Validation of models using basic parameters to differentiate intestinal tuberculosis from Crohn's disease: A multicenter study from Asia.

BACKGROUND: Data on external validation of models developed to distinguish Crohn's disease (CD) from intestinal tuberculosis (ITB) are limited. This study aimed to validate and compare models using clinical, endoscopic, and/or pathology findings to differentiate CD from ITB.
METHODS: Data from newly diagnosed ITB and CD patients were retrospectively collected from 5 centers located in Thailand or Hong Kong. The data was applied to Lee, et al., Makharia, et al., Jung, et al., and Limsrivilai, et al. model.
RESULTS: Five hundred and thirty patients (383 CD, 147 ITB) with clinical and endoscopic data were included. The area under the receiver operating characteristic curve (AUROC) of Limsrivilai's clinical-endoscopy (CE) model was 0.853, which was comparable to the value of 0.862 in Jung's model (p = 0.52). Both models performed significantly better than Lee's endoscopy model (AUROC: 0.713, p<0.01). Pathology was available for review in 199 patients (116 CD, 83 ITB). When 3 modalities were combined, Limsrivilai's clinical-endoscopy-pathology (CEP) model performed significantly better (AUROC: 0.887) than Limsrivilai's CE model (AUROC: 0.824, p = 0.01), Jung's model (AUROC: 0.798, p = 0.005) and Makharia's model (AUROC: 0.637, p<0.01). In 83 ITB patients, the rate of misdiagnosis with CD when used the proposed cutoff values in each original study was 9.6% for Limsrivilai's CEP, 15.7% for Jung's, and 66.3% for Makharia's model.
CONCLUSIONS: Scoring systems with more parameters and diagnostic modalities performed better; however, application to clinical practice is still limited owing to high rate of misdiagnosis of ITB as CD. Models integrating more modalities such as imaging and serological tests are needed.

Introduction

The incidence of inflammatory bowel disease or Crohn’s disease (CD) has been increasing in Asia over the last few decades [1]. Moreover, differentiation of CD from intestinal tuberculosis (ITB) is difficult due to the low sensitivities of currently available diagnostic tests. The 5.3–37.5% sensitivity of acid fast bacilli (AFB) specimen staining [2-4], the 23%-46% sensitivity of mycobacterial culture [5, 6], and the 36.4–67.9% sensitivity of tissue polymerase chain reaction (PCR) [4, 5, 7–9] are all too low to confidently distinguish between these two conditions and exclude a diagnosis of ITB. The Asia-Pacific guideline recommends antituberculous therapy (ATT) for 8–12 weeks in patients with diagnostic uncertainty due to the of risk of disseminated tuberculosis if patients with ITB are misdiagnosed CD and they are prescribed immunosuppressive therapy [10]. However, treatment with ATT has many side effects and may delay treatment in patients with CD, and this may cause severe relapse and the development of complications [11]. In response, many studies were conducted to identify and classify characteristics that can help to distinguish between these two diseases. Those studies found that some clinical, endoscopy, pathology, radiology, and serology findings can help to improve diagnostic accuracy in these patients [2, 5, 7, 12–21]. However, no single diagnostic parameter can distinguish between CD and ITB. As a result, many models were developed that include various factors and modalities, and many of those models have been reported to have high performance. However, the number of studies performed to externally validate those models are limited.

Accordingly, the aim of this study was to validate and compare among scoring systems that use clinical, endoscopic, and/or pathology findings to differentiate CD from ITB in the same large multicenter cohort of patients. In this study, we focused on the models that integrate only basic parameters because the diagnostic methods and variables that are included in these models are widely available, which means that these diagnostic models can be used by physicians that work in limited-resource settings.

Materials and methods

Study design and data source

This multicenter retrospective cohort study included adult patients aged >18 years with a diagnosis of CD or ITB who were diagnosed and treated at four major GI centers in Thailand (Siriraj Hospital, King Chulalongkorn Memorial Hospital, Phramongkutklao Hospital, or Songklanakarin Hospital) from September 2004 to November 2018 and one major GI center in Hong Kong (Prince of Wales Hospital, The Chinese University of Hong Kong) from January 2000 to November 2016. The data was collected in all centers between March 2017 and April 2019. The protocol for this study was approved by the institutional review board (IRB) of all participating centers including 1)The Joint Chinese University of Hong Kong—New Territories East Cluster Clinical Research Ethics Committee, 2)Siriraj Institutional Review Board, 3) The Institutional Review Board, Faculty of Medicine, Chulalongkorn University, 4) Institutional Review Board of Royal Thai Army Medical Department, and 5)Human Research Ethics Committee of Prince of Songkla University. The requirement to obtain written informed consent was waived due to the retrospective nature of this study.

Crohn’s disease was diagnosed based on clinical, endoscopic, and pathology findings with clinical response to CD treatment which was defined based on physicians’ notes in medical records that there was improvement of abdominal symptoms (such as abdominal pain, diarrhea, and bloody stool) and general well-being, in combination with the improvement of inflammatory biomarkers. The duration of at least six months of follow up was needed to confirm the clinical response. The criteria for diagnosis of ITB included any of the following: (i) presence of acid-fast bacilli or caseating granuloma in pathology specimens, (ii) tissue culture growing mycobacterial tuberculosis, (iii) presence of proven tuberculosis elsewhere in the body, or (iv) clinical and endoscopic response to ATT treatment without subsequent recurrence. A follow up endoscopy was performed at 2 to 6 months after initiation of treatment.

The clinical manifestations were manually reviewed in medical records. The pictures of endoscopic findings were reviewed by gastroenterologists of each center. The available pathologic slides were sent and reviewed by two pathologists from two centers. The gastroenterologists and pathologists were blinded to final diagnosis and any other predictive data. For pathological specimen slides, if the stain had faded out causing unclear images, repeat staining was performed on the slides.

