ArticleDigestive diseases and sciences2019
Analysis of Phenotypic Variables and Differentiation Between Untypical Crohn's Disease and Untypical Intestinal Tuberculosis.
Article in Digestive diseases and sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.
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4 citing papers in PubMed, 6 citations in OpenAlex.
- A novel multidisciplinary machine learning approach based on clinical, imaging, colonoscopy, and pathology features for distinguishing intestinal tuberculosis from Crohn's disease.Abdominal radiology (New York) · 2024Article
- Applying logistic LASSO regression for the diagnosis of atypical Crohn's disease.Scientific reports · 2022Article
- Differentiation of intestinal tuberculosis and Crohn's disease through an explainable machine learning method.Scientific reports · 2022Article
- Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe differentiation between untypical intestinal tuberculosis (UITB) and untypical Crohn's disease (UCD) is a challenge.
aimsTo analyze phenotypic variables and propose a novel prediction model for differential diagnosis of two conditions.
methodsA total of 192 patients were prospectively enrolled. The clinical, laboratory, endoscopic, and radiological features were investigated and subjected to univariable and multivariable analyses. The final prediction model for differentiation between UCD and UITB was developed by logistic regression analysis and Fisher discriminant analysis on the training set. The same discriminant function was tested on the validation set.
resultsTwenty-five candidates were selected from 52 phenotypic variables of typical Crohn's disease (TCD), UCD, and UITB patients. UCD's variables overlapped with both TCD and UITB. The percentages of tuberculosis history, positive PPD, and positive T-SPOT result in UCD were all significantly higher than that in TCD (11.6% vs. 0.0%, 27.9% vs. 0.0%, 25.6% vs. 4.5%, respectively, P < 0.05). The regression equations and Fisher discriminant function for discrimination between UCD and UITB were developed. In the training data, the area under the receiver operating characteristic of equations was 0.834, 0.69, and 0.648 in the clinical-laboratory, endoscopic, and radiological model, respectively. The accuracy of Fisher discriminant function for discrimination was 86% in UCD and 73% in UITB in the validation data.
conclusionsPhenotypes of UCD patients in TB-endemic countries may be associated with TB infection history. Fisher discriminant analysis is a good choice to differentiate UCD from UITB, which is worthy of verification in clinical practice.
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