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In JHEP reports : innovation in hepatology

Background & Aims : 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.

Methods : 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.

Results : 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.

Conclusions : 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.

Lay summary : 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.

Yu Qian, Xu Chuanjun, Li Qinyi, Ding Zhimin, Lv Yan, Liu Chuan, Huang Yifei, Zhou Jiaying, Huang Shan, Xia Cong, Meng Xiangpan, Lu Chunqiang, Li Yuefeng, Tang Tianyu, Wang Yuancheng, Song Yang, Qi Xiaolong, Ye Jing, Ju Shenghong


2D, 2-dimensional, 3D, 3-dimensional, ALBI, albumin–bilirubin, ALP, alkaline phosphatase, AUC, area under the curve, C-index, concordance index, CHE, cholinesterase, CHESS1701, CSPH, clinically significant portal hypertension, CT, CT, computed tomography, Cirrhosis, Decompensation, Deep learning, FCN, fully convolutional network, FIB-4, Fibrosis-4, HR, hazard ratio, HVPG, hepatic venous pressure gradient, Hb, haemoglobin, IDI, integrated discrimination improvement, LASSO, least absolute shrinkage and selection operator, LSM, liver stiffness measurement, MELD, model for end-stage liver disease, MRI, magnetic resonance imaging, NIT, non-invasive tool, NRI, net reclassification improvement, PLT, platelet, ROC, receiver operating characteristic curve, Spleen, Splenomegaly, TIPS, transjugular intrahepatic portosystemic shunt, WHO, World Health Organization