Frontiers in medicine · 2026
Association between the albumin-bilirubin score and gallstone prevalence: evidence from NHANES and an independent hospital-based cohort.
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Sun Qi, Gao Xizhuang, Zhang Hanning, Song Yuanjian
Аннотация
OBJECTIVE: This study aimed to evaluate the association between the albumin-bilirubin (ALBI) score and gallstone prevalence, and to develop machine-learning-based classification models for identifying participants with gallstones.
METHODS: Data from NHANES 2017-March 2020, including adults with complete albumin, bilirubin, and gallstone-status information, were analyzed. Weighted multivariable logistic regression and restricted cubic spline models were used to examine the association between ALBI score and gallstone prevalence. Fifteen feature-selection and machine-learning combinations were implemented to build classification models, with external validation conducted in a hospital-based cohort. Model performance was assessed using ROC curves, calibration plots, and decision curve analysis.
RESULTS: A total of 5,090 participants were included, including 490 with gallstones (9.63%, crude proportion). In the fully adjusted survey-weighted logistic regression model, continuous ALBI score was positively associated with gallstone prevalence (OR = 2.89; 95% CI: 1.68-4.99; P = 0.002). Restricted cubic spline analyses, stratified by sex, revealed a significant nonlinear association in women (P = 0.004) but not in men (P = 0.261), with a positive dose-response relationship when the ALBI score exceeded -2.60. In the machine-learning analysis, the leading cross-validated AUC values ranged from 0.718 to 0.720 across the top configurations, with internal test-set AUC values of 0.703 to 0.708; these differences were within the expected variability of the cross-validation procedure and the models should be regarded as indistinguishable in discriminative performance. In the external validation cohort, the AUC values ranged from 0.701 to 0.710, but the confidence intervals around these estimates are wide (approximately 0.62-0.80) owing to the small number of events (n = 42), and the external results provide only preliminary support for generalizability.
CONCLUSION: In a national sample of US adults, a higher ALBI score remained associated with higher odds of gallstone disease after multivariable adjustment. Machine-learning models incorporating ALBI achieved moderate discrimination (cross-validated AUC up to 0.720; internal test AUC up to 0.708), with preliminary external-validation support that requires confirmation in larger cohorts.
METHODS: Data from NHANES 2017-March 2020, including adults with complete albumin, bilirubin, and gallstone-status information, were analyzed. Weighted multivariable logistic regression and restricted cubic spline models were used to examine the association between ALBI score and gallstone prevalence. Fifteen feature-selection and machine-learning combinations were implemented to build classification models, with external validation conducted in a hospital-based cohort. Model performance was assessed using ROC curves, calibration plots, and decision curve analysis.
RESULTS: A total of 5,090 participants were included, including 490 with gallstones (9.63%, crude proportion). In the fully adjusted survey-weighted logistic regression model, continuous ALBI score was positively associated with gallstone prevalence (OR = 2.89; 95% CI: 1.68-4.99; P = 0.002). Restricted cubic spline analyses, stratified by sex, revealed a significant nonlinear association in women (P = 0.004) but not in men (P = 0.261), with a positive dose-response relationship when the ALBI score exceeded -2.60. In the machine-learning analysis, the leading cross-validated AUC values ranged from 0.718 to 0.720 across the top configurations, with internal test-set AUC values of 0.703 to 0.708; these differences were within the expected variability of the cross-validation procedure and the models should be regarded as indistinguishable in discriminative performance. In the external validation cohort, the AUC values ranged from 0.701 to 0.710, but the confidence intervals around these estimates are wide (approximately 0.62-0.80) owing to the small number of events (n = 42), and the external results provide only preliminary support for generalizability.
CONCLUSION: In a national sample of US adults, a higher ALBI score remained associated with higher odds of gallstone disease after multivariable adjustment. Machine-learning models incorporating ALBI achieved moderate discrimination (cross-validated AUC up to 0.720; internal test AUC up to 0.708), with preliminary external-validation support that requires confirmation in larger cohorts.