Exploring the prognostic value of serum albumin in critically ill cancer patients: an observational cohort study utilizing machine learning from large intensive care unit databases.

Objectives: Patients with malignant tumors are admitted to the ICU for diverse reasons. However, the clinical utility of serum albumin as a prognostic biomarker remains unclear. Materials and Methods: Patients with malignant tumors were screened from the Medical Information Mart for Intensive Care IV (MIMIC-IV, v3.1). This study employed Kaplan-Meier curves, Cox proportional-hazards models, restricted cubic splines (RCS), receiver operating characteristic (ROC) curves, and subgroup analyses to evaluate serum albumin associated with all-cause mortality. For mortality-risk prediction, we applied machine-learning algorithms and used SHapley Additive exPlanations (SHAP) to identify the most influential predictors among critically ill cancer patients. Results: A total of 1,739 patients with malignancy were included. The Kaplan-Meier curves showed significantly higher all-cause mortality in the hypoalbuminemia group (serum albumin < 30 g/L) than in the control group at each time point. Multivariable Cox regression models confirmed that hypoalbuminemia was independently associated with 28-day mortality (HR 1.74; 95% CI 1.34-2.27). Serum albumin exhibited a superior predictive capacity for long-term mortality (90-day and 1-year), with AUCs of 0.676 and 0.664, respectively, notably higher than those of the SOFA score (0.617 and 0.579). External validation using data from Tianjin Cancer Hospital yielded consistent results. The Machine learning model identified BUN, serum albumin, respiratory rate, heart rate, and SOFA as the top predictors for 14- and 28- day mortality. Conclusion: Hypoalbuminemia was independently associated with increased all-cause mortality. Serum albumin measured at ICU admission serves as a prognostic biomarker for identifying high-risk cancer patient groups.