Development and Validation of a Point-of-Care Triage Scorecard to Enhance Tuberculosis Case Detection During Active Community Screening in Yogyakarta, Indonesia
- 1Gadjah Mada University Faculty of Medicine Public Health and Nursing: Universitas Gadjah Mada Fakultas Kedokteran Kesehatan Masyarakat dan Keperawatan
Abstract
In primary healthcare centers lacking advanced imaging, community-based active tuberculosis (TB) case finding often relies on basic symptom screening. This approach often misses cases and leads to the inefficient allocation of rapid molecular testing (RMT). We aimed to develop and internally validate a simple clinical triage scorecard to improve TB detection and guide RMT use in resource-constrained settings. We conducted a retrospective cross-sectional study of 15,137 adults ([≥]18 years) evaluated within the Zero TB Yogyakarta program (2020-2025). Participants with complete clinical assessments and confirmatory GeneXpert results were included. Using multivariable logistic regression, we identified independent clinical predictors, which were subsequently transformed into an integer-based point scorecard. Model performance was evaluated via discrimination and calibration, utilizing bootstrap resampling (1,000 iterations) for internal validation. Among the 15,137 participants, 251 (1.7%) were GeneXpert-positive. The final multivariable model identified eight independent predictors: age, male sex, body mass index, prolonged cough, hemoptysis, unexplained weight loss, TB contact history, and diabetes mellitus. The model demonstrated strong predictive accuracy, with an optimism-adjusted AUROC of 0.836 and good calibration. When translated to the integer scorecard and compared directly to standard national symptom screening, the scorecard performed significantly better (AUROC 0.81 vs. 0.73; p<0.001). At a high sensitivity cut off score of [≥] 0, the tool achieved 93.63% sensitivity and 41.33% specificity. This point-of-care clinical scorecard provides higher diagnostic accuracy than standard symptom screening algorithms. By offering flexible operational thresholds, it empowers local health programs to dynamically balance the urgency of case detection with available diagnostic capacity, optimizing GeneXpert allocation where advanced radiological imaging is unavailable.
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BibTeX
@misc{catrianiningsih2026development,
title = {{Development and Validation of a Point-of-Care Triage Scorecard to Enhance Tuberculosis Case Detection During Active Community Screening in Yogyakarta, Indonesia}},
author = {D. Catrianiningsih and F. Felisia and A. S. Abdalla and S. Puspitasari and B. Dwihardiani and H. N. Mulia and A. Hidayat and R. Triasih},
howpublished = {medRxiv},
year = {2026},
month = aug,
doi = {10.64898/2026.08.27.26361569},
url = {https://www.medrxiv.org/content/10.64898/2026.08.27.26361569v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Development and Validation of a Point-of-Care Triage Scorecard to Enhance Tuberculosis Case Detection During Active Community Screening in Yogyakarta, Indonesia
AU - D. Catrianiningsih
AU - F. Felisia
AU - A. S. Abdalla
AU - S. Puspitasari
AU - B. Dwihardiani
AU - H. N. Mulia
AU - A. Hidayat
AU - R. Triasih
PY - 2026
DA - 2026/08/27
PB - medRxiv
DO - 10.64898/2026.08.27.26361569
UR - https://www.medrxiv.org/content/10.64898/2026.08.27.26361569v1
AB - In primary healthcare centers lacking advanced imaging, community-based active tuberculosis (TB) case finding often relies on basic symptom screening. This approach often misses cases and leads to the inefficient allocation of rapid molecular testing (RMT). We aimed to develop and internally validate a simple clinical triage scorecard to improve TB detection and guide RMT use in resource-constrained settings. We conducted a retrospective cross-sectional study of 15,137 adults ([≥]18 years) evaluated within the Zero TB Yogyakarta program (2020-2025). Participants with complete clinical assessments and confirmatory GeneXpert results were included. Using multivariable logistic regression, we identified independent clinical predictors, which were subsequently transformed into an integer-based point scorecard. Model performance was evaluated via discrimination and calibration, utilizing bootstrap resampling (1,000 iterations) for internal validation. Among the 15,137 participants, 251 (1.7%) were GeneXpert-positive. The final multivariable model identified eight independent predictors: age, male sex, body mass index, prolonged cough, hemoptysis, unexplained weight loss, TB contact history, and diabetes mellitus. The model demonstrated strong predictive accuracy, with an optimism-adjusted AUROC of 0.836 and good calibration. When translated to the integer scorecard and compared directly to standard national symptom screening, the scorecard performed significantly better (AUROC 0.81 vs. 0.73; p<0.001). At a high sensitivity cut off score of [≥] 0, the tool achieved 93.63% sensitivity and 41.33% specificity. This point-of-care clinical scorecard provides higher diagnostic accuracy than standard symptom screening algorithms. By offering flexible operational thresholds, it empowers local health programs to dynamically balance the urgency of case detection with available diagnostic capacity, optimizing GeneXpert allocation where advanced radiological imaging is unavailable.
ER -