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| 基于ALBI评分构建肝切除术后肝功能不全的风险预测模型 |
| Construction of the risk prediction model of hepatic insufficiency after hepatectomy based on ALBI score |
| 投稿时间：2025-01-06 |
| DOI：10.3969/j.issn.1000-0399.2025.11.009 |
| 中文关键词: 白蛋白-胆红素评分 肝切除 肝功能不全 预测模型 |
| 英文关键词: Albumin-bilirubin score Hepatectomy Hypohepatia Prediction model |
| 基金项目:河北省医学科学研究课题计划资助(编号:20220319) |
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| 中文摘要: |
| 目的 探究肝切除术后肝功能不全的风险因素，并基于白蛋白-胆红素（ALBI）评分构建风险识别模型。方法 回顾性分析2020年4月至2024年4月沧州市人民医院肝胆胰（微创）外科121例接受肝切除患者资料，依据术后是否发生肝功能不全将患者分为发生组（n=40）和未发生组（n=81）。分析术后发生肝功能不全的影响因素，构建风险预测模型并进行验证。结果 相比未发生组，发生组合并肝硬化、切除≥4肝段占比更高，术中出血量更大，ALBI评分更低（P<0.05）;logistic回归分析显示，合并肝硬化（OR=2.125,95%CI:1.872～2.379）、切除≥4肝段（OR=1.430,95%CI:1.354～2.507）、术中出血量大（OR=1.868,95%CI:1.754～2.982）是肝切除术后发生肝功能不全的危险因素，ALBI评分高是保护因素（OR=0.368,95%CI:0.201～0.658）;Hosmer-Lemeshow χ2=0.942,P=0.268。受试者工作特征（ROC）曲线结果显示，该模型预测术后肝功能不全的曲线下面积（AUC）为0.942（95%CI:0.881～0.978），灵敏度90.24%，特异度81.16%。结论 基于ALBI评分构建的风险预测模型具有优异的区分度及准确度。 |
| 英文摘要: |
| Objective To explore the risk factors for liver dysfunction after liver resection, and to construct a risk identification model based on the albumin-bilirubin（ALBI） score. Methods A retrospective analysis was conducted on 121 patients who underwent liver resection in the hospital from April 2020 to April 2024. These patients were divided into the group with postoperative liver dysfunction and the group without it. A risk prediction model was constructed based on the influencing factors of postoperative liver dysfunction and verified. Results There was a combination of cirrhosis and liver cirrhosis, a higher proportion of liver segments removed ≥4, and a greater intraoperative blood loss. The ALBI score was lower（P＜0.05）. Logistic regression analysis showed that the combination of cirrhosis（OR=2.125, 95%CI:1.872～2.379）, removal of ≥ 4 liver segments（OR=1.430,95%CI:1.354～2.507）, and large intraoperative blood loss were risk factors for liver dysfunction after liver resection（OR=1.868,95%CI:1.754～2.982）, while a high ALBI score was a protective factor（OR=0.368,95%CI:0.201～0.658）; Hosmer-Lemeshow χ2 = 0.942, P = 0.268. ROC results showed that the AUC of this model for predicting postoperative liver dysfunction was 0.942（95%CI:0.881～0.978）. The model of sensitivity was 90.24% and specificity was 81.16%. Conclusion The risk prediction model constructed based on the ALBI score has good discrimination and accuracy. |
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