Analysis of risk factors and a prediction model for rebleeding in liver cirrhosis patients with esophagogastric variceal bleeding

Abstract

BACKGROUND

Liver cirrhosis with esophagogastric variceal bleeding (EGVB) carries a high oneyear rebleeding rate (30%-50%) despite current therapies. Existing predictive tools lack specificity or invasiveness, necessitating a simple noninvasive model for rebleeding risk stratification.

AIM

To identify rebleeding risk factors and develop a noninvasive nomogram for predicting oneyear EGVB rebleeding in cirrhotic patients.

METHODS

This retrospective study enrolled 130 patients with cirrhosis and EGVB who were treated at the Second Affiliated Hospital of Hainan Medical University in 2024. All patients were followed up by telephone for 1-year. The sample size was determined based on the eventspervariable principle commonly used in logistic regression, aiming for at least 10 events per candidate predictor, which is consistent with the number of events observed in this study. With rebleeding within 1 year as the study endpoint, patients were divided into a rebleeding group and a non-rebleeding group. Relevant clinical data were collected, including demographics, laboratory tests, imaging findings, and endoscopic features. Basic data and laboratory/imaging examination data were statistically analyzed. Based on LASSO regression and multivariate logistic regression analyses, a nomogram model was constructed to predict the risk of EGVB rebleeding. This his graphical calculating tool, based on a multivariate regression model, visually represents the predicted probability of a patient experiencing a specific clinical event. The model was evaluated using the receiver operating characteristic (ROC) curve.

RESULTS

Of the 130 patients included, the mean age was 54 years. Univariate analysis identified the following risk factors for EGVB rebleeding in cirrhosis: Age (P = 0.034), red blood cell (RBC) (P < 0.001), lymphocyte (LYM) (P = 0.031), red cell distribution width-coefficient of variation (P = 0.004), RBC distribution width coefficient of variation to LYM ratio (P = 0.005), neutrophil-to-lymphocyte ratio (P < 0.001), creatine kinase isoenzyme (P = 0.034), blood urea nitrogen (BUN) (P < 0.001), total cholesterol (P = 0.001), procalcitonin (P < 0.001), ascites (P < 0.001), and gallbladder wall thickening (P < 0.001). Indicators with statistically significant differences in univariate analysis were included in LASSO regression, and a total of four predictors were selected: BUN, ascites, RBC, and thickening of the gallbladder wall (TGBW). Multivariate logistic regression analysis showed that BUN [odds ratio (OR) = 1.116, P = 0.013], RBC (OR = 0.519, P = 0.009), ascites (OR = 0.235, P = 0.001), and TGBW (OR = 0.199, P < 0.001) were independent influencing factors for rebleeding. The nomogram model demonstrated good discrimination with an area under the ROC curve of 0.853 (95%CI: 0.791-0.915). The optimal cut-off value was 0.343, corresponding to a sensitivity of 0.871 and a specificity of 0.706. These findings indicate that elevated BUN, decreased RBC, presence of ascites, and TGBW increase the incidence of EGVB rebleeding.

CONCLUSION

RBC, BUN, ascites, and TGBW are independent risk factors for EGVB rebleeding in cirrhosis. A nomogram for predicting the risk of EGVB rebleeding was established.

Keywords: Liver cirrhosis; Esophageal and gastric varices; Esophagogastric variceal rebleeding; Prediction model

Core Tip: This single-center retrospective study enrolled 130 cirrhotic patients with esophagogastric variceal bleeding (EGVB) in 2024 and conducted a one-year follow-up, including 62 rebleeding cases and 68 non-rebleeding cases, aiming to address the high mortality of EGVB rebleeding and the lack of simple non-invasive predictive tools for Hainan population. The study identified four independent risk factors for rebleeding -decreased red blood cell, elevated blood urea nitrogen, ascites, and thickening of the gallbladder wall (TGBW); notably, TGBW was originally validated as a novel non-invasive imaging predictor with an odds ratio value of 0.199 (P < 0.001). A reliable nomogram prediction model was further constructed with an original area under the curve (AUC) of 0.853 and a bootstrap internal validation AUC of 0.855, presenting favorable calibration and clinical applicability. Since this model merely requires routine blood tests and ultrasonic indicators, it can realize convenient and non-invasive risk stratification to guide secondary clinical prevention for local patients. Nevertheless, this research is limited by its retrospective nature, single-center setting and relatively small sample size; and further multi-center prospective studies are required to verify the generalizability of the model.