A Nomogram for Diagnosing Bile Acid Diarrhea Based on Postprandial Fecal Bile Acids and Serum FGF19

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Abstract

Background Bile acid diarrhea is a common cause of chronic diarrhea, but it is underdiagnosed clinically. Due to the lack of convenient diagnostic methods, the disease is rarely diagnosed in patients in China. The aim of this study is to explore the diagnostic value of postprandial fecal bile acid(FBA) detection for bile acid diarrhea, and to establish a comprehensive diagnostic model. Methods The study was conducted from January to December 2024. A total of 115 healthy volunteers and patients with IBS-D were enrolled. The total population was set as the training set, and 30% of the samples were randomly split as the internal validation set. Univariate and multivariate Logistic regression were used to identify independent predictors, based on which a nomogram model was constructed. Receiver operating characteristic (ROC) curve analysis, calibration curve analysis and decision curve analysis were used to comprehensively evaluate the discrimination ability, calibration degree and clinical net benefit of the nomogram. Results In the training set , Logistic regression analysis showed that fecal bile acids after a fatty meal, serum FGF19, and Bristol stool type were all independent predictors of BAD. The area under the curve (AUC) of the prediction model in the training set was 0.97 (95%CI: 0.94-1.00), and the AUC in the internal validation set was 0.89 (95%CI: 0.80–0.99). The calibration curve of the nomogram model showed that there was good agreement between the predicted probability and the actual probability. Clinical decision curve analysis further confirmed that the nomogram had good clinical application value. Conclusions The level of fecal bile acids(FBA) after a fat meal was significantly higher in patients with BAD than in those without BAD (P < 0.001). The nomogram model based on fecal bile acids after a fatty meal, serum FGF19 and Bristol stool type shows good clinical application potential in the diagnosis of BAD.

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