Simplifying Diagnosis of Bile Acid Diarrhea With Clinical and Biochemical Measurements on Blood and Single Stool Sample.

IF 11.6 1区医学 Q1 GASTROENTEROLOGY & HEPATOLOGY

Clinical Gastroenterology and Hepatology Pub Date : 2025-05-14 DOI:10.1016/j.cgh.2025.02.031

Saam Dilmaghani, Camille Lupianez-Merly, Joelle BouSaba, Priya Vijayvargiya, Irene Busciglio, Monique Ferber, Paula Carlson, Leslie J Donato, Michael Camilleri

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引用次数: 0

Abstract

Background and aims: Diagnosis of bile acid diarrhea (BAD) has been based on 48-hour fecal BA excretion; serum 7αC4 (C4) has been used to screen for BAD. Optimal diagnostic cutoffs for C4 and biochemical measurements in a single stool sample are unknown. We sought to examine the relationship between total BA concentration (TBAc) and percent primary BA (%PBA) in a single stool sample and serum C4 in patients with and without BAD and explore performance characteristics of stool consistency and biochemical (serum C4 and single-stool BA) parameters for diagnosis of BAD compared with gold standard 48-hour fecal BA.

Methods: Based on data from patients with BAD, irritable bowel syndrome with diarrhea (IBS-D), and healthy control subjects, we assessed correlations among stool and serum measurements. Machine learning models (based on data from 30 patients with BAD, 8 patients with IBS-D, and 26 healthy control subjects) were trained on 25 bootstrapped random samples, the superior model was identified, and optimal cutoffs of biological measurements to diagnose BAD were summarized.

Results: There were correlations between serum C4 and %PBA (R = 0.284, P < .001), and between %PBA and TBAc (R = 0.49, P < .001). Using a %PBA of 1.05% (25th percentile in BAD), the %PBA distinguished BAD from IBS-D (odds ratio, 3.06; 95% confidence interval, 1.35-7.46; P = .01). The multivariate logistic regression model had superior balance of variance and bias. Optimal cutoffs for predicting BAD using logistic regression were 4.5% PBA (P = .023) and 1.88 μmol/g TBAc (P = .016). Serum C4 >24 ng/mL and PBA >4.6% individually had 57% and 75.8% positive predictive value, respectively, but together had a 90.1% positive predictive value. Stool consistency was less informative.

Conclusions: New diagnostic cutoffs based on serum C4 and single-stool TBAc and % PBA provide potential alternatives for diagnosing BAD. Further validation is warranted.

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应用血液及单次粪便临床生化指标简化胆汁酸性腹泻的诊断。

背景：胆汁酸（BA）腹泻（BAD）的诊断基于48小时的粪便BA排泄；血清7αC4 （C4）用于BAD筛查。单个粪便样本中C4和生化测量的最佳诊断截止值尚不清楚。目的：探讨BAD患者和非BAD患者单次粪便中总BA浓度（TBAc）、原发BA百分比（%PBA）与血清C4的关系；与黄金标准48小时粪便BA相比，探讨粪便一致性和生化指标（血清C4和单大便BA）诊断BAD的性能特点。方法：基于BAD患者、IBS-D患者和健康对照者的数据，我们评估了粪便和血清测量之间的相关性。机器学习模型（基于30例BAD患者、8例IBS-D患者和26例健康患者的数据）在25个自举随机样本上进行训练，确定了最佳模型，并总结了诊断BAD的生物测量的最佳截止值。结果：血清C4与%PBA有相关性（R=0.284， BAD的Pth百分位数），%PBA区分BAD和IBS-D （OR 3.06 [95% CI: 1.35 ~ 7.46]， P=0.01）。多元logistic回归（LR）模型具有较好的方差平衡和偏倚性。利用LR预测BAD的最佳截止值为4.5% PBA （P=0.023）和1.88μmol/g TBAc （P=0.016）。血清C4 >24ng/mL和PBA>4.6% PPV分别为57%和75.8%，但合并PPV为90.1%。粪便稠度信息较少。结论：基于血清C4和单便TBAc和% PBA的新的诊断截止值为BAD的诊断提供了潜在的替代方法。进一步的验证是必要的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Clinical Gastroenterology and Hepatology 医学-胃肠肝病学

CiteScore

16.90

自引率

4.80%

发文量

903

审稿时长

22 days

期刊介绍： Clinical Gastroenterology and Hepatology (CGH) is dedicated to offering readers a comprehensive exploration of themes in clinical gastroenterology and hepatology. Encompassing diagnostic, endoscopic, interventional, and therapeutic advances, the journal covers areas such as cancer, inflammatory diseases, functional gastrointestinal disorders, nutrition, absorption, and secretion. As a peer-reviewed publication, CGH features original articles and scholarly reviews, ensuring immediate relevance to the practice of gastroenterology and hepatology. Beyond peer-reviewed content, the journal includes invited key reviews and articles on endoscopy/practice-based technology, health-care policy, and practice management. Multimedia elements, including images, video abstracts, and podcasts, enhance the reader's experience. CGH remains actively engaged with its audience through updates and commentary shared via platforms such as Facebook and Twitter.