Limited External Validation and Uncertain Clinical Utility of Prediction Models for Bacteriologically Negative Pulmonary Tuberculosis: A Systematic Review.
5 affiliations
- 1Department of Community Health, Pusat Kanser Tun Abdullah Ahmad Badawi, Universiti Sains Malaysia, Penang, MYS.
- 2Department of Thoracic Surgery, The First Affiliated Hospital of Henan Medical University, Xinxiang, CHN.
- 3Department of Pediatrics, The First Affiliated Hospital of Henan Medical University, Xinxiang, CHN.
- 4Life Science Research Center, The First Affiliated Hospital of Henan Medical University, Xinxiang, CHN.
- 5Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Henan Medical University, Xinxiang, CHN.
Abstract
Prediction models for bacteriologically negative pulmonary tuberculosis (BN-PTB) may support diagnostic triage, but their external validity and clinical utility remain uncertain. We searched PubMed, Embase, Web of Science, and Scopus from database inception through December 31, 2025. The review was reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020; risk of bias and applicability were assessed using Prediction model Risk Of Bias ASsessment Tool (PROBAST), and certainty of evidence was evaluated using an adaptation of Grading of Recommendations Assessment, Development and Evaluation (GRADE) for diagnostic test accuracy. Of 163 full-text reports, 160 were excluded because of population mismatch (n=63), target or reference-standard mismatch (n=52), methodological or design ineligibility (n=20), or model-use or reporting ineligibility (n=25). Three reports met the completed review eligibility criteria and contributed six distinct externally validated prediction models. Five models provided external-validation data sufficient for the extraction or reconstruction of model-specific 2×2 tables. The four Li QH et al. models had study-reported thresholds applied in the validation sample; for the Meng et al. model, the reported cutoff was 0.657, but its explicit linkage to the external-validation estimates was unclear. Consequently, the reconstructed Meng estimates were considered approximate. Model-specific sensitivities ranged from 0.877 to 0.982 and specificities from 0.864 to 0.986. No cross-model pooling was performed because four models shared one validation cohort and the models differed in predictor sets, threshold information, comparator spectra, and clinical settings. The Xu et al. nomogram reported an external C-index of 0.77 (95% confidence interval 0.68-0.86) but no fixed threshold or threshold-specific 2×2 data. Although several models showed favorable performance in their respective validation samples, the evidence was limited by shared validation data, disparate comparator spectra, incomplete threshold and calibration reporting, and the absence of repeated independent validation or prospective clinical-impact evaluation. The currently eligible evidence does not establish sufficient transportability or clinical utility for routine implementation.
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BibTeX
@article{zhai2026limited,
title = {{Limited External Validation and Uncertain Clinical Utility of Prediction Models for Bacteriologically Negative Pulmonary Tuberculosis: A Systematic Review.}},
author = {Chuntao Zhai and Baosheng Zhao and Yu Yu and Yue Zhao and Xiaohua Yang and Yuzhen Liu and Ernest Mangantig},
journal = {Cureus},
year = {2026},
month = sep,
doi = {10.7759/cureus.116983},
url = {https://europepmc.org/article/MED/42948785},
note = {CC BY},
}
RIS
TY - JOUR
TI - Limited External Validation and Uncertain Clinical Utility of Prediction Models for Bacteriologically Negative Pulmonary Tuberculosis: A Systematic Review.
AU - Chuntao Zhai
AU - Baosheng Zhao
AU - Yu Yu
AU - Yue Zhao
AU - Xiaohua Yang
AU - Yuzhen Liu
AU - Ernest Mangantig
PY - 2026
DA - 2026/09/26
JO - Cureus
DO - 10.7759/cureus.116983
UR - https://europepmc.org/article/MED/42948785
AB - Prediction models for bacteriologically negative pulmonary tuberculosis (BN-PTB) may support diagnostic triage, but their external validity and clinical utility remain uncertain. We searched PubMed, Embase, Web of Science, and Scopus from database inception through December 31, 2025. The review was reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020; risk of bias and applicability were assessed using Prediction model Risk Of Bias ASsessment Tool (PROBAST), and certainty of evidence was evaluated using an adaptation of Grading of Recommendations Assessment, Development and Evaluation (GRADE) for diagnostic test accuracy. Of 163 full-text reports, 160 were excluded because of population mismatch (n=63), target or reference-standard mismatch (n=52), methodological or design ineligibility (n=20), or model-use or reporting ineligibility (n=25). Three reports met the completed review eligibility criteria and contributed six distinct externally validated prediction models. Five models provided external-validation data sufficient for the extraction or reconstruction of model-specific 2×2 tables. The four Li QH et al. models had study-reported thresholds applied in the validation sample; for the Meng et al. model, the reported cutoff was 0.657, but its explicit linkage to the external-validation estimates was unclear. Consequently, the reconstructed Meng estimates were considered approximate. Model-specific sensitivities ranged from 0.877 to 0.982 and specificities from 0.864 to 0.986. No cross-model pooling was performed because four models shared one validation cohort and the models differed in predictor sets, threshold information, comparator spectra, and clinical settings. The Xu et al. nomogram reported an external C-index of 0.77 (95% confidence interval 0.68-0.86) but no fixed threshold or threshold-specific 2×2 data. Although several models showed favorable performance in their respective validation samples, the evidence was limited by shared validation data, disparate comparator spectra, incomplete threshold and calibration reporting, and the absence of repeated independent validation or prospective clinical-impact evaluation. The currently eligible evidence does not establish sufficient transportability or clinical utility for routine implementation.
SN - 2168-8184
ER -