医学
失代偿
内科学
慢性肝病
胃肠病学
多元分析
多元统计
心脏病学
脾脏
接收机工作特性
肝病
外科
临床实习
回顾性队列研究
试验预测值
慢性病
预测值
泌尿科
曲线下面积
作者
Yin Peng,Sitong Liu,Haoning Shen,Qinjun He,Hui Li,Junying Li,Jing Huang,Xiao Cheng,Weibin Wang,Minghan Tu,Xiaoqin Luo,Damei Zhou,Jinjun Chen,Xiaofeng Zhang
摘要
BACKGROUND AND AIMS: Although spleen stiffness measurement (SSM) via 100 Hz probe shows promise in predicting hepatic decompensation, its prognostic value across different etiological backgrounds remains insufficiently validated. We evaluated its efficacy in patients with compensated advanced chronic liver disease (cACLD) in a Chinese cohort. METHODS: This retrospective study included 713 cACLD patients. The optimal SSM cut-off was derived using the Fine-Grey competing risk model and validated by bootstrapping. The SSM-based model was compared against established models using time-dependent area under the curve (AUC), C-index, and decision curve analysis (DCA). RESULTS: During a median follow-up of 36.6 months, 28 patients (3.9%) developed decompensation. SSM was an independent predictor (sHR 1.03, 95% CI 1.02-1.05, p < 0.001). An SSM threshold of > 55 kPa was identified and demonstrated excellent stability (bootstrap 95% CI 39.8-57.3 kPa). The SSM > 55 kPa showed comparable discriminative ability to the multivariate NICER model at 1 year (AUC: 0.820 vs. 0.868, p = 0.328) and 2 years (AUC: 0.809 vs. 0.851, p = 0.271). Notably, SSM > 55 kPa significantly outperformed albumin-combined models (LSM-ALB, NICER-ALB) at 1-year prediction (all p < 0.05). DCA revealed that SSM > 55 kPa provided the highest net clinical benefit across all models. Patients with SSM > 55 kPa had a markedly higher decompensation risk (sHR 8.69, 95% CI 4.15-18.23, p < 0.001), with decompensation incidences of 15.2% vs. 2.0% compared to those below the threshold. CONCLUSION: In cACLD patients, a simple 100-Hz SSM threshold (> 55 kPa) effectively predicts hepatic decompensation with performance rivalling more complex models, offering a practical, non-invasive tool for risk stratification.
科研通智能强力驱动
Strongly Powered by AbleSci AI