Department of Severe Hepatopathy, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, Fujian Province, China.
Department of Hepatology, Hepatology Research Institute, the First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian Province, China.
Ann Med. 2024 Dec;56(1):2410403. doi: 10.1080/07853890.2024.2410403. Epub 2024 Oct 10.
Hepatic encephalopathy (HE) is a serious complication of acute-on-chronic liver failure (ACLF) that requires early detection and intervention to positively impact patient prognosis. This study aimed to develop a reliable model to predict HE in ACLF patients during hospitalization.
Retrospectively recruiting 255 hepatitis B-related ACLF patients, including 67 who developed HE during hospitalization, the study analysed clinical data and biochemical indices collected during the first week of admission. The least absolute shrinkage and selection operator (LASSO) was used to identify characteristic predictors for hospitalization HE events, and a logistic regression model was subsequently developed. Receiver operating characteristic (ROC) curves, calibration curves, and bootstrap resampling were used to evaluate the model's discrimination, consistency, and accuracy, and a nomogram was created to visualize the model. An external validation cohort of 236 liver failure patients collected from the same medical centre between 2007 and 2010 was used to validate the model.
The study found that blood urea nitrogen (BUN), alpha-fetoprotein (AFP), international normalized ratio (INR), serum ammonia, and infection complications during hospitalization were risk factors for HE in ACLF patients. The new model predicted the development of HE in ACLF patients with an area under the receiver operating characteristic curve (AUROC) of 85.2%, which was superior to other models. The best threshold for the new model was 0.28, resulting in a specificity of 81.4% and a sensitivity of 80.6%. In the validation group, the new model showed similar results, with an AUROC of 79% and a specificity of 83.6% and a sensitivity of 56.6%.
This study developed and validated a new prediction model for HE in ACLF patients offering a useful tool for early identification of patients with a high risk of HE in clinical settings. However, to ascertain the model's general effectiveness, future prospective multicentre studies are warranted.
肝性脑病（HE）是急性肝衰竭（ACLF）的严重并发症，需要早期发现和干预，以积极影响患者的预后。本研究旨在建立一种可靠的模型，以预测 ACLF 患者住院期间的 HE。
回顾性招募 255 例乙型肝炎相关 ACLF 患者，其中 67 例在住院期间发生 HE，分析患者入院第一周的临床资料和生化指标。采用最小绝对收缩和选择算子（LASSO）筛选住院期 HE 事件的特征预测因子，构建逻辑回归模型。采用受试者工作特征（ROC）曲线、校准曲线和 bootstrap 重采样评估模型的判别能力、一致性和准确性，并创建列线图可视化模型。使用来自同一医疗中心的 2007 年至 2010 年期间的 236 例肝衰竭患者的外部验证队列验证模型。
研究发现，住院期间血尿素氮（BUN）、甲胎蛋白（AFP）、国际标准化比值（INR）、血清氨和感染并发症是 ACLF 患者发生 HE 的危险因素。新模型预测 ACLF 患者 HE 发生的 AUC 为 85.2%，优于其他模型。新模型的最佳阈值为 0.28，特异性为 81.4%，敏感性为 80.6%。在验证组中，新模型也表现出类似的结果，AUC 为 79%，特异性为 83.6%，敏感性为 56.6%。
本研究建立并验证了一种 ACLF 患者 HE 预测新模型，为临床早期识别 HE 高危患者提供了有用的工具。然而，为了确定该模型的普遍有效性，需要进一步进行前瞻性多中心研究。
