Diagnostic utility of haematological parameters for tuberculosis in children living with HIV (0–14 years): a retrospective cohort study

Abstract

Background

Early detection of tuberculosis (TB) in children living with HIV (CLHIV) is crucial for improving clinical outcomes and reducing disease transmission. This study aimed to evaluate the association between hematological parameters, including the monocyte-to-lymphocyte ratio (MLR), neutrophil-to-lymphocyte ratio (NLR), and anemia status, and the presence of TB in CLHIV aged 0–14 years.

Methods

This retrospective cohort follow-up study included data from 276 CLHIV registered at the Antiretroviral Therapy (ART) Centre of a Tertiary Care Hospital in Gujarat, India, from January 2009 to March 2024. Demographic, clinical, and laboratory data were extracted from electronic medical records. Logistic regression models were developed to assess the predictive ability of hematological parameters for TB.

Results

Among the study population, 56.9% had moderate anemia, and 11.2% had severe anemia. Children with TB had significantly lower mean hemoglobin levels, higher absolute neutrophil counts, and higher median MLR (0.460 ± 0.117 vs. 0.167 ± 0.143, p = 0.001) and NLR ratios (2.1 ± 0.3 vs. 1.7 ± 0.7, p = 0.001) compared to those without TB. Model 1 (MLR and anemia status) showed that individuals with a greater MLR ratio had 15.62 times higher adjusted odds of having TB (95% CI: 6.84–35.67, p < 0.001). Model 2 (NLR and anemia status) revealed that a high NLR ratio was associated with 17.28 times higher adjusted odds of TB (95% CI: 7.41–40.35, p < 0.001). Model 3 (MLR, NLR, and anemia status) demonstrated the best predictive ability (AUC = 0.892, accuracy = 88%, sensitivity = 94.2%).

Conclusion

Higher MLR and NLR ratios were associated with increased odds of having TB in CLHIV. The combination of these hematological parameters, along with anemia status, exhibited promising predictive ability for TB in this population. These findings highlight the potential utility of routinely available hematological parameters in predicting TB disease in CLHIV.

Introduction

Tuberculosis (TB) remains a significant public health concern, particularly in resource-limited settings and among populations with compromised immune systems, such as those living with HIV/AIDS. Children living with HIV (CLHIV) are at an increased risk of developing TB, with a higher likelihood of disseminated and extrapulmonary forms of the disease [1]. Prompt diagnosis and treatment of TB in this vulnerable population are crucial for improving clinical outcomes and reducing mortality rates.

According to the World Health Organization (WHO), in 2023, an estimated 1.3 million children and young adolescents (aged 0–14 years) developed tuberculosis (TB), accounting for approximately 12% of the total TB cases globally. Children and young adolescents aged under 15 years accounted for approximately 166,000 deaths, representing about 15% of the total TB-related deaths [2]. Co-infection with HIV and TB poses a significant challenge, as HIV weakens the immune system and increases the risk of developing active TB [3]. Early detection of TB in PLHIV can be challenging, as the symptoms may be non-specific, and the disease progression can be rapid, particularly in those with advanced immunosuppression [4].

Hematological parameters, such as the monocyte-to-lymphocyte ratio (MLR) and neutrophil-to-lymphocyte ratio (NLR), have emerged as potential biomarkers for various inflammatory and infectious conditions, including TB [5, 6]. These ratios can be easily calculated from routine complete blood count (CBC) tests and may serve as cost-effective and accessible screening tools for TB in resource-limited settings.

Several studies have investigated the utility of MLR and NLR in predicting TB in adults living with HIV [7, 8]. However, there is a paucity of research exploring the diagnostic potential of these hematological parameters in CLHIV. Children have distinct immunological responses and disease manifestations compared to adults, necessitating specific investigations tailored to this age group.

This study aimed to evaluate the association between hematological parameters, including MLR, NLR, and anemia status, and the presence of TB in CLHIV aged 0–14 years. Additionally, the study aimed to assess the predictive ability of these parameters for TB in this population by developing logistic regression models. The findings from this study may contribute to the development of simple, cost-effective screening strategies for early detection of TB in children living with HIV, ultimately improving disease outcomes and reducing transmission.

