;\u0026plusmn;\u0026thinsp;84.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e293.86\u0026thinsp;\u0026plusmn;\u0026thinsp;755.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et=\u0026ndash;1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-BIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.10\u0026thinsp;\u0026plusmn;\u0026thinsp;110.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127.78\u0026thinsp;\u0026plusmn;\u0026thinsp;132.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et=\u0026ndash;1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.89\u0026thinsp;\u0026plusmn;\u0026thinsp;5.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.34\u0026thinsp;\u0026plusmn;\u0026thinsp;6.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.011*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.49\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.00\u0026thinsp;\u0026plusmn;\u0026thinsp;3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.76\u0026thinsp;\u0026plusmn;\u0026thinsp;2.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.780\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88.99\u0026thinsp;\u0026plusmn;\u0026thinsp;63.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.81\u0026thinsp;\u0026plusmn;\u0026thinsp;56.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.32\u0026thinsp;\u0026plusmn;\u0026thinsp;6.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.68\u0026thinsp;\u0026plusmn;\u0026thinsp;8.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et=\u0026ndash;1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.87\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et=\u0026ndash;1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.66\u0026thinsp;\u0026plusmn;\u0026thinsp;32.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111.26\u0026thinsp;\u0026plusmn;\u0026thinsp;114.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et=\u0026ndash;1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eserum sodium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137.38\u0026thinsp;\u0026plusmn;\u0026thinsp;4.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134.92\u0026thinsp;\u0026plusmn;\u0026thinsp;4.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.032*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCONUT score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.86\u0026thinsp;\u0026plusmn;\u0026thinsp;2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et=\u0026ndash;3.65\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMELD score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.83\u0026thinsp;\u0026plusmn;\u0026thinsp;7.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.19\u0026thinsp;\u0026plusmn;\u0026thinsp;8.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et=\u0026ndash;3.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.48\u0026thinsp;\u0026plusmn;\u0026thinsp;7.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.85\u0026thinsp;\u0026plusmn;\u0026thinsp;8.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127.04\u0026thinsp;\u0026plusmn;\u0026thinsp;30.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104.96\u0026thinsp;\u0026plusmn;\u0026thinsp;23.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVATA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.65\u0026thinsp;\u0026plusmn;\u0026thinsp;55.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.13\u0026thinsp;\u0026plusmn;\u0026thinsp;42.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSATA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104.05\u0026thinsp;\u0026plusmn;\u0026thinsp;54.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.25\u0026thinsp;\u0026plusmn;\u0026thinsp;75.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.06\u0026thinsp;\u0026plusmn;\u0026thinsp;9.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.34\u0026thinsp;\u0026plusmn;\u0026thinsp;7.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;3.57\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 \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 \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67 (85.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (85.71)\u003c/p\u003e \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\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (14.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (14.29)\u003c/p\u003e \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\u003ePrevious abdominal surgery\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 \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (57.14)\u003c/p\u003e \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\u003eWith\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (42.86)\u003c/p\u003e \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\u003eComplicated with diabetes\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 \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (89.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (80.95)\u003c/p\u003e \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\u003eWith\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (10.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (19.05)\u003c/p\u003e \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\u003eRFA\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 \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.280\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (89.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (100.00)\u003c/p\u003e \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\u003eWith\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (10.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \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\u003eTACE\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 \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (84.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (76.19)\u003c/p\u003e \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\u003eWith\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (15.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (23.81)\u003c/p\u003e \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\u003eUncontrollable variceal bleeding\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 \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 (78.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (90.48)\u003c/p\u003e \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\u003eWith\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (21.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (9.52)\u003c/p\u003e \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\u003eHepatic encephalopathy\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 \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 (91.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (80.95)\u003c/p\u003e \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\u003eWith\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (8.