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Record W4393952630 · doi:10.1016/s2214-109x(24)00052-4

Point-of-care C-reactive protein and Xpert MTB/RIF Ultra for tuberculosis screening and diagnosis in unselected antiretroviral therapy initiators: a prospective, cross-sectional, diagnostic accuracy study

2024· article· en· W4393952630 on OpenAlexfundno aff
Byron W P Reeve, Gcobisa Ndlangalavu, Hridesh Mishra, Zaida Palmer, Happy Tshivhula, Loren Rockman, Selisha Naidoo, Desiree L Mbu, C Naidoo, Brigitta Derendinger, Gerhard Walzl, Stephanus T. Malherbe, Paul D. van Helden, Fred C. Semitala, Christina Yoon, Rishi K Gupta, Mahdad Noursadeghi, Robin M. Warren, Grant Theron

Bibliographic record

VenueThe Lancet Global Health · 2024
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsnot available
FundersFaculté de médecine et des sciences de la santé, Université de SherbrookeEuropean and Developing Countries Clinical Trials PartnershipFogarty International CenterFaculty of Medicine and Health, University of SydneyDivision of Research Capacity DevelopmentNational Institutes of HealthUniversiteit StellenboschNational Institute of Allergy and Infectious DiseasesMedical Research CouncilSouth African Medical Research CouncilEuropean CommissionNational Institute of Biomedical Imaging and BioengineeringWellcome Trust
KeywordsMedicineAntiretroviral therapyCross-sectional studyTuberculosisHuman immunodeficiency virus (HIV)Point-of-care testingVirologyViral loadImmunologyPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Tuberculosis, a major cause of death in people living with HIV, remains challenging to diagnose. Diagnostic accuracy data are scarce for promising triage and confirmatory tests such as C-reactive protein (CRP), sputum and urine Xpert MTB/RIF Ultra (Xpert Ultra), and urine Determine TB LAM Ag (a lateral flow lipoarabinomannan [LF-LAM] test), without symptom selection. We evaluated novel triage and confirmatory tests in ambulatory people with HIV initiating antiretroviral therapy (ART). METHODS: 897 ART-initiators were recruited irrespective of symptoms and sputum induction offered. For triage (n=800), we evaluated point-of-care blood-based CRP testing, compared with the WHO-recommended four-symptom screen (W4SS). For sputum-based confirmatory testing (n=787), we evaluated Xpert Ultra versus Xpert MTB/RIF (Xpert). For urine-based confirmatory testing (n=732), we evaluated Xpert Ultra and LF-LAM. We used a sputum culture reference standard. FINDINGS: 463 (52%) of 897 participants were female. The areas under the receiver operator characteristic curves for CRP was 0·78 (95% CI 0·73-0·83) and for number of W4SS symptoms was 0·70 (0·64-0·75). CRP (≥10 mg/L) had similar sensitivity to W4SS (77% [95% CI 68-85; 80/104] vs 77% [68-85; 80/104]; p>0·99] but higher specificity (64% [61-68; 445/696] vs 48% [45-52; 334/696]; p<0·0001]; reducing unnecessary confirmatory testing by 138 (95% CI 117-160) per 1000 people and number-needed-to-test from 6·91 (95% CI 6·25-7·81) to 4·87 (4·41-5·51). Sputum samples with Xpert Ultra, which required induction in 49 (31%) of 158 of people (95% CI 24-39), had higher sensitivity than Xpert (71% [95% CI 61-80; 74/104] vs 56% [46-66; 58/104]; p<0·0001). Of the people with one or more confirmatory sputum or urine test results that were positive, the proportion detected by Xpert Ultra increased from 45% (26-64) to 66% (46-82) with induction. Programmatically done haemoglobin, triage test combinations, and urine tests showed comparatively worse results. INTERPRETATION: CRP is a more specific triage test than W4SS in those initiating ART. Sputum induction improves diagnostic yield. Sputum samples with Xpert Ultra is a more accurate confirmatory test than with Xpert. FUNDING: South African Medical Research Council, EDCTP2, US National Institutes of Health-National Institute of Allergy and Infectious Diseases.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.692

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.055
GPT teacher head0.433
Teacher spread0.378 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2024
Admission routes1
Has abstractyes

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