Early Diagnostic Utility of Genexpert for the Detection of <i>Mycobacterium tuberculosis</i>

Background: Tuberculosis (TB) remains a leading cause of morbidity and mortality worldwide, necessitating accurate and rapid diagnostic methods to improve early detection and treatment outcomes. Objective: To evaluate the diagnostic performance of the GeneXpert MTB/RIF assay compared with sputum smear microscopy in suspected pulmonary TB patients. Methodology: This was a descriptive, cross-sectional study conducted over a period of six months (January to June 2024) at THQ Hospital Jand, District Attock. This cross-sectional study was conducted on 150 patients with suspected pulmonary TB. Demographic and clinical data were recorded, and all participants underwent sputum smear microscopy and GeneXpert MTB/RIF testing. Smear microscopy was used as the reference standard to evaluate diagnostic performance in terms of sensitivity, specificity, predictive values, likelihood ratios, and accuracy. SPSS version 20 was used to analyze the data. Relationships between continuous variables were ascertained using the Pearson correlation coefficient. P-values less than 0.05 were deemed statistically significant. Results: The mean age of participants was 36.40 ± 17.12 years, with 74 (49.33%) males and 76 (50.67%) females. Most patients resided in rural areas (111; 74.0%) and were married (107; 71.33%), with farmers constituting the largest occupational group (94; 62.67%). Common symptoms included fatigue (140; 93.33%), chest pain (139; 92.67%), unintentional weight loss (137; 91.33%), and coughing up mucus or blood (117; 78.0%). GeneXpert detected TB in 123 (82.0%) patients compared to 85 (56.67%) by smear microscopy. Among smear-negative patients, GeneXpert identified 43 additional cases. The assay showed moderate diagnostic performance with strong agreement with smear microscopy. Conclusion: GeneXpert MTB/RIF assay offers a rapid and more sensitive alternative to smear microscopy, particularly beneficial for detecting smear-negative TB cases, thereby improving case detection in high-burden settings.