Communications medicine · 2025
Transparent and dynamic network-based risk assessment for development of hepatic encephalopathy.
СтатьяНазвание и аннотация: Перевод в очереди
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Wang Zixing, Jiang Suzhen, Liu Feng, Wang Xiao Xiao, Wang Guangchuan, Huang Rui, Kong Xiangsha, Fei Ran, Li Xiaohe, Cao Jinming, Yang Shuo, Wu Nan, Chen Dongbo, She Shaoping, Han Wei, Hu Yaoda, Wen Xin, Xue Tianyi, Song Guangjun, Rao Huiying
Аннотация
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.
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.