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (19.05)\u003c/p\u003e \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\u003eInfection before live transplantation\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 \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59 (75.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (90.48)\u003c/p\u003e \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\u003eWith\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (24.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (9.52)\u003c/p\u003e \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 \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003et: t-test, χ\u003csup\u003e2\u003c/sup\u003e: Chi-square test.\u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e Complications classified as Grade IIIa, including biliary leakage (n\u0026thinsp;=\u0026thinsp;5), bile duct stricture (n\u0026thinsp;=\u0026thinsp;3), peritoneal hemorrhage requiring drainage (n\u0026thinsp;=\u0026thinsp;7), symptomatic peritoneal effusion necessitating drainage (n\u0026thinsp;=\u0026thinsp;2), hemothorax (n\u0026thinsp;=\u0026thinsp;1), pleural effusion requiring drainage (n\u0026thinsp;=\u0026thinsp;2), and portal vein stenosis (n\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e \u003cp\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 analysis of risk factors for early postoperative complications after LT\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.76 (0.60\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCONUT score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.66 (1.10\u0026ndash;2.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMELD score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.10 (1.02\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(A, B) A 53-year-old male with decompensated cirrhosis developed posttransplant biliary leakage complications (SMI: 36.22; CONUT score: 8; MELD score: 28).\u003c/p\u003e \u003cp\u003e(C, D) A 44-year-old male with uncomplicated posttransplant (SMI: 59.49; CONUT score: 1; MELD score: 8).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of the prediction model\u003c/h2\u003e \u003cp\u003eThe predictive model exhibited excellent diagnostic performance, with an AUC of 0.856 (95% CI: 0.777\u0026ndash;0.938), an accuracy of 0.808, a sensitivity of 0.905, and a specificity of 0.782. The optimal cut-off value was 0.194, corresponding to a Youden index of 0.687 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The results demonstrated that the prediction model exhibited good discriminative ability.\u003c/p\u003e \u003cp\u003eThe Hosmer-Lemeshow goodness-of-fit test yielded a nonsignificant \u003cem\u003ep\u003c/em\u003e value of 0.339 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating no statistically significant deviation between the predicted and observed values, thus demonstrating adequate model calibration \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Decision curve analysis showed that most patients derived high net benefit from the predictive model, suggesting the model's clinical utility in both optimizing resource allocation and improving treatment outcomes and prognosis \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eThe nomogram was constructed to visually represent the predictive model outcomes, demonstrating the individual contributions of independent risk factors and generating a composite score for predicting the probability of early postoperative complications following LT, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eBiologic functions associated with IRI following LT\u003c/h2\u003e \u003cp\u003eBioinformatic analysis of the GEO dataset identified 606 significant DEGs \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, B\u003cb\u003e)\u003c/b\u003e. GO and KEGG enrichment analyses revealed significant enrichment of DEGs related to negative regulation of protein modification process, nuclear speck, DNA-binding transcription factor binding, TNF signaling pathway, NF-kappa B signaling pathway and IL-17 signaling pathway \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC, D\u003cb\u003e)\u003c/b\u003e. Immune infiltration analysis revealed distinct cellular profiles between groups: Control samples showed higher proportions of CD8⁺ T cells, regulatory T cells (Tregs), resting mast cells, monocytes, and plasma cells, whereas the HIRI group exhibited predominant infiltration of activated mast cells, eosinophils, resting CD4⁺ memory T cells, M0 macrophages, and naive B cells \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e. Compared with controls, the HIRI group exhibited significantly decreased proportions of infiltrating CD8⁺ T cells (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010), regulatory T cells (Tregs; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045), and resting mast cells (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010), whereas the proportions of activated mast cells (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010) and eosinophils (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031) were markedly increased \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study successfully developed and validated a predictive model for early postoperative complications following LT by integrating preoperative body composition parameters with established clinical risk scores. Our findings demonstrated that the SMI, CONUT score, and MELD score were independent risk factors for Clavien-Dindo grade\u0026thinsp;\u0026ge;\u0026thinsp;III complications, with the combined model exhibiting excellent discriminative ability (AUC: 0.856) and clinical utility. These results provided complementary evidence for current risk assessment strategies by incorporating objective CT-derived body composition metrics, which were frequently overlooked in preoperative evaluations.