Model validation

Although several models which included clinical, endoscopy, pathology, cross-sectional imaging, and serology have been reported [22], only the models using clinical, endoscopic, and pathologic findings were included in this study. The models using only basic parameters have an advantage that these are standard investigations for the diagnosis of Crohn’s disease or ITB in all countries including resource limited countries hence additional cost for investigations including interferon gamma release assay or cross sectional imagings are not required. The models that were validated in this study include the model by Lee, et al., which includes 8 endoscopic findings [12]; the model by Makharia, et al., which includes 4 parameters (2 clinical, 1 endoscopic, and 1 pathology) [7]; the model by Jung, et al., which includes 7 parameters (4 clinical and 3 endoscopic) [23]; and, and the model by Limsrivilai, et al., which includes 22 parameters (9 clinical, 8 endoscopic, and 5 pathology). A summary of each model is shown in Table 1. There were other two models which integrated clinical, endoscopic, and pathologic findings, but were not included in this study. The model by Yu et al. [14] integrated the presence of granuloma without specific characteristics, making it difficult to differentiate whether the granuloma was related to CD or ITB. The model by Li et al. [13] was also not included because of two reasons. First, this model integrated the presence of rodent-like ulcer without specific detail, and thus the interpretation was unclear. The other reason was the use of tuberculin skin test (TST) as one of the indicators for the diagnosis of ITB. Many patients in this cohort received BCG vaccine at birth, and it could cause a false positive TST. Therefore, physicians generally did not do TST in this setting. None of the patients in this cohort had this data available.

Table 1

Summary of the models included in this study.

Authors	Country	Study design	Model
Lee YJ, et al. Endoscopy 2006	Korea	ProspectiveCD 44, ITB 44	Favors CD (+1/each): longitudinal ulcer, aphthous ulcer, cobblestone appearance, anorectal involvementFavors ITB (-1/each): transverse ulcer, scars or pseudopolyps, a patulous ileocecal valve, involvement <4 segmentsFinal score: (+) Crohn’s disease, 0: indeterminate, (-) ITB
Makharia, et al. Am J Gastroenterol 2010	India	ProspectiveCD 53, ITB 53 (training)CD 20, ITB 20 (validation)	+ 2.3 × weight loss– 2.1 × blood in stool– 2.5 × involvement of sigmoid colon– 2.1 × focally enhanced colitis + 7
Jung Y, et al. Am J Gastroenterol 2016	Korea	RetrospectiveCD 79, ITB 49 for trainingCD 79, ITB 49 for validation	1/[1+e−(−4.423 + 0.037*age + 2.226*sex − 2.203*diarrhea + 2.345*transvers_ulcer − 1.911*longitudinal_ulcer − 2.123*sigmoid_colon + 5.606*pul_tbc)]
Limsrivilai, et al. Am J Gastroenterol 2017	Meta-analysis of 38 studies comprising 2,117 CD and 1,589 ITB		Model integrating 9 clinical, 8 endoscopic, 5 pathology, 5 CTE, and 1 IGRAbit.ly/ITBvsCD

Abbreviations: CD, Crohn’s disease; ITB, intestinal tuberculosis; CTE, computed tomography enterography; IGA, interferon gamma release assay

In this study, the model by Limsrivilai, et al. was alternatively named the ITBvsCD model, and it can be assessed via this link bit.ly/ITBvsCD. To compare the effectiveness of various combinations of parameters within the ITBvsCD model, we separated the diagnostic parameters, as follows: the clinical model (ITBvsCD-C), the endoscopy model (ITBvsCD-E), the clinical and endoscopy model (ITBvsCD-CE), and the clinical, endoscopy, and pathology model (ITBvsCD-CEP). The relative prevalence of ITB was required for this model calculation and the value of 0.28 was used. This relative prevalence of ITB was based on the total number of patients in this cohort, which included 427 CD and 163 ITB.

Data from patients who had available clinical and endoscopy data were applied to the Lee, et al., Jung, et al., and ITBvsCD (ITBvsCD-C, ITBvsCD-E, and ITBvsCD-CE) models. Data from patients who had clinical, endoscopy, and pathology data were applied to the ITBvsCD model (ITBvsCD-C, ITBvsCD-E, ITBvsCD-CE, and ITBvsCD-CEP), and the models from Jung, et al. and Makharia, et al. The performance of each model was assessed and compared to other models.

Statistical analysis

Descriptive statistics were used to summarize patient characteristics. Continuous variables are expressed as median and range or mean ± standard deviation, and categorical variables are presented as number of subjects and percentage. Standard two-group comparison methods were used, including independent t-test or Wilcoxon rank-sum test for continuous data, and chi-square test or Fisher’s exact test for categorical data. The performance of each model was assessed by area under the receiver operating characteristic curve (AUROC). DeLong test was employed to compare the performance of each model. The distribution of calculated probability of ITB from the ITBvsCD model is shown in box plots. The diagnostic performance of each model based on the proposed cutoff values from their original studies are reported as sensitivity, specificity, accuracy, positive and negative likelihood ratio (LR), positive predictive value (PPV), negative predictive value (NPV), and false-positive and false-negative rate. A two-tailed p-value of <0.05 was considered significant for all analyses. All analyses were performed using SAS 9.4 (SAS Institute Inc., North Carolina, US) and R program version 3.2 (R Foundation for Statistical Computing, Vienna, Austria). Package OptimalCutpoints [24], pROC [25], epiR [26], and ggplot2 [27] were used.

Results

Five hundred and ninety patients (427 CD and 163 ITB) were identified. Of those, 60 patients were excluded due to unavailable endoscopic data. The remaining 530 patients (383 CD and 147 ITB) with available clinical and endoscopic data were included in the analysis. Among ITB patients, 70 (47.6%) patients had pathological findings found either AFB or caseous granuloma, 35 (23.8%) patients had tissue culture growing mycobacterium tuberculosis, 36 (24.5%) patients had active tuberculosis elsewhere, and 29 (19.7%) patients were diagnosed based on response to empirical antituberculosis therapy. Nineteen (5%) of Crohn’s disease patients had received antituberculosis therapy without response before the diagnosis of Crohn’s disease was made. Demographic data, clinical manifestations, and endoscopic and pathology findings of study patients are shown in Table 2.

Table 2

Demographic, clinical, endoscopic, and pathology characteristics compared between Crohn’s disease (CD) and intestinal tuberculosis (ITB).

