Diagnostic Algorithms for Tuberculosis

In 2019, 10 million people fell ill and 1.4 million died from TB and in 2020 TB was the second cause of death in adults from an infectious agent, second only to COVID-19. In 2015, the World Health Organization (WHO) launched the End TB Strategy to reduce the number of TB cases by 90% by 2035. Although this strategy generated impetus for National TB control Programs, one of the major barriers to achieve its ambitious targets is the poor access to diagnostics for TB.

WHO recommends that all people with presumptive TB should be tested with WHO-recommended molecular diagnostics (WRDs). These tests are sensitive and specific, but require a good laboratory infrastructure and are usually located in distant reference laboratories. TB however is a disease of poverty and most people seek medical advice at health centres with basic laboratory facilities, limiting access to WRDs.

It is recognised that there is a major need for diagnostics suitable for health centres with limited resources, which are simple and provide rapid results locally. The tests should ideally identify as many patients with TB as possible (called a rule-in test), exclude those unlikely to have TB (called a rule out test) and identify individuals who need tests at the reference laboratories.

Although these idea diagnostics are not available, a few new diagnostics can partially meet these requirements. We therefore aim to use combinations of diagnostics that are able to 'patch up' the deficiencies of each other's' to develop diagnostic approaches that are efficient at the point of need. For example, Fujifilm's SILVAMP TB LAM (FujiLAM), a urine-based test, identified 66% and 70% of people with and without HIV who had TB in Nigeria, reaching a diagnosis within minutes.

We have also used C-Reactive Protein (CRP), a marker of inflammation, as a test of exclusion. With a 10-minute CRP test we demonstrated that patients with low CRP have less than 1% chance of having TB, allowing the rapid exclusion of the diagnosis in about 50% of patients. Moreover, although currently each sample is tested with one WRDs cartridge, samples from several patients can be pooled together for testing.

If a pooled test is negative, all samples are considered negative, while positive samples are re-tested to identify the positive sample. We have also assessed the pooling method and shown it reduces workloads and costs, making the use of WDRs more efficient. There is clearly potential to develop more efficient diagnostic algorithms.

We propose to develop diagnostic algorithms that use tests suitable for health facilities with limited facilities, which are rapid, patient-centred, cost-effective and articulated with the WHO current diagnostic algorithms based on WRDs.

We will start by developing mathematical models that test the performance of combinations of diagnostics that are suitable for the point of care in low resource settings. We will inform the models with data generated by our studies, or in the public domain, and include levels of uncertainty of their performance. We will estimate their sensitivity, specificity and proportions of patients achieving a diagnosis at the local and reference level.

We will simulate tables of performance with varying TB and HIV prevalence, to document their predictive value and how they would work in different scenarios. We will also evaluate the efficiency and acceptability of the models with patients, health service staff and stakeholders and conduct a pilot implementation study in Nigeria to estimate their cost-effectiveness.

Once the diagnostic algorithms are optimised and parametrised, we will seek funding to conduct large scale clinical trials in low income settings.