Background:Benign portal vein thrombosis (PVT) presents considerable technical challenges for portal vein reconstruction (PVR) during liver transplantation (LT). This study aimed to evaluate the surgical outcomes, safety profile, and clinical feasibility of ectopic physiological portal vein reconstruction (EPPVR) at non-hilar area for patients with complex benign PVT. Methods:We conducted a retrospective observational cohort study analyzing intraoperative parameters, postoperative complications, and survival outcomes in liver transplant recipients with benign PVT. Among 120 enrolled patients, 10 cases (8.3%) underwent EPPVR, including three varicose vein reconstructions (VVRs), one anterior transpancreatic bridge reconstruction, and five "pull-out" portal vein reconstructions (POPVRs) beneath the pancreas. Results:Compared with conventional orthotopic physiological portal vein reconstruction (OPPVR), EPPVR was associated with significantly longer anhepatic phase (154.50±45.43 vs. 79.77±34.56 mins, P<0.001) and greater intraoperative blood loss (1,930.00±1,515.15 vs. 766.36±707.50 mL, P=0.001). Postoperatively, the EPPVR group demonstrated markedly higher rates of elevated ascitic fluid amylase (60.0% vs. 5.5%, P<0.001) and required more frequent portal vein stent implantation in surviving patients (37.5% vs. 3.9%, P=0.008). Other complication rates and perioperative survival rate were comparable between these two groups. The above trends remained consistent in the subgroup analysis of patients with Yerdel grade 2-4 PVT, although the difference in stent implantation rates lost statistical significance (37.5% vs. 10.3%, P=0.15). Conclusions:Although EPPVR was associated with elevated perioperative risks, it did not decrease the perioperative survival rate in PVT patients. This reconstruction approach may represent a feasible alternative for complex PVT cases requiring non-standard reconstruction approaches, contingent upon thorough preoperative assessment and surgical planning.
Background: In many clinical trials, the outcome variables are often binary (or categorical), and the main interest is to investigate any relationship between these categorical outcomes and intervention strategies for measuring the effect size and strength of association. The two frequently applied statistical methods used for this purpose are the relative risk (RR) and odds ratio (OR). The aim of this paper is to explore the fundamental concepts, definitions, computational methods, interpretations, differences, and lastly the relationship between RR and OR in the context of gastroesophageal reflux disease (GERD) following two bariatric (weight loss) procedures, namely laparoscopic vertical sleeve gastrectomy (LVSG) vs. laparoscopic Roux-en-Y gastric bypass (LRYGB). Methods: From January 2015 to March 2024, several electronic databases were searched for randomized controlled trials (RCTs) comparing LVSG and LRYGB and their effects on postoperative GERD at 5-year follow-up. Computation of OR and RR was undertaken to determine this impact, the differences in the effect size and strength of association between these two methods, and to demonstrate the differences between these two concepts. Results: Our analysis has demonstrated that GERD is far more common in patients' population following a LVSG compared to LRYGB. The RR for GERD is 4.96 times higher for the LVSG cohort compared to the LRYGB group. Similarly, the OR has revealed that the odds of GERD for LVSG patients is 6.4 times that of LRYGB patients at 5-year follow-up. Conclusions: Although both OR and RR have demonstrated a large effect size and strong association for GERD following LVSG compared to LRYGB, the OR strength of association is more convincing. The results suggest the primary use of OR is the right approach, as it is showing greater strength of association between exposure, i.e., LVSG, and outcome, i.e., GERD at 5-year follow-up in our analysis.
Background:Lymph node metastasis is a key determinant of prognosis and adjuvant treatment in rectal cancer and is currently staged according to the American Joint Committee on Cancer (AJCC) 8th edition pathological N (pN) classification, which is based solely on the number of metastatic lymph nodes. However, prognostic heterogeneity exists among patients within the same pN category, particularly when lymph node retrieval is inadequate. Alternative nodal metrics, including the lymph node ratio (LNR) and log odds of positive lymph nodes (LODDS), have been proposed to improve risk stratification. Methods:This retrospective study included 170 patients with histologically confirmed rectal adenocarcinoma who underwent curative resection. Cancer-specific survival (CSS) was analyzed using the Kaplan-Meier method, and differences among groups were assessed with the log-rank test. Multivariate Cox proportional hazards models were employed to identify independent prognostic factors. Model performance was evaluated using the area under the curve (AUC), Akaike information criterion (AIC), and chi-square trend and likelihood ratio tests. Results:All three classification systems-pN, LNR, and LODDS-were significantly associated with CSS (log-rank P<0.001 for all). In multivariable analysis, each nodal classification system was independently associated with CSS after adjustment for covariates. Among them, LNR showed the slightly higher discriminative ability, with the highest AUC at 3 years (0.664), followed by pN (AUC =0.662) and LODDS (AUC =0.603). Prognostic performance improved notably in patients with ≥12 lymph nodes retrieved. Conclusions:pN, LNR, and LODDS are independent prognostic indicators in rectal cancer. Among them, LNR demonstrates slightly higher prognostic performance and may serve as a more effective tool for risk stratification than the AJCC 8th edition pN classification or LODDS, particularly when lymph node retrieval is adequate.
