Development and Validation of a Nomogram Predictive Model for Massive Ascites After Hepatectomy in Patients with Primary Hepatocellular Carcinoma

Development and Validation of a Nomogram Predictive Model for Massive Ascites After Hepatectomy in Patients with Primary Hepatocellular Carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and Validation of a Nomogram Predictive Model for Massive Ascites After Hepatectomy in Patients with Primary Hepatocellular Carcinoma Ao Men, Dapeng Sun, Xiuqing Sun, Luyuan Jin, Bailiang Liu, Fengyang Chen, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8441964/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: To develop and validate a nomogram for predicting massive ascites after hepatectomy in hepatocellular carcinoma (HCC) patients. Methods: A retrospective study of 232 HCC patients undergoing hepatectomy (Feb 2021–Jul 2025) was conducted. Patients were grouped by postoperative ascites status (massive, n=66; non-massive, n=166). Predictors were screened via univariate and multivariate logistic regression. A nomogram was built and internally validated using 1000 bootstrap samples. Performance was assessed via ROC analysis, calibration, and decision curve analysis (DCA)，Clinical Impact Curve（CIC). Results: Multivariate analysis identified four independent predictors: platelet count (OR=0.985), AST (OR=1.027), portal hypertension (OR=5.288), and operative time (OR=5.011). The nomogram achieved an AUC of 0.837 (95% CI: 0.781–0.892) with good calibration (H-L test, P=0.860). DCA showed clinical net benefit across thresholds [0.00–0.71] and [0.86–0.93]and the clinical impact curve showing good concordance at risk thresholds above 0.4. Conclusions: The nomogram accurately predicts massive ascites risk using four perioperative variables and demonstrates strong clinical utility for individualized management. Hepatectomy Hepatocellular carcinoma༛Postoperative complications༛ Ascites༛Nomogram༛ Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction According to the 2022 Global Cancer Statistics released by the International Agency for Research on Cancer (IARC), liver cancer ranks sixth in global incidence and third in cancer-related mortality worldwide [ 1 ] . In China—a region with a high burden of viral hepatitis—liver cancer is the fourth most commonly diagnosed malignancy and the second leading cause of cancer death [ 2 ] . Advances in surgical techniques and perioperative management have established hepatectomy as the primary curative treatment for hepatocellular carcinoma (HCC), significantly improving survival outcomes. However, HCC often arises in the context of underlying chronic liver disease, such as viral hepatitis, alcohol-related liver injury, or cirrhosis. In addition, the liver's complex anatomical architecture, abundant vascular supply, and central metabolic role make hepatectomy a technically demanding procedure with a substantial risk of postoperative complications [ 3 – 4 ] . These include hemorrhage, infection, liver failure, bile leakage, portal vein thrombosis, pleural effusion, and ascites.Among common complications following hepatectomy, ascites occurs in 5%–56% of cases [ 5 ] . Massive postoperative ascites can lead to a range of secondary sequelae, including abdominal infection, electrolyte imbalance, and hypoalbuminemia. In severe instances, it may precipitate liver failure, substantially prolong hospitalization and elevate healthcare expenditures. Beyond its clinical impact, massive ascites also adversely affects quality of life and may increase the risk of tumor recurrence [ 6 ] . The underlying pathophysiology involves multiple mechanisms, such as impaired hepatic albumin synthesis, dysregulation of fluid and sodium balance, portal hypertension, and altered hepatic lymph production [ 7 – 8 ] .However, most relevant studies have been performed under the condition of non-intervened liver status, thus failing to adequately reflect the pathogenesis of ascites in the context of clinically relevant hepatic impairment. Consequently, such research has inherent limitations, and the mechanisms proposed therein may not be generalizable to postsurgical ascites development. In recent years, several preoperative indicators—including Child–Pugh classification, indocyanine green retention rate at 15 minutes (ICG-R15), magnetic resonance elastography (MRE), and Mac-2 binding protein glycosylated isomer (M2BPGi)—have shown utility in predicting postoperative complications [ 9 – 11 ] . These tools contribute to improved risk stratification and support clinical efforts to reduce perioperative morbidity and mortality.Nevertheless, the prediction and management of massive ascites following hepatectomy warrant further in-depth investigation. Although the number of studies on post-hepatectomy ascites has been increasing globally, most remain focused on risk factor analysis, with a notable lack of systematic predictive model development [ 5 , 6 , 12 ] . As an intuitive and individualized predictive tool, the nomogram can integrate multiple predictors to provide a quantitative basis for clinical decision-making. Therefore, this study seeks to construct and validate a nomogram for predicting massive ascites after hepatectomy in patients with hepatocellular carcinoma (HCC), aiming to offer a novel approach for precise risk assessment and perioperative strategy optimization. Materials and Methods Research participants: We conducted a retrospective cohort study, systematically collecting clinical data from 312 patients who underwent hepatectomy for primary hepatocellular carcinoma (HCC) at the Department of Hepatobiliary and Pancreatic Surgery, General Hospital of Northern Theater Command, from February 2021 to July 2025.The inclusion criteria were as follows: (1) diagnosis of HCC consistent with the Guidelines for the Diagnosis and Treatment of Primary Liver Cancer (2024 Edition) [ 13 ] , with postoperative pathological confirmation; (2) availability of complete clinical data without critical missing information; and (3) undergoing primary hepatectomy as the initial treatment.Patients were excluded based on the following criteria: (1) preoperative receipt of any conversion therapy for liver cancer, such as transarterial chemoembolization (TACE), radiofrequency ablation (RFA), targeted therapy, immunotherapy, chemotherapy, or radiotherapy, which may influence intraoperative bleeding or postoperative recovery; (2) significant incompleteness of clinical data; (3) prior history of hepatic surgery (e.g. pericardial devascularization or portosystemic shunt) that could alter hepatic hemodynamics; (4) occurrence of postoperative complications such as bile leakage or hemorrhage, which could confound the assessment of ascites volume.Following rigorous application of the inclusion and exclusion criteria, 80 patients were excluded from the study. Specific reasons for exclusion included: preoperative conversion therapy (n = 14), substantially incomplete clinical data (n = 37), prior hepatic surgery for portal hypertension (specifically, pericardial devascularization, n = 1), previous liver surgery for hepatocellular carcinoma (n = 18), and postoperative complications such as bile leakage (n = 10). A total of 232 patients were consequently included in the final cohort.Comprehensive preoperative, intraoperative, and postoperative clinical data were collected for all enrolled patients. Based on the presence of massive postoperative ascites—defined as either a maximum daily drainage volume exceeding 10 mL/kg of preoperative body weight or a cumulative drainage volume greater than 1500 mL within one week after surgery [ 14 ] —patients were categorized into a massive ascites group (n = 66) and a non-massive ascites group (n = 166). Clinical Data Collection Patient data were systematically collected, encompassing demographic characteristics (age, sex, body mass index), comorbidities (coronary heart disease, hypertension, diabetes), laboratory test results, and tumor-related parameters. Laboratory assessments included platelet count, hemoglobin, alpha-fetoprotein, aspartate aminotransferase, alanine aminotransferase, albumin, total bilirubin, prothrombin activity, activated partial thromboplastin time, blood urea nitrogen, creatinine, and alkaline phosphatase. Disease-specific variables consisted of hepatitis history, portal hypertension status, and Child–Pugh classificationIntraoperative variables recorded included the presence of cirrhosis, extent of hepatectomy (major resection was defined as removal of ≥ 3 segments, minor as < 3 segments) [ 15 ] , duration of hepatic inflow occlusion, intraoperative blood transfusion, operative time, and surgical approach (laparoscopic, open, or conversion to laparotomy). Postoperative pathological data were also collected, with emphasis on microvascular invasion (MVI), capsular invasion, maximum tumor diameter, tumor number, histological grade, and the presence of vascular tumor thrombus. Perioperative Management and Surgical Technique All enrolled patients underwent comprehensive preoperative imaging to delineate tumor number, size, location, and anatomical relationships with intra- and extrahepatic vasculature and bile ducts. Patients with preoperative liver dysfunction received routine hepatoprotective therapy. All hepatectomies were performed by experienced attending surgeons (associate senior title or higher). Intraoperatively, perihepatic ligaments were mobilized, and tumor-feeding vessels were ligated. The Pringle maneuver was applied when indicated for hepatic inflow control. Liver transection was performed incrementally using an ultrasonic dissector, electrocautery, or the clamp-crush technique. Hemostasis at the resection margin was achieved by ligation, suture, or electrocoagulation, supplemented by absorbable hemostatic gauze. After thorough irrigation and confirmation of hemostasis and absence of bile leakage, drains were placed at the resection surface and Winslow’s foramen as needed. Postoperatively, abdominal drainage was closely monitored for volume, character, and color to promptly identify complications such as infection, hemorrhage, or bile leak. Non-contrast or contrast-enhanced computed tomography (CT) of the hepatobiliary-pancreatic system was routinely performed within one week after surgery to evaluate drainage catheter placement and detect fluid collections in the surgical area. When necessary, CT- or ultrasonography-guided percutaneous catheter drainage was utilized to manage inadequate drainage. All patients were managed according to a standardized postoperative protocol, which included prophylactic antibiotics, intravenous fluid support, and