Investigating the sequelae of pulmonary tuberculosis in Gambian children and adolescents

As of 2023, there were over 9 million pediatric TB survivors globally. Many of these children often face severe long-term respiratory impairments, including post-TB lung disease (PTLD). This condition arises due to the damage TB inflicts on the developing lungs during critical growth periods, leading to persistent respiratory deficits that significantly affect quality of life and productivity. However, the natural history of PTLD and its predictors remain poorly understood, particularly in pediatric populations in resource-limited settings where access to spirometry and other diagnostic tools is limited. The PI, Dr. Esin Nkereuwem, proposes an innovative approach to address these critical knowledge gaps by leveraging a well-characterized longitudinal cohort of pediatric TB survivors in The Gambia. This K43 project aims to integrate advanced longitudinal data analysis and cutting-edge artificial intelligence (AI)- powered radiological tools to improve the diagnosis and monitoring of PTLD in resource-constrained settings. Specifically, this study will address three aims: (1) to characterize respiratory health trajectories in pediatric TB survivors using repeated spirometry, chest X-rays, and quality-of-life assessments; (2) to identify host, environmental, and treatment-related predictors of long-term respiratory impairment and develop a predictive risk model for stratifying children based on their risk of adverse outcomes; and (3) to refine and validate an AI tool for detecting PTLD-specific radiological abnormalities and assessing longitudinal changes in lung health. The project will leverage an established infrastructure at the Medical Research Council Unit The Gambia, where Dr. Nkereuwem has conducted previous research with high participant retention and rigorous data collection. Building on preliminary findings showing significant respiratory impairments in pediatric TB survivors, the study will generate longitudinal data to better understand recovery patterns and predictors of poor outcomes. The AI tool will be refined and validated for pediatric use, offering a scalable alternative to spirometry in resource-limited settings. These innovations will contribute to developing evidence-based guidelines for the care and monitoring of pediatric TB survivors globally. This K43 Award will provide Dr. Nkereuwem with advanced training in longitudinal cohort analysis, predictive modeling, and AI-based radiological diagnostics under the mentorship of a multidisciplinary team of internationally recognized experts in pediatric TB, AI diagnostics, and global health. Through structured mentorship, workshops, and hands-on research, Dr. Nkereuwem will gain the skills necessary to establish an independent research program focused on improving respiratory outcomes in children. This training and research will ensure Dr. Nkereuwem’s contributions to the field are sustainable and impactful, aligning with the mission of the Fogarty International Center to address pressing global health challenges in LMICs.