To investigate the risk factors of massive ascites after liver resection for primary liver cancer (PLC) and construct a nomogram prediction model.

Clinical data of 739 PLC patients admitted to the Affiliated Hospital of North Sichuan Medical College from January 2018 to December 2022 were retrospectively analyzed. Among them, 585 patients were male and 154 female, aged from 21 to 89 years, with a median age of 60 years. According to the operation time and surgical department, all patients were divided into the training set (n=536) and validation set (n=203). The data in the training set were used for model construction, model evaluation and internal validation, and those in the validation set data were utilized for external validation. Clinical data of the enrolled patients were obtained by querying the scientific research data platform and special disease database of the hospital. In the training set, Lasso regression and binary Logistic regression analyses were used to construct the risk prediction model for postoperative massive ascites. Bootstrap method was adopted to conduct 1 000 repeated sampling in the training set for internal validation, and the data in the validation set were used for external validation. The area under the ROC curve (AUC) and calibration curve were employed to evaluate the prediction performance of this prediction model. Decision curve analysis (DCA) was utilized to evaluate clinical application value of the prediction model.

Lasso regression and Logistic regression analyses showed that liver cirrhosis, postoperative ALB, intraoperative blood loss and ALP were the risk factors for postoperative massive ascites in the training set (OR=3.107, 2.321, 2.472, 2.810; all P<0.05). Based on these four independent risk factors, a nomogram prediction model was constructed. The optimal cut-off value of the total score of the prediction model was 185.5. Patients with a total score of ≥185.5 were assigned into the high-risk group, and those with a total score <185.5 were allocated into the low-risk group. The AUC of the prediction model in the training and validation sets was 0.759 (95%CI: 0.716-0.802) and 0.805 (95%CI: 0.743-0.867), respectively. Calibration curve identified high consistency between the predicted risk and the actual risk estimated by the prediction model for of massive ascites after liver resection. DCA demonstrated that the prediction model had clinical value in predicting the risk of massive ascites, and its application could bring clinical benefits to the patients.

Prediction model for massive ascites after liver resection based on the independent risk factors including liver cirrhosis, postoperative ALB, intraoperative blood loss and ALP, in patients with PLC has high predictive ability and clinical applicability.