hepatoprotective therapy. Symptom-directed treatments—such as analgesics, acid suppressants, and antipyretics—were administered as needed, along with intermittent pneumatic compression for deep vein thrombosis prophylaxis.After the return of bowel function (evidenced by flatus or defecation), oral intake was gradually advanced from semi-liquid to liquid and eventually to a regular diet. Laboratory tests—including complete blood count and liver and kidney function panels—were performed daily for the first three postoperative days and every three days thereafter. Treatment plans were adjusted dynamically based on hepatic function recovery and strict fluid balance records. For patients diagnosed with massive ascites, intensive hepatoprotective therapy was initiated. Human albumin was supplemented when serum levels fell below 30 g/L, combined with intravenous loop diuretics (e.g., furosemide) and oral aldosterone antagonists (e.g. spironolactone) or arginine vasopressin V2 receptor antagonists (e.g. tolvaptan) until significant resolution of ascites was achieved. Statistical Analysis All data were managed in Microsoft Excel and analyzed using SPSS version 27.0.1. Study variables encompassed preoperative, intraoperative, and postoperative parameters. The Shapiro–Wilk test was used to assess normality. Normally distributed continuous variables were expressed as mean ± standard deviation (SD) and compared using the independent samples t-test; non-normally distributed variables were summarized as median with interquartile range [M (P25–P75)] and compared using the Mann–Whitney U test. Categorical variables were presented as frequencies and compared with the chi-square test. A two-tailed P-value < 0.05 was considered statistically significant. Univariate and multivariate binary logistic regression analyses were performed to identify risk factors for massive ascites, with results reported as odds ratios (ORs) and 95% confidence intervals (CIs). Variables significantly associated with the outcome (P < 0.05) in the multivariate model were considered independent risk factors.A nomogram was developed using the “rms” package in R (version 4.1.0) based on independent predictors identified through multivariate logistic regression. The model’s discriminative performance was evaluated by the area under the receiver operating characteristic (ROC) curve. Calibration was assessed using the Hosmer–Lemeshow test and a calibration plot. In addition, decision curve analysis (DCA) and clinical impact curve (CIC) were performed to quantify the net clinical benefit and clinical utility of the nomogram across different threshold probabilities. Results This study included 232 patients, comprising 191 (82.3%) males and 41 (17.7%) females. The mean age was 62 years, with a range of 24 to 84 years. Postoperative massive ascites occurred in 66 patients (28.4%), demarcating the massive ascites group, while the remaining 166 patients (71.6%) constituted the non-massive ascites group. The detailed baseline characteristics of all enrolled patients are summarized in Table 1 . Table 1 Baseline Characteristics of Hepatocellular Carcinoma Patients. Term Numerical value Gender[n(%)](Male/Female) 191(82.3)/41(17.7) Age (years) 62.0(57.0–69.0) BMI 20.13(18.96–23.31) Coronary Heart Disease (CHD)[n(%)]༈No/Yes༉ 220(94.8)/12(5.2) Diabetes mellitus(DM)[n(%)]༈No/Yes༉ 190(81.9)/42(18.1) Hypertension[n(%)]༈No/Yes༉ 173(74.6)/59(25.4) PLT(×10 9 /L) 149.00(125.00-176.00) HB(g/L) 136(120–148) AFP(ng/mL) 20.33(4.35–208.00) AST(U/L) 31.12 (22.58–45.21) ALT(U/L) 31.00(19.80–44.00) ALB(g/L) 35.35 ± 2.65 TB(µmol/L) 13.30(9.70–17.30) PTA(%) 94.70(84.40-104.80) APTT(S) 28.80(26.40–36.60) BUN(mmol/L) 5.47(4.48–6.56) Cr(µmol/L) 65.04(56.9-75.25) AKP(U/L) 84.17(70-104.5) Hepatitis[n(%)] No 37(15.9) HBV 158(68.1) HCV 32(13.8) HBV + HCV 5(2) Child-pugh[n(%)](A/B) 206(88.8)/26(11.2) Cirrhosis of the Liver[n(%)]༈No/Yes༉ 203(87.5)/29(12.5) Portal Hypertension[n(%)]༈No/Yes༉ 159(68.5)/73(31.5) Surgical Approach[n(%)] Laparoscope 148(63.8) Laparotomy 69(29.7) Laparoscopic Conversion to Laparotomy 15(6.5) Extent of Liver Resection[n(%)] Less than 3 Hepatic Segments 191(82.3) More than 3 Hepatic Segments 41(17.7) Pringle Maneuver Time 43.00(25.00–73.00) Intraoperative Blood Transfusion Status(No/Yes) 159(68.5)/73(31.5) Intraoperative Blood Loss 300(112.5–500.0) Operation time(≥180 mins/<180 mins) 199(85.8)/33(14.2) Pathological type Well-differentiated 44(19) Moderately to poorly differentiated 188(81) Presence or absence of tumor capsular invasion(No/Yes) 161(69.4)/71(30.6) Number of tumors(Solitary/Multiple) 207(89.2)/25(10.8) Maximum tumor diameter(≥ 5cm/<5cm) 87(37.5)/145(62.5) Ki-67(≥ 30%/<30%) 187(80.6)/45(19.4) MVI[n(%)]༈No/Yes༉ 180(77.6)/52(22.4) Univariate analysis of massive ascites in patients with HCC Univariate logistic regression was performed to identify factors associated with massive ascites following hepatectomy in 232 hepatocellular carcinoma patients. The analysis identified eight variables with statistically significant differences between the massive and non-massive ascites groups (all P < 0.05): platelet count (PLT), hemoglobin (HB), albumin (ALB), aspartate aminotransferase (AST), alanine aminotransferase (ALT), portal hypertension, hepatic portal clamping time, and operative time. These results indicate their potential association with the occurrence of postoperative massive ascites. Detailed results are provided in Table 2 . Table 2 univariate logistic regression analysis of postoperative massive ascites OR 95% Cl P-value Gender 1.286 0.591–2.799 0.526 Age (years) 0.983 0.954–1.012 0.248 BMI 1.005 0.919–1.098 0.920 CHD 0.831 0.218–3.169 0.786 DM 1.007 0.481–2.111 0.984 Hypertension 1.024 0.533–1.969 0.943 PLT(×109/L) 0.99 0.981-1 0.043 HB(g/L) 0.987 0.974-1 0.048 AFP(ng/mL) 1 1-1.001 0.348 AST(U/L) 1.044 1.025–1.063 <0.001 ALT(U/L) 1.036 1.020–1.053 <0.001 ALB(g/L) 0.895 0.802–0.999 0.048 TB(µmol/L) 1.008 0.981–1.036 0.554 PTA(%) 1 0.985–1.015 0.976 APTT(S) 1.024 0.980–1.070 0.297 BUN(mmol/L) 1.038 0.931–1.157 0.505 Cr(µmol/L) 0.476 0.978–1.010 0.994 AKP(U/L) 1 0.996–1.004 0.950 Hepatitis 1.859 0.773–4.472 0.166 Child-pugh 1.384 0.583–3.282 0.461 Cirrhosis of the Liver 2.748 0.918–8.231 0.071 Portal Hypertension 6.867 3.66-12.886 <0.001 Surgical Approach 1.170 0.714–1.849 0.500 Extent of Liver Resection 1.386 0.675–2.847 0.374 Pringle Maneuver Time 1.012 1.004–1.019 0.003 Intraoperative Blood Transfusion Status 1.806 0.994–3.281 0.052 Intraoperative Blood Loss 1.001 1-1.001 0.086 Operation time 6.774 1.568–29.264 0.010 Pathological type 0.806 0.380–1.709 0.574 Presence or absence of tumor capsular invasion 1.591 0.871–2.905 0.131 Number of tumors 1.209 0.495–2.954 0.677 Maximum tumor diameter 0.852 0.470–1.546 0.599 Ki-67 1.496 0.693–3.229 0.305 MVI 1.103 0.552–2.204 0.782 Multivariate analysis of massive ascites in patients with HCC Eleven variables with a significance level of P < 0.05 in the univariate analysis were included in the multivariate binary logistic regression model. These consisted of platelet count (PLT), hemoglobin (HB), albumin (ALB), aspartate aminotransferase (AST), alanine aminotransferase (ALT), portal hypertension, hepatic portal clamping time, liver cirrhosis, intraoperative blood transfusion, intraoperative blood loss, and operative duration. Multivariate analysis identified four independent risk factors for massive postoperative ascites: PLT (OR = 0.985, 95% CI: 0.973–0.996), AST (OR = 1.027, 95% CI: 1.001–1.054), portal hypertension (OR = 5.288, 95% CI: 2.535–11.033), and operative duration (OR = 5.011, 95% CI: 1.034–24.296).Detailed results are provided in Table 3 . Table 3 Multivariate logistic regression analysis of postoperative massive ascites Variables Estimates SE Wald OR P-value 95%Cl PLT -0.016 0.006 6.705 0.985 0.01 0.973–0.996 AST 0.026 0.013 4.045 1.027 0.026 1.001–1.054 Portal-Hypertension 1.665 0.375 19.7 5.288 <0.001 2.535–11.033 Operation Time 1.612 0.805 4.004 5.011 0.045 1.034–24.296 Establishment of the Nomogram Model for predicting postoperative massive ascites Based on the independent predictors identified by multivariate logistic regression—PLT, AST, portal hypertension, and operative duration—we constructed a nomogram for predicting massive ascites (Fig. 1 ). The predictive performance of the nomogram was evaluated using receiver operating characteristic (ROC) analysis. As shown in Fig. 2 , the model achieved an area under the curve (AUC) of 0.837 (95% CI: 0.781–0.892), indicating strong discriminative ability in stratifying the risk of postoperative massive ascites. The calibration of the nomogram was evaluated using a calibration curve (Fig. 3 ) and the Hosmer–Lemeshow (HL) test. The HL test result (χ² = 3.968, P = 0.860) indicated no significant deviation between predicted and observed outcomes, suggesting good model fit. Both the calibration plot and HL test confirmed that the nomogram is well-calibrated, with predicted probabilities closely aligning with actual event rates. This supports its reliability as a quantitative tool for individualized risk assessment of massive ascites. Decision curve analysis (DCA) was conducted to evaluate the clinical utility of the nomogram (Fig. 4 ). The results indicated that the model provides a positive net benefit across a wide range of threshold probabilities, specifically from 0.00 to 0.71 and again from 0.86 to 0.93. The clinical impact curve (CIC) further supported these findings (Fig. 5 ). At threshold probabilities above 0.4, the number of individuals classified as high-risk by the model closely aligned with the actual number of observed events, demonstrating high concordance between prediction and observation and confirming the model's practical diagnostic value. Discussion The reported incidence of massive postoperative ascites varies considerably, ranging from 3.5% to 37% across studies [ 11 – 13 ] , largely attributable to inconsistent diagnostic criteria. Some studies define it as a daily drainage volume exceeding 500 mL beyond the third postoperative day until drain removal [ 6 , 16 ] , although this criterion does not account for variations in patient body weight. Ishizawa et al. proposed an alternative definition based on a maximum daily drainage volume of > 10 mL/kg of preoperative body weight [ 5 , 14 ] . Nevertheless, daily drainage volumes can be influenced by multiple transient factors, such as tube patency, patient positioning, peritoneal lavage, and wound exudate. To improve reliability, our study defined massive ascites as either a maximum daily drainage volume > 10 mL/kg of preoperative body weight or a cumulative drainage volume > 1500 mL within the first