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Reviews of "Diagnostic Accuracy of Chest X-Ray Computer Aided Detection Software and Blood Biomarkers for Detection of Prevalent and Incident Tuberculosis in Household Contacts Followed up for 5 Years"

Reviewers: A Yassi (The University of British Columbia) | 📒📒📒 ◻️◻️

Published onNov 07, 2024
Reviews of "Diagnostic Accuracy of Chest X-Ray Computer Aided Detection Software and Blood Biomarkers for Detection of Prevalent and Incident Tuberculosis in Household Contacts Followed up for 5 Years"
key-enterThis Pub is a Review of
Diagnostic accuracy of Chest X-Ray Computer Aided Detection software and blood biomarkers for detection of prevalent and incident tuberculosis in household contacts followed up for 5 years
Diagnostic accuracy of Chest X-Ray Computer Aided Detection software and blood biomarkers for detection of prevalent and incident tuberculosis in household contacts followed up for 5 years
Description

Abstract Background WHO Tuberculosis (TB) screening guidelines recommend computer-aided detection (CAD) software for chest radiograph (CXR) interpretation. However, studies evaluating their diagnostic and prognostic accuracy are limited.Methods We conducted a prospective cohort study of household TB contacts in South Africa. Participants all underwent baseline CXR and sputum investigation (routine [single spontaneous] and enhanced [additionally 2-3 induced] sputum investigation and passive and active follow-up for incident TB. CXR were processed comparing 3 CAD softwares (CAD4TBv7.0, qXRv3.0.0, and Lunit INSIGHT CXR 3.1.4.111). We evaluated their performance to detect routine and enhanced prevalent, and incident TB, comparing the performance to blood-based biomarkers (Xpert MTB host-response, Erythrocyte Sedimentation Rate, C-Reactive Protein, QuantiFERON) in a subgroup.Findings 483 participants were followed-up for 4.6 years (median). There were 23 prevalent (7 routinely diagnosed) and 38 incident TB cases. The AUC ROC to identify prevalent TB for CAD4TB, qXR and Lunit INSIGHT CXR were 0.87 (95% CI 0.77-0.96), 0.88 (95% CI 0.79-0.97) and 0.91 (95% CI 0.83-0.99) respectively. >30% with scores above recommended CAD thresholds who were bacteriologically negative on routine baseline sputum were subsequently diagnosed by enhanced baseline sputum investigation or during follow-up. The AUC performance of baseline CAD to identify incident cases ranged between 0.60-0.65. The diagnostic performance of CAD for prevalent TB was superior to blood-based biomarkers.Interpretation Our findings suggest that the potential of CAD-CXR screening for TB is not maximised as a high proportion of those above current thresholds but with a negative routine confirmatory sputum have true TB disease that may benefit intervention.Funding UKRI-MRCSummary We found that the diagnostic accuracy of CAD-CXR to identify prevalent TB cases in household TB contacts was high but >30% with scores above recommended CAD thresholds who were bacteriologically negative on routine testing baseline were subsequently diagnosed suggest that the potential of CAD-CXR screening is not maximised.

To read the original manuscript, click the link above.

Summary of Reviews: The preprint explores the efficacy of computer-aided detection (CAD) software in identifying prevalent tuberculosis (TB) with high diagnostic accuracy, outperforming traditional blood-based biomarkers. The findings suggest that CAD can detect TB cases that are missed in routine sputum testing, particularly in individuals who later test positive on follow-up. One key implication is that CAD-positive cases might benefit from additional sputum testing, as relying solely on CAD could lead to unnecessary treatment. Reviewer critiques emphasize the need for clearer definitions of the study's gold standard for AUC measurements and questioning why blood biomarkers were compared given their limited predictive value. Additionally, the study could improve by considering high-risk subgroups such as silica-exposed workers, whose CXR findings might mimic TB.

Reviewer 1 (Annalee Y…) | 📒📒📒 ◻️◻️

RR\ID Strength of Evidence Scale Key

📕 ◻️◻️◻️◻️ = Misleading

📙📙 ◻️◻️◻️ = Not Informative

📒📒📒 ◻️◻️ = Potentially Informative

📗📗📗📗◻️ = Reliable

📘📘📘📘📘 = Strong

To read the reviews, click the links below. 

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