Etiology and Prognostic Analysis of Acute Liver Failure in Chinese Children

Overview

Research Background: Pediatric acute liver failure (PALF) refers to the sudden onset of severe liver injury in children without known chronic liver disease, leading to multi-system organ dysfunction, with a mortality rate as high as 50%-70%. The etiology of PALF is complex and varied, including infections, metabolic disorders, and toxins. In developed countries, it is often caused by drug and toxin exposure, while in developing countries, viral infections are the primary cause. Additionally, 30%-50% of PALF cases have an unknown etiology, which increases the difficulty of treatment. Current treatment options include medical management, artificial liver support, and liver transplantation. Liver transplantation is the only proven effective treatment, but issues such as organ shortages and the timing of transplantation affect treatment outcomes. Improving diagnostic capabilities for the etiology and exploring optimal treatment strategies are of significant importance in enhancing the clinical success rate of PALF management. Research Objective: To explore the etiology and prognostic factors of pediatric acute liver failure (PALF), analyze the relationship between different causes of PALF and prognosis, and the relationship between different treatment modalities and prognosis. This study aims to investigate the correlation between etiology, treatment methods, and outcomes, providing scientific evidence to improve the precision in diagnosis and treatment of PALF and to enhance decision-making and timing judgments for liver transplantation.

Data were organized and analyzed using WindRays statistical software. Quantitative data that followed a normal distribution were expressed as mean ± standard deviation (x ± s), and intergroup comparisons were performed using t-tests. For non-normally distributed quantitative data, median (interquartile range) \[M (P25, P75)\] was used, and intergroup comparisons were conducted using the Mann-Whitney U test. Qualitative data were presented as frequencies (n) and percentages (%), with intergroup comparisons made using χ² or Fisher's exact test. Multivariate logistic and Cox regression analyses were performed to adjust for potential confounding factors in the association between PALF etiology, treatment, and prognosis. Results were presented as odds ratios (ORs) with 95% confidence intervals (CIs). A multivariate-adjusted restricted cubic spline model was constructed to examine possible non-linear dose-response relationships between PALF etiology, treatment, and prognosis. Variables were selected using stepwise regression, lasso regression, and best subset regression. Risk scoring models were established using regression analysis, nomograms, scoring systems, decision trees, and machine learning algorithms. Sensitivity analyses were conducted using propensity score matching (PSM), regression analysis, and stratified analysis. The predictive efficacy of the model for PALF prognosis was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), with AUC ≥ 0.80 indicating good predictive value. Data with missing values less than 5% were deleted directly, and data with missing values between 5% and 20% were imputed using multiple imputation methods. Handling of lost to follow-up: Multiple contact information and emergency contacts were collected at the beginning of the study to maintain regular communication with the patients and their guardians to reduce the risk of lost follow-up. If follow-up failed, multiple attempts were made to contact participants via phone, email, or other methods. Once lost to follow-up, multiple imputation methods were used to estimate missing data, and sensitivity analyses were conducted (if possible).