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Archives of Infectious Diseases & Therapy(AIDT)

ISSN: 2577-8455 | DOI: 10.33140/AIDT

Impact Factor: 1.385*

Comparative Study between Genexpert and Smear Microscopy for the Diagnosis of Tuberculosis in Samples of Patients Suspected of Pulmonary Tuberculosis: the Case Study of General Hospital, Awo-Omamma, Imo State, Nigeria

Abstract

Osuoha CB, Njoku-Obi TN, Nwofor CN and Ohalete CN

Tuberculosis (TB), caused by Mycobacterium tuberculosis, has remained a major scourge of humanity all over the world, with the greatest mortality occurrences noted, in developing countries. The cannot-be-over- emphasized burden of TB in Nigeria is among the highest in Africa. The study on hand was therefore aimed at comparing Cepheid GeneXpert MTB/ RIF assay for direct detection of Mycobacterium tuberculosis Complex (MTBC) and Rifampicin (RIF) resistance with the traditional smear microscopy method-the ZN technique. Sensitivity and specificity of diagnostic yields were high points of comparison. A carefullydesigned cross-sectional study was drawn and executed at the General Hospital, Awo-Omamma, covering patients’ inflow from August, 2016 to May 2017. Amongst the numerous patients presenting, a total of 120 samples were collected from patients with highest pulmonary concerns, having been assessed prognostically.

Sixty-two patients (51.67%) were males, fifty-eight (48.33%) were females and all having mean ages of 42.2+16 years. Thirty patients (25%) had chronic lung diseases. Out of the 120 samples examined, 36 samples (30.00%) were MTBC positive by Smear microscopy while 42 (35.00%) were positive by GeneXpert. Placing both methods (GeneXpert and Smear microscopy) side-by-side, GeneXpert gave 85% sensitivity and 98.5% specificity GeneXpert indeed detected 6 (7.2%) additional positive cases as compared to Smear microscopy. Only 5 clinical isolates of the entire patients were resistant to Rifampicin. The study therefore conduced that GeneXpert was a better and more reliable diagnostic tool compared to Smear microscopy and can significantly reduce false-negatives and very i

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