Tuberculosis (TB) continues to be a global public health problem with 10.4 million new cases and 1.4 million TB deaths annually. About 600 000 of these new TB cases are resistant to rifampicin, the most important first line drug. In this PhD project I aim to bring Whole Genome Sequencing (WGS) as a diagnostic method, currently mostly used in research, to the patient. Interpretation of WGS data requires expertise which is unavailable in high burden countries, I will bridge this gap by developing a software suite which automatically interprets the WGS data and recommends the optimal individualized treatment regimen for each patient, also taking clinical patient information into account. Additionally, the software will also be able to, dependent on the region and based on the prevalence of drug resistance in that region, detect unexpected drug resistance patterns in patients. For these patients, additional drug susceptibility tests (DSTs) will be recommended.
The software will have an easy to use interface where health care workers can enter the clinical patient data. The WGS results will be combined with the clinical information and the results will be made available to the health care worker. Depending on the expertise of the health care worker, additional details describing the decision process towards the optimal regimen and DST recommendation can be made available. This all-inclusive software suite will allow for easier diagnosis and treatment of drug resistant TB.