PDF(Pubmed)
Abstract：
UNASSIGNED: Non-invasive stratification of the liver decompensation risk remains unmet in people with compensated cirrhosis. This study aimed to develop a non-invasive tool (NIT) to predict hepatic decompensation.
UNASSIGNED: This retrospective study recruited 689 people with compensated cirrhosis (median age, 54 years; 441 men) from 5 centres from January 2016 to June 2020. Baseline abdominal computed tomography (CT), clinical features, and liver stiffness were collected, and then the first decompensation was registered during the follow-up. The spleen-based model was designed for predicting decompensation based on a deep learning segmentation network to generate the spleen volume and least absolute shrinkage and selection operator (LASSO)-Cox. The spleen-based model was trained on the training cohort of 282 individuals (Institutions I-III) and was validated in 2 external validation cohorts (97 and 310 individuals from Institutions IV and V, respectively) and compared with the conventional serum-based models and the Baveno VII criteria.
UNASSIGNED: The decompensation rate at 3 years was 23%, with a 37.6-month median (IQR 21.1-52.1 months) follow-up. The proposed model showed good performance in predicting decompensation (C-index ≥0.84) and outperformed the serum-based models (C-index comparison test p <0.05) in both the training and validation cohorts. The hazard ratio (HR) for decompensation in individuals with high risk was 7.3 (95% CI 4.2-12.8) in the training and 5.8 (95% CI 3.9-8.6) in the validation (log-rank test, p <0.05) cohorts. The low-risk group had a negligible 3-year decompensation risk (≤1%), and the model had a competitive performance compared with the Baveno VII criteria.
UNASSIGNED: This spleen-based model provides a non-invasive and user-friendly method to help predict decompensation in people with compensated cirrhosis in diverse healthcare settings where liver stiffness is not available.
UNASSIGNED: People with compensated cirrhosis with larger spleen volume would have a higher risk of decompensation. We developed a spleen-based model and validated it in external validation cohorts. The proposed model might help predict hepatic decompensation in people with compensated cirrhosis when invasive tools are unavailable.
UNASSIGNED: This retrospective study recruited 689 people with compensated cirrhosis (median age, 54 years; 441 men) from 5 centres from January 2016 to June 2020. Baseline abdominal computed tomography (CT), clinical features, and liver stiffness were collected, and then the first decompensation was registered during the follow-up. The spleen-based model was designed for predicting decompensation based on a deep learning segmentation network to generate the spleen volume and least absolute shrinkage and selection operator (LASSO)-Cox. The spleen-based model was trained on the training cohort of 282 individuals (Institutions I-III) and was validated in 2 external validation cohorts (97 and 310 individuals from Institutions IV and V, respectively) and compared with the conventional serum-based models and the Baveno VII criteria.
UNASSIGNED: The decompensation rate at 3 years was 23%, with a 37.6-month median (IQR 21.1-52.1 months) follow-up. The proposed model showed good performance in predicting decompensation (C-index ≥0.84) and outperformed the serum-based models (C-index comparison test p <0.05) in both the training and validation cohorts. The hazard ratio (HR) for decompensation in individuals with high risk was 7.3 (95% CI 4.2-12.8) in the training and 5.8 (95% CI 3.9-8.6) in the validation (log-rank test, p <0.05) cohorts. The low-risk group had a negligible 3-year decompensation risk (≤1%), and the model had a competitive performance compared with the Baveno VII criteria.
UNASSIGNED: This spleen-based model provides a non-invasive and user-friendly method to help predict decompensation in people with compensated cirrhosis in diverse healthcare settings where liver stiffness is not available.
UNASSIGNED: People with compensated cirrhosis with larger spleen volume would have a higher risk of decompensation. We developed a spleen-based model and validated it in external validation cohorts. The proposed model might help predict hepatic decompensation in people with compensated cirrhosis when invasive tools are unavailable.
摘要：
未经证实：代偿性肝硬化患者肝脏失代偿风险的非侵入性分层仍未得到满足。本研究旨在开发一种非侵入性工具（NIT）来预测肝功能失代偿。
UNASSIGNED：这项回顾性研究招募了689名代偿期肝硬化患者（中位年龄，54岁；441名男性），从2016年1月到2020年6月，来自5个中心。基线腹部计算机断层扫描（CT），临床特征,收集肝脏硬度，然后在随访期间记录了第一次失代偿。基于脾脏的模型被设计用于基于深度学习分割网络来预测失代偿以生成脾脏体积和最小绝对收缩和选择算子(LASSO)-Cox。基于脾脏的模型在282个人（机构I-III）的训练队列中进行了训练，并在2个外部验证队列中进行了验证（来自机构IV和V的97和310个人，分别）并与传统的基于血清的模型和BavenoVII标准进行比较。
未经评估：3年失代偿率为23%，中位随访时间为37.6个月（IQR21.1-52.1个月）。所提出的模型在预测失代偿（C指数≥0.84）方面表现良好，并且在训练和验证队列中均优于基于血清的模型（C指数比较检验p<0.05）。高风险个体失代偿的风险比（HR）在训练中为7.3（95％CI4.2-12.8），在验证中为5.8（95％CI3.9-8.6）（对数秩检验，p<0.05)队列。低风险组的3年失代偿风险可忽略不计（≤1％），与BavenoVII标准相比，该模型具有竞争力。
UNASSIGNED：这种基于脾脏的模型提供了一种非侵入性且用户友好的方法，可以帮助预测在无法获得肝硬度的不同医疗机构中代偿性肝硬化患者的代偿失调。
非ASSIGNED：脾体积较大的代偿性肝硬化患者代偿失调的风险较高。我们开发了一个基于脾脏的模型，并在外部验证队列中进行了验证。当侵入性工具不可用时，所提出的模型可能有助于预测代偿性肝硬化患者的肝功能失代偿。
UNASSIGNED：这项回顾性研究招募了689名代偿期肝硬化患者（中位年龄，54岁；441名男性），从2016年1月到2020年6月，来自5个中心。基线腹部计算机断层扫描（CT），临床特征,收集肝脏硬度，然后在随访期间记录了第一次失代偿。基于脾脏的模型被设计用于基于深度学习分割网络来预测失代偿以生成脾脏体积和最小绝对收缩和选择算子(LASSO)-Cox。基于脾脏的模型在282个人（机构I-III）的训练队列中进行了训练，并在2个外部验证队列中进行了验证（来自机构IV和V的97和310个人，分别）并与传统的基于血清的模型和BavenoVII标准进行比较。
未经评估：3年失代偿率为23%，中位随访时间为37.6个月（IQR21.1-52.1个月）。所提出的模型在预测失代偿（C指数≥0.84）方面表现良好，并且在训练和验证队列中均优于基于血清的模型（C指数比较检验p<0.05）。高风险个体失代偿的风险比（HR）在训练中为7.3（95％CI4.2-12.8），在验证中为5.8（95％CI3.9-8.6）（对数秩检验，p<0.05)队列。低风险组的3年失代偿风险可忽略不计（≤1％），与BavenoVII标准相比，该模型具有竞争力。
UNASSIGNED：这种基于脾脏的模型提供了一种非侵入性且用户友好的方法，可以帮助预测在无法获得肝硬度的不同医疗机构中代偿性肝硬化患者的代偿失调。
非ASSIGNED：脾体积较大的代偿性肝硬化患者代偿失调的风险较高。我们开发了一个基于脾脏的模型，并在外部验证队列中进行了验证。当侵入性工具不可用时，所提出的模型可能有助于预测代偿性肝硬化患者的肝功能失代偿。
