AMASSMENT - Algorithm Modelled & Applied in Sabah for Smear Negative Pulmonary Tuberculosis

Research Question: Can a clinical algorithm be used to predict pulmonary tuberculosis (PTB) in a suspected patient with negative sputum smears for acid-fast bacilli (AFB)? Background: Pulmonary Tuberculosis (PTB) remain a major lung infection in lower- and middle-income countries (LMICS). The World Health Organization (WHO) estimates that 10 million people have TB disease each year, yet only 7 million are diagnosed and notified. Direct microscopy, which detects AFB from sputum smears is the primary method of diagnosis. Negative sputum smears often lead to missed or delayed diagnosis. Finding the 'Missing Millions' is a key target as missed diagnoses contribute to silent transmissions. Sabah state in Malaysia has one of the highest incidences of TB in the country. Smear negative TB accounts for 20% of all TB cases. The WHO (2020) and Malaysian (2021) guidelines recommend using gene-Xpert to improve the diagnosis of smear negative TB. However, due to the high cost, this test is not widely available in many LMICS. Hence, this study aimed to develop a clinical algorithm for detecting smear negative PTB and to test the validity of this algorithm. Methodology: This study has two phases. Phase One enlists a panel of experts to develop the clinical algorithm using a modified Delphi method. Phase Two will recruit symptomatic patients who are smear negative for AFB to carry out gene-Xpert test and Mycobacterium TB (MTB) culture to determine the sensitivity and specificity of the developed algorithm using MTB culture as the gold standard. A sample of 700 will be collected. Questions to discuss: 1. Can a clinical algorithm predict the diagnosis of pulmonary TB in patients whose sputum smears are negative for AFB? 2. If the clinical algorithm is valid, will this algorithm be applicable to other LMICS?