	Total (n = 530)	CD (n = 383)	ITB (n = 147)	p
Age (years), (mean±SD)	41.6±18.0	37.6±17.1	52.2±16.0	<0.01
Male gender	311 (58.7%)	234 (61.1%)	77 (52.4%)	0.07
Clinical presentation, n (%)
Duration of symptoms (months), median [IQR]	6 [2–12]	7 [3–15]	3 [1–6]	<0.01
Abdominal pain	352/520 (67.7%)	265/374 (70.9%)	87/146 (59.6%)	0.01
Diarrhea	277/521 (53.2%)	224/375 (58.6%)	53/146 (36.3%)	<0.01
Hematochezia	158/519 (30.4%)	123/373 (33.0%)	35/146 (24.0%)	0.045
Clinical gut obstruction	39/519 (7.5%)	28/373 (7.5%)	11/146 (7.5%)	>0.99
Fever	102/518 (19.7%)	50/372 (13.4%)	52/146 (35.6%)	<0.01
Night sweats	8/503 (1.6%)	5/360 (1.4%)	3/143 (2.1%)	0.69
Anemia	206/515 (40%)	143/369 (38.8%)	63/146 (43.2%)	0.36
Weight loss	250/517 (48.4%)	169/371 (45.6%)	81/146 (56.5%)	0.04
Perianal disease	89/522 (17.1%)	84/376 (22.3%)	5/146 (3.4%)	<0.01
Extraintestinal manifestations	47/520 (9.0%)	41/374 (11.0%)	6/146 (4.1%)	0.01
Lung involvement	39/516 (7.6%)	2/370 (0.5%)	37/146 (25.3%)	<0.01
Ascites	5/516 (1%)	1/370 (0.27%)	4/146 (2.7%)	<0.01
Endoscopy, n (%)
Longitudinal ulcer	100 (18.9%)	91 (23.8%)	9 (6.1%)	<0.01
Cobblestone appearance	48 (9.1%)	48 (12.5%)	0 (0.0%)	<0.01
Aphthous ulcer	197 (37.2%)	165 (43.1%)	32 (21.8%)	<0.01
Transverse ulcer	81 (15.3%)	26 (6.8%)	55 (37.4%)	<0.01
Patulous ileocecal valve*	44/521 (8.5%)	25/376 (6.6%)	19/145 (13.1%)	0.02
Intestinal luminal narrowing	86 (16.2%)	73 (19.1%)	13 (8.8%)	<0.01
Mucosal bridging	7 (1.3%)	7 (1.8%)	0 (0.0%)	0.20
Pseudopolyps	90 (17.0%)	77 (20.1%)	13 (8.8%)	<0.01
Segment involved**
Ileal involvement	258/511 (50.5%)	192/374 (51.3%)	66/137 (48.2%)	0.53
Cecal involvement	209/520 (40.2%)	139/376 (37.0%)	70/144 (48.6%)	0.02
Ascending colon involvement	181/521 (34.7%)	132/377 (35.0%)	49/144 (34.0%)	0.83
Transverse colon involvement	148/523 (28.3%)	121/379 (31.9%)	27/144 (18.8%)	<0.01
Descending colon involvement	126/530 (23.8%)	105/383 (27.4%)	21/147 (14.3%)	<0.01
Sigmoid colon involvement	150/530 (28.3%)	138/383 (36.0%)	12/147 (8.2%)	<0.01
Rectal involvement	119/530 (22.5%)	114/383 (29.8%)	5/147 (3.4%)	<0.01
Less than 4 segments involvement	415/530 (78.3%)	287/383 (74.9%)	128/147 (87.1%)	<0.01
Pathology (n = 199), n (%)		(n = 116)	(n = 83)
Presence of granuloma	80 (40.2%)	21 (18.0%)	59 (71.1%)	<0.01
•Confluent granuloma	43 (53.8%)	5 (23.8%)	38 (64.4%)	<0.01
•Large granuloma	45 (56.3%)	7 (33.3%)	38 (64.4%)	0.01
•More than 5 granuloma per section	18 (22.5%)	1 (4.8%)	17 (28.8%)	0.03
•Mucosal granuloma	69 (86.3%)	13 (61.9%)	56 (94.9%)	<0.01
•Microgranuloma	54 (67.5%)	15 (71.4%)	39 (66.1%)	0.65
•Cuffing lymphocytes around granuloma	53 (67.1%)	10 (47.6%)	43 (74.1%)	0.03
Ulcer lined by histiocytes	64 (32.2%)	27 (23.3%)	37 (44.6%)	<0.01
Disproportionate inflammation	87 (43.7%)	48 (41.4%)	39 (47.0%)	0.43
Focally enhanced colitis	132 (66.3%)	79 (68.1%)	53 (63.9%)	0.53

A p-value<0.05 indicates statistical significance

*Patulous ileocecal valve could not be evaluated in some patients because they had undergone hemicolectomy or flexible sigmoidoscopy

**Endoscopic findings could not be evaluated in some patients because they had undergone hemicolectomy, they had impassable stricture, they had undergone flexible sigmoidoscopy, or the terminal ileum was not accessed

Abbreviations: CD, Crohn’s disease; ITB, intestinal tuberculosis; SD, standard deviation; IQR, interquartile range

Model validation and comparison

The data of 530 patients with available clinical and endoscopy data was used to validate the models integrating only clinical and endoscopy parameters. Of 530 patients, 199 patients (116 CD and 83 ITB) had pathology specimens available for review and those patients were included in the validation of all models.

Validation of models integrating clinical and endoscopic parameters

The data of 530 patients with available clinical and endoscopy data were applied to the ITBvsCD model and the models by Lee, et al. and Jung, et al. for model validation. In Lee’s model, 143 patients obtained a score of zero, which reflects an indeterminate diagnosis. Among the remaining 387 patients, the sensitivity, specificity, and accuracy of diagnosis of ITB was 96%, 47%, and 61.2%, respectively. The AUROC was 0.713 (95% confidence interval [CI]: 0.677–0.748). Subgroup analysis in both study countries showed comparable model accuracy. The accuracy was 61.5% and 60.9% for Thai and Hong Kong cohort, respectively (S1 Table).

In the ITBvsCD model, the data was applied to the ITBvsCD-C, ITBvsCD-E, and ITBvsCD-CE models. The results of that analysis are shown in Fig 1. The AUROC of ITBvsCD-C and ITBvsCD-E was 0.756 (95% CI: 0.711–0.801) and 0.792 (95% CI: 0.752–0.831), respectively (p = 0.21). When the clinical and endoscopy were combined, the AUROC of the ITBvsCD-CE was 0.853 (95% CI: 0.817–0.888), which was significantly higher than both clinical alone and endoscopy alone (both p<0.01).

