RESUMO
Objective: To explore the risk factors of short-term prognosis of severe Budd-Chiari syndrome (BCS) patients,established and verified the nomogram prediction model for these BCS patients and evaluated its clinical application value. Methods: This study is a retrospective cohort study. The clinical data of 171 patients with severe BCS diagnosed were retrospectively analyzed in the Department of Hepatopancreatobiliary Surgery First Affiliated Hospital of Zhengzhou University from January 2018 to December 2023. There were 105 males and 66 females, aged (52.1±12.8) years (range: 18 to 79 years). The patients were divided into two groups based on whether they died within 28 days: the death group (n=38) and the survival group (n=133). The risk factors for short-term death of patients were analyzed,and independent risk factors were screened by univariate and multivariate analysis. Furthermore,these factors were used to establish the nomogram prediction model. The area under the curve(AUC),the Bootstrap Resampling,the Hosmer-Lemeshow test and the Decision Curve Analysis(DCA) were used to verify the model's differentiation,internal verification,calibration degree and clinical effectiveness,respectively. Results: Univariate and multivariate Logistics regression analysis showed that the history of hepatic encephalopathy,white blood cell,glomerular filtration rate and prothrombin time were independent risk factors (P<0.05). The above factors were used to successfully establish the prediction model with 0.908 of AUC and 0.895 of the internal verification of AUC,indicating that the predictive model was valuable. The 0.663 P-values in the Hosmer-Lemeshow test indicated the high calibration degree of the model. The clinical effectiveness of the model was proved by the 18% clinical benefit population using the DCA curve with the 17% probability threshold. Conclusions: The independent risk factors are the history of hepatic encephalopathy,white blood cell,glomerular filtration rate and prothrombin time. An adequate basis was acquired by establishing a nomogram prediction model of the short-term prognosis of severe BCS,which was helpful for early clinical screening and identification of high-risk patients with severe BCS who could die in the short term and timely providing timely intervention measures for improving the prognosis.