Integrating tuberculosis and COVID-19 molecular testing in Lima, Peru: a cross-sectional, diagnostic accuracy study
Bibliographic record
Abstract
BACKGROUND: Integrated molecular testing could be an opportunity to detect and provide care for both tuberculosis and COVID-19. Many high tuberculosis burden countries, such as Peru, have existing GeneXpert systems for tuberculosis testing with GeneXpert Xpert MTB/RIF Ultra (Xpert Ultra), and a GeneXpert SARS-CoV-2 assay, GeneXpert Xpert Xpress SARS-CoV-2 (Xpert Xpress), is also available. We aimed to assess the feasibility of integrating tuberculosis and COVID-19 testing using one sputum specimen with Xpert Ultra and Xpert Xpress in Lima, Peru. METHODS: In this cross-sectional, diagnostic accuracy study, we recruited adults presenting with clinical symptoms or suggestive history of tuberculosis or COVID-19, or both. Participants were recruited from a total of 35 primary health facilities in Lima, Peru. Participants provided one nasopharyngeal swab and one sputum sample. For COVID-19, we tested nasopharyngeal swabs and sputum using Xpert Xpress; for tuberculosis, we tested sputum using culture and Xpert Ultra. We compared diagnostic accuracy of sputum testing using Xpert Xpress with nasopharyngeal swab testing using Xpert Xpress. Individuals with positive Xpert Xpress nasopharyngeal swab results were considered COVID-19 positive, and a positive culture indicated tuberculosis. To assess testing integration, the proportion of cases identified in sputum by Xpert Xpress was compared with Xpert Xpress on nasopharyngeal swabs, and sputum by Xpert Ultra was compared with culture. FINDINGS: Between Jan 11, 2021, and April 26, 2022, we recruited 600 participants (312 [52%] women and 288 [48%] men). In-study prevalence of tuberculosis was 13% (80 participants, 95% CI 11-16) and of SARS-CoV-2 was 35% (212 participants, 32-39). Among tuberculosis cases, 13 (2·2%, 1·2-3·7) participants were concurrently positive for SARS-CoV-2. Regarding the diagnostic yield of integrated testing, Xpert Ultra detected 96% (89-99) of culture-confirmed tuberculosis cases (n=77), and Xpert Xpress-sputum detected 67% (60-73) of COVID-19 cases (n=134). All five study staff reported that integrated molecular testing was easy and acceptable. INTERPRETATION: The diagnostic yield of Xpert Xpress on sputum was moderate, but integrated testing for tuberculosis and COVID-19 with GeneXpert was feasible. However, systematic testing for both diseases might not be the ideal approach for everyone presenting with presumptive tuberculosis or COVID-19, as concurrent positive cases were rare during the study period. Further research might help to identify when integrated testing is most worthwhile and its optimal implementation. FUNDING: Canadian Institutes of Health Research and International Development Research Centre. TRANSLATION: For the Spanish translation of the abstract see Supplementary Materials section.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".