背景:失代偿期肝硬化(DLC)是肝硬化进展的一个阶段,死亡率很高。
目的:建立并验证一种新颖且易于使用的预测列线图,以评估DLC患者的预后。
方法:选取南昌大学第一附属医院(南昌,江西省,中国)2013年12月至2019年8月。将患者分为两组:衍生组(n=329)和验证组(n=164)。进行单因素和多因素Cox回归分析以评估预后因素。列线图的性能取决于其校准,歧视,和临床有用性。
结果:年龄,机械通风应用,终末期肝病模型(MELD)评分,平均动脉血压,和动脉氧分压/吸入氧浓度构建模型。推导组和验证组的列线图C指数分别为0.780(95CI:0.670-0.889)和0.792(95CI:0.698-0.886),分别。在两组中,校准曲线与实际观察曲线均表现出良好的一致性。此外,决策曲线分析表明,我们的列线图在临床实践中是有用的.
结论:建立并验证了基于大型亚洲队列的简单易用的新颖列线图,与Child-Turcotte-Pugh和MELD评分相比,表现出改善的表现。对于DLC患者,所提出的列线图可能有助于指导临床医生进行治疗分配,并有助于预后预测.
BACKGROUND: Decompensated liver cirrhosis (DLC) is a stage in the progression of liver cirrhosis and has a high mortality.
OBJECTIVE: To establish and validate a novel and simple-to-use predictive nomogram for evaluating the prognosis of DLC patients.
METHODS: A total of 493 patients with confirmed DLC were enrolled from The First Affiliated Hospital of Nanchang University (Nanchang, Jiangxi Province,
China) between December 2013 and August 2019. The patients were divided into two groups: a derivation group (n = 329) and a validation group (n = 164). Univariate and multivariate Cox regression analyses were performed to assess prognostic factors. The performance of the nomogram was determined by its calibration, discrimination, and clinical usefulness.
RESULTS: Age, mechanical ventilation application, model for end-stage liver disease (MELD) score, mean arterial blood pressure, and arterial oxygen partial pressure/inhaled oxygen concentration were used to construct the model. The C-indexes of the nomogram in the derivation and validation groups were 0.780 (95%CI: 0.670-0.889) and 0.792 (95%CI: 0.698-0.886), respectively. The calibration curve exhibited good consistency with the actual observation curve in both sets. In addition, decision curve analysis indicated that our nomogram was useful in clinical practice.
CONCLUSIONS: A simple-to-use novel nomogram based on a large Asian cohort was established and validated and exhibited improved performance compared with the Child-Turcotte-Pugh and MELD scores. For patients with DLC, the proposed nomogram may be helpful in guiding clinicians in treatment allocation and may assist in prognosis prediction.