Will partial splenic embolization followed by splenectomy increase intraoperative bleeding?

Abstract

BACKGROUND Partial splenic embolization (PSE) has been suggested as an alternative to splenectomy in the treatment of hypersplenism. However, some patients may experience recurrence of hypersplenism after PSE and require splenectomy. Currently, there is a lack of evidence-based medical support regarding whether preoperative PSE followed by splenectomy can reduce the incidence of complications. AIM To investigate the safety and therapeutic efficacy of preoperative PSE followed by splenectomy in patients with cirrhosis and hypersplenism. METHODS Between January 2010 and December 2021, 321 consecutive patients with cirrhosis and hypersplenism underwent splenectomy at our department. Based on whether PSE was performed prior to splenectomy, the patients were divided into two groups: PSE group (n = 40) and non-PSE group (n = 281). Patient characteristics, postoperative complications, and follow-up data were compared between groups. Propensity score matching (PSM) was conducted, and univariable and multivariable analyses were used to establish a nomogram predictive model for intraoperative bleeding (IB). The receiver operating characteristic curve, Hosmer-Lemeshow goodness-of-fit test, and decision curve analysis (DCA) were employed to evaluate the differentiation, calibration, and clinical performance of the model. RESULTS After PSM, the non-PSE group showed significant reductions in hospital stay, intraoperative blood loss, and operation time (all P = 0.00). Multivariate analysis revealed that spleen length, portal vein diameter, splenic vein diameter, and history of PSE were independent predictive factors for IB. A nomogram predictive model of IB was constructed, and DCA demonstrated the clinical utility of this model. Both groups exhibited similar results in terms of overall survival during the follow-up period. CONCLUSION Preoperative PSE followed by splenectomy may increase the incidence of IB and a nomogram-based prediction model can predict the occurrence of IB.