Utility of Hematological Indices in Predicting the Sputum Conversion at the End of the Intensive Phase of Treatment for Patients With Pulmonary TB
DOI:
https://doi.org/10.21276/apalm.3789Keywords:
sputum Conversion, predictor, Hematological, Pulmonary TBAbstract
Background: Early sputum conversion is a crucial marker of treatment response and infectivity reduction in pulmonary tuberculosis (TB). However, conventional microbiological monitoring methods such as culture are time-consuming and resource-intensive. Hematological indices derived from routine complete blood counts are simple, inexpensive markers of systemic inflammation and immune response and may provide additional prognostic information during treatment.Aim: To evaluate the predictive value of baseline hematological indices and their dynamic changes in identifying sputum conversion at the end of the intensive phase of anti-tubercular therapy (ATT) in patients with pulmonary TB.
Methods: This observational longitudinal study included 56 treatment-naïve, CBNAAT-positive pulmonary TB patients. Neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), neutrophil-monocyte-to-lymphocyte ratio (NMLR), and systemic immune-inflammation index (SII) were assessed at baseline and after two months of ATT. Sputum conversion was determined using smear microscopy and culture. Predictive performance was evaluated using receiver operating characteristic (ROC) curve analysis and logistic regression.
Results: Sputum conversion was achieved in 47 (83.9%) patients. Baseline values of all hematological indices were significantly higher in non-converters (p < 0.001) and declined significantly among converters following treatment. NMLR demonstrated the strongest predictive performance (AUC 0.922; OR 180), followed by MLR and NLR.
Conclusion: Hematological indices, particularly NMLR, MLR, and NLR, are effective, low-cost predictors of early microbiological response in pulmonary TB and may serve as useful adjuncts to routine treatment monitoring, especially in resource-limited settings.
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