The diagnostic performance of novel skin-based in-vivo tests for tuberculosis infection compared with purified protein derivative tuberculin skin tests and blood-based in vitro interferon-γ release assays: a systematic review and meta-analysis
Notice bibliographique
Résumé
BACKGROUND: Novel skin-based tests for tuberculosis infection might present suitable alternatives to current tests; however, diagnostic performance of new tests compared with the purified protein derivative-tuberculin skin test (TST) or interferon-γ release assays (IGRA) needs systematic assessment. METHODS: In this systematic review and meta-analysis, we searched English (Medline OVID), Chinese (Chinese Biomedical Literature Database and the China National Knowledge Infrastructure), and Russian (e-library) databases from the inception of each database to May 15, 2019, (with updated search of the Russian and English databases on Oct, 20 2020) using terms "ESAT6" OR "CFP10" AND "skin test" AND "Tuberculosis" OR "C-Tb" OR "Diaskintest". We included studies reporting on the performance of index tests alone or compared with a comparator. Inclusion criteria varied according to review objectives and performance outcome, but reporting of test cut-offs for positivity applied to study population was required from all studies. We used a hierarchy of reference standards for tuberculosis infection consistent with the 2020 WHO framework to evaluate diagnostic performance. Two authors independently reviewed the titles and abstracts for English and Chinese (LF and MK) and Russian studies (MK and VN). Study quality was assessed with QUADAS-2. Pooled random-effects estimates are presented when appropriate for total agreement proportion, sensitivity in microbiologically confirmed tuberculosis and specificity in cohorts with low risk of tuberculosis infection. This study is registered with PROSPERO, CRD42019135572. FINDINGS: We identified 1466 original articles, of which 37 (2·5%) studies, including 10 915 individuals (7111 Diaskintest, 2744 C-Tb, 887 EC, 173 DPPD), were included in the qualitative analysis (29 [78%] studies of Diaskintest, five [15%] studies of C-Tb, two [5%] studies of EC-skintest, and one [3%] study of DPPD). 22 (1·5%) studies including 5810 individuals (3143 Diaskintest, 2129 C-Tb, 538 EC-skintest) were included in the quantitative analysis: 15 (68%) of Diaskintest, five (23%) of C-Tb, and two (9%) of EC-skintest. Tested sub-populations included individuals with HIV, children (0-18 years), and individuals exposed to tuberculosis. Studies were heterogeneous with moderate to high risk of bias. Nine head-to-head studies of index test versus TST and IGRA permitted direct comparisons and pooling. In a mixed cohort of people with and without tuberculosis, Diaskintest pooled agreement with IGRA was 87·16% (95% CI 79·47-92·24) and 55·45% (46·08-64·45) with TST-5 mm cut-off (TST5 mm). Diaskintest sensitivity was 91·18% (95% CI 81·72-95·98) compared with 88·24% (78·20-94·01) for TST5 mm, 89·66 (78·83-95·28) for IGRA QuantiFERON, and 90·91% (79·95-96·16) for TSPOT.TB. C-Tb agreement with IGRA in individuals with active tuberculosis was 79·80% (95% CI 76·10-83·07) compared with 78·92% (74·65-82·63) for TST5 mm/15 mm cut-off (TST5 mm/15 mm). TST5/15mm reflects threshold in cohorts that applied stratified cutoffs: 5 mm for HIV-infected, immunocompromised, or BCG-naive individuals, and 15mm for BCG-vaccinated immunocompetent individuals. C-Tb sensitivity was 74·52% (95% CI 70·39-78·25) compared with a sensitivity of 78·18% (67·75-85·94) for TST5 mm/15 mm, and 71·67% (63·44-78·68) for IGRA. Specificity was 97·85% (95% CI 93·96-99·25) for C-Tb versus 93·31% (90·22-95·48) for TST 15 mm cut-off and 99·15% (79·66-99·97) for IGRA. EC-skintest sensitivity was 86·06% (95% CI 82·39-89·07). INTERPRETATION: Novel skin-based tests for tuberculosis infection appear to perform similarly to IGRA or TST; however, study quality varied. Evaluation of test performance, patient-important outcomes, and diagnostic use in current clinical algorithms will inform implementation in key populations. FUNDING: StopTB (New Diagnostics Working Group) and FIND. TRANSLATIONS: For the Chinese and Russian translations of the abstract see Supplementary Materials section.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,007 | 0,001 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».