Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association · 2026
Predictive value of the albumin-bilirubin score for all-cause mortality in individuals with stroke: machine learning prediction models with SHAP-based interpretation.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Zhao Haoming, Chen Ruixue, Zhang Jing, Xu Ming, Zhao Shangshang, Ai Kun
Аннотация
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 1838 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.
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 1838 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.