A Risk Matrix Model for the Prediction of Intestinal Tuberculosis and Differentiation from Crohn’s Disease

Research Article

Austin J Gastroenterol. 2015; 2(5): 1052.

A Risk Matrix Model for the Prediction of Intestinal Tuberculosis and Differentiation from Crohn’s Disease

Larsson G1,2*, Shenoy KT³, Ramasubramanian R4, Thayumanavan L5, Balakumaran LK³, Cvancarova M6, Bjune GA7 and Moum BA8

¹Department of Medicine, Lovisenberg Diaconal Hospital, Norway

²Faculty of Medicine, University of Oslo, Norway

³Population Health and Research Institute, Medical College, India

4Thoothukudi Government Medical College, India

5Madurai Medical College, India

6Oslo University Hospital, Department of Oncology, Norway

7Institute of Health and Society, University of Oslo, Norway

8Department of Gastroenterology and Hepatology, Oslo University Hospital Ullevål, Norway

*Corresponding author: Larsson G, Unger-Vetlesen Institute, Department of Medicine, Lovisenberg Diaconal Hospital, NO-0440 Oslo, Norway

Received: July 14, 2015; Accepted: September 07, 2015; Published: September 09, 2015


Background: Intestinal Tuberculosis (ITB) can be difficult to distinguish from Crohn’s Disease (CD), especially in resource-limited areas. By combining independent risk factors measured at diagnosis, we aimed to construct a visual risk matrix model that could predict ITB.

Methods: Treatment naïve patients with ITB (n=38) and CD (n=37) were prospectively recruited from routine clinical practice in four Indian medical centres between October 2009 and July 2012.Records from case histories, clinical examination, endoscopy and histopathology of biopsies were collected prior to sampling for faecal- and serum calprotectin and C-reactive protein. Patients with malignancy, human immunodeficiency virus infection or age below 18 years were excluded from the study.

Risk factors associated with ITB and CD diagnoses were identified from univariate analysis and entered into multiple models. The probabilities of ITB diagnosis were calculated for selected levels of risk factors and the results were arranged in a prediction matrix.

Results: Four variables were significantly associated with ITB or CD diagnosis and were combined in the final matrix. Predictors of ITB were weight loss, mucosal nodularity and faecal calprotectin = 200μg/g; predictors of CD were multi-segment involvement and faecal calprotectin < 200μg/g. The probability of ITB at diagnosis ranged from 19 to 91% and for CD from 9 to 81%, depending of the level of the risk factors.

Conclusion: A visual matrix model in which faecal calprotectin is combined with clinical and endoscopic risk factors could become a rapid, easy and pointof- care tool to differentiate between ITB and CD in clinics with limited resources.

Keywords: Intestinal tuberculosis; Crohn’s disease; Diagnosis; Risk factors; Calprotectin


ATT: Anti-Tuberculous Chemotherapy; CD: Crohn’s Disease; CI: Confidence Interval; CRP: Creactive Protein; FC: Faecal Calprotectin; IQR: Inter-Quartile Range; ITB: Intestinal Tuberculosis; M.tb: Mycobacterium tuberculosis; POC: Point-Of-Care; rs: Spearman rank correlation coefficient; SC: Serum Calprotectin; SCORE: Systematic Coronary Risk Evaluation Chart; TB: Tuberculosis


The presentation and pathological findings in Intestinal Tuberculosis (ITB) may vary, be nonspecific and can easily be confounded with other gastrointestinal diseases [1,2]. In Tuberculosis (TB) endemic areas, Crohn’s disease (CD) is recurrently mistaken for ITB because of similar clinical, radiological, endoscopic and histopathological appearance and because of limited information on CD epidemiology [2-4]. Conversely, in Western countries where CD is more frequently seen, the lack of awareness of ITB and the difficulty of confirming tuberculosis (TB) by bacteriological methods can cause ITB to be mistaken for CD [5-6]. Consequently, prescribing immunosuppressants with the intention to treat CD in a patient undiagnosed with TB could be catastrophic. Endoscopic features favouring ITB diagnosis are mucosal nodularity, transverse ulcers and patolous ileocoecal valve, whereas longitudinal ulcers and multisegment involvement are typical of CD. Although granuloma with caseous necrosis is pathognomonic of ITB, the majority of patients do not present with this feature on histopathology [1-9].

“Gold standard” ITB diagnostics include expensive modalities, require highly qualified staff and hence, are not readily available in economically deprived TB endemic areas. Thus, as opposed to practice in developed countries where laboratory diagnosis of ITB can usually be achieved, clinicians in financially challenged countries are often left with empiric Antituberculous Chemotherapy (ATT) as the only available diagnostic tool [3-12]. Hence, there is a demand for new, sensitive, rapid, easy and affordable Point-Of-Care (POC) diagnostics.

