肝脏 ›› 2022, Vol. 27 ›› Issue (2): 152-159.

• 肝功能衰竭 • 上一篇    下一篇

不同评分模型对慢加急性肝衰竭患者短期预后的预测价值

吴欢, 伍龙, 祝娟娟, 张权, 申晓旭   

  1. 550004 贵阳 贵州医科大学附属医院感染科(吴欢,祝娟娟,张权,申晓旭),肛肠外科(伍龙)
  • 收稿日期:2021-04-26 出版日期:2022-02-28 发布日期:2022-04-19
  • 通讯作者: 伍龙,Email:hanjun523@126.com
  • 基金资助:
    贵州省科技支撑计划项目(黔科合支撑[2021]一般094)

Predictive value of different scoring models for short-term prognosis of patients with aute-on-chronic liver failure

WU Huan1, WU Long2, ZHU Juan-juan1, ZHANG Quan1, SHEN Xiao-xu1   

  1. 1. Department of Infectious Diseases,The Affiliated Hospital of Guizhou Medical University,Guiyang 550004,China;
    2. Department of Anus & Intestine Surgery,The Affiliated Hospital of Guizhou Medical University,Guiyang 550004,China
  • Received:2021-04-26 Online:2022-02-28 Published:2022-04-19
  • Contact: WU Long,Email:hanjun523 @126.com

摘要: 目的 比较不同评估模型对慢加急性肝衰竭(acute-on-chronic liver failure,ACLF)短期预后的预测价值。方法 选取2018年7月至2020年9月在贵州医科大学附属医院感染科住院治疗的ACLF患者246例,根据治疗终点及出院后3个月的临床结局分为生存组和死亡组,比较两组患者的临床资料,应用受试者工作特征曲线下面积评价各评分模型对ACLF短期预后的判断能力。结果 生存组与死亡组患者的年龄、白细胞、中性粒细胞、中性粒细胞/淋巴细胞、血红蛋白、血小板、甲胎蛋白、总胆红素、直接胆红素、间接胆红素、肌酐、钠、凝血酶原时间、国际标准化比值、凝血酶原活动度、乳酸和氨差异均有统计学意义(均P<0.05)。死亡组患者ACLF相关并发症(肺部感染、肝性脑病、肝肾综合征、上消化道出血、腹水、自发性腹膜炎)的发生率均明显高于生存组。多因素logistic回归分析结果显示,年龄、NLR、IBil、HE、腹水是影响ACLF患者短期预后的独立危险因素。肝衰竭早期、中期和晚期的病死率分别为11.54%(6/52)、32.89%(25/76)、77.12%(91/118),差异有统计学意义(P<0.01)。肝衰竭早期不同评分模型平均值明显低于肝衰竭中期、晚期,肝衰竭中期不同评分模型平均值明显低于肝衰竭晚期。除LRM评分模型外,其余9种评分模型在肝衰竭分期组间以及生存组与死亡组间比较差异均有统计学意义(均P<0.05)。所有评分模型不同分值区间比较差异均有统计学意义(均P<0.01)。ARRC评分模型的AUC为0.765,其后依次为CTP、ABIC、iMELD、MDF,以上评分模型AUC均>0.7,LRM评分模型AUC最小(0.586),预测价值最差。结论 除LRM评分模型外,CTP、ARRC、MELD、MELD-Na、iMELD、MESO、ABIC、MDF、ALBI评分模型均能较好地预测ACLF患者短期预后,其中ARRC评分模型具有更高的评估价值。

关键词: 慢加急性肝衰竭, 评分模型, 预后

Abstract: Objective To investigate the independent risk factors of prognosis in patients with acute-on-chronic liver failure (ACLF), and compare the predictive value of different assessment models for short-term prognosis. Methods A total of 246 patients with ACLF admitted to our hospital from July 2018 to September 2020 were enrolled, and divided into survival group and death group according to the treatment end-point and the clinical outcome 3 months after discharge. SPSS and Yibei statistical software were used to compare the clinical data of the patients in the two groups. Receiver operator characteristic (ROC) curve and area under the curve (AUC) were used to evaluate the predictive efficiency of each scoring model on the short-term prognosis. Results There were significant differences of age, white blood cell (WBC), neutrophils (NEU), neutrophils/nymphocytes (NLR), hemoglobin (Hgb), platelets (PLT), alpha-fetoprotein (AFP), total bilirubin (TBil), direct bilirubin (DBil), indirect bilirubin (IBil), creatinine (CR), natrium (Na), prothrombin time (PT), international normalized ratio (INR), prothrombin activity (PTA), lactic acid (LA) and ammonia (AMM) between the 2 groups. The incidence rates of ACLF-related complications, including lung infection and hepatic encephalopathy (HE), hepatorenal syndrome (HS), upper gastrointestinal bleeding, ascites, spontaneous bacterial peritonitis (SBP), in death group were significantly higher than those in survival group. Multivariate logistic regression analysis showed that age, NLR, IBIL, HE, and ascites were independent risk factors affecting short-term prognosis in patients with ACL. The mortality rates of early, middle and end stages of liver failure were 11.54%, 32.89%, and 77.12%, respectively, which were significantly different between the two groups (P<0.001). The average value of different scoring models in the different stages from high to low were end stage of liver failure, middle stage of liver failure, and early stage of liver failure. Except for the Logstic Regression Model (LRM), the differences among the different liver failure stage groups compared by the other 9 scoring models were statistically significant. The values of Child-Turcotte-Pugh score (CTP), end-stage liver disease score (MELD), MELD-Na score (MELD-Na), MESO score (MESO), integrated MELD score (iMELD), Maddrey discriminant function (MDF), Asia Pacific Association for the Study of Liver Diseases ACLF Research Alliance-ACLF (AARC), Age-Bilirubin-INR-Creatinine (ABIC) and Albumin-bilirubin score (ALBI) in survival group were statistically different from those in death group. Divide the different scoring models into 2-3 intervals according to the scores. Fisher exact test was used when the sample size was less than 5. Pearson chi-square test was used when the sample size in the group was greater than 5. The results showed that all the scoring models had significant differences in the comparison of different score intervals between groups (P<0.001). The AUC values of the 10 scoring models were between 0.5 and 0.8, suggesting a certain predictive value for the prognosis of patients with ACLF. Among them, the AUC value of the ARRC scoring model was the largest (AUC=0.765), followed by CTP, ABIC, iMELD, MDF and the AUC values of the above scoring model were all > 0.7. The AUC value of the LRM scoring model was the smallest (AUC=0.586), indicating it had the worst predictive value. Conclusion In addition to the LRM scoring model, the remaining scoring models CTP, ARRC, MELD, MELD-Na, iMELD, MESO, ABIC, MDF, ALBI can better predict the short-term prognosis of ACLF patients, and the ARRC scoring model has the highest predictive value.

Key words: Acute-on-chronic liver failure, Scoring model, Prognosis