\u003c/p\u003e \u003cp\u003eThe predictive performance of the SMI aligned with emerging evidence on the role of sarcopenia in post-LT outcomes[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Skeletal muscle mass, reflected by the SMI, played a crucial role in the body's physiological functions. A lower SMI indicated sarcopenia, which could compromise wound healing and immune function through mechanisms such as chronic inflammation, metabolic dysregulation, and impaired ammonia metabolism[\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Recipients with low SMI demonstrated a significantly increased risk of Grade\u0026thinsp;\u0026ge;\u0026thinsp;III postoperative complications. This association was possibly attributed to reduced skeletal muscle mass leading to diminished amino acid reserves, which subsequently impairs the supply of adequate amino acids for cellular repair. Consequently, this deficiency likely contributed to vascular and biliary complications, as well as impaired wound healing[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Furthermore, skeletal muscle released glutamine to activate lymphocytes, thereby enhancing immune function. Reduced muscle mass would compromise this immunomodulatory effect, leading to immunosuppression in transplant recipients. Additionally, decreased respiratory muscle mass could impair postoperative cough efficiency, potentially contributing to pulmonary complications[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Our CT-based quantification provided a more precise assessment than traditional anthropometric measures such as BMI, which failed to show significance in our cohort. This finding supported recent recommendations by Shafaat et al[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] to incorporate body composition analysis in transplant candidate selection. The association between low SMD and complications in the univariate analysis further suggested that myosteatosis might contribute to adverse outcomes, although it did not retain independence in the multivariate modelling.\u003c/p\u003e \u003cp\u003eThe CONUT score's predictive value underscored the impact of malnutrition on post-LT recovery. Unlike isolated serum albumin levels, this composite metric integrated nutritional reserve (total cholesterol) and immune competence (lymphocytes), reflecting multiple pathways through which malnutrition exacerbated surgical risk[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The CONUT score was calculated on the basis of three clinically relevant parameters, all of which were routinely monitored in standard patient care. This made data acquisition timely and convenient, while the scoring system itself remained simple to implement. The score's association with complications persisted after adjusting for liver disease severity, suggesting that nutritional interventions pretransplant could mitigate risk.\u003c/p\u003e \u003cp\u003eOur model's incorporation of the MELD score confirmed its enduring relevance in predicting technical and physiological challenges during early post-LT recovery. In patients with end-stage liver disease, impaired hepatic synthetic function reduced cholesterol production by hepatocytes. As cholesterol was an essential component of cell membranes and played a critical role in maintaining cellular structure and function, its deficiency might compromise cellular repair mechanisms, potentially contributing to postoperative complications[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, the modest effect size (OR: 1.10, 95% CI: 1.02\u0026ndash;1.19) highlighted its limitations as a standalone predictor, emphasizing the added value of body composition and nutritional metrics. Notably, conventional liver function tests (ALT, AST, bilirubin) showed no significant associations, reinforcing the need for multidimensional risk assessment.\u003c/p\u003e \u003cp\u003eMethodologically, our study advanced prior work through rigorous CT protocol standardization and blinded radiologist analyses, minimizing measurement variability. The model's strong calibration (Hosmer-Lemeshow \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.339) and net benefit across decision curve thresholds supported its clinical applicability for identifying high-risk patients who could benefit from enhanced monitoring or prehabilitation.\u003c/p\u003e \u003cp\u003eVascular complications, such as portal vein stenosis and hepatic artery thrombosis, were partly driven by endothelial dysfunction due to IRI. Our bioinformatic analysis identified 606 DEGs in IRI-affected grafts, with TNF/NF-kappaB signaling as the most enriched pathway. This aligns with prior work showing TNF-α drived hepatocyte apoptosis during reperfusion [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], while NF-kappaB activation amplifies pro-inflammatory cytokine release[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. And the upregulation of C5AR1 promotes neutrophil infiltration and microthrombosis[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].Additionally, biliary complications were a major concern post-LT, with IRI damaging cholangiocytes via oxidative stress and immune activation. The upregulation of IL1RN suggested a counter-regulatory response to IL-1β-driven inflammation, which was pivotal in biliary injury[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The IL-17 pathway, identified in our analysis, was known to promote fibrotic remodeling in bile ducts, contributing to cholangiopathy[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur Cibersort analysis revealed that the HIRI group exhibited a pro-inflammatory immune landscape, with elevated activated mast cells and eosinophils. Mast cells release histamine and vascular endothelial growth factor, promoting vascular leakage[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], while eosinophil-derived major basic protein could directly damage biliary epithelia leading to immune microenvironment shifts[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Conversely, depleted CD8⁺ T cells and Tregs suggest compromised immune regulation, permitting unchecked inflammation.\u003c/p\u003e \u003cp\u003eThis study had several limitations that should be acknowledged. First, its single-center retrospective design might introduce selection bias and limit the generalizability of our findings. External validation in multicenter prospective cohorts was necessary to confirm the model's robustness. Second, postoperative management protocols (e.g., immunosuppression regimens, surgical techniques) were not adjusted for in the analysis, which could influence complication rates. Third, while bioinformatic analysis implicated HIRI in LT complications, further functional experiments are needed to validate these molecular mechanisms. Finally, the small sample size would affect the model's long-term predictive performance. Future studies should incorporate larger, multiethnic cohorts and assess whether preoperative physical prehabilitation can mitigate the risk of complications.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study established a novel, clinically practical model integrating the SMI, CONUT, and MELD scores to predict early post-LT complications. By bridging body composition analysis with established risk metrics, our approach enabled more comprehensive preoperative risk stratification. These findings advocated for routine CT-based body composition assessment in transplant candidacy evaluations and underscored malnutrition and sarcopenia as modifiable risk factors deserving targeted interventions. Bioinformatic findings implicated HIRI in LT complications. Clinical implementation of CT-based assessment and mechanistic investigations will be warranted.