Unknown

Dataset Information

0

A modified MELD model for Chinese pre-ACLF and ACLF patients and it reveals poor prognosis in pre-ACLF patients.


ABSTRACT:

Background & aims

Acute-on-chronic liver failure (ACLF) is one of the most deadly, prevalent, and costly diseases in Asia. However, no prognostic model has been developed that is based specifically on data gathered from Asian patients with ACLF. The aim of the present study was to quantify the survival time of ACLF among Asians and to develop a prognostic model to estimate the probability of death related to ACLF.

Methods

We conducted a retrospective observational cohort study to analyze clinical data from 857 patients with ACLF/pre-ACLF who did not undergo liver transplantation. Kaplan-Meier and Cox proportional hazards regression model were used to estimate survival rates and survival affected factors. The area under the receiver operating characteristic curve (auROC) was used to evaluate the performance of the models for predicting early mortality.

Results

The mortality rates among patients with pre-ACLF at 12 weeks and 24 weeks after diagnosis were 30.5% and 33.2%, respectively. The mortality rates among patients with early-stage ACLF at 12 weeks and 24 weeks after diagnosis were 33.9% and 37.1%, respectively. The difference in survival between pre-ACLF patients and patients in the early stage of ACLF was not statistically significant. The prognostic model identified 5 independent factors significantly associated with survival among patients with ACLF and pre-ACLF: the model for end-stage liver disease (MELD) score; age, hepatic encephalopathy; triglyceride level and platelet count.

Conclusion

The findings of the present study suggest that the Chinese diagnostic criteria of ACLF might be broadened, thus enabling implementation of a novel model to predict ACLF-related death after comprehensive medical treatment.

SUBMITTER: Xia Q 

PROVIDER: S-EPMC3673980 | biostudies-literature |

REPOSITORIES: biostudies-literature

Similar Datasets

| S-EPMC9472060 | biostudies-literature
| S-EPMC6007067 | biostudies-literature
| S-EPMC5400346 | biostudies-literature
| S-EPMC10023372 | biostudies-literature
| S-EPMC5482636 | biostudies-literature
| S-EPMC9986060 | biostudies-literature
2013-09-16 | GSE45142 | GEO
2013-09-16 | E-GEOD-45142 | biostudies-arrayexpress
2023-04-04 | GSE228610 | GEO