Spleen volume-based non-invasive tool for predicting hepatic decompensation in people with compensated cirrhosis (CHESS1701)
Qian Yu,
Chuanjun Xu,
Qinyi Li,
Zhimin Ding,
Yan Lv,
Chuan Liu,
Yifei Huang,
Jiaying Zhou,
Shan Huang,
Cong Xia,
Xiangpan Meng,
Chunqiang Lu,
Yuefeng Li,
Tianyu Tang,
Yuancheng Wang,
Yang Song,
Xiaolong Qi,
Jing Ye,
Shenghong Ju
Affiliations
Qian Yu
Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
Chuanjun Xu
Department of Radiology, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, China
Qinyi Li
Department of Radiology, The Affiliated Third Hospital of Jiangsu University, Zhenjiang, China
Zhimin Ding
Department of Radiology, Yijishan Hospital of Wannan Medical College, Wuhu, China
Yan Lv
Department of Medical Imaging, Subei People’s Hospital, Medical School of Yangzhou University, Yangzhou, China
Chuan Liu
Center of Portal Hypertension, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
Yifei Huang
CHESS Center, Institute of Portal Hypertension, The First Hospital of Lanzhou University, Lanzhou, China
Jiaying Zhou
Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
Shan Huang
Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
Cong Xia
Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
Xiangpan Meng
Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
Chunqiang Lu
Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
Yuefeng Li
Department of Radiology, The Affiliated Hospital of Jiangsu University, Zhenjiang, China
Tianyu Tang
Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
Yuancheng Wang
Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
Yang Song
MR Scientific Marketing, Siemens Healthineers Ltd., Shanghai, China
Xiaolong Qi
Center of Portal Hypertension, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
Jing Ye
Department of Medical Imaging, Subei People’s Hospital, Medical School of Yangzhou University, Yangzhou, China
Shenghong Ju
Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China; Corresponding author. Address: Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China. Tel.: +86-83272121.
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.
