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TRUVACE RECORD VERSION
record: TRV-2026-1266
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-03T06:56:56.483775Z
status: published
lens: g_space
sector: health
headline: Predictive Value of the Albumin-Bilirubin Score for All-Cause Mortality in Individuals with Stroke: Machine Learning Prediction Models with SHAP-Based Interpretation
dek: 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…
gain_title: In 1,838 stroke survivors, machine learning models incorporating the albumin-bilirubin score predicted all-cause mortality and enabled risk stratification for individualized management.
problem_title: (none)
trace_subject: (none)
gain_reading: In 1,838 stroke survivors, machine learning models incorporating the albumin-bilirubin score predicted all-cause mortality and enabled risk stratification for individualized management.
gain_evidence: In stroke survivors, the ALBI score independently predicts ACM and may aid risk stratification and individualized management. | Among 1,838 stroke survivors (median follow-up: 78 months)
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers evaluated the albumin-bilirubin score as a predictor of all-cause mortality in 1,838 stroke survivors from NHANES 1999-2018, with 78 months median follow-up. They selected variables with Boruta, tested associations with Kaplan-Meier and Cox models, and built five machine learning models with 10-fold cross-validation assessed by C-index, Brier score, time-dependent AUC, and calibration, interpreting the best model with SHAP.

The finding that ALBI independently predicts mortality could help clinicians stratify long-term risk in stroke survivors and tailor follow-up, but the supplied text does not report model performance numbers, external validation, or clinical implementation outcomes, leaving uncertainty about generalizability and bedside utility as of the October 2026 publication date.
limitation: 
tag: Evidence-backed gain
key_points: Study analyzed NHANES 1999-2018 data for 1,838 stroke survivors with median follow-up of 78 months. | Boruta algorithm selected variables including age, sex, cardiovascular disease, lymphocyte count, uric acid, and creatinine alongside ALBI. | Five ML models were built with 10-fold cross-validation and evaluated by C-index, integrated Brier score, time-dependent AUC, and calibration curves. | SHapley Additive exPlanations (SHAP) was used to interpret the best-performing model.
rundown: The analysis used National Health and Nutrition Examination Survey data from 1999-2018 and applied the Boruta algorithm to select predictors including marital status, body mass index, aspartate/alanine aminotransferase, polyunsaturated fatty acids, and basophil percentage.

Associations were examined with Kaplan-Meier curves, Cox regression, and restricted cubic splines to assess nonlinearity between ALBI and mortality risk, with log-rank testing showing survival differences across quartiles.
sources:
- peer_reviewed | Journal of Stroke and Cerebrovascular Diseases | https://doi.org/10.1016/j.jstrokecerebrovasdis.2026.108759 | 2026-10-01
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