Background: Hepatocellular carcinoma (HCC) often has an insidious onset and high metastatic potential. Early death, defined as death within 3 months of initial diagnosis, is common in metastatic HCC. However, few studies have used current staging systems to predict this outcome. Objectives: This study aimed to develop and internally validate a predictive model for early death in mHCC (American Joint Committee on Cancer, 8th edition, M1 stage IVB) using the Surveillance, Epidemiology, and End Results (SEER) database and to construct a nomogram for individual risk estimation. Methods: Data from patients diagnosed with metastatic HCC between 2018 and 2023 were extracted from the SEER database (version 9.0.43.0). A total of 5,877 patients were randomly divided into training (n = 4,114) and validation (n = 1,763) cohorts at a ratio of 7:3. Univariable and multivariable logistic regression analyses were performed to identify predictors. A nomogram was subsequently constructed, and its performance was evaluated using the concordance index (C-index), calibration curves, receiver operating characteristic curves, and decision curve analysis. Results: The early death rate was 62.5% in the training cohort and 63.4% in the validation cohort. The final multivariable model retained nine predictors: age, tumor size, alpha-fetoprotein, bone metastasis, brain metastasis, lung metastasis, marital status, radiotherapy, and chemotherapy. The nomogram achieved a C-index of 0.740 in the training cohort and 0.746 in the validation cohort, outperforming the conventional tumor-node-metastasis staging system (C-index: 0.531). Calibration curves demonstrated close agreement between predicted and observed outcomes, and DCA indicated net clinical benefit. A reduced model excluding radiotherapy and chemotherapy had a validation C-index of 0.605. Conclusions: This SEER-based nomogram integrates nine readily available clinical variables to predict early death in patients with mHCC and demonstrates robust discrimination, calibration, and clinical utility. It provides an intuitive quantitative tool to identify high-risk patients and guide individualized treatment and follow-up strategies.
Background: Kindlin-2 is a focal adhesion protein involved in cell-cell and cell-extracellular matrix adhesion. These functions influence cell behavior and processes such as fibrosis. Kindlin-2 has recently been reported to play important roles in fibrotic diseases. Objectives: This study aimed to assess the association between serum kindlin-2 levels and liver cirrhosis. Methods: The study included 48 individuals with liver cirrhosis and 40 healthy controls. Serum kindlin-2 levels were measured using an enzyme-linked immunosorbent assay. Complete blood count parameters and liver function tests were assessed. Liver fibrosis was evaluated using noninvasive indices, including the aspartate aminotransferase-to-platelet ratio index and fibrosis-4 index. Mortality risk was evaluated using the Model for End-Stage Liver Disease-Sodium score. Results: Serum kindlin-2 levels were lower in the liver cirrhosis group (P = 0.014). Kindlin-2 levels were inversely associated with alanine aminotransferase, aspartate aminotransferase, and APRI scores and positively associated with platelet count. Low kindlin-2 levels were independently associated with liver cirrhosis. Conclusions: Low serum kindlin-2 levels were associated with liver cirrhosis. Correlations were observed between kindlin-2 levels and markers of liver fibrosis, as well as certain liver function tests. These findings may be a consequence of the cirrhosis process.
Background: Esophageal variceal bleeding (EVB) is a life-threatening complication of cirrhosis and a major contributor to morbidity and mortality. Although early endoscopic intervention is recommended, the optimal timing of endoscopy remains debated, and robust tools for predicting rebleeding risk are lacking. Objectives: To evaluate the effects of different endoscopy-timing strategies on short-term outcomes in cirrhotic patients with acute EVB and to develop a predictive model for 30-day rebleeding risk. Methods: This retrospective cohort study was conducted at a tertiary medical center from January 2018 to December 2023. A total of 350 patients with cirrhosis and acute EVB were included and categorized into 3 groups based on endoscopy timing: < 6 hours (super-early), 6 - 12 hours (standard), and > 12 hours (delayed). Multivariable logistic regression was used to identify independent predictors of 30-day rebleeding. A nomogram was constructed for individualized risk estimation, and its performance was evaluated using receiver operating characteristic analysis and Hosmer-Lemeshow calibration testing. Results: Among 583 screened patients, 350 met the inclusion criteria. Of these, 50 underwent super-early endoscopy, 180 underwent standard-timing endoscopy, and 120 underwent delayed endoscopy. The standard group had the lowest 30-day rebleeding rate (5%), compared with the super-early (10%) and delayed (12%) groups (P = 0.03). The 30-day mortality rate did not differ significantly among the groups (super-early, 6%; standard, 4%; delayed, 8%; P = 0.12). Independent predictors of rebleeding included endoscopy timing, Child-Pugh class, shock at admission, hemoglobin level, and international normalized ratio (INR). The predictive nomogram demonstrated strong discriminatory ability (area under the curve [AUC] = 0.82) and good calibration (Hosmer-Lemeshow P = 0.09). Conclusions: In this retrospective cohort, endoscopy performed within 6 - 12 hours was associated with a lower 30-day rebleeding rate, whereas 30-day mortality was similar among the timing groups. The proposed nomogram may facilitate individualized estimation of 30-day rebleeding risk and support clinical decision-making.
