Meta-Analytic Bayesian Model For Differentiating Intestinal Tuberculosis from Crohn's Disease

Abstract

Objectives: Distinguishing intestinal tuberculosis (ITB) from Crohn's disease (CD) is difficult, although studies have reported clinical, endoscopic, imaging, and laboratory findings that help to differentiate these two diseases. We aimed to produce estimates of the predictive power of these findings and construct a comprehensive model to predict the probability of ITB vs. CD.

Methods: A systematic literature search for studies differentiating ITB from CD was conducted in MEDLINE, PUBMED, and EMBASE from inception until September 2015. Fifty-five distinct meta-analyses were performed to estimate the odds ratio of each predictive finding. Estimates with a significant difference between CD and ITB and low to moderate heterogeneity (I²<50%) were incorporated into a Bayesian prediction model incorporating the local pretest probability.

Results: Thirty-eight studies comprising 2,117 CD and 1,589 ITB patients were included in the analyses. Findings in the model that significantly favored CD included male gender, hematochezia, perianal disease, intestinal obstruction, and extraintestinal manifestations; endoscopic findings of longitudinal ulcers, cobblestone appearance, luminal stricture, mucosal bridge, and rectal involvement; pathological findings of focally enhanced colitis; and computed tomographic enterography (CTE) findings of asymmetrical wall thickening, intestinal wall stratification, comb sign, and fibrofatty proliferation. Findings that significantly favored ITB included fever, night sweats, lung involvement, and ascites; endoscopic findings of transverse ulcers, patulous ileocecal valve, and cecal involvement; pathological findings of confluent or submucosal granulomas, lymphocyte cuffing, and ulcers lined by histiocytes; a CTE finding of short segmental involvement; and a positive interferon-γ release assay. The model was validated by gender, clinical manifestations, endoscopic, and pathological findings in 49 patients (27 CD, 22 ITB). The sensitivity, specificity, and accuracy for diagnosis of ITB were 90.9%, 92.6%, and 91.8%, respectively.

Conclusions: A Bayesian model based on the meta-analytic results is presented to estimate the probability of ITB and CD calibrated to local prevalence. This model can be applied to patients using a publicly available web application.

Figure 1

Flow diagram of study selection. CD, Crohn's disease; IGRA, interferon-γ-releasing assay; ITB, intestinal tuberculosis.

Figure 2

Forest plot presenting odds ratios, 95% confidence intervals, and I² of all predictor variables. “Clinical manifestation of intestinal obstruction” and “CT enterography finding of asymmetrical wall thickening” became significant in sensitivity analyses using only the studies with low bias. ASCA, anti-Saccharomyces cerevisiae antibody; CT, computed tomography; IC, ileocecal valve; IGRA, interferon-γ-releasing assay.

Figure 3

Receiver operating characteristic curve for three models for differentiating intestinal tuberculosis from Crohn's disease based on gender, clinical manifestations, and endoscopic findings. Model 1 includes the significant parameters with low heterogeneity (I²<50) and likelihood ratio (LR) ≥2, Model 2 includes the significant parameters with low heterogeneity (I²<50), and Model 3 includes the significant parameters, regardless of heterogeneity and LR. AUC, area under the curve.

Figure 4

Receiver operating characteristic curve of the final Bayesian model for differentiating intestinal tuberculosis from Crohn's disease based on gender, clinical manifestation, endoscopic, and pathological findings.