Department of Infectious Disease, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, 410005, Hunan Province, China.
Department of Infectious Diseases, Key Laboratory of Viral Hepatitis of Hunan, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China.
Eur J Med Res. 2024 Nov 21;29(1):556. doi: 10.1186/s40001-024-02141-7.
Hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is frequently accompanied by short-term morbidity and mortality. However, there have been no studies on the associations between baseline clinicopathologic characteristics at hospital admission and clinical prognosis after receiving artificial liver therapy. Therefore, the current study aimed to develop a prognostic nomogram for predicting the outcomes of patients with HBV-ACLF following artificial liver support.
A retrospective study of 110 consecutive patients who were diagnosed with HBV-ACLF between January 2018 and August 2022 was conducted. First, univariate and multivariate logistic regression analyses were performed to determine the independent prognostic factors significantly associated with patient outcomes. Moreover, a predictive nomogram model underlying the prognostic factors was established and further evaluated. The area under the curve (AUC) was used to gauge the predictive accuracy. The calibration curve and decision curve analysis (DCA) were employed to assess the discriminability and clinical effectiveness, respectively.
In patients with HBV-ACLF, multivariate logistic analysis revealed that age ≥ 40 years (OR 6.76, p = 0.025), middle-stage liver failure (OR 49.96, p < 0.001), end-stage liver failure (OR 19.27, p = 0.002), hepatic encephalopathy (OR 7.06, p = 0.032), upper gastrointestinal hemorrhage (OR 47.24, p = 0.047), and artificial liver therapy consisting of plasma exchange (PE) + plasma exchange double plasma molecular adsorption system (DPMAS) (OR 0.26, p = 0.04) were identified as prognostic factors. Then, we established and evaluated a predictive nomogram with an AUC of 0.885, which showed better predictive accuracy than the model for end-stage liver disease (MELD) score (AUC of 0.634) and the Child-Pugh score (AUC of 0.611). Moreover, the calibration curve showed good agreement between the ideal and bias-corrected curves. Decision curve analysis confirmed the better clinical utility of this approach.
We developed and evaluated a unique nomogram that was more accurate than conventional prognostic models for predicting the clinical prognosis of HBV-ACLF patients receiving artificial liver therapy. As a result, the nomogram may be a helpful tool in clinical decision-making to predict the outcomes of patients with HBV-ACLF.
乙型肝炎病毒相关慢加急性肝衰竭（HBV-ACLF）常伴有短期发病率和死亡率。然而，目前尚无研究探讨入院时的基线临床病理特征与接受人工肝治疗后的临床预后之间的关系。因此，本研究旨在为接受人工肝支持治疗的 HBV-ACLF 患者建立预测预后的列线图。
对 2018 年 1 月至 2022 年 8 月期间连续收治的 110 例 HBV-ACLF 患者进行回顾性研究。首先，采用单因素和多因素 logistic 回归分析确定与患者结局显著相关的独立预后因素。此外，建立并进一步评估基于预后因素的预测列线图模型。曲线下面积（AUC）用于评估预测准确性。校准曲线和决策曲线分析（DCA）分别用于评估区分度和临床有效性。
在 HBV-ACLF 患者中，多因素 logistic 分析显示年龄≥40 岁（OR 6.76，p=0.025）、中晚期肝功能衰竭（OR 49.96，p<0.001）、终末期肝功能衰竭（OR 19.27，p=0.002）、肝性脑病（OR 7.06，p=0.032）、上消化道出血（OR 47.24，p=0.047）和人工肝治疗包括血浆置换（PE）+双重血浆分子吸附系统（DPMAS）（OR 0.26，p=0.04）是预后因素。然后，我们建立并评估了一个 AUC 为 0.885 的预测列线图，其预测准确性优于终末期肝病模型（MELD）评分（AUC 为 0.634）和 Child-Pugh 评分（AUC 为 0.611）。此外，校准曲线显示理想和偏倚校正曲线之间具有良好的一致性。决策曲线分析证实了该方法的更好的临床实用性。
我们开发并评估了一个独特的列线图，该列线图比传统的预后模型更准确地预测接受人工肝治疗的 HBV-ACLF 患者的临床预后。因此，该列线图可能是临床决策中预测 HBV-ACLF 患者结局的有用工具。