Fig 1

Validation of the models integrating clinical and endoscopy in 530 patients who had clinical and endoscopic data.

Data shown as area under the receiver operating characteristic curve (AUROC), and a p-value <0.05 indicates statistical significance.

When the data was applied to Jung’s model, the AUROC was 0.862 (95% CI: 0.829–0.895). The performance between the ITBvsCD-CE model and Jung’s model was not significantly different (p = 0.52), but both performed significantly better than Lee’s model (both p<0.01), which used only endoscopic findings. Subgroup analyses in the Thai and Hong Kong cohorts relative to the validation of the ITBvsCD model and Jung’s model are shown in Table 3. Except for Jung’s model, which performed significantly better in the Hong Kong cohort, the other comparisons between countries were non-significantly different.

Table 3

Subgroup analysis of validation of the model integrating clinical and endoscopic findings for each model compared among cohorts.

	AUROC
Model	Total cohort (N = 530)	Thai cohort (n = 241)	Hong Kong cohort (n = 289)	p
ITBvsCD-C	0.756 (0.711–0.801)	0.744 (0.681–0.806)	0.714 (0.637–0.791)	0.55
ITBvsCD-E	0.792 (0.752–0.831)	0.761 (0.703–0.819)	0.778 (0.705–0.831)	0.72
ITBvsCD-CE	0.853 (0.817–0.888)	0.831 (0.781–0.882)	0.827 (0.760–0.894)	0.92
Jung’s model	0.862 (0.829–0.895)	0.810 (0.757–0.863)	0.885 (0.833–0.938)	0.047

A p-value<0.05 indicates statistical significance

Abbreviation: AUROC, area under the receiver operating characteristic curve; ITB, intestinal tuberculosis; CD, Crohn’s disease; C, clinical findings; E, endoscopic findings; CE, clinical and endoscopic findings

Validation of all models among the cohort of 199 patients with available clinical, endoscopy and pathology data

The ITBvsCD model (ITBvsCD-C, ITBvsCD-E, ITBvsCD-CE, and ITBvsCD-CEP), and the models by Jung, et al. and Makharia, et al. were validated in this cohort.

Regarding the ITBvsCD model (as shown in Fig 2A), the model that integrated pathology findings further improved the performance compared to the ITBvsCD-CE model. The AUROC significantly increased from 0.824 (95% CI: 0.766–0.881) to 0.887 (95% CI: 0.841–0.933) (p = 0.01). Fig 2B shows that the difference of the calculated probability of ITB between the patients with CD and ITB was most in ITBvsCD-CEP when compared to ITBvsCD-C, ITBvsCD-E and ITBvsCD-CE.

Fig 2

(A) Validation of the 4 ITBvsCD models in 199 patients who had clinical, endoscopic, and pathology data. Data shown as area under the receiver operating characteristic curve (AUROC), and a p-value <0.05 indicates statistical significance. (B) Distribution of the calculated probability of intestinal tuberculosis for each of the 4 ITBvsCD models. The bottom and top of each box represent the 25th and 75th percentiles, giving the interquartile range. The line through the box indicates the median, and the error bars indicate the 10th and 90th percentiles.

Comparison among the ITBvsCD-CEP model, Jung’s model, and Makharia’s model is shown in Fig 3. The ITBvsCD-CEP model, which includes 22 variables from clinical, endoscopy, and pathology, performed significantly better than the model by Jung, et al. which includes 7 parameters from only clinical and endoscopy (AUROC: 0.798, 95% CI: 0.738–0.858), and the model by Makharia, et al., which includes only 4 variables from clinical, endoscopy, and pathology (AUROC: 0.637, 95% CI: 0.561–0.713).

Fig 3

Validation of the models integrating clinical, endoscopic, and pathology data in 199 patients who had clinical, endoscopic, and pathology data.

Data shown as area under the receiver operating characteristic curve (AUROC), and a p-value <0.05 indicates statistical significance.

The sensitivity, specificity, accuracy, positive and negative LR, PPV, NPV, and false-positive and false-negative rates for diagnosis of intestinal tuberculosis for each model at the cutoff values proposed in the original studies, which included the calculated ITB probability of 20% in the ITBvsCD-CEP model [21], 0.35 in the model by Jung, et al. [23], and 5.1 in the model by Makharia, et al. [7], are summarized in Table 4. For the ITBvsCD-CEP model, other cutoff values were assessed to identify the most suitable cutoff value for use in clinical practice.

Table 4

Performance of all models in diagnosis of intestinal tuberculosis in 199 patients with available clinical, endoscopic, and pathology findings.

Cutoff value	Sensitivity	Specificity	Accuracy	Positive LR	Negative LR	PPV	NPV	CD misdiagnosed as ITB	ITB misdiagnosed as CD
ITBvsCD-CEP: AUROC 0.887 (0.841–0.933)
59.47%	74%	90%	83%	7.10	0.30	84%	83%	12/116	22/83
20%	90%	71%	79%	3.08	0.14	69%	91%	34/116	8/83
10%	90%	63%	74%	2.44	0.15	64%	90%	43/116	8/83
5%	96%	53%	71%	2.03	0.07	59%	95%	55/116	3/83
ITBvsCD-CE: AUROC 0.824 (0.766–0.881)
5%	93%	41%	62%	1.56	0.18	53%	89%	69/116	6/83
Model by Jung, et al.: AUROC 0.798 (0.738–0.858)
0.35	84%	56%	68%	1.92	0.28	58%	83%	51/116	13/83
Model by Makharia, et al.: AUROC 0.637 (0.561–0.713)
5.1	66%	51%	57%	1.35	0.66	49%	68%	59/116	55/83

Abbreviations: LR, likelihood ratio; PPV, positive predictive value; NPV, negative predictive value; ITB, intestinal tuberculosis; CD, Crohn’s disease; AUROC, area under receiver operating characteristic curve

For the ITBvsCD-CEP model, the best cutoff value for differentiating CD from ITB was 59.47%. At this cutoff, the model diagnosed patients accurately in 82.9% of cases. However, the negative predictive value (NPV) was only 83%, which means there would have been 22 of 83 patients with ITB (26.5%) misdiagnosed as CD, and those patients would have received immunosuppressive agents. At the cutoff of 20%, which was reported to have an NPV of 100% in the original study [21], the NPV was 91% when applied to this cohort, which means that 8 of 83 ITB patients (9.6%) would have been treated as CD. To decrease the false-negative rate, the cutoff value was set to a lower value. To obtain an NPV of 95%, the cutoff value had to be set at 5%. However, at this cutoff value, as high as 55 of 116 CD patients (47.4%) would receive ATT without need.

