Haematological markers as predictive tools for tuberculosis in PLHIV: a retrospective cohort study in Gujarat, India.

Abstract

Background: Tuberculosis (TB) remains a significant health challenge among people living with HIV (PLHIV), underscoring the need for early diagnosis and prompt treatment. Hematological parameters have emerged as potential markers for predicting and monitoring TB disease. This study aimed to assess the utility of hematological parameters in predicting TB disease among PLHIV.

Methods: This retrospective cohort study was conducted at an Antiretroviral Therapy (ART) Centre in Gujarat, India, including PLHIV registered between January 2018 and March 2024. Hematological parameters, including hemoglobin levels, platelet counts, white blood cell differentials, and derived ratios (monocyte-lymphocyte ratio [MLR] and neutrophil-lymphocyte ratio [NLR]), were extracted from medical records. TB diagnosis was based on bacteriological confirmation or clinical criteria. Cox proportional hazards models and Kaplan-Meier survival analyses were performed to assess the association between hematological parameters and TB disease, adjusting for potential confounders.

Results: Among 810 PLHIV, 202 (25%) had TB disease. PLHIV with TB had a higher prevalence of anemia (91.6% vs. 60.0%, p < 0.001), leucocytosis (16.3% vs. 7.6%, p = 0.0004), and neutrophilia (25.2% vs. 9.4%, p < 0.0001) compared to those without TB. A higher MLR ratio (> 0.23) was associated with an increased risk of TB (HR: 5.44, 95% CI: 3.94-7.50, p < 0.001), independent of anemia. Anemia was also an independent predictor of TB (HR: 4.37, 95% CI: 2.72-7.02, p < 0.001).

Conclusions: Hematological parameters, particularly MLR ratio and anemia status, showed strong associations with TB disease among PLHIV. An MLR > 0.23 was associated with a 5.44-fold increased risk of TB, while anemia increased the risk by 4.37-fold. These readily available and cost-effective markers could enhance early TB detection and risk stratification in PLHIV, especially in resource-limited settings. Integration of these parameters into existing screening protocols may improve targeted interventions and patient outcomes.