Methodology

Study design

This retrospective cohort follow-up study was conducted at the ART (Antiretroviral Therapy) Centre of a Tertiary Care Hospital in Gujarat. The study included data from ART CLHIV aged 0–14 years registered from Jan 2009- March 2024.

Study population

The study population consisted of children living with HIV (CLHIV) aged ≤ 14 years, who were registered at the ART Centre during the specified period. Both newly diagnosed and follow-up cases were included in the analysis.

TB definition and diagnosis

For this study, TB was defined as either bacteriologically confirmed or clinically diagnosed pulmonary TB (PTB).

• 1. Bacteriologically Confirmed TB:

• Sputum smear microscopy positive for acid-fast bacilli (AFB).

• Culture-positive for Mycobacterium tuberculosis complex.

• Positive GeneXpert MTB/RIF assay.

• 2. Clinically Diagnosed TB:

• Suggestive clinical features (e.g., persistent cough, fever, night sweats, weight loss).

• Radiological findings consistent with TB (e.g., cavities, infiltrates, pleural effusion on chest X-ray or CT scan).

• Strong clinical suspicion by a physician, leading to a decision to initiate a full course of anti-TB treatment.

• 3. Algorithm for TB Diagnosis in CLHIV: (a) Symptom screening: All CLHIV were screened for TB symptoms at each visit using the WHO four-symptom screen (current cough, fever, night sweats, or weight loss). (b) If symptomatic:

• Sputum samples were collected for AFB smear microscopy and GeneXpert MTB/RIF testing.

• Chest X-ray was performed.

• In cases of suspected EPTB or negative pulmonary tests, further investigations like IGRA, FNAC, or radiological studies were conducted based on clinical presentation.

• CLHIV with advanced HIV disease (CD4 count < 200 cells/µL) or those not on ART underwent TB screening with GeneXpert MTB/RIF.

• 4. TB Treatment Initiation:

• Anti-TB treatment was initiated based on the bacteriological confirmation or strong clinical suspicion, as per national guidelines.

• Patients were closely monitored for treatment response and adverse effects.

In the present study, only pulmonary Tuberculosis was included.

Figure 1 shows the participant selection process.

Fig. 1

participants selection process

Baseline characteristics such as age, gender, CD4 count, WHO clinical stage, preventive therapy received, and ART regimen were collected for the study participants.

Data collection

Data were retrieved from the electronic medical records (EMRs) of the ART Centre. The following information was collected:

• 1. Demographic details: Age, gender.

• 2. Clinical data: WHO clinical stage, presence of tuberculosis (TB), opportunistic infections, only pulmonary tuberculosis was included in the analysis.

• 3. Laboratory parameters: CD4 count, complete blood count (CBC) with differential count, hemoglobin levels.

• 4. Treatment details: Antiretroviral therapy (ART) regimen, cotrimoxazole preventive therapy (CPT), isoniazid preventive therapy (IPT).

The monocyte-to-lymphocyte ratio (MLR) and neutrophil-to-lymphocyte ratio (NLR) were calculated from the CBC differential count. Hematological parameters were measured at the time of ART initiation for newly diagnosed patients and at the most recent clinic visit prior to TB diagnosis for patients already on ART. For patients without TB, the most recent measurements prior to the end of the study period were used.

Definitions

• 1. Anaemia was defined and classified according to WHO guidelines based on hemoglobin levels as shown in Table 1 [9].

• 2. A high MLR was defined as a value greater than 0.378, and a high NLR was defined as a value greater than 2 [10, 11].

Table 1 Classification of Anemia according to WHO guidelines based on hemoglobin levels

Data quality assurance

Data extraction and validation: Established standardized protocols for data extraction from medical records, with clear guidelines for handling missing or incomplete data.

Data cleaning and quality checks: Perform regular data cleaning and quality checks to identify and address potential inconsistencies, outliers, or missing values in the dataset. This may involve range checks, logical consistency checks, and cross-validation with other data sources.

Data security and confidentiality: Implement robust data security measures to protect the confidentiality and integrity of the study data, including secure data storage, access controls, and deidentification procedures.

Statistical analysis

Descriptive statistics were used to summarize the baseline characteristics of the study population. Continuous variables were reported as means and standard deviations, while categorical variables were reported as frequencies and percentages.

