Journal of Clinical Hepatology
This article investigates the clinical characteristics and predictive factors for achieving recompensation in patients with decompensated liver cirrhosis associated with autoimmune hepatitis (AIH). The study included 211 patients, of whom 61 achieved recompensation, and their baseline indicators and prognostic relationships were analyzed. The results showed that the recompensation group had significantly higher levels of ALT, AST, TBil, MELD score, Child-Pugh score, and SMA positivity rate than the persistent decompensation group, suggesting that more severe initial liver injury and obvious liver cell inflammation activity may actually predict a better treatment response. Multivariate Cox regression analysis found that baseline ALT elevation, SMA positivity, lower IgG levels, and hormone therapy were independent predictive factors for recompensation (P<0.05), with the HR for hormone therapy reaching 20.651, highlighting its key role. The study systematically reveals the predictive value of SMA positivity and IgG levels in recompensation and constructs a nomogram model (C-index=0.87), providing a quantitative prediction tool for clinical use. The innovation lies in: one, challenging traditional cognition and finding that patients with high MELD/Child-Pugh scores may still achieve recompensation; two, clearly identifying SMA positivity as an independent favorable prognostic factor; three, establishing an integrated recompensation prediction model with clinical, biochemical, and immunological indicators, which has important clinical guiding significance.
This study focuses on the clinical characteristics and predictive factors for recompensation in decompensated liver cirrhosis associated with autoimmune hepatitis (AIH), which has important clinical significance. Firstly, although liver cirrhosis caused by AIH often progresses to the decompensation stage, some patients can achieve recompensation and improve their prognosis after standardized treatment. However, there is currently a lack of effective tools for predicting recompensation. This study clarifies the independent predictive factors such as hormone therapy, baseline ALT, TBil, and IgG levels through the construction of a nomogram prediction model, providing a basis for individualized treatment. In particular, hormone therapy has been confirmed as a strong independent predictive factor for recompensation (HR=20.651, P<0.001), supporting the feasibility of cautiously using hormone therapy in early decompensation patients under strict monitoring. In addition, the study finds that patients with more severe liver injury (such as higher MELD/Child-Pugh scores) may still achieve recompensation after effective immunosuppressive treatment, challenging the traditional cognition that late-stage liver disease is irreversible. This model helps identify high-probability recompensation populations, optimize clinical decision-making, and improve long-term survival rates.
Objective: To investigate the clinical features and outcomes of recompensation in patients with autoimmune hepatitis (AIH) - related de…
