# A clinical decision tool to optimize the selection of antibiotics for patients with rifampicin-resistant Tuberculosis > **NIH NIH R01** · YALE UNIVERSITY · 2024 · $647,922 ## Abstract PROJECT SUMMARY/ABSTRACT Tuberculosis (TB) remains a major public health concern worldwide with more than 1.4 million estimated deaths in 2022. Despite recent declines in global TB incidence, the emergence and spread of drug-resistant Mycobacterium tuberculosis have complicated the control of TB in many settings. Drug-resistant TB is associated with higher mortality and morbidity and requires longer duration of treatment with multiple second- line antibiotics that often have severe side effects. With the widespread adoption of Xpert MTB/RIF (a molecular test for the rapid detection of TB and resistance to rifampicin) over the last 10 years, a growing number of individuals with rifampicin-resistant TB (RR-TB) are being detected and notified in many high- burden settings. To determine an effective treatment regimen for a patient with RR-TB, the selection of antibiotics would ideally be made based on the results of drug susceptibility tests (DSTs). However, because of limited access to DSTs and lengthy delays in receiving DST results, the treatment of RR-TB in most settings remains empiric (i.e., without the results of DSTs) and according to standardized second-line regimens, which are endorsed at the global level. This results in many patients with RR-TB receiving suboptimal treatments, which exposes them to a higher risk of treatment failure, increased toxicity, and the emergence of additional resistance. To mitigate these issues, this project develops a clinical decision support (CDS) tool to optimize medications for individuals with RR-TB, at the point of care, and based on the patient’s basic demographic and clinical information (e.g., age, residence in urban or rural area, and history of TB treatment). The proposed tool combines spatiotemporal machine learning and decision models to synthesize data from clinical trials of anti- TB drugs, local surveillance systems of drug-resistant TB, and studies of cost and loss in quality of life due to illness, treatment toxicity, treatment failure, and emergence of additional resistance. Employing a user- centered design approach with direct input from stakeholders (e.g., TB practicing physicians, health services researchers, laboratory specialist, and policymakers), this project develops a prototype of a user interface for the proposed CDS tool with the potential to be implemented in routine clinical care and a follow-up randomized clinical trials. This project also evaluates the effectiveness and cost-effectiveness of treatment recommendations that are customized according to the local epidemiology of drug-resistant TB and/or according to patients’ basic demographic and clinical information compared with the standardized treatment regimens, which are determined at the global level. The proposed research is significant because it provides TB clinicians in low-resource, high-burden settings with essential evidence and tools to improve the treatment outcomes of patients with RR-TB while containing cost and... ## Key facts - **NIH application ID:** 10980109 - **Project number:** 1R01AI177326-01A1 - **Recipient organization:** YALE UNIVERSITY - **Principal Investigator:** Reza YAESOUBI - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $647,922 - **Award type:** 1 - **Project period:** 2024-07-18 → 2029-06-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10980109 ## Citation > US National Institutes of Health, RePORTER application 10980109, A clinical decision tool to optimize the selection of antibiotics for patients with rifampicin-resistant Tuberculosis (1R01AI177326-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10980109. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*