Clinical Performance of a Molecular-based Lateral Flow Assay for Rapid and Simultaneous Detection of Tuberculosis and Non-Tuberculous Mycobacteria in Indonesia.
DOI:
https://doi.org/10.36497/sb890673Keywords:
nucleic-acid lateral immunoflow assay, rapid molecular test, TB diagnosisAbstract
Background: Rapid molecular diagnostic for Tuberculosis (TB) and Non-Tuberculosis Mycobacteria (NTM) is urgently needed, especially in high burden area, as molecular methods are focused on easy implementation for routine TB diagnosis in low-resource countries, and to decrease the turnaround time and increase sensitivity significantly. Our objectives is to evaluate the diagnostic performance of a new multi-target and rapid molecular assay based on universal lateral flow assay to visualize the amplification results - the PaxView® TB/NTM Multiplex PCR (MPCR)- Universal Lateral Flow Assay (ULFA) Kit recommended by the World Health Organization (WHO).
Methods: The study was conducted in Bandung, Indonesia, using sputum samples from outpatient TB clinic in Hasan Sadikin General Hospital. Sputum samples were collected consecutively from each patient. All the eligible samples underwent microbiological work-up and Xpert MTB/RIF testing with different sputum taken previously. The collected sputum was extracted, amplified, and then visualized using the PaxView® TB/NTM MPCR-ULFA kit.
Results: The sensitivity and specificity of the PaxView® MPCR-ULFA were 95.1% and 82.4%, respectively. The positivity rate for MPCR ULFA showed significantly higher result (43.0%) compared to culture (32.8%), smear microscopy (26.3%), and comparable with the Xpert MTB/RIF assay (43.5%). A total of 23 confirmed NTM positive cases were found in this study (2.6%).
Conclusion: The PaxView® MPCR-ULFA demonstrated promising performance in detecting MTB and NTM. The sensitivity and specificity of this kit are comparable to other molecular diagnostic modalities. The simple platform using a test strip and straightforward visualization might be appropriately applied in poorly-setting laboratories.
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Copyright (c) 2026 Lidya Chaidir, Nabilla Ghina Zavitri, Adi Imam Cahyadi, Claudia Selviyanti, Dyshelly Nurkartika Pascapurnama, Jessi Annisa, Bachti Alisjahbana, Prayudi Santoso

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