Nomogram for prediction of portal vein system thrombosis after splenectomy for hypersplenism in patients with Wilson disease

Background: The occurrence of portal vein system thrombosis (PVST) after splenectomy in patients with Wilson disease (WD) can lead to serious complications. The early identification of high-risk patients can help improve patient prognosis. This study aimed to establish and validate a personalized nomogram for assessing the risk of PVST after splenectomy in patients with WD and hypersplenism.Methods: We retrospectively collected the data from 81 patients with WD and hypersplenism who underwent splenectomy. Based on whether PVST occurred within a month after the operation, they were divided into the PVST group and the non-PVST group. The clinical data of the 2 groups were compared, and univariate analysis was used to select the statistically significant features and incorporated into the least absolute shrinkage and selection operator (LASSO) regression model for optimization. Multivariate logistic regression analysis was used to determine the independent risk factors for PVST after splenectomy, which were then applied to establish a personalized nomogram. We calculated the concordance (C)-index and drew the receiver operating characteristic (ROC) curve, the model calibration curve, and the clinical decision analysis (DCA) curve to evaluate the accuracy, calibration, and clinical applicability of the model, respectively. We used bootstrapping for internal validation of the model.Results: Univariate analysis showed that the differences in preoperative portal vein diameter and velocity of portal blood flow, postoperative mean platelet volume (MPV), mean platelet distribution width (PDW), D-dimer, prothrombin time (PT), and the increase of platelet count (PLT) were of statistical significance (P<0.05). According to the results of the LASSO and multivariate logistic regression analyses, a model including preoperative portal vein diameter, preoperative portal blood flow velocity, postoperative D-dimer, and the increase of PLT was established to predict the risk of PVST after splenectomy. The model showed good accuracy with a C-index of 0.838 (95% CI: 0.750-0.92 6) and had a well-fitted calibration curve. Furthermore, internal validation showed it achieved a moderate C-index of 0.805. The DCA curve indicated that the model has clinical applicability when patients are treated at thresholds of 2-100%.Conclusions: Establishing a predictive model for the risk of PVST in patients with WD and hypersplenism after splenectomy can help clinicians identify patients at high risk of PVST who require intervention measures.