Development and validation of the HEALTH scoring system = A novel and non-invasive laboratory panel to differentiate intestinal tuberculosis from  
other gastrointestinal diseases

Introduction

World Health Organization (WHO) reported through the Global Tuberculosis Report 2022 that Indonesia is in second place with the highest burden of tuberculosis in 2021.¹ Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (MTB). These bacteria primarily infect the lung tissue but can also spread to other body parts. When TB affects areas outside the lungs, it is referred to as extrapulmonary tuberculosis (EPTB). The prevalence of EPTB worldwide is 16%, while in Indonesia is 10–19%.² Intestinal tuberculosis (ITB) as part of EPTB has a prevalence of around 3–16%.^3–5 The proportion of TB colitis in Dr. Cipto Mangunkusumo National Central General Hospital, reported by Suparmin, was 8/60 (13.3%).⁶

Although pulmonary TB (PTB) is more common, ITB can present with vague, non-specific gastrointestinal symptoms, such as abdominal pain, diarrhea, weight loss, and bloating. It remains a diagnostic challenge due to its overlap with other gastrointestinal diseases, such as Crohn’s disease (CD) and colon cancer. The management of ITB, CD, and colon cancer is known to be very different.^7–10 While ITB can be cured with anti-TB therapy, CD typically persists and may relapse. Misdiagnosis of CD as ITB results in unnecessary anti-TB therapy, increased risk of toxicity, and late treatment of the primary disease. Otherwise, misdiagnosis of ITB as CD results in fatal ITB.^7–14 Misdiagnosis of ITB as colon cancer can lead to unnecessary surgical interventions, such as hemicolectomy, which could have been avoided with accurate diagnosis. Misdiagnosing colon cancer as ITB can delay necessary cancer treatments, potentially worsening patient outcomes. Both conditions require different treatment approaches, and incorrect treatment due to misdiagnosis can lead to increased morbidity and even mortality.^15,16

The routine diagnosis of ITB is based on clinical manifestations, Ziehl-Neelsen (ZN) acid-fast bacterial (AFB) staining examination of stool, stool culture, colonoscopy, and histopathology.^4,14,17–19 Each of these ITB diagnostic examinations has advantages and limitations. Considering that some laboratory parameters often available in healthcare facilities and the impact of inflammation in ITB produces an abnormal immune response in leukocytes and differential count, erythrocyte sedimentation rate (ESR), neutrophil to lymphocyte ratio (NLR), monocyte to lymphocyte ratio (MLR), interferon-gamma (IFN-γ), adenosine deaminase (ADA), and the antimicrobial protein human beta defensin-2 (HBD-2)^20–26 have proven to be valuable in differentiating ITB and other gastrointestinal diseases. Furthermore, using non-invasive laboratory samples, especially stool and blood, has become a pivotal area in modern diagnostics. These biological specimens offer significant advantages in patient comfort, non-invasive, ease of sample collection, and the potential for early detection of various diseases.²⁷

In recent years, numerous studies have been attempted to create model panels that could improve the accuracy and success rate of ITB management,^28–33 but they used different diagnostic models and scoring systems, some of which are not user-friendly, as they may need a calculator or a computer, and cannot be applied in healthcare facilities that lack access to colonoscopy and histopathology services. Hence, in this study, we aimed to develop and validate a non-invasive laboratory panel using a scoring system to differentiate ITB from other gastrointestinal diseases.

Methods

Results

Determination of scoring system

The scoring system based on the regression coefficient β value and standard errors of the independent diagnostic parameters in multiple regression analysis is displayed in Table 4. After rounding off to the nearest integer, the scores of each parameter in the scoring system are defined as follows: 1 point for stool HBD-2 levels, ESR, blood ADA activity, lymphocytes, NLR, and 2 points for stool TB PCR. This combination of 6 biomarkers is referred to as the HEALTH scoring system.

