Sometimes in clinical practice, it is a great challenge to distinguish Crohn's disease (CD) and intestinal tuberculosis (ITB), we conducted this study to identify simple and useful algorithm for distinguishing them. We retrospectively reviewed the medical history of the patients who were diagnosed as ITB or CD. We firstly identified ITB patients, and then the patients diagnosed with CD were matched by age, sex, and admission time in a 1:1 ratio. Patients who admitted between May 1, 2013 and April 30, 2019 were regarded as training cohort, and patients admitted between May 1, 2019 and May 1, 2020 were regarded as validation cohort. We used multivariate analysis to identify the potential variables, and then we used R package rpart to build the classification and regression tree (CART), and validated the newly developed model. In total, the training cohort included 84 ITB and 84 CD patients, the validation cohort included 22 ITB and 22 CD patients. Multivariate analysis showed that, positive interferon-gamma release assays (IGRAs), ≥ 4 segments involved, longitudinal ulcer, circular ulcer, and aphthous ulcer were confirmed as independent discriminating factors. Using these parameters to build the CART model made an overall accuracy rate was 88.64%, with sensitivity, specificity, NPV, and PPV being 90.91%, 86.36%, 90.48% and 86.96%, respectively. We developed a simple and novel algorithm model covering laboratory, imaging, and endoscopy parameters with CART to differentiate ITB and CD with good accuracy. Positive IGRAs and circular ulcer were suggestive of ITB, while ≥ 4 segments involved, longitudinal ulcer, and aphthous ulcer were suggestive of CD.

中文翻译：

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克罗恩病和肠结核鉴别诊断新算法模型的开发和验证：结合实验室、影像学和内镜特征

有时在临床实践中，区分克罗恩病（CD）和肠结核（ITB）是一个很大的挑战，我们进行了这项研究，以找出简单而有用的算法来区分它们。我们回顾性地回顾了被诊断为 ITB 或 CD 的患者的病史。我们首先确定 ITB 患者，然后将诊断为 CD 的患者按年龄、性别和入院时间按 1:1 的比例进行匹配。2013年5月1日至2019年4月30日期间入院的患者为训练队列，2019年5月1日至2020年5月1日期间入院的患者为验证队列。我们使用多变量分析来识别潜在变量，然后我们使用 R 包 rpart 构建分类和回归树（CART），并验证新开发的模型。总共，培训队列包括 84 名 ITB 和 84 名 CD 患者，验证队列包括 22 名 ITB 和 22 名 CD 患者。多因素分析显示，阳性干扰素-γ释放试验（IGRAs）、≥4个节段受累、纵向溃疡、圆形溃疡和口疮性溃疡被确认为独立的判别因素。使用这些参数构建CART模型，总体准确率为88.64%，敏感性、特异性、NPV和PPV分别为90.91%、86.36%、90.48%和86.96%。我们使用 CART 开发了一个简单而新颖的算法模型，涵盖实验室、成像和内窥镜参数，以高精度区分 ITB 和 CD。IGRA 阳性和圆形溃疡提示 ITB，而涉及≥ 4 个节段、纵向溃疡和阿弗他溃疡提示 CD。