Predictive Value of the Albumin-Bilirubin Score for All-Cause Mortality in Individuals with Stroke: Machine Learning Prediction Models with SHAP-Based Interpretation.

BACKGROUND: The albumin-bilirubin (ALBI) score, an objective liver function and prognostic indicator, has shown predictive value in various diseases, but its long-term prognostic significance in stroke survivors remains unclear. OBJECTIVE: To evaluate the ALBI score's predictive value for all-cause mortality (ACM) in individuals with stroke. METHODS: Data from the National Health and Nutrition Examination Survey (1999-2018) were analyzed. The Boruta algorithm selected key variables associated with ACM. Kaplan-Meier curves, Cox regression, and restricted cubic splines examined associations and nonlinearity between ALBI and ACM risk. Five machine learning (ML) models were built with 10-fold cross-validation; performance was assessed via C-index, integrated Brier score, time-dependent AUC, and calibration curves. SHapley Additive exPlanations (SHAP) interpreted the best model. RESULTS: Among 1,838 stroke survivors (median follow-up: 78 months), Boruta-selected variables included age, sex, race, marital status, body mass index, cardiovascular disease, lymphocyte count, aspartate/alanine aminotransferase, uric acid (UA), polyunsaturated fatty acids, basophil percentage, and creatinine. Kaplan-Meier curves showed significant survival differences across ALBI quartiles (log-rank P<0.001), with higher ALBI linked to lower survival. Cox regression confirmed ALBI as an independent risk factor (Model 3: HR=2.297, 95% CI: 1.662-3.174, P<0.001). Among ML models, gradient boosting machine performed best (C-index=0.749, 95% CI: 0.733-0.764), with 1-, 3-, and 5-year td-AUCs of 0.820, 0.798, and 0.771, respectively. SHAP identified age as the top predictor, followed by UA, creatinine, ALBI, marital status, and others. CONCLUSION: In stroke survivors, the ALBI score independently predicts ACM and may aid risk stratification and individualized management.