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

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.

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.

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.

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.