The total score of ITB subjects showed significantly higher than Non-ITB subjects (mean, 4.27 vs. 2.17, p < 0.0001). In this scoring system, we calculated the probability of ITB in each score, as shown in Table 5. It was observed that there is a synergistic relationship between an increasing total score and a higher percentage of subjects with ITB. To determine a cut-off score that indicates whether a subject is considered to have ITB, a statistical cut-off score between sensitivity and specificity was searched for, which can be seen in Table 6.

Table 5. Probability of ITB in each score.

Total scores	ITB		Total	Probability (%)
	No	Yes
0	1	0	1	0.46
1	25	0	25	3.19
2	53	2	55	2.20
3	36	5	41	13.63
4	6	5	11	52.55
5	0	5	5	88.68
6	0	5	5	98.20

Table 6. Sensitivity and specificity of scores at different cut-off point based on ROC curve analysis.

Cut-off score	Sensitivity	Specificity	Classified	LR+	LR-
(≥0)	100.00%	0.00%	15.38%	1
(≥1)	100.00%	0.83%	16.08%	1.0083	0
(≥2)	100.00%	21.49%	33.57%	1.2737	0
(≥3)	90.91%	65.29%	69.23%	2.619	0.1392
(≥4)	68.18%	95.04%	90.91%	13.75	0.3348
(≥5)	45.45%	100.00%	91.61%		0.5455
(≥6)	22.73%	100.00%	88.11%		0.7727
(>6)	0.00%	100.00%	84.62%		1

According to the ROC curve, the cut-off score of ≥ 4 yielded the largest AUC of 0.8978 (95% CI 0.823–0.971) with a sensitivity of 68.18% (95% CI 45.13%–86.14%) and specificity of 95.04% (95% CI 89.52%–98.16%) ( Figure 1). The HEALTH scoring system was then applied to our data and the subjects were split into two groups: Scores < 4 (Non-ITB) and scores ≥ 4 (ITB) as shown in Table 7. It was found that the HEALTH scoring system with a cut-off score of ≥ 4 successfully predicted 15 of 22 ITB cases and the scores < 4 successfully predicted 115 of 121 Non-ITB cases, yielding positive predictive value of 71.43% (95% CI 52.14%–85.16%) and negative predictive value of 94.26% (95% CI 89.90%–96.81%). The new proposed scoring system for differentiating ITB and Non-ITB can be seen in Table 8.

Figure 1. Sensitivity and specificity of scoring system based on ROC curve analysis.

Table 7. Validation of the HEALTH scoring system for differentiating ITB and Non-ITB subjects.

Total scores	Actual pathological diagnosis
	ITB	Non-ITB
<4	7 (5.74%)	115 (94.26%)**
≥4	15 (71.43%)*	6 (28.57%)
Total	22	121

* Positive Predictive Value = 71.4%.

** Negative Predictive Value = 94.3%.

Table 8. New proposed of the HEALTH scoring system for differentiating ITB and Non-ITB subjects.

Diagnostic parameters	Scores
Stool HBD-2 levels (ng/mL)
<31.14	1
≥31.14	0
ESR (mm)
≥33.5	1
<33.5	0
Blood ADA activity (IU/mL)
≥12.56	1
<12.56	0
Lymphocytes (%)
<26.85	0
≥26.85	1
Stool TB PCR
Positive	2
Negative	0
NLR
≥2.32	1
<2.32	0
Total Scores
≥4	ITB
<4	Non-ITB

Discussion

Scoring systems in laboratory diagnostics play a crucial role in improving the accuracy and efficiency of disease diagnosis. By integrating specific biomarkers and clinical data, these systems enhance the ability to distinguish between similar diseases, predict disease progression, and guide treatment decisions. The development and validation of these systems across various diseases highlight their potential to transform clinical practice and improve patient outcomes. In Indonesia, a laboratory panel using a scoring system to diagnose ITB has not been developed.