The hematological profiles of CLHIV with and without TB were compared using independent t-tests for continuous variables and Mann-Whitney U tests for non-parametric variables.

Logistic regression models were developed to assess the association between hematological parameters (MLR, NLR, and anemia status) and the presence of TB. Three models were constructed:

• 1. Model 1: MLR and anemia status as predictors.

• 2. Model 2: NLR and anemia status as predictors.

• 3. Model 3: MLR, NLR, and anemia status as predictors.

Crude and adjusted odds ratios (CORs and AORs) with 95% confidence intervals (CIs) were calculated. The models were adjusted for potential confounders such as age, gender, baseline CD4 count, WHO clinical stage, CPT, IPT, and ART regimen.

Model performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), as well as accuracy, sensitivity, and specificity. Sensitivity, specificity, and accuracy were calculated using standard formulas, with TB diagnosis as the gold standard. As defined in the study, a high MLR was considered a value greater than 0.378, and a high NLR was defined as a value greater than 2. These cutoffs were used to classify cases as high or low risk for TB in the respective models. For the combined model (Model 3), a case was considered high risk if either MLR > 0.378 or NLR > 2.

Sensitivity was calculated as true positives / (true positives + false negatives), specificity as true negatives / (true negatives + false positives), and accuracy as (true positives + true negatives) / total sample size. True positives were defined as TB-positive cases correctly identified as high risk by the model, while true negatives were TB-negative cases correctly identified as low risk. Model fit was assessed using McFadden’s R-squared and Nagelkerke’s R-squared.

All statistical analyses were performed using R version 4.1.2 (R Foundation for Statistical Computing, Vienna, Austria) and SPSS version 27 (IBM Corp., Armonk, NY, USA)., and a p-value < 0.05 was considered statistically significant.

Ethical considerations

The study protocol was reviewed and approved by the institutional review board (Shri MP Shah Medical College and Guru Gobind Govt Hospital) (REF No: 284/03/2023, dated 22/02/2024) Confidentiality and anonymity of the participants were maintained throughout the study. This study was consented to by Gujarat State AIDS Control Society (GSACS) (GSACS/SIMU/Research/2023-24/3/4240, dated 14/03/2024).

Results

The study population consisted of 276 children living with HIV (CLHIV). Table 2 shows the Baseline Characteristics of the Study Population (N = 276). The mean age of the participants was 6.8 ± 3.98 years, with 170 males and 106 females. The majority (74.6%) had a CD4 count greater than 350 cells/µL, and 63% received cotrimoxazole preventive therapy (CPT). The mean baseline hemoglobin level was 9.7 ± 1.7 g/dL. Regarding anemia status, 20.3% had no anemia, 11.6% had mild anemia, 56.9% had moderate anemia, and 11.2% had severe anemia.

Table 2 Baseline characteristics of the study population (N = 276)

Table 3 compares the hematological profiles of children living with HIV with and without tuberculosis (TB). The mean hemoglobin level was significantly lower in those with TB (9.30 ± 1.46 g/dL) than those without TB (9.9 ± 1.95 g/dL), with a p-value of 0.023. The mean platelet count was similar between the two groups (p-value = 0.266). Children with TB had a higher mean white blood cell count (8.94 ± 6.3 × 10³/µL vs. 8.43 ± 3.8 × 10³/µL, p-value = 0.460) and a significantly higher mean absolute neutrophil count (6.1 ± 4.7 × 10³/µl vs. 4.6 ± 2.3 × 10³/µl, p-value < 0.001) compared to those without TB. The mean CD4 count was lower in those with TB (560 ± 353 cells/µL) compared to those without TB (883 ± 714 cells/µL), with a p-value of 0.002. The mean MCV was similar between the groups (p-value = 0.872). However, children with TB had a significantly higher median MLR ratio (0.460 ± 0.117 vs. 0.167 ± 0.143, p-value = 0.001) and NLR ratio (2.1 ± 0.3 vs. 1.7 ± 0.7, p-value = 0.001) compared to those without TB.

Table 3 Comparison of hematological profiles of CLHIV with and without TB (N = 276)

Three logistic regression models were developed to assess the predictive ability of hematological parameters for TB in this study population.

