Xu Yuan, Li Yufeng, Li Shenglin, Xue Shouxiao, Liu Jianli
Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China.
Second Clinical School, Lanzhou University, Lanzhou, China.
Insights Imaging. 2023 Mar 29;14(1):51. doi: 10.1186/s13244-023-01393-x.
Liver cirrhosis-acute decompensation (LC-AD) has rapid short-term disease progression and difficult early risk stratification. The purpose is to develop and validate a model based on dual-energy CT quantification of extracellular liver volume (ECV) for predicting the occurrence of acute-on-chronic liver failure (ACLF) within 90 days in patients with hepatitis B (HBV) LC-AD.
The retrospective study included patients with HBV LC-AD who underwent dual-energy CT scans of the liver from January 2018 to March 2022 and were randomized to training group (215 patients) and validation group (92 patients). The primary outcome was the need for readmission within 90 days due to ACLF. Based on the training group data, independent risk factors for disease progression in clinical and dual-energy CT parameters were identified and modeled by logistic regression analysis. Based on the training and validation groups data, receiver operating characteristic (ROC) curves, calibration curves, and decision analysis curves (DCA) were used to verify the discrimination, calibration, and clinical validity of the nomogram.
Chronic liver failure consortium-acute decompensation score (CLIF-C ADs) (p = 0.008) and ECV (p < 0.001) were independent risk factors for ACLF within 90 days. The AUC of the model combined ECV and CLIF-C ADs were 0.893 and 0.838 in the training and validation groups, respectively. The calibration curves show good agreement between predicted and actual risks. The DCA indicates that the model has good clinical application.
The model combined ECV and CLIF-C ADs can early predict the occurrence of ACLF within 90 days in HBV LC-AD patients.
肝硬化急性失代偿(LC-AD)疾病短期进展迅速,早期风险分层困难。目的是开发并验证一种基于双能CT定量肝细胞外容积(ECV)的模型,以预测乙型肝炎(HBV)LC-AD患者在90天内发生慢加急性肝衰竭(ACLF)的情况。
这项回顾性研究纳入了2018年1月至2022年3月期间接受肝脏双能CT扫描的HBV LC-AD患者,并将其随机分为训练组(215例患者)和验证组(92例患者)。主要结局是因ACLF在90天内再次入院的需求。基于训练组数据,通过逻辑回归分析确定临床和双能CT参数中疾病进展的独立危险因素并建立模型。基于训练组和验证组数据,采用受试者工作特征(ROC)曲线、校准曲线和决策分析曲线(DCA)来验证列线图的区分度、校准度和临床有效性。
慢性肝衰竭协作组急性失代偿评分(CLIF-C ADs)(p = 0.008)和ECV(p < 0.001)是90天内发生ACLF的独立危险因素。在训练组和验证组中,结合ECV和CLIF-C ADs的模型的AUC分别为0.893和0.838。校准曲线显示预测风险与实际风险之间具有良好的一致性。DCA表明该模型具有良好的临床应用价值。
结合ECV和CLIF-C ADs的模型可以早期预测HBV LC-AD患者在90天内发生ACLF的情况。