postoperative week, thus integrating cumulative volume to mitigate the influence of transient or incidental factors. We analyzed preoperative, intraoperative, and postoperative variables from 232 patients who underwent hepatectomy for hepatocellular carcinoma. Multivariate analysis identified platelet count, aspartate aminotransferase level, portal hypertension, and operative time as independent risk factors for massive postoperative ascites. These factors were incorporated into a nomogram that demonstrated strong discriminative ability, with an area under the receiver operating characteristic curve (AUC) of 0.837. The nomogram also exhibited satisfactory calibration and clinical utility, supporting its use as a practical predictive tool. The pathophysiology of postoperative ascites following hepatectomy in hepatocellular carcinoma (HCC) patients, though previously explored, remains incompletely understood. Impaired hepatic functional reserve is widely recognized as a pivotal contributor. In this study, the mean platelet count (PLT) was significantly lower in the massive ascites group than in the non-massive ascites group (142 × 10⁹/L vs. 151 × 10⁹/L). This difference may be partly explained by hypersplenism secondary to cirrhotic portal hypertension, a condition that also promotes ascites formation, which in turn may further accelerate platelet consumption [ 17 ] . These observations align with our finding that portal hypertension constitutes an independent risk factor for postoperative ascites. Notably, platelet count remained significant in the multivariate regression, suggesting that thrombocytopenia alone cannot fully account for poor outcomes. Emerging evidence indicates that platelets may also facilitate HCC progression and liver fibrosis through the release of cytokines such as VEGF and TGF-β, thereby indirectly contributing to the pathophysiology of ascites [ 18 ] . Furthermore, accumulating evidence has established preoperative thrombocytopenia as an independent predictor of postoperative complications and perioperative mortality in liver surgery, corroborating our findings [ 5 , 11 , 12 , 19 – 25 ] . Aspartate aminotransferase (AST), a sensitive marker of hepatocellular injury, reflects the degree of hepatic inflammation and sinusoidal endothelial dysfunction. Elevated AST levels are associated with increased sinusoidal permeability, which facilitates the extravasation of albumin and fluid into the peritoneal space. This process may also promote intrahepatic fibrogenesis, thereby contributing to a self-sustaining cycle in ascites pathogenesis. Previous studies have consistently linked high preoperative AST levels with an increased risk of postoperative complications, including ascites [ 19 ] , which aligns with our results.Preoperative portal hypertension not only exacerbates underlying hepatic dysfunction but also serves as a critical prognostic indicator of post-hepatectomy complications [ 6 , 27 – 28 ] . Mechanistically, hepatectomy disrupts intrahepatic portosystemic shunts, leading to a sharp rise in sinusoidal and splanchnic vascular pressure, which in turn promotes fluid transudation. Additionally, portal hypertension stimulates hepatic lymph production beyond the drainage capacity of the thoracic duct, resulting in lymphatic leakage into the abdominal cavity [ 29 – 30 ] .It has also been suggested that this elevated venous pressure, combined with reduced colloid osmotic pressure due to impaired albumin synthesis, disrupts the Starling equilibrium, establishing a vicious cycle that perpetuates ascites accumulation [ 26 ] . In line with these pathophysiological insights, the American Association for the Study of Liver Diseases guidelines recognize preoperative portal hypertension as a relative contraindication to hepatectomy, providing further support for our conclusions [ 31 ] . Our findings suggest that the risk of massive postoperative ascites is not solely dependent on liver function parameters, but rather stems from the interplay of multiple determinants. Operative duration emerged as a significant risk factor in our analysis, consistent with previous reports [ 5 – 6 ] . A large-scale national analysis of 21,443 hepatectomy cases demonstrated that procedures exceeding 3 hours may induce immunosuppression, tissue hypoperfusion, and infectious complications, thereby increasing postoperative morbidity and mortality [ 32 ] . While some studies have reported associations between surgical approach and massive ascites incidence [ 33 – 35 ] ,we did not observe significant differences in this regard. Nevertheless, we speculate that as our institution's experience with laparoscopic hepatectomy expands and surgical techniques continue to evolve, the physiological advantages of minimally invasive approaches—including preserved hepatic hemodynamics, reduced surgical trauma, and earlier recovery of oral intake—may become particularly beneficial for cirrhotic patients. These benefits could potentially translate into a substantial reduction in post-hepatectomy ascites risk with the accumulation of experience and technical refinement. Based on our findings, preoperative thrombocytopenia, elevated aspartate aminotransferase (AST), prolonged operative time, and the presence of portal hypertension were identified as significant risk factors for massive postoperative ascites. Accordingly, we propose the following clinical recommendations.For patients with progressive preoperative thrombocytopenia, timely platelet transfusion should be considered to correct coagulation dysfunction. In cases of preoperative AST elevation or early signs of ascites, perioperative hepatoprotective therapy should be promptly initiated to facilitate AST normalization and prevent worsening of fluid accumulation.Intraoperatively, surgical efficiency should be optimized—through standardized hilar dissection and judicious inflow occlusion—and minimally invasive approaches should be prioritized when feasible to reduce operative duration.The primary innovation of this study lies in the development of the first nomogram specifically designed to predict massive ascites following hepatectomy for hepatocellular carcinoma. The model demonstrated robust predictive accuracy and tangible clinical utility.Several limitations of this study should be acknowledged. First, its retrospective and single-center design introduces potential selection bias and unmeasured confounding, despite statistical adjustments. Second, the modest sample size limits the generalizability of the findings; external validation through multicenter prospective studies is warranted. Third, certain potential predictors of ascites—such as tumor diameter and liver stiffness—were not incorporated into the model. Finally, the lack of postoperative follow-up precluded an assessment of the long-term prognostic impact of massive ascites on survival or recurrence. In summary, massive ascites after hepatectomy for hepatocellular carcinoma arises from a complex interplay of perioperative factors. The developed nomogram provides a valuable tool for personalizing postoperative management. Future prospective, multi-center studies with larger cohorts are essential to refine and validate this model for broader clinical implementation. Abbreviations HB Hemoglobin AFP Alpha-Fetoprotein AST Aspartate Transaminase ALT Alanine Transaminase ALB Albumin TB Total Bilirubin PTA Prothme Activity BUN Blood Urea Nitrogen Cr Creatinine AKP Alkaline Phosphatase HBV Hepatitis B Virus HCV Hepatitis C Virus ROC Receiver Operating Curve AUC Area Under Curve CIC Clinical Impact Curve DCA Decision Curve Analysis Declarations Ethics approval and consent to participate ： The protocol for this retrospective study was reviewed and approved by the Ethics Committee of the General Hospital of the Northern Theater Command on July 2, 2025 (Approval No: Y(2025)302).This retrospective study was conducted in accordance with the Declaration of Helsinki.The requirement for informed consent was waived by the same committee due to the retrospective nature of the study. Consent for publication ： Not applicable. Availability of data and materials ： The datasets generated and analysed during the current study are not publicly available due to patient privacy and confidentiality concerns but are available from the corresponding author on reasonable request Competing Interests ： None Funding ： This work was supported by Independent Scientific Research Project of General Hospital of Northern Theater Command（ZZKY2024036），Science and Technology Planning Project of Liaoning Province（2025-BS-0947） Authors' contributions ： All authors have reviewed and approved the final version of the manuscript. The specific contributions of each author were as follows:Ao Men, Dapeng Sun, and Xiuqing Sun contributed to the study design, data analysis, and manuscript drafting. Luyuan Jin, Bailiang Liu, Fengyang Chen, Boyuan Nan, and Wenxin Wang were responsible for data collection and analysis. Wei Zhang and Lei Han supervised the overall research, provided critical revisions to the manuscript, and approved the final version. Acknowledgements ： We would like to thank Professors Wei Zhang and Lei Han for their guidance on the topic selection, revisions to the manuscript, and financial support for this paper. We also express our gratitude to all medical staff and patients who made contributions to this article.This work was supported by grants from the Independent Scientific Research Project of General Hospital of Northern Theater Command (ZZKY2024036) and the Science and Technology Planning Project of Liaoning Province (2025-BS-0947). Declarations of interest: none Clinical trial number ： not applicable References Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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World J Surg 2017; 41: 2095-2100. https://doi.org/10.1007/s00268-017-4003-x Zhou J, Sun H, Wang Z, Cong W, Zeng M, Zhou W, et al. China Liver Cancer Guidelines for the Diagnosis and Treatment of Hepatocellular Carcinoma (2024 Edition). Liver Cancer 2024; Epub ahead of print. https://doi.org/10.1159/000546574 Berardi G, Morise Z, Sposito C, Igarashi K, Panetta V, Simonelli I, et al. Development of a nomogram to predict outcome after liver resection for hepatocellular carcinoma in Child-Pugh B cirrhosis. J Hepatol 2020; 72: 75-84. https://doi.org/10.1016/j.jhep.2019.08.032 Belghiti J, Hiramatsu K, Benoist S, Massault P, Sauvanet A, Farges O, et al. Seven hundred forty-seven hepatectomies in the 1990s: an update to evaluate the actual risk of liver resection. J Am Coll Surg 2000; 191: 38-46. https://doi.org/10.1016/s1072-7515(00)00261-1 Harimoto N, Araki K, Ishii