Early mortality prediction score for tuberculosis patients: a clinical implementation review
DOI:
https://doi.org/10.14456/taj.2025.10Keywords:
tuberculosis, early mortality, risk prediction scoring system, clinical implementation reviewAbstract
Early mortality during tuberculosis treatment remains a significant global public health concern, with mortality rates reaching up to 50% within the first 2-3 months of treatment. The development of risk prediction scoring systems is therefore crucial for screening and treatment planning. This study aimed to analyze current scoring systems by examining their components and key prognostic variables, evaluating their clinical effectiveness and limitations, and identifying factors contributing to successful implementation across different healthcare settings. The methodology involved a systematic literature review of clinical implementation studies. Results revealed an evolution from basic scoring systems using 5-8 clinical variables to more complex systems integrating clinical parameters, laboratory findings, and socio-demographic factors, including specialized systems for high-risk patients. The developed systems demonstrated predictive accuracy (area under the curve, AUC) ranging from 0.81 to 0.89 and achieved a 30-40% reduction in early mortality rates within the first two months. Despite limitations in accuracy across diverse populations and implementation complexities, emerging trends focusing on artificial intelligence and new technologies, coupled with flexible system development, show promise in enhancing predictive efficiency and contextual adaptability across different settings.
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