For Jung’s model, at the cutoff value of 0.35, the sensitivity, specificity, accuracy, and positive and negative predictive value were 84%, 56%, 67.8%, 58%, and 83%, respectively. The corresponding values in the original study were 98.0%, 92.4%, 95.2%, 88.9%, and 98.6%, respectively. For Makharia’s model, at the cutoff value of 5.1, the sensitivity and specificity were 66% and 51%, respectively. The corresponding values in the original report were 90% and 60%, respectively, as shown in Table 4.

Subgroup analysis in patients with intestinal tuberculosis who were diagnosed by response to empirical treatment with antituberculosis therapy

Of 147 ITB, 29 patients had negative results for all diagnostic tests, and were diagnosed by response to empirical antituberculosis therapy. The models were applied to this group of patients. For ITBvsCD-CEP score, the median calculated ITB probability was 45.82% (range 2.81–99.96%). At the score cutoff value of 5%, 10%, and 20%, 2/29 (6.9%), 6/29 (20.7%), and 9/29 (31.0%%) ITB would be diagnosed with Crohn’s disease, respectively. For Jung’s score, the median score was 0.584 (range 0.009–0.999). At the cutoff value of 0.35, 7/29 (24.1%) ITB would be diagnosed with Crohn’s disease. For Makharia’s score, 23 patients had available pathological specimens for review. The median score was 7.2 (range 2.6–9.3). At the cutoff value of 5.1, 8/23 (34.8%) of ITB would be diagnosed with Crohn’s disease.

Discussion

Many models for differentiating CD from ITB have been developed by many different research groups. Our study is the first to validate and compare these models in the same large multicenter cohort; however, our comparative analysis of models was intentionally limited to the models that include only clinical (alone and in combination), endoscopic (alone and in combination), and pathology (in combination only) parameters. Models that include radiographic and/or laboratory parameters were not included since these are considered advanced investigations and they are not available and/or affordable in many healthcare settings. We found that the ITBvsCD-CEP model, which includes clinical, endoscopy, and pathology, performed better than the models that include only clinical and endoscopy parameters (ITBvsCD-CE and Jung’s model). Furthermore, the models that include both clinical and endoscopy performed better than the clinical only model (ITBvsCD-C) and the endoscopy only models (ITBvsCD-E and Lee’s model). We also found that the models that include clinical, endoscopic, and pathology parameters may not perform very well if the number of variables is low. For example, although it was found to be highly effective for differentiating ITB from CD in their cohort, the model by Makharia, et al., which includes only 4 parameters, may not be generalizable to other populations that may have different characteristics. The concept of adding more variables to improve the diagnostic performance of a model is supported by two recent studies. First, Mao, et al. reported that adding significant CT enterography findings, including segmental involvement and comb sign, to the endoscopic score by Lee, et al. improved diagnostic accuracy from 66.7% to 95.2% [18]. Second, Bae, et al. integrated radiologic findings of pulmonary or small bowel involvement and serological tests, including IGRA and anti-Saccharomyces cerevisiae antibody (ASCA), into their original endoscopic score, and they found that the accuracy of their model improved from 81.2% to 96.3% [20].

Even though the AUROC for the ITBvsCD-CEP model is high at 0.887, there is still an important limitation relative to its application in clinical practice. Due to the risk of fatal complications if immunosuppressive agents are given to ITB patients who are misdiagnosed with CD, a tool needed is the one that can conclusively exclude ITB among patients with uncertain diagnosis. In the ITBvsCD-CEP model, at the best cutoff value of 59.4%, the false-negative rate was high at 26.5%, with 22 of 83 ITB patients being misdiagnosed. Although a lower threshold was set to lower the false-negative rate to even 5%, there would still be 3 ITB patients that would be misdiagnosed. Moreover, at this cutoff, about half of CD patients would receive ATT without actual need. Additionally, when applied all models to ITB patients with negative results of all diagnostic tests, and were diagnosed by empirical ATT, substantial numbers of patients in this group would have been misdiagnosed with Crohn’s disease, emphasizing the limited value of these models. At the proposed cutoff value in the original studies, the percentage of misdiagnosis was 31.0% for ITBvsCD-CEP at the cutoff score of 20%, 24.1% for Jung’ score at the cutoff of 0.35, and 34.8% for Makharia’s score at the cutoff of 5.1. As such, the search for better models must continue. There are at least 2 ways to improve the ITBvsCD model. First, some basic parameters could also be beneficial. As shown in Table 2, most of the significant variables are concordant with the results of meta-analysis [21], and they have been included in the ITBvsCD model. However, there are two significant findings that are not included in the ITBvsCD model—age and presenting duration. For age, the results of meta-analysis showed a trend of more advanced age in ITB, but this factor was not found to be statistically significant, so age was not included in the model. However, many studies published later that have not been included in meta-analysis, including studies by Jung, et al. [23], Bae, et al. [20], and He, et al. [28], reported that ITB patients were older than CD patients. Including these studies could have changed the result of meta-analysis. Regarding presenting duration, meta-analysis also showed presenting duration in CD to be longer than that in ITB. However, owing to the limitation of the ITBvsCD model, which can include only variables with dichotomous results, presenting duration could not be included in the model. The second way to improve the model is to add more advanced investigations, including CT enterography and serological tests, such as interferon gamma release assay. The ITBvsCD model was designed to include these variables, but the benefit of these advanced investigations needs to be proven in further studies.

The main strength of this study is that we included a large number of patients from two countries and from a total of 5 tertiary care centers. Furthermore, the results are quite comparable between the two countries, which suggests that our results may be generalizable to all Asian populations.