Table 4: Model 1 included MLR status (greater vs. less) and anemia status (mild, moderate, and severe vs. normal) as predictors. The model had an R² of 0.297, indicating that it explained 29.7% of the variation in the outcome. Individuals with a greater MLR ratio had 15.62 times higher odds of having TB than those with a lower MLR ratio (95% CI: 6.84–35.67, p-value < 0.001). Anemia is not statistically significant in multivariant analysis. The model had an accuracy of 87.3% and an AUC of 83.1 with a specificity of 62%, a sensitivity of 92.9%,

Table 4 Logistic regression model for predicting tuberculosis in CLHIV using MLR and Anemia status (Model 1)

Table 5: Model 2 included anemia status and NLR ratio (low vs. high) as predictors. The model had an R² of 0.308, explaining 30.8% of the variation in the outcome. Individuals with a high NLR ratio had 17.28 times higher odds of having TB compared to those with a low NLR ratio (95% CI: 7.41–40.35, p-value < 0.001). The model had an accuracy of 85.9% and an AUC of 84.8%, with a specificity of 68% and a sensitivity of 89.8%.

Table 5 Logistic regression model for predicting tuberculosis in CLHIV using NLR and Anemia status (Model 2)

Table 6: Model 3 included MLR status, NLR ratio, and anemia status as predictors. The model had an R² of 0.387, explaining 38.7% of the variation in the outcome. Individuals with a higher MLR ratio had 4.61 times higher odds of having TB compared to those with a lower MLR ratio (95% CI: 1.72–12.38, p-value-0.002). Individuals with a high NLR ratio had 5.34 times higher odds of having TB compared to those with a low NLR ratio (95% CI: 1.97–14.48, p-value < 0.001). The model had an accuracy of 88% and an AUC of 89.2% with a specificity of 60%, and a sensitivity of 94.2%.

Table 6 Logistic regression model for predicting tuberculosis in CLHIV using MLR, NLR, and Anemia status (Model 3)

Figure 2 shows the ROC curves of models 1,2 and 3.

Fig. 2

ROC curves of model-1,2 and 3

In summary, the study found that higher MLR ratios and NLR ratios were associated with increased odds of having TB in children living with HIV. The models incorporating these hematological parameters, along with anemia status, demonstrated good to excellent predictive ability for TB in this pediatric population, as evidenced by the R² values, odds ratios, and AUC values. These findings highlight the potential utility of routinely available hematological parameters in predicting TB disease in children living with HIV.

Discussion

This study investigated the association between hematological parameters, including the monocyte-to-lymphocyte ratio (MLR), neutrophil-to-lymphocyte ratio (NLR), and anemia status, and the presence of tuberculosis (TB) in a pediatric population living with HIV. The findings contribute to the growing body of evidence exploring the potential utility of these routinely available hematological markers as screening tools for TB in vulnerable populations, particularly in resource-limited settings.

The baseline characteristics of the study population highlight the high prevalence of anemia among CLHIV, with 56.9% having moderate anemia and 11.2% having severe anemia. Anemia is a well-recognized complication of HIV infection and is associated with disease progression, increased morbidity, and mortality [12,13,14]. The observed high rates of anemia in this study population underscore the need for vigilant monitoring and appropriate management strategies.

This study also reveals significant differences in hematological profiles between CLHIV with and without TB. Notably, children with TB had lower mean hemoglobin levels, higher absolute neutrophil counts, lower CD4 counts [15, 16], and significantly higher MLR and NLR ratios compared to those without TB [5, 17]. These findings are consistent with previous studies that have reported altered hematological parameters in HIV-TB co-infection [5, 15,16,17]. The higher MLR and NLR ratios observed in children with TB may reflect the underlying inflammatory and immune response to the TB infection, which alters the balance between different leukocyte populations [5, 17, 18].

The logistic regression models demonstrate the potential predictive ability of MLR, NLR, and anemia status for TB in this pediatric population. In Model 1, individuals with a greater MLR ratio had significantly higher adjusted odds of having TB (AOR = 15.62, 95% CI: 6.84–35.67, p < 0.001) compared to those with a lower MLR ratio. Similarly, in Model 2 (Table 5), a high NLR ratio was associated with increased adjusted odds of TB (AOR = 17.28, 95% CI: 7.41–40.35, p < 0.001). These findings align with previous studies that have reported the usefulness of MLR and NLR in predicting TB in adult populations living with HIV [5, 17, 18].