N, Muranushi R, Hoshino K, Hagiwara K, et al. Predictors of postoperative ascites after hepatic resection in patients with hepatocellular carcinoma. *Anticancer Res* 2020; 40: 4343-4349. https://doi.org/10.21873/anticanres.14437 Aryal B, Yamakuchi M, Shimizu T, Kadono J, Furoi A, Gejima K, et al. Deciphering platelet kinetics in diagnostic and prognostic evaluation of hepatocellular carcinoma. Can J Gastroenterol Hepatol 2018; 2018: 9142672. https://doi.org/10.1155/2018/9142672 Pavlovic N, Rani B, Gerwins P, Heindryckx F. Platelets as key factors in hepatocellular carcinoma. Cancers (Basel) 2019; 11: 1022. https://doi.org/10.3390/cancers11071022 Bennett JJ, Blumgart LH. Assessment of hepatic reserve prior to hepatic resection. J Hepatobiliary Pancreat Surg 2005; 12: 10-15. https://doi.org/10.1007/s00534-004-0950-3 Kaneko K, Shirai Y, Wakai T, Yokoyama N, Akazawa K, Hatakeyama K, et al. Low preoperative platelet counts predict a high mortality after partial hepatectomy in patients with hepatocellular carcinoma. World J Gastroenterol 2005; 11: 5888-5892. https://doi.org/10.3748/wjg.v11.i37.5888 Luo JC, Hwang SJ, Chang FY, Chu CW, Lai CR, Wang YJ, et al. Simple blood tests can predict compensated liver cirrhosis in patients with chronic hepatitis C. Hepatogastroenterology 2002; 49: 478-481. Jarnagin WR, Gonen M, Fong Y, DeMatteo RP, Ben-Porat L, Little S, et al. Improvement in perioperative outcome after hepatic resection: analysis of 1,803 consecutive cases over the past decade. Ann Surg 2002; 236: 397-406. https://doi.org/10.1097/01.SLA.0000029003.66466.B3 Lu SN, Wang JH, Liu SL, Hung CH, Chen CH, Tung HD, et al. Thrombocytopenia as a surrogate for cirrhosis and a marker for the identification of patients at high-risk for hepatocellular carcinoma. Cancer 2006; 107: 2212-2222. https://doi.org/10.1002/cncr.22242 Venkat R, Hannallah JR, Krouse RS, Maegawa FB. Preoperative thrombocytopenia and outcomes of hepatectomy for hepatocellular carcinoma. J Surg Res 2016; 201: 498-505. https://doi.org/10.1016/j.jss.2015.08.038 Boleslawski E, Petrovai G, Truant S, Dharancy S, Duhamel A, Salleron J, et al. Hepatic venous pressure gradient in the assessment of portal hypertension before liver resection in patients with cirrhosis. Br J Surg 2012; 99: 855-863. https://doi.org/10.1002/bjs.8753 Kaplan DE, Ripoll C, Thiele M, Fortune BE, Simonetto DA, Garcia-Tsao G, et al. AASLD Practice Guidance on risk stratification and management of portal hypertension and varices in cirrhosis. Hepatology 2024; 79: 1180-1211. https://doi.org/10.1097/HEP.0000000000000647 Piano S, Tonon M, Angeli P. Management of ascites and hepatorenal syndrome. Hepatol Int 2018; 12(Suppl 1): 122-134. https://doi.org/10.1007/s12072-017-9815-0 Rockey DC. Noninvasive assessment of liver fibrosis and portal hypertension with transient elastography. Gastroenterology 2008; 134: 8-14. https://doi.org/10.1053/j.gastro.2007.11.053 Jeong J, Tanaka M, Iwakiri Y. Hepatic lymphatic vascular system in health and disease. J Hepatol 2022; 77: 206-218. https://doi.org/10.1016/j.jhep.2022.01.025 Chung C, Iwakiri Y. The lymphatic vascular system in liver diseases: its role in ascites formation. Clin Mol Hepatol 2013; 19: 99-104. https://doi.org/10.3350/cmh.2013.19.2.99 Singal AG, Llovet JM, Yarchoan M, Mehta N, Heimbach JK, Dawson LA, et al. AASLD Practice Guidance on prevention, diagnosis, and treatment of hepatocellular carcinoma. Hepatology 2023; 78: 1922-1965. https://doi.org/10.1097/HEP.0000000000000466 Chacon E, Eman P, Dugan A, Davenport D, Marti F, Ancheta A, et al. Effect of operative duration on infectious complications and mortality following hepatectomy. HPB (Oxford) 2019; 21: 1727-1733. https://doi.org/10.1016/j.hpb.2019.05.001 El-Gendi A, El-Shafei M, El-Gendi S, Shawky A. Laparoscopic versus open hepatic resection for solitary hepatocellular carcinoma less than 5 cm in cirrhotic patients: a randomized controlled study. J Laparoendosc Adv Surg Tech A 2018; 28: 302-310. https://doi.org/10.1089/lap.2017.0518 Belli G, Fantini C, D'Agostino A, Cioffi L, Langella S, Russolillo N, et al. Laparoscopic versus open liver resection for hepatocellular carcinoma in patients with histologically proven cirrhosis: short- and middle-term results. Surg Endosc 2007; 21: 2004-2011. https://doi.org/10.1007/s00464-007-9503-6 Kanazawa A, Tsukamoto T, Shimizu S, Kodai S, Yamazoe S, Yamamoto S, et al. Impact of laparoscopic liver resection for hepatocellular carcinoma with F4-liver cirrhosis. Surg Endosc 2013; 27: 2592-2597. https://doi.org/10.1007/s00464-013-2795-9 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-8441964","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587614107,"identity":"743947aa-7bef-4322-8094-aae752b0feb2","order_by":0,"name":"Ao Men","email":"","orcid":"","institution":"General Hospital of Shenyang Military Region","correspondingAuthor":false,"prefix":"","firstName":"Ao","middleName":"","lastName":"Men","suffix":""},{"id":587614108,"identity":"dbc880ce-6db2-4f38-91cb-549946673c63","order_by":1,"name":"Dapeng Sun","email":"","orcid":"","institution":"General Hospital of Shenyang Military 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10:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8441964/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8441964/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102375642,"identity":"c34d1890-27fc-4ee2-bf32-97b79b0ac22d","added_by":"auto","created_at":"2026-02-11 05:21:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":54960,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting massive ascites after hepatectomy\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8441964/v1/a81a65064a9f3a46e71015cf.png"},{"id":102375645,"identity":"62a87a9b-e369-4324-ae3f-15ce5749d36f","added_by":"auto","created_at":"2026-02-11 05:21:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":38319,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of the nomogram for predicting postoperative massive ascites\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8441964/v1/dda7cb587c8c4a69237d53e9.png"},{"id":102397958,"identity":"0c76df6c-ed93-4483-9eff-036a410ed271","added_by":"auto","created_at":"2026-02-11 10:20:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":82674,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve of the nomogram for predicting postoperative massive ascites\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8441964/v1/57ccffbc3cf662ae620e32b8.png"},{"id":102375644,"identity":"8b11530a-f9c5-45b6-a57e-05a3ea08d33a","added_by":"auto","created_at":"2026-02-11 05:21:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":52579,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis of the nomogram for postoperative massive ascites\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8441964/v1/1c301342ae44ab978b0bfd83.png"},{"id":102404106,"identity":"8ee8bae9-7e8a-48e4-a1e2-2a106df5eaed","added_by":"auto","created_at":"2026-02-11 11:00:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":70530,"visible":true,"origin":"","legend":"\u003cp\u003eClinical impact curve of the nomogram for postoperative massive ascites\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8441964/v1/7bada1adfa32af1763d85ea8.png"},{"id":104742566,"identity":"e33a90e4-00d7-4b74-8277-fcbc5c9488bd","added_by":"auto","created_at":"2026-03-16 16:41:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1135064,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8441964/v1/176b53d1-5fa1-4258-8088-fd501115e0ea.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a Nomogram Predictive Model for Massive Ascites After Hepatectomy in Patients with Primary Hepatocellular Carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccording to the 2022 Global Cancer Statistics released by the International Agency for Research on Cancer (IARC), liver cancer ranks sixth in global incidence and third in cancer-related mortality worldwide\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. In China\u0026mdash;a region with a high burden of viral hepatitis\u0026mdash;liver cancer is the fourth most commonly diagnosed malignancy and the second leading cause of cancer death \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Advances in surgical techniques and perioperative management have established hepatectomy as the primary curative treatment for hepatocellular carcinoma (HCC), significantly improving survival outcomes. However, HCC often arises in the context of underlying chronic liver disease, such as viral hepatitis, alcohol-related liver injury, or cirrhosis. In addition, the liver's complex anatomical architecture, abundant vascular supply, and central metabolic role make hepatectomy a technically demanding procedure with a substantial risk of postoperative complications \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. These include hemorrhage, infection, liver failure, bile leakage, portal vein thrombosis, pleural effusion, and ascites.Among common complications following hepatectomy, ascites occurs in 5%\u0026ndash;56% of cases \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Massive postoperative ascites can lead to a range of secondary sequelae, including abdominal infection, electrolyte imbalance, and hypoalbuminemia. In severe instances, it may precipitate liver failure, substantially prolong hospitalization and elevate healthcare expenditures. Beyond its clinical impact, massive ascites also adversely affects quality of life and may increase the risk of tumor recurrence \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. The underlying pathophysiology involves multiple mechanisms, such as impaired hepatic albumin synthesis, dysregulation of fluid and sodium balance, portal hypertension, and altered hepatic lymph production \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.However, most relevant studies have been performed under the condition of non-intervened liver status, thus failing to adequately reflect the pathogenesis of ascites in the context of clinically relevant hepatic impairment. Consequently, such research has inherent limitations, and the mechanisms proposed therein may not be generalizable to postsurgical ascites development. In recent years, several preoperative indicators\u0026mdash;including Child\u0026ndash;Pugh classification, indocyanine green retention rate at 15 minutes (ICG-R15), magnetic resonance elastography (MRE), and Mac-2 binding protein glycosylated isomer (M2BPGi)\u0026mdash;have shown utility in predicting postoperative complications \u003csup\u003e[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. These tools contribute to improved risk stratification and support clinical efforts to reduce perioperative morbidity and mortality.Nevertheless, the prediction and management of massive ascites following hepatectomy warrant further in-depth investigation. Although the number of studies on post-hepatectomy ascites has been increasing globally, most remain focused on risk factor analysis, with a notable lack of systematic predictive model development \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. As an intuitive and individualized predictive tool, the nomogram can integrate multiple predictors to provide a quantitative basis for clinical decision-making. Therefore, this study seeks to construct and validate a nomogram for predicting massive ascites after hepatectomy in patients with hepatocellular carcinoma (HCC), aiming to offer a novel approach for precise risk assessment and perioperative strategy optimization.