Machine learning prediction model for 28-day mortality among hepatic failure patients complicated by acute respiratory distress syndrome.

Objectives: Hepatic failure is a common and severe condition among intensive care unit (ICU) patients. Its complication with acute respiratory distress syndrome (ARDS) is consistently associated with poor clinical outcomes and a significant disease burden. Early identification of high-risk patients is essential for improving clinical outcomes. This study aimed to develop and validate a machine learning (ML) model to predict 28-day mortality in ICU patients with hepatic failure complicated by ARDS. Methods: Data were extracted from the Medical Information Mart for Intensive Care IV database, focusing on patients with hepatic failure complicated by ARDS. The cohort was randomly divided into an 80% training set and a 20% validation set. Six ML algorithms were applied to analyze clinical characteristics. Shapley Additive Explanations (SHAP) were used to interpret the optimal model. Results: A total of 884 patients with hepatic failure and concurrent ARDS were included, with a 28-day mortality rate of 47.4%. Random forest models demonstrated superior performance, achieving an area under the curve of 0.823 (95% confidence interval: 0763-0.883) in the validation set. SHAP analysis identified eight clinically significant predictors of mortality, ranked by importance: age, neutrophil count, pulse transit time, direct bilirubin, heart rate, fibrinogen, serum sodium concentration, and prothrombin time. SHAP enhanced model interpretability, supporting clinical decision-making and potentially improving patient outcomes. Conclusions: ML approaches exhibited promising performance in predicting 28-day mortality among hepatic failure patients complicated by ARDS. These models may aid in guiding treatment decisions for patients with hepatic failure patients.