Faecal Calprotectin (FC) is used as a biomarker in patients with CD to monitor relapse of disease and treatment response [13]. During the last decade, POC devices for rapid FC measurements have been developed [14]. These are easy to perform, less costly and less personnel dependent than conventional enzyme-linked immuno-sorbent assays. C-Reactive Protein (CRP) may be used to investigate systemic inflammation in CD, in which increasing levels indicate a more severe disease [15]. Serum Calprotectin (SC) may be used to monitor CD patients on anti-TNFa therapy, in whom falling levels have been associated with a positive treatment response [16]. Recently, we found high levels of FC, SC and CRP in patients with ITB [17]. Because immunological mechanisms and cytokine release differ slightly between CD and ITB [10], calprotectin levels could vary between the two diseases.

The Systematic Coronary Risk Evaluation Chart (SCORE) has become one of the most widely used risk models in clinical medicine [18]. The SCORE predicts the 10-year probability of cardiovascular mortality by combining well-known risk factors at diagnosis, which are arranged into a simple visual colour matrix wherein each box corresponds to a specific risk profile. Similar visual risk matrix models have later been developed to predict the risk of advanced disease in patients with CD [19]. Recently, by prospectively including newly diagnosed patients with ITB or CD from routine clinical practice in Southern India, we established demographic, clinical and endoscopic risk factors of the two diseases [2]. By using the SCORE system as an example, and by combining the above independent risk factors with calprotectin and CRP measurements, we aimed to construct a visual risk matrix model, which could predict the diagnosis of ITB and differentiate it from CD.

Materials and Methods

Newly diagnosed and treatment naïve ITB and CD patients were prospectively recruited by senior gastroenterologists at four South Indian medical centres in a consecutive manner from October 2009 to July 2012 (Appendix) [2]. Diagnostic criteria for ITB and CD were used according to internationally published guidelines [1,20,21]. Demographic, clinical, endoscopic and histologic features were recorded by site investigators in standardized electronic questionnaires and collected in a database. Patients were scheduled for follow-up clinical visits after two and six months of treatment. Clinical remission after ATT was regarded confirmatory for ITB diagnosis. Exclusion criteria were malignancy, age below 18 years and human immunodeficiency virus infection. Samples for biochemistry were obtained prior to initiation of treatment and faeces spot samples were collected prior to or minimum three days after endoscopy. CRP was analysed in blood serum by use of CRP turbilatex assay (Spinreact, Girona, Spain) and automated turbidometry (Beckman Coulter AU480, Cal, USA) at a local ISO certified laboratory. Faecal aliquots and separated blood serum vials were stored at -20°C until analysis. FC and SC were analysed with enzyme-linked immuno-sorbent assays using EK-CAL and MRP 8/14 kits respectively, according to the manufacturer’s recommendations (B�hlmann Laboratories AG, Basel, Switzerland).


All variables included in the analyses were recorded at diagnosis. Due to the skewed distribution of data and limited sample size, the continuous variables were described with medians and ranges and crude differences between groups were assessed with Mann-Whitney Wilcoxon tests. The categorical variables were listed as counts and percentages and differences between groups were evaluated with Chisquare or Fischer’s exact tests (when appropriate). The strength of association between continuous variables was assessed by calculating the Spearman rank correlation coefficient (rs).

As our aim was to quantify probabilities of ITB or CD based on observed or measured variables, we constructed a prediction matrix. First, the following risk factors were established from the results of our previous study [2]: weight loss (from onset of symptoms, qualitative); right inferior abdominal pain (on physical examination at diagnosis); multi-segment involvement (endoscopically apparent lesions in =3 of 6 pre-defined anatomic sub-divisions); and mucosal nodularity (round elevated nodules 2-6 mm in diameter detected upon endoscopy) (Table 1). All four variables were regarded as dichotomous. Then, univariate logistic regression models were fitted and variables which differed significantly between the groups (p < 0.05) were included into further analyses. Risk factors that were highly associated with each other were excluded to avoid multicollinearity. FC, SC and CRP were measured as continuous variables, followed by categorization into dichotomous variables. Several cut-off levels for FC were tested, based on both statistical properties and clinical recommendations [17-24]. Cut-off levels were evaluated separately as well as combined with the previously established predictors. The final cut-off level was chosen based on the most optimal separation between ITB and CD. In the next step, several logistic regression models were fitted. Due to the limited number of patients we included up to four risk factors in one model. The best model was chosen based on its prediction power and the Aikaike Information Criterion [25]. Finally, the odds computed with the selected logistic regression model were transformed into probabilities with 95% Confidence Intervals (CI) and the results were arranged in a risk matrix. The analyses were conducted with Predictive Analytics Software (Version 18.1; IBM, New York, USA).

Citation: Larsson G, Shenoy KT, Ramasubramanian R, Thayumanavan L, Balakumaran LK, Cvancarova M, et al. A Risk Matrix Model for the Prediction of Intestinal Tuberculosis and Differentiation from Crohn’s Disease. Austin J Gastroenterol. 2015; 2(5): 1052. ISSN : 2381-9219