作者
Ashok Choudhury,Ankur Jindal,Rakhi Maiwall,Manoj Kumar Sharma,Brijesh C. Sharma,Viniyendra Pamecha,Mamun Al Mahtab,Salimur Rahman,Yogesh Chawla,Sunil Taneja,Soek Siam Tan,Harshad Devarbhavi,Zhongping Duan,Yu Chen,Qin Ning,Ji Dong Jia,Deepak Amarapurkar,C E Eapen,Ashish Goel,Hamid Salehiniya,Amna Subhan Butt,Wasim Jafri,D. J. Kim,Hasmik Ghazinian,Guan Huei Lee,Ajit Sood,Laurentius A. Lesmana,Zaigham Abbas,Gamal Shiha,Diana A. Payawal,A. Kadir Dokmeci,José D. Sollano,Gian Carpio,George Lau,Fazal Karim,Padaki Nagaraja Rao,Richard Moreau,Priyanka Jain,P. Bhatia,Guresh Kumar,Shiv Kumar Sarin
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.