Improving Early Identification of Drug Resistant Tuberculosis in HIV Prevalent Settings
This K23 award will provide Dr. Metcalfe with the support necessary to accomplish the following goals: (1) to improve scientific understanding of the burden and impact of the multidrug resistant tuberculosis (MDR TB) epidemic within HIV prevalent settings in order to craft effective public health responses; (2) to apply laboratory research techniques for molecular drug susceptibility testing of clinical specimens; (3) to apply novel biostatistical methods to improve prediction of at-risk patients and to evaluate the incremental value of new diagnostic tests; and (4) to develop an independent clinical research career. To achieve these goals, Dr. Metcalfe has assembled a mentoring team comprised of a primary mentor, Dr. Philip Hopewell, UCSF Professor of Medicine and world-renowned authority on global TB control; and two co-mentors: Dr. Arthur Reingold, UC Berkeley Professor of Epidemiology, Associate Dean for Research, and internationally recognized infectious disease epidemiologist, and Dr. Laurence Huang, UCSF Professor of Medicine, an expert researcher in HIV-associated pulmonary diseases. The convergence of the drug-resistant TB and HIV epidemics represents a major threat to global TB control. The lack of rapid drug susceptibility testing has been a major impediment to effective prevention, treatment and epidemiologic research of drug resistant TB. While recently introduced molecular assays dramatically reduce detection times, these techniques are not routinely available in most settings. Patients with MDR TB in low-income settings are typically only identified after multiple failed treatment attempts. Dr. Metcalfe's research will capitalize on an extensive pre-existing research infrastructure in Zimbabwe centered on an NIH Division of AIDS-funded Clinical Trials Unit (5U01AI069436-04) to improve early identification of patients with MDR TB through novel prediction rules applicable to low-income, high-HIV burden settings (Aim 1), determine which patients are most likely to benefit from molecular drug susceptibility testing (Aim 2), and provide pilot data towards the first prospective study of the contribution of microbial factors to drug resistant TB transmission in a high HIV prevalence setting (Aim 3). This research will form the basis for a future R01-level proposal utilizing longitudinal patient- and pathogen-specific data to characterize development of drug resistant TB in real-time.
MDR TB now accounts for 3.6% of global TB cases, and it is estimated that only 7% of the 440,000 annual cases of MDR TB are currently detected. In most settings, MDR TB patients are routinely identified only after multiple failed treatment attempts. Improved clinical prediction models, long used in the cardiovascular and cancer literature to augment precision of diagnoses and inform decisions about treatment, would allow cost-effective application of molecular assays in areas where available, and improve early detection of high risk MDR TB suspects where they are not.