Interestingly, Model 3, which included both MLR and NLR as predictors, along with anemia status, exhibited the best performance in terms of model fit (McFadden’s R² = 0.387, Nagelkerke R² = 0.501) and predictive ability (AUC = 0.892, accuracy = 88%, sensitivity = 94.2%). This suggests that combining these hematological parameters may enhance the predictive power for TB in CLHIV, potentially providing a more comprehensive assessment of the underlying immunological and inflammatory processes associated with TB co-infection.

While anemia status alone did not show a significant association with TB in the adjusted models, it is noteworthy that moderate anemia was associated with increased odds of TB in Model 1 (AOR = 2.15, 95% CI: 0.65–7.09, p = 0.209), albeit not statistically significant. This aligns with previous findings suggesting that anemia may be a predictor of TB in HIV-infected individuals [19, 20].

Limitations

Retrospective study design: The retrospective nature of the study relies on existing medical records and data, which may be subject to potential biases and inaccuracies in documentation. Single-center study: The study was conducted at a single tertiary care hospital, which may limit the generalizability of the findings to other healthcare settings or populations with different demographic or clinical characteristics. Potential confounding factors: While the study adjusted for some potential confounders, there may be other unmeasured or unaccounted factors that could have influenced the observed associations. Lack of longitudinal data: The study may have benefited from longitudinal data on hematological parameters and clinical outcomes to better understand the dynamic changes and their prognostic value over time. A key limitation of this study is the relatively small number of CLHIV with PTB (50 out of 276 participants. This may limit the generalizability of our findings to broader populations of CLHIV. Future large-scale, multi-center studies with a higher number of PTB cases are crucial to validate and extend our findings.

Despite these limitations, the strengths of this study include the relatively large sample size of 276 CLHIV and the comprehensive analysis of multiple hematological parameters of TB disease. The utilization of routinely available laboratory data also highlights the potential practical applicability of these findings in resource-limited settings, where access to more advanced diagnostic tools may be limited.

Future prospective studies, incorporating additional biomarkers and clinical outcomes, are warranted to further validate and refine the use of hematological parameters in the diagnosis, monitoring, and risk stratification of CLHIV with TB disease. Moreover, the integration of these readily available and cost-effective markers into existing diagnostic algorithms and clinical decision-making processes could potentially improve the overall management and outcomes for this vulnerable patient population, especially in resource-limited settings.

Recommendations

Based on the findings of this study, the following implications and recommendations can be made for the HIV/TB program:

• 1. Integrate hematological parameters in screening protocols: Incorporate MLR, NLR, and anemia assessment into existing TB screening algorithms for CLHIV to enhance early detection.

• 2. Implement risk stratification: Prioritize CLHIV with elevated MLR and NLR ratios for intensive monitoring and early intervention.

• 3. Utilize in resource-limited settings: Leverage these cost-effective parameters where advanced diagnostics are unavailable to optimize resource allocation.

• 4. Update clinical guidelines: Incorporate these findings into national and international TB/HIV management guidelines.

• 5. Conduct further validation: Perform prospective multi-center studies to validate these findings across diverse settings and populations.

By incorporating the assessment of hematological parameters into existing HIV/TB programs, healthcare providers can leverage readily available and cost-effective tools to enhance early detection, risk stratification, and targeted interventions, ultimately improving the management and outcomes for this vulnerable patient population.

Conclusion

This study demonstrates that elevated monocyte-to-lymphocyte ratio (MLR) and neutrophil-to-lymphocyte ratio (NLR) are significantly associated with tuberculosis in children living with HIV. The combination model incorporating both ratios with anemia status showed excellent predictive ability (AUC = 0.892, sensitivity = 94.2%), outperforming individual parameter models. These readily available hematological markers offer potential as cost-effective screening tools for TB in resource-limited settings. Integration of these parameters into existing screening algorithms could facilitate earlier TB detection in this vulnerable population, potentially improving clinical outcomes and reducing transmission. Health policies should consider incorporating these accessible biomarkers into pediatric HIV care guidelines, particularly in high-TB-burden regions with limited diagnostic capabilities.