A non-invasive model based on the virtual portal pressure gradient to predict the first variceal hemorrhage in cirrhotic patients

Background and aims This study aimed to establish a non-invasive model based on the virtual portal pressure gradient (vPPG) to predict the first variceal hemorrhage (VH) in patients with cirrhosis. Methods This single-center study prospectively enrolled cirrhotic patients as the training and validation cohorts during different time periods. The PPG-detection software (PPGS 1.0) was used to perform vPPG calculation, which involves 2 steps including three-dimensional (3D) reconstruction of portal vein tree and subsequent application of computational fluid dynamics. All patients were given standard primary prophylaxis against VH and followed up for 2 years. Data from the training cohort were assessed using univariate and multivariate Cox regression and Kaplan–Meier analyses, by which a nomogram with its dynamic form was developed to estimate the probability of VH. Results In the training cohort ( n = 128), 37 (28.9%) experienced VH during 2-year follow-up. Four variables including vPPG ≥ 10.5 mmHg ( p < 0.001), PLT < 56 × 10 9 /L ( p = 0.048), albumin < 32 g/L ( p < 0.001) and INR ≥ 1.2 ( p = 0.022) were identified as independent risk factors of VH, among which vPPG showed the best diagnostic performance (AUC 0.875). Subsequently, these predictors were incorporated into the nomogram, of which C -indexes were 0.891 and 0.926 for the training and validation cohorts, respectively. Calibration curves demonstrated a great calibration ability of the model. At the threshold probabilities of 0.1–0.6 (1 year) and 0.1–1.0 (2 years), this nomogram could offer more net benefits in decision curve analysis. Conclusions The vPPG-based nomogram could be used for risk stratification of the first VH in patients with cirrhosis.