肝癌肝切除术后感染风险预测模型的建立与评价.

Objective To investigate the risk factors of infection after hepatectomy for liver cancer, and to establish and validate a risk prediction model. Methods The clinical data of 167 patients with primary liver cancer who underwent hepatectomy in People's Hospital of Wuhan University from January 2020 to March 2022 were retrospectively collected. All patients were divided into postoperative infection group (n=28) and non-infection group (n=139) according to whether postoperative infection complications occurred. The t-test or Mann-Whitney U test was used for comparison of continuous data between two groups and the chi-square test was used for comparison of categorical data between two groups. Univariate analysis and logistic regression analysis were used to screen the risk factors of infection after hepatectomy for hepatocellular carcinoma, and a nomogram risk prediction model for postoperative infection was established. All patients were randomly divided into training cohort (n=119) and the validation cohort (n=48) according to the ratio of 7∶ 3, the Bootstrap method was used for internal validation of the model, and the model calibration curve and ROC curve were used to evaluate the calibration and discrimination of the nomogram model. Results Postoperative infection occurred in 28 of 167 patients (16.8%). Logistic regression analysis showed that diabetes, CONUT score ≥4 points, preoperative NLR, operation time, intraoperative blood loss, and drainage tube placement time > 7 d were independent risk factors for infection after hepatectomy for liver cancer (all P < 0.05). Based on the nomogram constructed from the above six risk factors, the area under the ROC curve of the training cohort and the validation cohort was 0.848, and 0.853, respectively. The calibration curve of the nomogram model shows that the predicted value is basically consistent with the actual observed value, indicating that the accuracy of the nomogram model prediction is better. Conclusion The individualized nomogram risk prediction model based on diabetes, CONUT score ≥4 points, preoperative NLR, operation time, intraoperative blood loss, and drainage tube placement time > 7 d has good predictive performance and has high predictive value for high-risk patients. [ABSTRACT FROM AUTHOR]