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch participants:\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective cohort study, systematically collecting clinical data from 312 patients who underwent hepatectomy for primary hepatocellular carcinoma (HCC) at the Department of Hepatobiliary and Pancreatic Surgery, General Hospital of Northern Theater Command, from February 2021 to July 2025.The inclusion criteria were as follows: (1) diagnosis of HCC consistent with the Guidelines for the Diagnosis and Treatment of Primary Liver Cancer (2024 Edition) \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, with postoperative pathological confirmation; (2) availability of complete clinical data without critical missing information; and (3) undergoing primary hepatectomy as the initial treatment.Patients were excluded based on the following criteria: (1) preoperative receipt of any conversion therapy for liver cancer, such as transarterial chemoembolization (TACE), radiofrequency ablation (RFA), targeted therapy, immunotherapy, chemotherapy, or radiotherapy, which may influence intraoperative bleeding or postoperative recovery; (2) significant incompleteness of clinical data; (3) prior history of hepatic surgery (e.g. pericardial devascularization or portosystemic shunt) that could alter hepatic hemodynamics; (4) occurrence of postoperative complications such as bile leakage or hemorrhage, which could confound the assessment of ascites volume.Following rigorous application of the inclusion and exclusion criteria, 80 patients were excluded from the study. Specific reasons for exclusion included: preoperative conversion therapy (n\u0026thinsp;=\u0026thinsp;14), substantially incomplete clinical data (n\u0026thinsp;=\u0026thinsp;37), prior hepatic surgery for portal hypertension (specifically, pericardial devascularization, n\u0026thinsp;=\u0026thinsp;1), previous liver surgery for hepatocellular carcinoma (n\u0026thinsp;=\u0026thinsp;18), and postoperative complications such as bile leakage (n\u0026thinsp;=\u0026thinsp;10). A total of 232 patients were consequently included in the final cohort.Comprehensive preoperative, intraoperative, and postoperative clinical data were collected for all enrolled patients. Based on the presence of massive postoperative ascites\u0026mdash;defined as either a maximum daily drainage volume exceeding 10 mL/kg of preoperative body weight or a cumulative drainage volume greater than 1500 mL within one week after surgery \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e\u0026mdash;patients were categorized into a massive ascites group (n\u0026thinsp;=\u0026thinsp;66) and a non-massive ascites group (n\u0026thinsp;=\u0026thinsp;166).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClinical Data Collection\u003c/h3\u003e\n\u003cp\u003ePatient data were systematically collected, encompassing demographic characteristics (age, sex, body mass index), comorbidities (coronary heart disease, hypertension, diabetes), laboratory test results, and tumor-related parameters. Laboratory assessments included platelet count, hemoglobin, alpha-fetoprotein, aspartate aminotransferase, alanine aminotransferase, albumin, total bilirubin, prothrombin activity, activated partial thromboplastin time, blood urea nitrogen, creatinine, and alkaline phosphatase. Disease-specific variables consisted of hepatitis history, portal hypertension status, and Child\u0026ndash;Pugh classificationIntraoperative variables recorded included the presence of cirrhosis, extent of hepatectomy (major resection was defined as removal of \u0026ge;\u0026thinsp;3 segments, minor as \u0026lt;\u0026thinsp;3 segments) \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e, duration of hepatic inflow occlusion, intraoperative blood transfusion, operative time, and surgical approach (laparoscopic, open, or conversion to laparotomy). Postoperative pathological data were also collected, with emphasis on microvascular invasion (MVI), capsular invasion, maximum tumor diameter, tumor number, histological grade, and the presence of vascular tumor thrombus.\u003c/p\u003e\n\u003ch3\u003ePerioperative Management and Surgical Technique\u003c/h3\u003e\n\u003cp\u003eAll enrolled patients underwent comprehensive preoperative imaging to delineate tumor number, size, location, and anatomical relationships with intra- and extrahepatic vasculature and bile ducts. Patients with preoperative liver dysfunction received routine hepatoprotective therapy. All hepatectomies were performed by experienced attending surgeons (associate senior title or higher). Intraoperatively, perihepatic ligaments were mobilized, and tumor-feeding vessels were ligated. The Pringle maneuver was applied when indicated for hepatic inflow control. Liver transection was performed incrementally using an ultrasonic dissector, electrocautery, or the clamp-crush technique. Hemostasis at the resection margin was achieved by ligation, suture, or electrocoagulation, supplemented by absorbable hemostatic gauze. After thorough irrigation and confirmation of hemostasis and absence of bile leakage, drains were placed at the resection surface and Winslow\u0026rsquo;s foramen as needed. Postoperatively, abdominal drainage was closely monitored for volume, character, and color to promptly identify complications such as infection, hemorrhage, or bile leak.\u003c/p\u003e \u003cp\u003eNon-contrast or contrast-enhanced computed tomography (CT) of the hepatobiliary-pancreatic system was routinely performed within one week after surgery to evaluate drainage catheter placement and detect fluid collections in the surgical area. When necessary, CT- or ultrasonography-guided percutaneous catheter drainage was utilized to manage inadequate drainage. All patients were managed according to a standardized postoperative protocol, which included prophylactic antibiotics, intravenous fluid support, and hepatoprotective therapy. Symptom-directed treatments\u0026mdash;such as analgesics, acid suppressants, and antipyretics\u0026mdash;were administered as needed, along with intermittent pneumatic compression for deep vein thrombosis prophylaxis.After the return of bowel function (evidenced by flatus or defecation), oral intake was gradually advanced from semi-liquid to liquid and eventually to a regular diet. Laboratory tests\u0026mdash;including complete blood count and liver and kidney function panels\u0026mdash;were performed daily for the first three postoperative days and every three days thereafter. Treatment plans were adjusted dynamically based on hepatic function recovery and strict fluid balance records.\u003c/p\u003e \u003cp\u003eFor patients diagnosed with massive ascites, intensive hepatoprotective therapy was initiated. Human albumin was supplemented when serum levels fell below 30 g/L, combined with intravenous loop diuretics (e.g., furosemide) and oral aldosterone antagonists (e.g. spironolactone) or arginine vasopressin V2 receptor antagonists (e.g. tolvaptan) until significant resolution of ascites was achieved.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll data were managed in Microsoft Excel and analyzed using SPSS version 27.0.1. Study variables encompassed preoperative, intraoperative, and postoperative parameters. The Shapiro\u0026ndash;Wilk test was used to assess normality. Normally distributed continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and compared using the independent samples t-test; non-normally distributed variables were summarized as median with interquartile range [M (P25\u0026ndash;P75)] and compared using the Mann\u0026ndash;Whitney U test. Categorical variables were presented as frequencies and compared with the chi-square test. A two-tailed P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003eUnivariate and multivariate binary logistic regression analyses were performed to identify risk factors for massive ascites, with results reported as odds ratios (ORs) and 95% confidence intervals (CIs). Variables significantly associated with the outcome (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the multivariate model were considered independent risk factors.A nomogram was developed using the \u0026ldquo;rms\u0026rdquo; package in R (version 4.1.0) based on independent predictors identified through multivariate logistic regression. The model\u0026rsquo;s discriminative performance was evaluated by the area under the receiver operating characteristic (ROC) curve. Calibration was assessed using the Hosmer\u0026ndash;Lemeshow test and a calibration plot. In addition, decision curve analysis (DCA) and clinical impact curve (CIC) were performed to quantify the net clinical benefit and clinical utility of the nomogram across different threshold probabilities.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThis study included 232 patients, comprising 191 (82.3%) males and 41 (17.7%) females. The mean age was 62 years, with a range of 24 to 84 years. Postoperative massive ascites occurred in 66 patients (28.4%), demarcating the massive ascites group, while the remaining 166 patients (71.6%) constituted the non-massive ascites group. The detailed baseline characteristics of all enrolled patients are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics of Hepatocellular Carcinoma Patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumerical value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender[n(%)](Male/Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e191(82.3)/41(17.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.0(57.0\u0026ndash;69.0)\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\u003e20.13(18.96\u0026ndash;23.31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary Heart Disease (CHD)[n(%)]༈No/Yes༉\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e220(94.8)/12(5.