Scholarlink

Background <p>Hepatic encephalopathy is a debilitating and resource-intensive complication of cirrhosis, with high prevalence, frequent hospitalizations, and poor prognosis. Precise and dynamic identification of at-risk individuals remains a major clinical challenge.</p> Methods <p>Combining expert knowledge with data-driven methodologies, we proposed a network-based risk assessment model by analyzing the inter-connections between cirrhotic complications and using routinely available blood test results. The model was developed (<i>n</i> = 2789) and validated (<i>n</i> = 698) in multi-etiology cirrhosis cohorts.</p> Results <p>Here we show hepatic encephalopathy as a pivotal nexus in the cirrhotic complication cascade. The presence of ascites (adjusted relative risk: 4.8), gastroesophageal varices (3.6), peritonitis (2.2), hepatorenal syndrome (2.1), gastroesophageal variceal hemorrhage (1.9) and hepatocellular carcinoma (1.5) is related with subsequent hepatic encephalopathy; all <i>p</i> &lt; 0.05. Nine of 980 tests (ammonia, international normalized ratio, red cell distribution width standard deviation, fibrinogen, triglycerides, mean corpuscular hemoglobin, absolute neutrophil count, sodium, and total CO<sub>2</sub>) are selected through a stringent process encompassing clinical utility, expert agreement, risk direction clarity, and information non-redundancy. The network-based model accurately predicts hepatic encephalopathy risk in both cross-sectional and longitudinal settings, AUROC = 0.926 (95% confidence interval: 0.882-0.962) and 0.962 (0.933-0.984), respectively. It is compatible with missing data (i.e., using partially observed information) and offers flexible clinical implementation through a simple tool-free method and smart device integration. Clinical utility spans patient risk stratification, dynamic risk monitoring, optimized screening, and hepatic encephalopathy-related complication prevention.</p> Conclusions <p>This network-based approach provides transparent, precise and dynamic risk assessment in hepatic encephalopathy management, with potential to improve clinical outcomes in cirrhotic patients.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Transparent and dynamic network-based risk assessment for development of hepatic encephalopathy

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月10日

查看全文
  • Zixing Wang,
  • Suzhen Jiang,
  • Feng Liu,
  • Xiao Xiao Wang,
  • Guangchuan Wang,
  • Rui Huang,
  • Xiangsha Kong,
  • Ran Fei,
  • Xiaohe Li,
  • Jinming Cao,
  • Shuo Yang,
  • Nan Wu,
  • Dongbo Chen,
  • Shaoping She,
  • Wei Han,
  • Yaoda Hu,
  • Xin Wen,
  • Tianyi Xue,
  • Guangjun Song,
  • Huiying Rao

摘要

Background

Hepatic encephalopathy is a debilitating and resource-intensive complication of cirrhosis, with high prevalence, frequent hospitalizations, and poor prognosis. Precise and dynamic identification of at-risk individuals remains a major clinical challenge.

Methods

Combining expert knowledge with data-driven methodologies, we proposed a network-based risk assessment model by analyzing the inter-connections between cirrhotic complications and using routinely available blood test results. The model was developed (n = 2789) and validated (n = 698) in multi-etiology cirrhosis cohorts.

Results

Here we show hepatic encephalopathy as a pivotal nexus in the cirrhotic complication cascade. The presence of ascites (adjusted relative risk: 4.8), gastroesophageal varices (3.6), peritonitis (2.2), hepatorenal syndrome (2.1), gastroesophageal variceal hemorrhage (1.9) and hepatocellular carcinoma (1.5) is related with subsequent hepatic encephalopathy; all p < 0.05. Nine of 980 tests (ammonia, international normalized ratio, red cell distribution width standard deviation, fibrinogen, triglycerides, mean corpuscular hemoglobin, absolute neutrophil count, sodium, and total CO2) are selected through a stringent process encompassing clinical utility, expert agreement, risk direction clarity, and information non-redundancy. The network-based model accurately predicts hepatic encephalopathy risk in both cross-sectional and longitudinal settings, AUROC = 0.926 (95% confidence interval: 0.882-0.962) and 0.962 (0.933-0.984), respectively. It is compatible with missing data (i.e., using partially observed information) and offers flexible clinical implementation through a simple tool-free method and smart device integration. Clinical utility spans patient risk stratification, dynamic risk monitoring, optimized screening, and hepatic encephalopathy-related complication prevention.

Conclusions

This network-based approach provides transparent, precise and dynamic risk assessment in hepatic encephalopathy management, with potential to improve clinical outcomes in cirrhotic patients.