This study also has some limitations. First, due to the retrospective nature of this study, some clinical parameters might not have been available in medical records. Furthermore, the lack of mention of a particular finding in the official endoscopic and pathologic reports may not be meant as an absence of that finding. We have attempted to minimize this limitation by performing a review of pictures of endoscopic findings and specimen slides of pathologic findings by gastroenterologists and pathologists, respectively whereby an initial definitive diagnosis could not be made. For endoscopic findings, all available pictures of endoscopic findings of each patient were reviewed. For pathologic findings, 199 patients had slide specimens for review. The specimen slides of each patient were retrieved and reviewed by gastrointestinal pathologists. However, there may be some variations in the subjective evaluation of the findings of endoscopic lesions and pathology findings. Establishing clear definitions, or using central readers or advanced technology, such as artificial intelligence, for interpretation of the findings may be helpful. Second, models that include more than clinical, endoscopic, and pathology parameters, as shown in S2 Table [16–18, 29] and S3 Table [20, 28, 30, 31], were not able to be included in our study. Owing to the retrospective and real-life study design of this paper, not all subjects had undergone cross sectional imaging. Most of the imaging studies in our cohort were conventional CT abdomen and not CT enterography. Since the majority of the previous studies had used CT findings obtained from CT enterography for their models, extrapolating some of these findings to other models might not be accurate. Furthermore, many laboratory variables, including IGRA and ASCA were also not available. Further prospective studies aiming to validate other models which incorporated radiological imaging and serology will be necessary and this will form our future study. Third, the value of external model validation is limited by the small number of difficult to diagnose ITB. Only 29 (19.7%) patients had negative workup and were diagnosed by response to empirical anti-tuberculosis therapy.

Conclusion

Scoring systems with more parameters and diagnostic modalities seemed to have significantly better ability to differentiate ITB from CD; however, application to clinical practice is still limited owing to high rate of misdiagnosis of ITB as CD. Validation of models integrating more modalities such as imaging and serological tests is needed.

Validation of the score by Lee, et al. in our total cohort of 530 patients.

(DOCX)

Click here for additional data file.

Models integrating computed tomography enterography.

(DOCX)

Click here for additional data file.

Models integrating clinical, endoscopic, pathology, imaging, and laboratory findings.

(DOCX)

Click here for additional data file.

(CSV)

Click here for additional data file.

16 Jun 2020

PONE-D-20-14222

Validation of models using basic parameters to differentiate intestinal tuberculosis from Crohn's disease: A multicenter study from Asia

PLOS ONE

Dear Dr. Ng,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

I can understand that this MS has previously been evaluated for another journal and by chance, one reviewer that reject the paper has reviewed it again. However, in spite of his previously concerns he favor acceptance.

The other reviewer recommend rejection, primarily due to the retrospective design that are not clearly presented. In addition, it is not clear how these three models were chosen among several.

I can see some potentials in the MS, so I allow you to revise accordingly the reviewers raised issues.

Please submit your revised manuscript by Jul 27 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

Kind regards,

Pal Bela Szecsi, M.D. D.M.Sci.

Academic Editor

PLOS ONE

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Reviewers' comments:

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Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: No

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

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PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The study is interesting, largely well written and has a large data set. However, there are important methodological issues which might affect the validity of the findings of the study.

Major Concerns

• It is surprising that only three models were chosen for evaluation. In a recent review (See Intestinal Res https://doi.org/10.5217/ir.2019.09142), there are a large number of models which have been reported. How did the authors choose these three models and ignore others. Also, radiology has a very important role and exclusion of radiological models is concerning.

• It is surprising that the authors give an impression that the model is validated in a prospective manner . See the wording in abstract “newly diagnosed ITB and CD patients”. It is in the methods that it becomes clear that this data has been validated in a retrospective cohort. The word retrospective should be mentioned in the abstract

• The major concern about this study is that this is a validation study on a retrospective cohort. Since a number of parameters would not be recorded the performance of a particular algorithm/model will not be truly evaluated. This is a single factor which compromises to a great extent the findings of the study.

• Also, in retrospective studies the lack of mention of a particular finding may be taken as absence of that finding. Eg. An endoscopist may ignore or not mention cobble-stoning unless the preformat mentions that this findings be reported OR endoscopist may ignore small/apthous ulcers when larger ulcers are present. This is a problem inherent in retrospective studies and therefore compromises the validity of findings.

• In some of the figures numbers don’t match: See night sweats, EIM in Table 2 ; Weight loss there is a gross mismatch. This gives an impression of carelessness in manuscript preparation and leaves the reviewer unsure whether to trust these findings

Minor Concerns

• It is incorrect to report that none of the models has been externally validated. The Limsrivilai model was created on basis of a Bayesian methodology using all published literature. This was then validated in a single center which is an external validatation (outside the dataset from which it was derived) .

• What is the definition of clinical response fo Crohn disease. Was CDAI used or were endoscopies done and SES-CD calculated

• One of the models also takes into account the underlying prevalence of ITB or CD. What was the prevalence chosen. One believes that since it is a cross country study the prevalence would differ

• How was response to ATT defined: was it endoscopic or clinical and what was the timing of response

Reviewer #2: This study looks at predictive models to differentiate between ITB and CD and looks at retrospective data sets to identify their reproducibility . It is a two center study and looks at three predictive models .

**********

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Reviewer #1: No

Reviewer #2: No

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14 Jul 2020

Dear Professor Pal Bela Szecsi,

We would like to thank the editor and reviewers for examining our manuscript entitled, “Validation of models using basic parameters to differentiate intestinal tuberculosis from Crohn’s disease: A multicenter study from Asia” (PONE-D-20-14222). We have reviewed each of the reviewers’ comments and provided a point-by-point response as below. We are submitting our revised manuscript for consideration and hope you find this version acceptable for publication in PLOS ONE.

Thank you very much

Yours sincerely,

Julajak Limsrivilai, M.D., MSc.

Choon Kin Lee, M.D.

Siew C Ng, M.D., Ph.D.

Reviewer #1: The study is interesting, largely well written and has a large data set. However, there are important methodological issues which might affect the validity of the findings of the study.

Major Concerns

• It is surprising that only three models were chosen for evaluation. In a recent review (See Intestinal Res https://doi.org/10.5217/ir.2019.09142), there are a large number of models which have been reported. How did the authors choose these three models and ignore others. Also, radiology has a very important role and exclusion of radiological models is concerning.