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus(DM)[n(%)]༈No/Yes༉\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e190(81.9)/42(18.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension[n(%)]༈No/Yes༉\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e173(74.6)/59(25.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT(\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e149.00(125.00-176.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHB(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136(120\u0026ndash;148)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFP(ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.33(4.35\u0026ndash;208.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.12 (22.58\u0026ndash;45.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.00(19.80\u0026ndash;44.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.35\u0026thinsp;\u0026plusmn;\u0026thinsp;2.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB(\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.30(9.70\u0026ndash;17.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTA(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94.70(84.40-104.80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPTT(S)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.80(26.40\u0026ndash;36.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.47(4.48\u0026ndash;6.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr(\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.04(56.9-75.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAKP(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.17(70-104.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatitis[n(%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37(15.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e158(68.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(13.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBV\u0026thinsp;+\u0026thinsp;HCV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChild-pugh[n(%)](A/B)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e206(88.8)/26(11.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCirrhosis of the Liver[n(%)]༈No/Yes༉\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e203(87.5)/29(12.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePortal Hypertension[n(%)]༈No/Yes༉\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e159(68.5)/73(31.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgical Approach[n(%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaparoscope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e148(63.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaparotomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69(29.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaparoscopic Conversion to Laparotomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(6.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtent of Liver Resection[n(%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 3 Hepatic Segments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e191(82.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than 3 Hepatic Segments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41(17.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePringle Maneuver Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.00(25.00\u0026ndash;73.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntraoperative Blood Transfusion Status(No/Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e159(68.5)/73(31.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntraoperative Blood Loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300(112.5\u0026ndash;500.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperation time(\u0026ge;180 mins/\u0026lt;180 mins)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e199(85.8)/33(14.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathological type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell-differentiated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44(19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerately to poorly differentiated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e188(81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresence or absence of tumor capsular invasion(No/Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e161(69.4)/71(30.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of tumors(Solitary/Multiple)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e207(89.2)/25(10.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum tumor diameter(\u0026ge;\u0026thinsp;5cm/\u0026lt;5cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87(37.5)/145(62.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKi-67(\u0026ge;\u0026thinsp;30%/\u0026lt;30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e187(80.6)/45(19.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMVI[n(%)]༈No/Yes༉\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180(77.6)/52(22.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate analysis of massive ascites in patients with HCC\u003c/h2\u003e \u003cp\u003eUnivariate logistic regression was performed to identify factors associated with massive ascites following hepatectomy in 232 hepatocellular carcinoma patients. The analysis identified eight variables with statistically significant differences between the massive and non-massive ascites groups (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05): platelet count (PLT), hemoglobin (HB), albumin (ALB), aspartate aminotransferase (AST), alanine aminotransferase (ALT), portal hypertension, hepatic portal clamping time, and operative time. These results indicate their potential association with the occurrence of postoperative massive ascites. Detailed results are provided in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eunivariate logistic regression analysis of postoperative massive ascites\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% Cl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.591\u0026ndash;2.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.526\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.954\u0026ndash;1.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.919\u0026ndash;1.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.218\u0026ndash;3.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.481\u0026ndash;2.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.533\u0026ndash;1.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT(\u0026times;109/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.981-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHB(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.974-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFP(ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1-1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.025\u0026ndash;1.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.020\u0026ndash;1.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.802\u0026ndash;0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB(\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.981\u0026ndash;1.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.554\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTA(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.985\u0026ndash;1.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPTT(S)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.980\u0026ndash;1.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.931\u0026ndash;1.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr(\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.978\u0026ndash;1.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAKP(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.996\u0026ndash;1.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.773\u0026ndash;4.472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChild-pugh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.583\u0026ndash;3.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCirrhosis of the Liver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.918\u0026ndash;8.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePortal Hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.66-12.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgical Approach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.714\u0026ndash;1.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtent of Liver Resection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.675\u0026ndash;2.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePringle Maneuver Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.004\u0026ndash;1.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntraoperative Blood Transfusion Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.994\u0026ndash;3.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntraoperative Blood Loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1-1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperation time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.568\u0026ndash;29.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathological type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.380\u0026ndash;1.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresence or absence of tumor capsular invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.871\u0026ndash;2.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of tumors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.495\u0026ndash;2.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum tumor diameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.470\u0026ndash;1.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.599\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKi-67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.693\u0026ndash;3.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.552\u0026ndash;2.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMultivariate analysis of massive ascites in patients with HCC\u003c/h3\u003e\n\u003cp\u003eEleven variables with a significance level of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the univariate analysis were included in the multivariate binary logistic regression model. These consisted of platelet count (PLT), hemoglobin (HB), albumin (ALB), aspartate aminotransferase (AST), alanine aminotransferase (ALT), portal hypertension, hepatic portal clamping time, liver cirrhosis, intraoperative blood transfusion, intraoperative blood loss, and operative duration.