There is currently no stand-alone laboratory test in ITB management, which creates opportunities for developing laboratory examinations using various biomarkers to diagnose ITB according to its pathophysiology. These examinations include routine examinations (such as hematology, IGRA, and blood ADA activity) and specific examinations (such as TB PCR). This study used multiple regression and ROC curves to determine independent diagnostic parameters. After multivariate analysis, we developed and validated a combination of 6 biomarkers from blood and stool specimens to differentiate ITB and other gastrointestinal diseases, including stool HBD-2 level, ESR, blood ADA activity, lymphocytes, stool TB PCR, and NLR.

Stool HBD-2 levels in the ITB subjects were lower than Non-ITB subjects. Researchers have noted limited studies on stool HBD-2 levels in ITB subjects. The stool HBD-2 levels can stimulated through two pathways: one involves pro-inflammatory cytokines (IL-1β, IL-1α, and TNF-α), while the other involves microbial components like lipopolysaccharides and peptidoglycan. The elevated stool HBD-2 levels in Non-ITB subjects may result from activation through both pathways: the NFκB pathway and mitogen-activated protein kinase (MAPK), which produce pro-inflammatory cytokines similar to those seen in IBD, as well as pathways activated by microbial component in infectious colitis or ileitis. Stool HBD-2 is a crucial antimicrobial protein that mediates the innate immune response. The production of stool HBD-2 through the microbial component pathway is non-specific and does not distinguish between MTB and non-MTB microbes. The ITB subjects in this study were suspected to have chronic infections, which may account for their relatively low stool HBD-2 levels.^34,35

The ESR in the ITB subjects was significantly higher compared to Non-ITB subjects. The ESR test, which has a high sensitivity of 81%, can effectively serve as a screening tool for ITB. However, there are limited studies explicitly focusing on ESR in ITB cases. Most studies have primarily examined ESR in pulmonary TB cases, as documented by Khalil et al.,³⁶ Bashir et al.,³⁷ and Chong.³⁸ The ESR test measures acute-phase proteins produced in acute and chronic diseases. In ITB, pro-inflammatory cytokines stimulate the liver to produce these proteins, including fibrinogen, which can elevate ESR results. Chronic diseases like TB often lead to increased immunoglobulin levels as part of the immune response to persistent infection. These positively charged immunoglobulin proteins can bind to the negatively charged surface of erythrocytes, promoting the formation of erythrocyte rouleaux and thus resulting in a rapid ESR. Despite its usefulness, the ESR test has limitations. It is influenced by anemia and hypoalbuminemia, leading to a false increase in the ESR. Therefore, it is crucial to consider the presence of anemia and hypoalbuminemia when interpreting ESR results in ITB subjects.

Blood ADA activity in ITB subjects was significantly higher than Non-ITB subjects. This finding aligns with Salmanzadeh et al., who observed significantly higher blood ADA activity in subjects with pulmonary tuberculosis than those with pneumonia, lung malignancies, and healthy controls.³⁹ It is important to highlight that ADA activity measured in the blood does not pinpoint the exact production source but reflects the overall ADA activity from activated lymphocytes and monocytes throughout the body, including areas affected by MTB. Currently, ADA tests cannot be performed on stool specimens or biopsy tissue, making it impossible to determine local ADA production in the intestine in ITB. The increased number of monocytes in ITB subjects correlates with the elevated ADA activity, suggesting that monocytes play a more significant role than other cells in the context of ITB.^40,41

The percentage of lymphocytes in the ITB subjects showed no significant difference than Non-ITB subjects and was still within the normal range (reference value: 20–40%). In TB infection, the percentage of lymphocytes in the blood often appears in the normal range due to a complex mechanism of immune responses. While some lymphocyte subsets may be changed, the overall lymphocytes can remain within normal ranges. Infection of MTB can cause shifts in lymphocyte homeostasis, with some subsets like CD8+ T-cells and B-cells increasing, while others like CD4+ T-cells decrease. These changes can balance out, resulting in a normal overall percentage of lymphocytes.^42,43

