Impact of Replacing Smear Microscopy with Xpert MTB/RIF for Diagnosing Tuberculosis in Brazil: A Stepped-Wedge Cluster-Randomized Trial
Bibliographic record
Abstract
BACKGROUND: Abundant evidence on Xpert MTB/RIF accuracy for diagnosing tuberculosis (TB) and rifampicin resistance has been produced, yet there are few data on the population benefit of its programmatic use. We assessed whether the implementation of Xpert MTB/RIF in routine conditions would (1) increase the notification rate of laboratory-confirmed pulmonary TB to the national notification system and (2) reduce the time to TB treatment initiation (primary endpoints). METHODS AND FINDINGS: We conducted a stepped-wedge cluster-randomized trial from 4 February to 4 October 2012 in 14 primary care laboratories in two Brazilian cities. Diagnostic specimens were included for 11,705 baseline (smear microscopy) and 12,522 intervention (Xpert MTB/RIF) patients presumed to have TB. Single-sputum-sample Xpert MTB/RIF replaced two-sputum-sample smear microscopy for routine diagnosis of pulmonary TB. In total, 1,137 (9.7%) tests in the baseline arm and 1,777 (14.2%) in the intervention arm were positive (p<0.001), resulting in an increased bacteriologically confirmed notification rate of 59% (95% CI = 31%, 88%). However, the overall notification rate did not increase (15%, 95% CI = -6%, 37%), and we observed no change in the notification rate for those without a test result (-3%, 95% CI = -37%, 30%). Median time to treatment decreased from 11.4 d (interquartile range [IQR] = 8.5-14.5) to 8.1 d (IQR = 5.4-9.3) (p = 0.04), although not among confirmed cases (median 7.5 [IQR = 4.9-10.0] versus 7.3 [IQR = 3.4-9.0], p = 0.51). Prevalence of rifampicin resistance detected by Xpert was 3.3% (95% CI = 2.4%, 4.3%) among new patients and 7.4% (95% CI = 4.3%, 11.7%) among retreatment patients, with a 98% (95% CI = 87%, 99%) positive predictive value compared to phenotypic drug susceptibility testing. Missing data in the information systems may have biased our primary endpoints. However, sensitivity analyses assessing the effects of missing data did not affect our results. CONCLUSIONS: Replacing smear microscopy with Xpert MTB/RIF in Brazil increased confirmation of pulmonary TB. An additional benefit was the accurate detection of rifampicin resistance. However, no increase on overall notification rates was observed, possibly because of high rates of empirical TB treatment. TRIAL REGISTRATION: ClinicalTrials.gov NCT01363765. Please see later in the article for the Editors' Summary.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".