\u003c/p\u003e \u003cp\u003eMultivariate analysis identified four independent risk factors for massive postoperative ascites: PLT (OR\u0026thinsp;=\u0026thinsp;0.985, 95% CI: 0.973\u0026ndash;0.996), AST (OR\u0026thinsp;=\u0026thinsp;1.027, 95% CI: 1.001\u0026ndash;1.054), portal hypertension (OR\u0026thinsp;=\u0026thinsp;5.288, 95% CI: 2.535\u0026ndash;11.033), and operative duration (OR\u0026thinsp;=\u0026thinsp;5.011, 95% CI: 1.034\u0026ndash;24.296).Detailed results are provided in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate logistic regression analysis of postoperative massive ascites\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\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\u003eEstimates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95%Cl\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.973\u0026ndash;0.996\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\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.001\u0026ndash;1.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePortal-Hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.535\u0026ndash;11.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperation Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.034\u0026ndash;24.296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eEstablishment of the Nomogram Model for predicting postoperative massive ascites\u003c/h3\u003e\n\u003cp\u003eBased on the independent predictors identified by multivariate logistic regression\u0026mdash;PLT, AST, portal hypertension, and operative duration\u0026mdash;we constructed a nomogram for predicting massive ascites (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe predictive performance of the nomogram was evaluated using receiver operating characteristic (ROC) analysis. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the model achieved an area under the curve (AUC) of 0.837 (95% CI: 0.781\u0026ndash;0.892), indicating strong discriminative ability in stratifying the risk of postoperative massive ascites.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe calibration of the nomogram was evaluated using a calibration curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and the Hosmer\u0026ndash;Lemeshow (HL) test. The HL test result (χ\u0026sup2; = 3.968, P\u0026thinsp;=\u0026thinsp;0.860) indicated no significant deviation between predicted and observed outcomes, suggesting good model fit. Both the calibration plot and HL test confirmed that the nomogram is well-calibrated, with predicted probabilities closely aligning with actual event rates. This supports its reliability as a quantitative tool for individualized risk assessment of massive ascites.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDecision curve analysis (DCA) was conducted to evaluate the clinical utility of the nomogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The results indicated that the model provides a positive net benefit across a wide range of threshold probabilities, specifically from 0.00 to 0.71 and again from 0.86 to 0.93.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe clinical impact curve (CIC) further supported these findings (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). At threshold probabilities above 0.4, the number of individuals classified as high-risk by the model closely aligned with the actual number of observed events, demonstrating high concordance between prediction and observation and confirming the model's practical diagnostic value.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe reported incidence of massive postoperative ascites varies considerably, ranging from 3.5% to 37% across studies \u003csup\u003e[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, largely attributable to inconsistent diagnostic criteria. Some studies define it as a daily drainage volume exceeding 500 mL beyond the third postoperative day until drain removal \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, although this criterion does not account for variations in patient body weight. Ishizawa et al. proposed an alternative definition based on a maximum daily drainage volume of \u0026gt;\u0026thinsp;10 mL/kg of preoperative body weight \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Nevertheless, daily drainage volumes can be influenced by multiple transient factors, such as tube patency, patient positioning, peritoneal lavage, and wound exudate. To improve reliability, our study defined massive ascites as either a maximum daily drainage volume\u0026thinsp;\u0026gt;\u0026thinsp;10 mL/kg of preoperative body weight or a cumulative drainage volume\u0026thinsp;\u0026gt;\u0026thinsp;1500 mL within the first postoperative week, thus integrating cumulative volume to mitigate the influence of transient or incidental factors.\u003c/p\u003e \u003cp\u003eWe analyzed preoperative, intraoperative, and postoperative variables from 232 patients who underwent hepatectomy for hepatocellular carcinoma. Multivariate analysis identified platelet count, aspartate aminotransferase level, portal hypertension, and operative time as independent risk factors for massive postoperative ascites. These factors were incorporated into a nomogram that demonstrated strong discriminative ability, with an area under the receiver operating characteristic curve (AUC) of 0.837. The nomogram also exhibited satisfactory calibration and clinical utility, supporting its use as a practical predictive tool.\u003c/p\u003e \u003cp\u003eThe pathophysiology of postoperative ascites following hepatectomy in hepatocellular carcinoma (HCC) patients, though previously explored, remains incompletely understood. Impaired hepatic functional reserve is widely recognized as a pivotal contributor. In this study, the mean platelet count (PLT) was significantly lower in the massive ascites group than in the non-massive ascites group (142 \u0026times; 10⁹/L vs. 151 \u0026times; 10⁹/L). This difference may be partly explained by hypersplenism secondary to cirrhotic portal hypertension, a condition that also promotes ascites formation, which in turn may further accelerate platelet consumption \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. These observations align with our finding that portal hypertension constitutes an independent risk factor for postoperative ascites. Notably, platelet count remained significant in the multivariate regression, suggesting that thrombocytopenia alone cannot fully account for poor outcomes. Emerging evidence indicates that platelets may also facilitate HCC progression and liver fibrosis through the release of cytokines such as VEGF and TGF-β, thereby indirectly contributing to the pathophysiology of ascites \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFurthermore, accumulating evidence has established preoperative thrombocytopenia as an independent predictor of postoperative complications and perioperative mortality in liver surgery, corroborating our findings \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20 CR21 CR22 CR23 CR24\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Aspartate aminotransferase (AST), a sensitive marker of hepatocellular injury, reflects the degree of hepatic inflammation and sinusoidal endothelial dysfunction. Elevated AST levels are associated with increased sinusoidal permeability, which facilitates the extravasation of albumin and fluid into the peritoneal space. This process may also promote intrahepatic fibrogenesis, thereby contributing to a self-sustaining cycle in ascites pathogenesis. Previous studies have consistently linked high preoperative AST levels with an increased risk of postoperative complications, including ascites\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, which aligns with our results.Preoperative portal hypertension not only exacerbates underlying hepatic dysfunction but also serves as a critical prognostic indicator of post-hepatectomy complications\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Mechanistically, hepatectomy disrupts intrahepatic portosystemic shunts, leading to a sharp rise in sinusoidal and splanchnic vascular pressure, which in turn promotes fluid transudation. Additionally, portal hypertension stimulates hepatic lymph production beyond the drainage capacity of the thoracic duct, resulting in lymphatic leakage into the abdominal cavity \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e .It has also been suggested that this elevated venous pressure, combined with reduced colloid osmotic pressure due to impaired albumin synthesis, disrupts the Starling equilibrium, establishing a vicious cycle that perpetuates ascites accumulation \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. In line with these pathophysiological insights, the American Association for the Study of Liver Diseases guidelines recognize preoperative portal hypertension as a relative contraindication to hepatectomy, providing further support for our conclusions \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur findings suggest that the risk of massive postoperative ascites is not solely dependent on liver function parameters, but rather stems from the interplay of multiple determinants. Operative duration emerged as a significant risk factor in our analysis, consistent with previous reports \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. A large-scale national analysis of 21,443 hepatectomy cases demonstrated that procedures exceeding 3 hours may induce immunosuppression, tissue hypoperfusion, and infectious complications, thereby increasing postoperative morbidity and mortality \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile some studies have reported associations between surgical approach and massive ascites incidence \u003csup\u003e[\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e,we did not observe significant differences in this regard. Nevertheless, we speculate that as our institution's experience with laparoscopic hepatectomy expands and surgical techniques continue to evolve, the physiological advantages of minimally invasive approaches\u0026mdash;including preserved hepatic hemodynamics, reduced surgical trauma, and earlier recovery of oral intake\u0026mdash;may become particularly beneficial for cirrhotic patients. These benefits could potentially translate into a substantial reduction in post-hepatectomy ascites risk with the accumulation of experience and technical refinement.