Background: Gallbladder cancer (GBC) exhibits significant heterogeneity in postoperative prognosis, and the traditional tumor-nodes-metastasis (TNM) staging system has limitations in individualized survival prediction. This study aimed to develop and validate a nomogram integrating preoperative systemic status, ultrasonographic features, and postoperative pathological parameters for quantitatively predicting the overall survival (OS) of GBC patients after surgery, to aid clinical risk stratification and decision-making. Methods: Clinicopathological data of 61 GBC patients who underwent surgical treatment at Peking Union Medical College Hospital (PUMCH) between August 2008 and December 2022 were retrospectively collected. Univariate and multivariate Cox proportional hazards regression models were employed to screen for independent prognostic factors. The model was optimized based on the Akaike Information Criterion (AIC), and a nomogram for predicting 1-year, 3-year, and 5-year OS was constructed. Internal validation was performed using the Bootstrap method (500 resamples). The model's discrimination and calibration were comprehensively assessed using the concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves, and calibration curves. The optimal cutoff value for the risk score was determined using the maximal selected rank statistics (MaxStat) method. Results: During the median follow-up period, 54.1% of patients died. Multivariate Cox regression analysis identified four independent adverse prognostic factors: elevated American Society of Anesthesiologists (ASA) classification [grade III vs. grade I: hazard ratio (HR) =9.68, 95% confidence interval (CI): 2.46-38.01, P=0.001], preoperative low serum albumin (Alb) (per 1 g/L decrease: HR =1.10, P=0.03), lymph node metastasis (N1/N2 vs. N0: HR =4.72, 95% CI: 1.90-11.74, P<0.001), and hyperechoic tumor appearance on ultrasonography (hyperechoic vs. hypoechoic: HR =7.69, 95% CI: 1.35-43.70, P=0.02). The nomogram constructed based on these factors demonstrated excellent discrimination (C-index =0.733). The area under the curve (AUC) values for predicting 1-year, 3-year, and 5-year survival rates were 0.882, 0.845, and 0.840, respectively. Calibration curves showed high agreement between predicted probabilities and actual observations. The risk stratification system divided patients into low-risk and high-risk groups, with the high-risk group having a median survival of only 14.5 months (P<0.001). Conclusions: The nomogram developed in this study innovatively integrates multidimensional indicators reflecting patients' physiological reserve (ASA classification), nutritional/immune status (Alb), and tumor biological behavior (ultrasonographic echogenicity, N stage). Notably, the discovery of "hyperechoic" appearance as an independent adverse prognostic imaging marker holds significant clinical implication. This model demonstrates high accuracy and strong practicality, effectively identifying high-risk postoperative populations and guiding individualized adjuvant therapy and follow-up strategies.
Background:Noninvasive liver disease assessment (NILDA) tools, such as the fibrosis-4 (FIB-4) index, are widely used to identify patients with metabolic dysfunction-associated steatotic liver disease (MASLD) at risk of advanced fibrosis (≥ F3). However, their predictive accuracy for fibrosis staging and long-term outcomes remains limited. This study aimed to develop and validate FibroX, an explainable machine learning-based model for improving the detection of advanced fibrosis and the prediction of long-term clinical outcomes in adults with MASLD. Methods:FibroX was developed using data from adults with MASLD (N=1,487) in the National Health and Nutrition Examination Survey (NHANES) 2017-2020. Fibrosis stage was determined using a two-step approach-initial NILDA screening followed by vibration-controlled transient elastography (TE)-in accordance with 2024 American Association for the Study of Liver Diseases guidance. The model was built using extreme gradient boosting (XGBoost), optimized at a 95% specificity threshold, and internally validated using 5-fold cross-validation. External validation was performed in two independent cohorts: 337 biopsy-confirmed MASLD patients and 4,276 participants from NHANES III with up to 30 years of mortality follow-up. Model interpretability was evaluated using SHapley Additive Explanations (SHAP). Results:In NHANES 2017-2020, FibroX demonstrated superior discrimination for advanced fibrosis compared with FIB-4 [area under the receiver operating characteristic curve (AUROC) 0.97 vs. 0.62; P<0.001]. In biopsy-confirmed MASLD patients, FibroX also outperformed FIB-4 (AUROC 0.84 vs. 0.82; P<0.001). In NHANES III, FibroX predicted all-cause mortality (C-statistic 0.80) and remained independently associated with cardiovascular mortality after adjustment for established risk factors [adjusted hazard ratio (HR) 1.22; 95% confidence interval (CI): 1.01-1.47]. SHAP analysis identified platelet count, age, hemoglobin A1c, aspartate aminotransferase (AST), and estimated glomerular filtration rate (eGFR) as the most influential predictors. Conclusions:FibroX is a transparent and accurate machine learning model that improves detection of advanced fibrosis and prediction of long-term cardiovascular mortality compared with FIB-4. These findings support the use of explainable machine learning-based noninvasive tools for risk stratification and outcome prediction in patients with MASLD.
