MICA: Radio+ TB trial development: Determining the optimal regimen for bacteriologically negative, radiographically apparent TB diagnosed usingnewtest

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Tuberculosis (TB) is the leading cause of death from an infectious disease globally, with most of the cases in Africa and Asia. There is now a strong, international political commitment to ending the TB epidemic over the next 15 years. At the moment, each year only 70% (7 million) of the world's estimated 10 million TB cases are picked up and started on treatment. A major goal of the End-TB strategy over the next few years is to "find these missing millions" starting at least 90% of TB cases on treatment. To do this it is recognised that we need a much more proactive approach and need to embrace technological advances. Digital chest X-Ray (where image appears on a computer screen rather than a film) is increasingly being recommended as an important screening tool to do this as it can pick up the earliest signs of disease on the lungs before the patient has any symptoms and therefore identify people who need testing for TB (on a coughed up sample of sputum). Each year millions of people in Africa and Asia are now being screened for TB with digital chest X-Ray (>400,000 in one city in Pakistan alone). Although this has proved very effective, an emerging issue with this approach is that it finds lots of people who look like they have TB on the chest X-ray but their sputum test is negative. We know some of these people are at very high risk to developing TB in subsequent years, but there is currently no agreed simplified approach on how to treat this group in these settings and they often are not followed up and become part of the lost TB cases. Part of the problem is than no clinical trials have been done for this group for over 35 years. We want to directly address this problem in a modern trial to find out, how much of what drug treatment is needed to treat people who have abnormal chest X-Ray but negative sputum tests for TB to stop them from developing TB in the future. We will use two new tests to identify those most likely to have TB and benefit from treatment, these tests are ready to be used if found to work well. Firstly we will use software which uses artificial intelligence to analyse the chest x-ray image to see if the pattern suggests TB. This software provides an answer in minutes at low cost and now is so sophisticated that it performs better and more consistently than human readers. If the software thinks the chest X-Ray looks like TB, we will conduct a second test on a small volume of blood (ultimately may only be a finger prick). This blood sample is placed directly into a cartridge and loaded onto a machine and analysed for a genetic pattern which suggests the body is fighting TB, with an answer available in 2 hours. This new blood test is made by the same company (Cepheid) as the most widely used sputum test for TB (Xpert) and run on exactly the same machine, therefore the machine is even found on mobile X-ray vans that travel into hard to reach communities to find TB.In order to undertake this trial we need to address some key questions which we propose to do in this development grant namely:1) There are several artificial intelligence software packages that have been trained in different ways. What is the best artificial intelligence software package for our trial, to detect and predict TB in the settings we are interested in?2) By how much will adding the new TB blood test improve this detection/prediction?3) If a thousand people were screened with X-Ray how many will the artificial intelligence software call abnormal and how many of those will have a positive TB blood test? 4) What do patients and their doctors think of our approach and what would they value the most in treatment? Short duration, high success rate or certainty of diagnosis?5) Where should we conduct our trial? Which setting has the right combination of seeing enough patients and expertise to conduct a trial?