Bi-directional Diagnosis of Tuberculosis (TB) Using Xpert® MTB/RIF Ultra and Determine® TB-LAM Ag Testing Methodologies in a Parallel Algorithm: Pathway Towards Identifying the Nigerian Missing TB Cases.
Year: 2024
Type: article
Abstract: Background: The endemic nature of TB infection in Nigeria requires that the nation’s TB diagnostic tools and algorithms be reassessed towards meeting the current global trends in TB case-detection. Also, the incidence and prevalence of both Pulmonary tuberculosis (PTB) and Extra-Pulmonary tuberculosis (EPTB) needs to be well ascertained for Nigeria to know her true TB prevalence. Methods: 365 presumptive and consenting TB clients participated in this cross-sectional study. Collected sputum and urine samples were tested with Xpert® MTB/RIF ultra and the Determine® TB-LAM Ag®; following standard laboratory operating processes. Obtained data were statistically analyzed using the student’s t-test, the Pearson’s correlation statistics, and simple percentages with the Excel® Data Analysis ToolPak. Results: 30.4% (111/365) combined Mtb cases were detected in this study. Using Student’s t-test at 95% CL, the obtained p-value (P 0.018104), and a calculated correlation coefficient (r=1) obtained reveals a significantly strong association and a perfect positive correlation between the bi-directional diagnosis of TB using this parallel TB screening methodologies, and an increased Mtb cases detection. Conclusion: In this study, the high sensitivity of the Xpert® MTB/RIF Ultra at detecting PTB and the high specificity of the Determine® TB-LAM Ag® at detecting EPTB has been synergized in a parallel algorithm. It is believed that the application of this testing methodology in a parallel algorithm would help to optimize Mtb cases detection and TB diagnosis in Nigeria.
Source: The Nigerian Health Journal
Authors: Segun, Adeniyi David, John, Tongvwam Princewill, Chikezie, Okonkwo Patrick, Emmanuel, Owolagba Femi, Eke, Ofuche, Ifeyinwa, Onwuatuelo, Osi, Samuels Jay, Prosper, Okonkwo
Institution: AIDS Prevention Initiative in Nigeria
Language: English
Subfield: Infectious Diseases
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W6977427170
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