Liver failure determines the outcome in patients of acute-on-chronic liver failure (ACLF): comparison of APASL ACLF research consortium (AARC) and CLIF-SOFA models

医学 肝衰竭 结直肠外科 内科学 结果(博弈论) 肝病学 胃肠病学 移植手术 腹部外科 重症监护医学 数理经济学 数学
作者
Ashok Choudhury,Ankur Jindal,Rakhi Maiwall,Manoj Kumar,B. C. Sharma,Viniyendra Pamecha,M.A. Mahtab,S Rahman,Yogesh Chawla,Sunil Taneja,S. S. Tan,Harshad Devarbhavi,Zhongping Duan,Yú Chen,Qin Ning,Ji Dong Jia,Deepak Amarapurkar,C. E. Eapen,Ashish Goel,Saeed Hamid
出处
期刊:Hepatology International [Springer Nature]
卷期号:11 (5): 461-471 被引量:194
标识
DOI:10.1007/s12072-017-9816-z
摘要

Acute-on-chronic liver failure (ACLF) is a progressive disease associated with rapid clinical worsening and high mortality. Early prediction of mortality and intervention can improve patient outcomes. We aimed to develop a dynamic prognostic model and compare it with the existing models. A total of 1402 ACLF patients, enrolled in the APASL-ACLF Research Consortium (AARC) with 90-day follow-up, were analyzed. An ACLF score was developed in a derivation cohort (n = 480) and was validated (n = 922). The overall survival of ACLF patients at 28 days was 51.7%, with a median of 26.3 days. Five baseline variables, total bilirubin, creatinine, serum lactate, INR and hepatic encephalopathy, were found to be independent predictors of mortality, with AUROC in derivation and validation cohorts being 0.80 and 0.78, respectively. AARC-ACLF score (range 5–15) was found to be superior to MELD and CLIF SOFA scores in predicting mortality with an AUROC of 0.80. The point scores were categorized into grades of liver failure (Gr I: 5–7; II: 8–10; and III: 11–15 points) with 28-day cumulative mortalities of 12.7, 44.5 and 85.9%, respectively. The mortality risk could be dynamically calculated as, with each unit increase in AARC-ACLF score above 10, the risk increased by 20%. A score of ≥11 at baseline or persisting in the first week was often seen among nonsurvivors (p = 0.001). The AARC-ACLF score is easy to use, dynamic and reliable, and superior to the existing prediction models. It can reliably predict the need for interventions, such as liver transplant, within the first week.
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