Background: Noninvasive predictors of incident metabolic dysfunction-associated steatotic liver disease (MASLD) in patients with chronic hepatitis B (CHB) are limited. We evaluated the predictive value of the triglyceride-glucose body mass index (TyG-BMI) compared with that of inflammatory-lipid indices. Objectives: This study aimed to compare the predictive value of TyG-BMI with that of six metabolic and inflammatory-lipid indices for incident MASLD in patients with CHB and to assess whether predictive performance varied according to antiviral treatment, HBeAg status, and HBV DNA load. Methods: This retrospective cohort study included 131 patients with CHB and no baseline MASLD. Incident MASLD occurred in 41 patients (31.3%). Cox regression, time-dependent receiver operating characteristic (ROC) analysis, and decision curve analysis were used to evaluate predictive performance. Results: TyG-BMI was the only one of the seven indices to reach statistical significance in the fully adjusted Cox model (HR = 1.021, P = 0.027). The highest TyG-BMI quartile was associated with a 4.12-fold higher risk than the lowest quartile (P = 0.027). TyG-BMI achieved time-dependent area under the curve (AUC) values above 0.70 and demonstrated numerically higher predictive performance than the other indices. Numerical differences in predictive performance were observed across virological subgroups. Calibration and decision curves indicated good model performance. Conclusions: In this exploratory cohort, TyG-BMI showed a numerically stronger association with incident MASLD than individual metabolic and inflammatory-lipid composite indices. External prospective validation is needed before clinical application.
Background: The severity of metabolic dysfunction-associated fatty liver disease (MAFLD) is closely associated with metabolic factors. Early identification of severe fatty liver is critical for timely clinical intervention; however, simple and intuitive tools for assessing the risk of severe fatty liver are lacking. Objectives: This study aimed to develop and validate a nomogram-based classification model for severe fatty liver disease using routine clinical indicators. Methods: A total of 159 patients with fatty liver were retrospectively enrolled, including 39 patients in the severe group and 119 in the mild-to-moderate group. Univariate analyses were performed to screen indicators associated with fatty liver severity, followed by binary logistic regression to identify independent correlating factors. A nomogram model was constructed based on these factors. Model discrimination was assessed using receiver operating characteristic (ROC) curve analysis, calibration accuracy using a calibration curve, and clinical utility using decision curve analysis (DCA). Results: Univariate analysis revealed significant differences in Body Mass Index (BMI), total cholesterol (CHOL), and the triglyceride-glucose (TyG) index between the 2 groups (P < 0.05). Multivariate logistic regression showed that BMI (odds ratio [OR] = 1.585; 95% CI, 1.342 - 1.871) and CHOL (OR = 1.647; 95% CI, 1.081 - 2.509) were independent predictors of severe fatty liver. The nomogram model based on these 2 factors achieved an area under the curve (AUC) of 0.888 in ROC analysis, with a sensitivity of 82% and a specificity of 80%. The calibration curve demonstrated high agreement between the predicted probability and the observed risk, and DCA indicated a high clinical net benefit within a threshold probability range of 5%-80%. Conclusions: The nomogram classification model based on BMI and CHOL demonstrated favorable discrimination, calibration, and clinical utility. It may serve as a simple screening tool for severe fatty liver, facilitate the early identification of high-risk individuals, and guide individualized interventions. However, owing to the small sample size, which included only 39 patients with severe fatty liver, and the cross-sectional design, the stability of the model requires further validation in large-sample, multicenter prospective cohorts.