\u003c/p\u003e \u003cp\u003eBased on our findings, preoperative thrombocytopenia, elevated aspartate aminotransferase (AST), prolonged operative time, and the presence of portal hypertension were identified as significant risk factors for massive postoperative ascites. Accordingly, we propose the following clinical recommendations.For patients with progressive preoperative thrombocytopenia, timely platelet transfusion should be considered to correct coagulation dysfunction. In cases of preoperative AST elevation or early signs of ascites, perioperative hepatoprotective therapy should be promptly initiated to facilitate AST normalization and prevent worsening of fluid accumulation.Intraoperatively, surgical efficiency should be optimized\u0026mdash;through standardized hilar dissection and judicious inflow occlusion\u0026mdash;and minimally invasive approaches should be prioritized when feasible to reduce operative duration.The primary innovation of this study lies in the development of the first nomogram specifically designed to predict massive ascites following hepatectomy for hepatocellular carcinoma. The model demonstrated robust predictive accuracy and tangible clinical utility.Several limitations of this study should be acknowledged. First, its retrospective and single-center design introduces potential selection bias and unmeasured confounding, despite statistical adjustments. Second, the modest sample size limits the generalizability of the findings; external validation through multicenter prospective studies is warranted. Third, certain potential predictors of ascites\u0026mdash;such as tumor diameter and liver stiffness\u0026mdash;were not incorporated into the model. Finally, the lack of postoperative follow-up precluded an assessment of the long-term prognostic impact of massive ascites on survival or recurrence.\u003c/p\u003e \u003cp\u003eIn summary, massive ascites after hepatectomy for hepatocellular carcinoma arises from a complex interplay of perioperative factors. The developed nomogram provides a valuable tool for personalizing postoperative management. Future prospective, multi-center studies with larger cohorts are essential to refine and validate this model for broader clinical implementation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"871\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19.3267%;\"\u003e\n \u003cp\u003eHB \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80.5294%;\"\u003e\n \u003cp\u003eHemoglobin \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19.3267%;\"\u003e\n \u003cp\u003eAFP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80.5294%;\"\u003e\n \u003cp\u003e\u0026nbsp;Alpha-Fetoprotein \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19.3267%;\"\u003e\n \u003cp\u003eAST \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80.5294%;\"\u003e\n \u003cp\u003e\u0026nbsp;Aspartate Transaminase \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19.3267%;\"\u003e\n \u003cp\u003eALT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80.5294%;\"\u003e\n \u003cp\u003eAlanine Transaminase \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19.3267%;\"\u003e\n \u003cp\u003eALB \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80.5294%;\"\u003e\n \u003cp\u003eAlbumin \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19.3267%;\"\u003e\n \u003cp\u003eTB \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80.5294%;\"\u003e\n \u003cp\u003e\u0026nbsp;Total Bilirubin \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19.3267%;\"\u003e\n \u003cp\u003ePTA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80.5294%;\"\u003e\n \u003cp\u003e\u0026nbsp;Prothme Activity \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19.3267%;\"\u003e\n \u003cp\u003eBUN \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80.5294%;\"\u003e\n \u003cp\u003e\u0026nbsp; Blood Urea Nitrogen \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19.3267%;\"\u003e\n \u003cp\u003eCr \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80.5294%;\"\u003e\n \u003cp\u003e\u0026nbsp; Creatinine \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19.3267%;\"\u003e\n \u003cp\u003eAKP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80.5294%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Alkaline Phosphatase \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19.3267%;\"\u003e\n \u003cp\u003eHBV \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80.5294%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Hepatitis B Virus \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19.3267%;\"\u003e\n \u003cp\u003eHCV \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80.5294%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Hepatitis C Virus \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 92.8571%;\"\u003e\n \u003cp\u003eROC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Receiver Operating Curve \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAUC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Area Under Curve \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCIC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Clinical Impact Curve \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eDCA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Decision Curve Analysis \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe protocol for this retrospective study was reviewed and approved by the Ethics Committee of the General Hospital of the Northern Theater Command on July 2, 2025 (Approval No: Y(2025)302).This retrospective study was conducted in accordance with the Declaration of Helsinki.The requirement for informed consent was waived by the same committee due to the retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThe datasets generated and analysed during the current study are not publicly available due to patient privacy and confidentiality concerns but are available from the corresponding author on reasonable request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThis work was supported by Independent Scientific Research Project of General Hospital of Northern Theater Command（ZZKY2024036），Science and Technology Planning Project of Liaoning Province（2025-BS-0947）\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eAll authors have reviewed and approved the final version of the manuscript. The specific contributions of each author were as follows:Ao Men, Dapeng Sun, and Xiuqing Sun contributed to the study design, data analysis, and manuscript drafting. Luyuan Jin, Bailiang Liu, Fengyang Chen, Boyuan Nan, and Wenxin Wang were responsible for data collection and analysis. Wei Zhang and Lei Han supervised the overall research, provided critical revisions to the manuscript, and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eWe would like to thank Professors Wei Zhang and Lei Han for their guidance on the topic selection, revisions to the manuscript, and financial support for this paper. We also express our gratitude to all medical staff and patients who made contributions to this article.This work was supported by grants from the Independent Scientific Research Project of General Hospital of Northern Theater Command (ZZKY2024036) and the Science and Technology Planning Project of Liaoning Province (2025-BS-0947).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations of interest:\u0026nbsp;\u003c/strong\u003enone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003enot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. 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Hepatic venous pressure gradient in the assessment of portal hypertension before liver resection in patients with cirrhosis. \u003cem\u003eBr J Surg\u003c/em\u003e 2012; 99: 855-863. https://doi.org/10.1002/bjs.8753\u003c/li\u003e\n\u003cli\u003eKaplan DE, Ripoll C, Thiele M, Fortune BE, Simonetto DA, Garcia-Tsao G, et al. AASLD Practice Guidance on risk stratification and management of portal hypertension and varices in cirrhosis. \u003cem\u003eHepatology\u003c/em\u003e 2024; 79: 1180-1211. https://doi.org/10.1097/HEP.0000000000000647\u003c/li\u003e\n\u003cli\u003ePiano S, Tonon M, Angeli P. Management of ascites and hepatorenal syndrome. \u003cem\u003eHepatol Int\u003c/em\u003e 2018; 12(Suppl 1): 122-134. https://doi.org/10.1007/s12072-017-9815-0\u003c/li\u003e\n\u003cli\u003eRockey DC. 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Impact of laparoscopic liver resection for hepatocellular carcinoma with F4-liver cirrhosis. \u003cem\u003eSurg Endosc\u003c/em\u003e 2013; 27: 2592-2597. https://doi.org/10.1007/s00464-013-2795-9\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hepatectomy, Hepatocellular carcinoma༛Postoperative complications༛ Ascites༛Nomogram༛","lastPublishedDoi":"10.21203/rs.3.rs-8441964/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8441964/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e To develop and validate a nomogram for predicting massive ascites after hepatectomy in hepatocellular carcinoma (HCC) patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA retrospective study of 232 HCC patients undergoing hepatectomy (Feb 2021–Jul 2025) was conducted. Patients were grouped by postoperative ascites status (massive, n=66; non-massive, n=166). Predictors were screened via univariate and multivariate logistic regression. A nomogram was built and internally validated using 1000 bootstrap samples. Performance was assessed via ROC analysis, calibration, and decision curve analysis (DCA)，Clinical Impact Curve（CIC).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Multivariate analysis identified four independent predictors: platelet count (OR=0.985), AST (OR=1.027), portal hypertension (OR=5.288), and operative time (OR=5.011). The nomogram achieved an AUC of 0.837 (95% CI: 0.781–0.892) with good calibration (H-L test, P=0.860). DCA showed clinical net benefit across thresholds [0.00–0.71] and [0.86–0.93]and the clinical impact curve showing good concordance at risk thresholds above 0.4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe nomogram accurately predicts massive ascites risk using four perioperative variables and demonstrates strong clinical utility for individualized management.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Nomogram Predictive Model for Massive Ascites After Hepatectomy in Patients with Primary Hepatocellular Carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-11 05:20:55","doi":"10.21203/rs.3.rs-8441964/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"991dc810-751a-4c72-a8e8-6bc098123033","owner":[],"postedDate":"February 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-16T16:39:25+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-11 05:20:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8441964","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8441964","identity":"rs-8441964","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}