Blood RNA biomarkers and C-reactive protein for triage of adult patients with tuberculosis lymphadenitis and pericarditis in South Africa: a single-centre, prospective, observational, diagnostic accuracy study
Bibliographic record
Abstract
BACKGROUND: Data on the diagnostic accuracy of blood RNA biomarker signatures for extrapulmonary tuberculosis are scarce. We aimed to address this question among people investigated for tuberculosis lymphadenitis and tuberculosis pericarditis. METHODS: This prospective, observational, diagnostic accuracy study was done at a tertiary hospital in Cape Town, South Africa. We enrolled consecutive symptomatic adults (aged 18 years or older) with presumptive tuberculosis lymphadenitis (Jan 25, 2017, to Oct 9, 2019) or tuberculosis pericarditis (Nov 24, 2016, to Oct 28, 2019). We used microbiological testing of samples from the site of disease as the reference standard. We evaluated the diagnostic accuracy of seven previously reported blood RNA signatures by area under the receiver operating characteristic curve (AUROC) and sensitivity and specificity at prespecified thresholds using two SDs above the mean of a healthy reference control group, benchmarked against blood C-reactive protein and WHO target product profile for a tuberculosis triage test. Decision curve analysis was used to evaluate clinical utility of the best-performing blood RNA signature and C-reactive protein. FINDINGS: The pooled cohort included 440 individuals, 374 of whom (275 with lymphadenitis and 99 with pericarditis) had at least one microbiological test from the site of disease, blood C-reactive protein, and RNA measurements available and were included in the analysis. 181 (48%) participants were female and 193 (52%) were male. The diagnostic accuracy of blood RNA signatures was similar across patients with lymphadenitis and pericarditis. In pooled analysis of both cohorts, all RNA signatures had similar discrimination, with AUROC point estimates ranging from 0·77 (95% CI 0·72-0·82) to 0·82 (0·77-0·86), and greater than that of C-reactive protein (0·61 [0·56-0·67]). The best-performing signature (Roe3) did not meet the WHO target product profile benchmark for a triage test. At the prespecified threshold, Roe3 had 78% (95% CI 72-83) sensitivity and 69% (62-75) specificity; C-reactive protein at a threshold of 10 mg/L had 83% (77-87) sensitivity and 35% (29-43) specificity. In this setting, decision curve analysis showed that Roe3 offered greater net benefit than other approaches for services aiming to reduce the number needed to investigate with confirmatory testing to fewer than four to identify each individual with tuberculosis. INTERPRETATION: Our results suggest RNA biomarkers show better accuracy and clinical utility than C-reactive protein to trigger confirmatory tuberculosis testing in patients with tuberculosis lymphadenitis and tuberculosis pericarditis, but still fall short of the WHO target product profile for tuberculosis triage tests. FUNDING: South African Medical Research Council, European and Developing Countries Clinical Trials Partnership 2, National Institutes of Health/National Institute of Allergy and Infectious Diseases, Wellcome Trust, National Institute for Health and Care Research, and Royal College of Physicians.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".