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.

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.

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Publication Details

Journal
TNHJPH
Published
2024-10-27
DOI
https://doi.org/10.71637/tnhj.v25i1.944
Primary Topic
Tuberculosis Research and Epidemiology
Type
article
Field-Weighted Citation Impact
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article

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.

TNHJPH
Tuberculosis Research and Epidemiology
article

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.

article en

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.

TNHJPH
AIDS Prevention Initiative in Nigeria (NG)
Good health and well-being
Openalex Percentile: Top 49%
Tuberculosis Research and Epidemiology
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