Response: Thank you for the positive comments and for raising an important issue. We appreciate that several other models have been reported which included cross-sectional imaging and serology. Owing to the retrospective and real life study design of this paper, not all subjects had undergone cross sectional imaging. Most of the imaging studies in our cohort were conventional CT abdomen and not CT enterography. Since the majority of the previous studies had used CT findings obtained from CT enterography for their models, extrapolating some of these findings to other models might not be accurate. We have included these points in the limitations under discussion (page 19, line 351-358). In addition, the models using only clinical, endoscopic, and pathologic findings have an advantage that these are standard investigations for the diagnosis of Crohn’s disease or ITB in all countries including resource limited countries hence additional cost for investigations including interferon gamma release assay or cross sectional imagings are not required. Validation of their performance is also warranted.

We have included details on how we selected these four models (Lee et al., Makharia et al., Jung et al., Limsrivilai et al.) in the Methods section and the section on model validation (page 6, line 113-118). We have also included under limitations that further prospective studies aiming to validate other models which incorporated radiological imaging will be necessary and this will form our future study.

• It is surprising that the authors give an impression that the model is validated in a prospective manner. See the wording in abstract “newly diagnosed ITB and CD patients”. It is in the methods that it becomes clear that this data has been validated in a retrospective cohort. The word retrospective should be mentioned in the abstract

Response: Thank you for the comments. We have added the word “retrospectively” in the abstract to better reflect our cohort.

• The major concern about this study is that this is a validation study on a retrospective cohort. Since a number of parameters would not be recorded the performance of a particular algorithm/model will not be truly evaluated. This is a single factor which compromises to a great extent the findings of the study.

Response: Thank you very much for the comments. We agree with the reviewer and have included this in the limitations of this study on page 18-19, line 338-346.

• Also, in retrospective studies the lack of mention of a particular finding may be taken as absence of that finding. Eg. An endoscopist may ignore or not mention cobble-stoning unless the preformat mentions that this findings be reported OR endoscopist may ignore small/apthous ulcers when larger ulcers are present. This is a problem inherent in retrospective studies and therefore compromises the validity of findings.

Response: Thank you very much for the comments. We have attempted to minimize this limitation by performing a review of pictures of endoscopic findings and specimen slides of pathologic findings by gastroenterologists and pathologist, respectively whereby an initial definitive diagnosis could not be made. For endoscopic findings, all available pictures of endoscopic findings of each patient were reviewed. For pathologic findings, 199 patients had slide specimens for review. The specimen slides of each patient were retrieved and reviewed by gastrointestinal pathologists. If the stain had faded out, resulting in unclear images, repeat staining was performed on the slides. We have added this information in the Methods section on page 6, line 107-111 and also emphasize this limitation in the discussion on page 18-19, line 338-346.

• In some of the figures numbers don’t match: See night sweats, EIM in Table 2 ; Weight loss there is a gross mismatch. This gives an impression of carelessness in manuscript preparation and leaves the reviewer unsure whether to trust these findings

Response: We appreciate the reviewer for the careful review and comments. We have corrected the typos and also rechecked other findings for accuracy.

Minor Concerns

• It is incorrect to report that none of the models has been externally validated. The Limsrivilai model was created on basis of a Bayesian methodology using all published literature. This was then validated in a single center which is an external validatation (outside the dataset from which it was derived).

Response: Thank you for the comments. We have revised the sentence “Models developed to distinguish Crohn’s disease (CD) from intestinal tuberculosis (ITB) have not been externally validated.” to “ Data on external validation of models developed to distinguish Crohn’s disease (CD) from intestinal tuberculosis (ITB) are limited.” in the abstract, and the sentence “However, none of these models have been externally validated and compared with the other models” to “However, the number of studies performed to externally validate those models are limited” in the first paragraph on page 4, line 73-74.

• What is the definition of clinical response for Crohn disease. Was CDAI used or were endoscopies done and SES-CD calculated

Response: Clinical response was defined based on physicians’ notes in medical records that there was improvement of abdominal symptoms (such as abdominal pain, diarrhea, and bloody stool) and general well-being, in combination with the improvement of inflammatory biomarkers. As data in this study were collected retrospectively, neither CDAI nor SES-CD was routinely recorded. The duration of at least six months of follow up was needed to confirm the clinical response. We have added this detail in the Methods section on page 5, line 98-101.

• One of the models also takes into account the underlying prevalence of ITB or CD. What was the prevalence chosen. One believes that since it is a cross country study the prevalence would differ

Response: Thank you for the comments, We used 0.28 for the prevalence of ITB and applied it to the ITB vs CD model. This number is based on the total number of patients in our cohort (427 CD and 163 ITB). We have added this detail in the Methods section on page 7, line 128-131.

• How was response to ATT defined: was it endoscopic or clinical and what was the timing of response.

Response: Among patients with negative results for the diagnosis of ITB, a diagnosis of ITB was based on empirical response to ATT. In this case, the response to ATT was defined as those having both a clinical and endoscopic response. A follow up endoscopy was performed at 2 to 6 months after initiation of treatment. This has been included in the methods section in page 6, line 105-106.

Reviewer #2: This study looks at predictive models to differentiate between ITB and CD and looks at retrospective data sets to identify their reproducibility. It is a two center study and looks at three predictive models.

Response: We thank the reviewer for the comments and summary of our paper. However, we do not see any suggestions. Please let us know if we miss something.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

16 Sep 2020

PONE-D-20-14222R1

Validation of models using basic parameters to differentiate intestinal tuberculosis from Crohn's disease: A multicenter study from Asia

PLOS ONE

Dear Dr. Ng,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

The manuscript has greatly improved, by especially one of the reviewers has issues that needs to be addressed.

Please submit your revised manuscript by Oct 31 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Pal Bela Szecsi, M.D. D.M.Sci.

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

Reviewer #3: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Yes

Reviewer #3: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: The comments have addressed all points. I have no further comments . The authors have addressed all limitations.

Reviewer #3: The authors have done an interesting study comparing the existent models on differentiating CD and ITB. Though well presented, there are certain queries that need to be resolved-

1. The authors mention that they have compared the models which have utilized the only clinical, endoscopic and pathologic features so as to make it applicable in resource constraint countries. However, there are two other models by Yu et al (Digestion 2012;85:202-209.) and Li et al (Dig Dis Sci 2011;56:188-196.) which the authors have not included. Authors should discuss why these were excluded

2. One of these models (Li et al) includes tuberculin skin test which is easily available in resource constraint countries as well. Did the authors have data on TST in their cohort? If yes, then including this would add to the study.