In this study, the proportion of positive stool TB PCR was significantly higher in the ITB subjects than Non-ITB subjects. The results of stool TB PCR in this study are similar to those reported by Suparmin et al.⁶ The high proportion of positive stool TB PCR can be influenced by the high endemicity of TB in the population. This study also showed “acceptable” specificity (100%) for stool TB PCR test in differentiating ITB and Non-ITB subjects. This finding aligns with Gaur et al., who reported that stool TB PCR tests showed high specificity in EPTB subjects.⁴⁴ PCR is a recent advanced diagnostic method increasingly used to diagnose TB. Unlike biopsy, stool TB PCR is non-invasive and less prone to sampling error. The detailed mechanism of MTB DNA shedding in stool remains to be investigated. A recent study indicates a two-way interaction between the gut and lung (gut-lung axis) in the transfer of metabolites, immune cells, and bacterial fragments via the bloodstream.⁴⁵ However, these findings require further investigation, as the mechanistic basis of ITB is still unclear. A major challenge in diagnosing ITB is its non-specific and diverse clinical manifestations, which delay confirmation. Therefore, detecting MTB DNA in ITB subjects’ stool may serve as a valuable rule-in test.

In the present study, the NLR, an indicator of acute infection or inflammation, remained within the normal range and showed no significant difference between ITB and Non-ITB subjects. This finding contrasts with the results reported by Rees et al., who noted higher NLR levels in symptomatic pulmonary TB cases compared to controls.²¹ This discrepancy may be attributed to the role of neutrophils in non-granulomatous and acute inflammatory conditions. NRL is a newly identified biomarker of inflammation that is simpler to measure and relatively stable compared to other inflammatory biomarkers. However, the relationship between NLR and clinical outcomes can be influenced by several factors, including comorbidities, immune status, and virulence of the tuberculosis strain. Additionally, NLR can vary significantly between individuals based on age, gender, and ethnicity. It can also fluctuate over time due to stress, medications, and physiological conditions.

Developing laboratory panels using a scoring system based on subjects’ clinical biomarkers is nothing new. For managing the risk of patient severity or differentiating the disease from another disease, several previous studies developed laboratory scoring systems for infectious diseases, such as a lab score system for predicting COVID-19 patient severity, differentiating subjects with severe and mild H1N1 infection, and discrimination of TB infection from both PTB and EPTB disease.^46–48 However, researchers have pointed out that there are still very few reports regarding laboratory panels for differentiating ITB from other gastrointestinal diseases. Makharia et al. developed scoring system with 4 parameters (blood in stool, weight loss, histologically focally enhanced colitis, and involvement of sigmoid colon) to differentiate intestinal tuberculosis and Crohn’s disease.²⁸ Shi et al. mentioned the use of various parameters such as good response to ATT and positive IGRA, combined with clinical, endoscopy, radiology, and histopathology to diagnose intestinal tubeculosis, but not in a scoring system panel.¹⁴

The strength of the HEALTH scoring system is that this is the first study based on laboratory findings that predicted the probability of ITB. Additionally, the laboratory tests involved in this scoring system are user-friendly and non-invasive, as they only require blood and stool samples. In Indonesia, this scoring system can be utilized by healthcare facilities, regardless of whether they have colonoscopy and histopathology services. Healthcare facilities that lack these services can use the HEALTH scoring system to assist clinicians in managing ITB. On the other hand, healthcare facilities equipped with colonoscopy and histopathology services can use the HEALTH scoring system to identify patients who should undergo colonoscopy, thereby reducing waiting times for colonoscopy examinations.

It should be noted that our study has several limitations. This study was a single-center cross-sectional study, and the reagents used to examine stool IFN-γ in Indonesia do not include in vitro diagnostic reagents. Independent external validation, cost-effectiveness analysis, and multicenter randomized prospective study must be designed to validate the HEALTH scoring system.

Conclusion

This study developed and validated a laboratory panel called the HEALTH scoring system based on clinical biomarkers of stool HBD-2 level, ESR, blood ADA activity, lymphocytes, stool TB PCR, and NLR, which could be used to differentiate ITB from other gastrointestinal diseases.

Software availability

Software availability statement: The statistical analyses performed in this article using SPSS version 20 and STATA 14 software can be conducted using the freely accessible software R (programming language) https://www.r-project.org/