Development and validation of a nomogram for predicting postoperative venous thromboembolism risk in patients with hepatocellular carcinoma

建立和验证用于预测肝细胞癌患者术后静脉血栓栓塞风险的列线图

阅读:1

Abstract

BACKGROUND: Few studies have specifically modeled the risk of venous thromboembolism (VTE) for postoperative hepatocellular carcinoma (HCC) patients, although HCC is the third leading cause of cancer death worldwide. This study aimed to develop and validate a nomogram that accurately predicts the risk of VTE in patients after HCC surgery. AIM: To develop and validate a nomogram to accurately predict the risk of VTE in postoperative HCC patients by integrating clinical and laboratory risk factors. The model seeks to provide a user-friendly tool for identifying high-risk individuals who may benefit from targeted anticoagulation therapy, thereby improving clinical decision-making and patient outcomes. METHODS: Data from patients who underwent HCC surgery at Chongqing University Cancer Hospital in China were analyzed. Through univariate and multivariate logistic regression analyses, independent risk factors for VTE were identified and integrated into a nomogram. The predictive performance of the nomogram was assessed via receiver operating characteristic curves, calibration curves, decision curve analysis and other relevant metrics. RESULTS: Of 905 postoperative HCC patients were included in the study. The nomogram incorporated eight independent risk factors for VTE: Karnofsky Performance Scale, base disease, cancer stage (tumor-node-metastasis), chemotherapy, D-dimer concentration, white blood cell count, hemoglobin, and fibrinogen. The C-index for the nomogram model was 0.825 in the training cohort and 0.820 in the validation cohort, indicating good discriminative ability. Calibration plots of the model revealed high concordance between the predicted probabilities and observed outcomes. CONCLUSION: We developed and validated a novel nomogram that can accurately estimate the risk of VTE in individual postoperative HCC patients. This model can identify high-risk patients who may benefit from targeted anticoagulation therapy.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。