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eLT liver transplantation\u003c/p\u003e\u003cp\u003eSMI skeletal muscle index\u003c/p\u003e\u003cp\u003eCONUT controlling nutritional status\u003c/p\u003e\u003cp\u003eMELD model for end-stage liver disease\u003c/p\u003e\u003cp\u003eHIRI hepatic ischemia-reperfusion injury\u003c/p\u003e\u003cp\u003eIRI ischemia-reperfusion injury\u003c/p\u003e\u003cp\u003eROC receiver operating characteristic\u003c/p\u003e\u003cp\u003eDCA decision curve analysis\u003c/p\u003e\u003cp\u003eGEO Gene Expression Omnibus\u003c/p\u003e\u003cp\u003eGO Gene Ontology\u003c/p\u003e\u003cp\u003eKEGG Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\u003cp\u003eDEGs differentially expressed genes\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eManuscript drafting and manuscript revision for important intellectual content, J.H.Z.; literature research, J.H.Z. and Z.L.Y.; statistical analysis, J.H.Z. and Y.J.Z.; manuscript editing, J.H.Z., Y.J.Z., H.X.J. and J.Q.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; approval of final version of submitted manuscript, all authors; agrees to ensure any questions related to the work are appropriately resolved, all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eScience and Technology Program of Jiangxi Provincial Health Commission (Grant NO.202510320).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used in this study are not publicly available due to regulatory restrictions but can be obtained from the corresponding author upon reasonable request following institutional approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe retrospective study was conducted in accordance with the ethical principles of the Declaration of Helsinki and approved by the Biomedical Research Ethics Committee of the Second Affiliated Hospital of Nanchang University(No. V1.0\u0026times;20250104). The committee waived the requirement for informed consent due to the retrospective nature of the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdam R, Karam V, Delvart V, et al. Evolution of indications and results of liver transplantation in Europe. A report from the European Liver Transplant Registry (ELTR). 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Published 2021 Jul 21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms22157785\u003c/span\u003e\u003cspan address=\"10.3390/ijms22157785\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamazaki K, Gleich GJ, Kita H. Bile acids induce eosinophil degranulation by two different mechanisms. Hepatology. 2001;33(3):582\u0026ndash;90. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1053/jhep.2001.22168\u003c/span\u003e\u003cspan address=\"10.1053/jhep.2001.22168\" 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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-gastroenterology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmge","sideBox":"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmge/default.aspx","title":"BMC Gastroenterology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Liver transplantation, Early-stage postoperative complications, Diagnostic model, Molecular mechanism","lastPublishedDoi":"10.21203/rs.3.rs-6533495/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6533495/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e This study aimed to identify risk factors, elucidate molecular mechanisms, and establish a prediction model for early complications following liver transplantation (LT).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e This study analysed 99 patients who underwent their first orthotopic liver transplantation, stratified by Clavien-Dindo grade ≥III complications into complication (n=21) and noncomplication (n=78) groups, with intergroup comparisons of body composition and clinically relevant parameters. Univariate and multivariate logistic regression identified risk factors for posttransplant complications, culminating in the development of a predictive model for postoperative complications following LT. The discriminative ability and calibration of the model were assessed via receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and the Hosmer-Lemeshow test, while its clinical utility was evaluated via decision curve analysis (DCA). Gene expression profiles were analysed via array datasets obtained from the Gene Expression Omnibus (GEO) database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eMultivariate logistic regression analysis revealed that the SMI, CONUT score, and MELD score were independent risk factors for Grade III or higher early postoperative complications following LT. A predictive model incorporating the SMI, CONUT score, and MELD score demonstrated excellent discriminative ability, with an area under the ROC curve (AUC) of 0.856. The Hosmer-Lemeshow test confirmed good model fit (\u003cem\u003eP\u003c/em\u003e=0.339). DCA demonstrated superior net benefit compared with default strategies across clinically relevant threshold probabilities. Transcriptomic and immune profiling revealed pro-inflammatory pathway activation and distinct immune cell alterations in hepatic ischemia-reperfusion injury (HIRI) following LT.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e The predictive model incorporating the SMI, CONUT score, and MELD score as independent risk factors for Grade III or higher early post-LT complications showed high clinical utility, while molecular insights highlighted potential therapeutic targets for mitigating post-LT complications.\u003c/p\u003e","manuscriptTitle":"Construction of a diagnostic model for early-stage postoperative complications after liver transplantation and molecular mechanism research","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-04 09:21:00","doi":"10.21203/rs.3.rs-6533495/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-06-19T23:07:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"322078067563033365577418729848381520281","date":"2025-06-13T14:00:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-30T15:29:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-26T08:36:19+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-09T04:55:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-07T01:47:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Gastroenterology","date":"2025-05-07T01:46:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-gastroenterology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmge","sideBox":"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmge/default.aspx","title":"BMC Gastroenterology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"542335ec-16a6-49ca-9351-ab1d1f363363","owner":[],"postedDate":"June 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-06-04T09:21:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-04 09:21:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6533495","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6533495","identity":"rs-6533495","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}