3. The endoscopic and pathologic slides were reviewed by Gastroenterologist and Pathologists. Was central reading done or was it reviewed at different centers?

4. 29 of 147 ITB patients (20%) only had indeterminate diagnosis of ITB and rest had a definite diagnosis. Given the poor sensitivity of current diagnostic techniques for ITB, 80% definite diagnosis rate for ITB is rather surprising.

5. Authors should mention the proportion of patients who satisfied the different diagnostic criteria for ITB (e.g. positive AFB, culture, caseating necrosis, etc.)

6. Similarly among patients with CD, how many received ATT trial and how many were diagnosed upfront?

7. Among the comparison of pathologic findings, authors should also mention about the prevalence of caseation necrosis in either group

8. The real value of these models lies in predicting the diagnosis of ITB in patients with indeterminate TB. With only 29 such patients the study becomes grossly limited in its aim of external validation of these models. This point should be clearly highlighted in discussion.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

Reviewer #3: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

12 Oct 2020

Dear Professor Pal Bela Szecsi,

We would like to thank the editor and reviewers again for examining our first revised manuscript entitled, “Validation of models using basic parameters to differentiate intestinal tuberculosis from Crohn’s disease: A multicenter study from Asia” (PONE-D-20-14222R1). We have reviewed each of the reviewers’ comments and provided a point-by-point response as below. We are submitting our revised manuscript for consideration and hope you find this version acceptable for publication in PLOS ONE.

Thank you very much

Yours sincerely,

Julajak Limsrivilai, M.D., MSc.

Choon Kin Lee, M.D.

Siew C Ng, M.D., Ph.D.

Reviewer #2: The comments have addressed all points. I have no further comments. The authors have addressed all limitations.

Thank you very much.

Reviewer #3: The authors have done an interesting study comparing the existent models on differentiating CD and ITB. Though well presented, there are certain queries that need to be resolved-

1. The authors mention that they have compared the models which have utilized the only clinical, endoscopic and pathologic features so as to make it applicable in resource constraint countries. However, there are two other models by Yu et al (Digestion 2012;85:202-209.) and Li et al (Dig Dis Sci 2011;56:188-196.) which the authors have not included. Authors should discuss why these were excluded

Thank you for raising this important issue. We were aware of these two models when we designed this study; however, we decided not to include them. The model by Yu et al. integrated the presence of granuloma without specific characteristics, making it difficult to differentiate whether the granuloma was related to CD or ITB. We also excluded the model by Li et al. because of two reasons. First, this model integrated the presence of rodent-like ulcer without specific detail, and thus the interpretation was unclear. The other reason was the use of tuberculin skin test (TST) as one of the indicators for the diagnosis of ITB. Many patients in our cohort received BCG vaccine at birth, and it could cause a false positive tuberculin skin test. Therefore, physicians generally did not do TST in this setting. None of the patients in our cohort had this data available. We have added this information in the section Materials and methods, subsection Model validation on page 6-7, line 124-133.

2. One of these models (Li et al) includes tuberculin skin test which is easily available in resource constraint countries as well. Did the authors have data on TST in their cohort? If yes, then including this would add to the study.

We agree that if tuberculin skin tests were available, it might have helped to distinguish ITB from CD. However, there is a concern about using TST in our patients as we mentioned above.

3. The endoscopic and pathologic slides were reviewed by Gastroenterologist and Pathologists. Was central reading done or was it reviewed at different centers?

The endoscopic findings were reviewed by experts of each center. The available pathologic slides were sent and reviewed by two experts from two centers. We added this details in the methods section on page 6 line 107-110. We recognized interobserver disagreement, and thus stated this issue in the limitations of this study. We have added that using central readers could minimize the variation in the interpretation in the limitations of study on page 19, line 363 as well.

4. 29 of 147 ITB patients (20%) only had indeterminate diagnosis of ITB and rest had a definite diagnosis. Given the poor sensitivity of current diagnostic techniques for ITB, 80% definite diagnosis rate for ITB is rather surprising.

We agree with the reviewer and the detailed information about the diagnosis of ITB in this study is provided in the answer to question#5. The numbers of ITB patients whose diagnosis required response to empirical antituberculosis agents in this cohort is lower than we expected as well.

5. Authors should mention the proportion of patients who satisfied the different diagnostic criteria for ITB (e.g. positive AFB, culture, caseating necrosis, etc.)

Among 147 ITB patients, 70 (47.6%) patients had pathological findings found either AFB or caseous granuloma, 35 (23.8%) patients had tissue culture growing mycobacterium tuberculosis, 36 (24.5%) patients had proven tuberculosis elsewhere, and 29 (19.7%) patients were diagnosed based on response to empirical antituberculosis therapy. We have added this information in the first paragraph of the results section on page 9 line 171-175.

6. Similarly among patients with CD, how many received ATT trial and how many were diagnosed upfront?

Nineteen (5%) of Crohn’s disease patients had received anti-tuberculous therapy without response before the diagnosis of Crohn’s disease was made. We have added this information in the first paragraph of the results section on page 9 line 175-177. This proportion is quite low. The reason could be that this study was conducted in tertiary-referral centers where some physicians might have experience with Crohn’s disease more than general physicians, resulting in more confident to treat Crohn’s disease when all TB tests were negative.

7. Among the comparison of pathologic findings, authors should also mention about the prevalence of caseation necrosis in either group.

The prevalence of caseation in each group was not presented in the table 2 because the caseous necrosis was present only in patients with ITB.

8. The real value of these models lies in predicting the diagnosis of ITB in patients with indeterminate TB. With only 29 such patients the study becomes grossly limited in its aim of external validation of these models. This point should be clearly highlighted in discussion.

Thank you for pointing out this issue. We do agree that the value of these models are limited to those with indeterminate diagnosis of ITB, which was quite a small number in this study, and we have added this limitation on page 20 line 373-376. Nonetheless, all diagnostic models were derived from patients with both indeterminate and definite ITB, and thus we opted to validate these models with similar patient population.

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11 Nov 2020

Validation of models using basic parameters to differentiate intestinal tuberculosis from Crohn's disease: A multicenter study from Asia

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14 Nov 2020

PONE-D-20-14222R2

Validation of models using basic parameters to differentiate intestinal tuberculosis from Crohn’s disease: A multicenter study from Asia

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