A combined pre- and intra-operative nomogram in evaluation of degrees of liver cirrhosis predicts post-hepatectomy liver failure: a multicenter prospective study.

IF 5.5 2区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY ACS Applied Nano Materials Pub Date : 2024-04-03 Epub Date: 2023-03-15 DOI:10.21037/hbsn-22-410

Bin-Yong Liang, Er-Lei Zhang, Jian Li, Xin Long, Wen-Qiang Wang, Bi-Xiang Zhang, Zhi-Wei Zhang, Yi-Fa Chen, Wan-Guang Zhang, Bin Mei, Zhen-Yu Xiao, Jin Gu, Zun-Yi Zhang, Shuai Xiang, Han-Hua Dong, Lei Zhang, Peng Zhu, Qi Cheng, Lin Chen, Zhan-Guo Zhang, Bin-Hao Zhang, Wei Dong, Xiao-Feng Liao, Tao Yin, Dong-De Wu, Bin Jiang, Yu-Feng Yuan, Zhong-Lin Zhang, Yao-Bing Chen, Kai-Yan Li, Wan Yee Lau, Xiao-Ping Chen, Zhi-Yong Huang

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Abstract

Background: Adequate evaluation of degrees of liver cirrhosis is essential in surgical treatment of hepatocellular carcinoma (HCC) patients. The impact of the degrees of cirrhosis on prediction of post-hepatectomy liver failure (PHLF) remains poorly defined. This study aimed to construct and validate a combined pre- and intra-operative nomogram based on the degrees of cirrhosis in predicting PHLF in HCC patients using prospective multi-center's data.

Methods: Consecutive HCC patients who underwent hepatectomy between May 18, 2019 and Dec 19, 2020 were enrolled at five tertiary hospitals. Preoperative cirrhotic severity scoring (CSS) and intra-operative direct liver stiffness measurement (DSM) were performed to correlate with the Laennec histopathological grading system. The performances of the pre-operative nomogram and combined pre- and intra-operative nomogram in predicting PHLF were compared with conventional predictive models of PHLF.

Results: For 327 patients in this study, histopathological studies showed the rates of HCC patients with no, mild, moderate, and severe cirrhosis were 41.9%, 29.1%, 22.9%, and 6.1%, respectively. Either CSS or DSM was closely correlated with histopathological stages of cirrhosis. Thirty-three (10.1%) patients developed PHLF. The 30- and 90-day mortality rates were 0.9%. Multivariate regression analysis showed four pre-operative variables [HBV-DNA level, ICG-R15, prothrombin time (PT), and CSS], and one intra-operative variable (DSM) to be independent risk factors of PHLF. The pre-operative nomogram was constructed based on these four pre-operative variables together with total bilirubin. The combined pre- and intra-operative nomogram was constructed by adding the intra-operative DSM. The pre-operative nomogram was better than the conventional models in predicting PHLF. The prediction was further improved with the combined pre- and intra-operative nomogram.

Conclusions: The combined pre- and intra-operative nomogram further improved prediction of PHLF when compared with the pre-operative nomogram.

Trial registration: Clinicaltrials.gov Identifier: NCT04076631.

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评估肝硬化程度的术前和术中结合列线图预测肝切除术后肝功能衰竭：一项多中心前瞻性研究

背景：充分评估肝硬化程度对于肝细胞癌（HCC）患者的手术治疗至关重要。肝硬化程度对肝切除术后肝功能衰竭（PHLF）预测的影响仍未明确。本研究旨在利用前瞻性多中心数据，构建并验证基于肝硬化程度的术前和术后联合提名图，以预测 HCC 患者的 PHLF：在2019年5月18日至2020年12月19日期间，在5家三级医院连续接受肝切除术的HCC患者。术前肝硬化严重程度评分（CSS）和术中直接肝脏硬度测量（DSM）与 Laennec 组织病理学分级系统相关。将术前提名图和术前术中联合提名图预测 PHLF 的性能与传统的 PHLF 预测模型进行了比较：结果：在本研究的 327 例患者中，组织病理学研究显示，HCC 患者无肝硬化、轻度肝硬化、中度肝硬化和重度肝硬化的比例分别为 41.9%、29.1%、22.9% 和 6.1%。CSS或DSM与肝硬化的组织病理学分期密切相关。33名患者（10.1%）发展为PHLF。30天和90天的死亡率分别为0.9%。多变量回归分析显示，四个术前变量（HBV-DNA 水平、ICG-R15、凝血酶原时间（PT）和 CSS）和一个术中变量（DSM）是 PHLF 的独立风险因素。根据这四个术前变量和总胆红素构建了术前提名图。术前和术中组合提名图是通过添加术中 DSM 而构建的。在预测 PHLF 方面，术前提名图优于传统模型。结论：结论：与术前提名图相比，术前和术中联合提名图进一步提高了对 PHLF 的预测能力：试验注册：Clinicaltrials.gov Identifier：试验注册：Clinicaltrials.gov Identifier：NCT04076631。

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来源期刊

ACS Applied Nano Materials Multiple-

CiteScore

8.30

自引率

3.40%

发文量

1601

期刊介绍： ACS Applied Nano Materials is an interdisciplinary journal publishing original research covering all aspects of engineering, chemistry, physics and biology relevant to applications of nanomaterials. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important applications of nanomaterials.

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