Published online Feb 27, 2024. doi: 10.4254/wjh.v16.i2.251
Peer-review started: November 11, 2023
First decision: December 14, 2023
Revised: December 24, 2023
Accepted: January 15, 2024
Article in press: January 15, 2024
Published online: February 27, 2024
Acute liver failure (ALF) and acute on chronic liver failure (ACLF) are the most common causes of liver disease death. As an effective treatment, Artificial liver support system (ALSS) can reduce bilirubin in a short term, improve inflammatory storm, regulate immunity, which has been widely used in the treatment of ALF and ACLF. However, there is no clinical predict model could evaluate the prognosis of patients with ALF and ACLF treated with ALSS. G3BP1 could inhibit inflammatory response, and the increased expression of G3BP1 was positively correlated with the prognosis of liver failure. However, there is no correlation between G3BP1 and the prognosis for the liver failure patients treated by ALSS.
The significant characteristics of ALF and ACLF are rapid progression of the disease, which can lead to rapid occurrence of extrahepatic organ failure, manifested as liver coma or hepatorenal syndrome, leading to high mortality rate and extremely poor prognosis. Therefore, early judgment of prognosis and timely intervention during the progression of ALF and ACLF are of great significance to improving survival rate.
The purpose of this study was to verify the predictive efficacy of G3BP1 in patients with ALF and ACLF treated with ALSS, and to provide reference for the clinical development of a new prediction model. Based on the above studies, we investigated whether G3BP1 can predict the prognosis of patients with ALF and ACLF, and provide a basis for clinical decision-making programs and timing.
A total of 244 patients with ALF and ACLF were enrolled in this study. The levels of G3BP1 on admission and at discharge were detected. The validation set of 514 patients was collected to verify the predicted effect of G3BP1 and the viability of prognosis. Univariate and multivariate Cox proportional hazards models were used to detect G3BP1, clinical biomarkers [lactate dehydrogenase (LDH), alpha-fetoprotein (AFP) and prothrombin time (PT), etc.], and inflammatory factors [tumor necrosis factor-α (TNF-α), interlenkin (IL)-1β and IL-18] for the risk of progression after ALSS treatment. We treated biomarkers as continuous traits (log-transformed) or as 3-level categorical variables (low, medium, and high) defined according to the tertials of the biomarker level. We developed a reference Cox proportional hazards model for progression after ALSS treatment of liver failure in the training cohort and tested whether the inclusion of biomarker levels further improved risk prediction. The random forest success rate curve model of R software was used to select the survival prognosis factors of patients in the training set. Stepwise Cox regression was used to evaluate the number of variables with the concordance index (C-index) value to obtain the best C-index value with fewer prognostic factors.
The value of difference of G3BP1 between the value of discharge and admission (difG3BP1) was then cut into two groups with 0 as the cut-off point. Group 0 had a nearly 10-fold increased risk compared to group ≥ 0. According to the Kaplan-Meier curve, survival rate in the 0-test set was lower than in the difG3BP1 ≥ 0 group (P < 0.001), which was also similarly in the validation set. For the subgroup analysis, group 0 presented a very high risk of progression to the disease regardless of model for end-stage liver disease scores in high-risk or low-risk groups. This indicated that G3BP1 was good for prognostic assessment. Moreover, TNF-α and IL-1β were closely related to the prognosis of liver failure before and after adjustment. However, its risk coefficient was lower when compared with G3BP1, suggesting that G3BP1 was more effective in predicting liver failure than the inflammatory factors. For the random forest prediction model, the most importance evaluation index were LDH and AFP. Compared with other indicators, G3BP1 had the highest C index value and better consistency. It was also more stable in the validation set. Modeling with AFP + LDH + G3BP1, the C index in the test set and validation set were respectively 0.84 and 0.8, which could better assist clinical practice. Therefore, the combination of G3BP1, AFP and LDH modeling ability index could well predict the endpoint of ALF and ACLF patients treated with ALSS.
For the clinical characteristics and laboratory indicators of ALF and ACLF patients treated with ALSS, G3BP1, AFP, LDH, TNF-α and IL-1β were independent risk factors. G3BP1 was the most effective predictor of liver failure. The model established by G3BP1, AFP and LDH had high predictive value. The selected laboratory indexes are objective, and easy to detect, which has low cost and was simple to calculate. This model could help clinicians effectively identify the risk of death in ALF and ACLF patients treated ALSS.
G3BP1 could effectively predict liver failure, which has great value to timely provide liver transplantation opportunity for patients who have failed for drug and ALSS treatment. The combination of G3BP1, AFP and LDH could accurately evaluate the disease condition and predict the clinical endpoint of patients. Based on the predicted role of G3BP1, it can effectively reduce the fatality rate and improve the prognosis of patients.