P3-S5.04 The diagnostic accuracy of rapid point of care tests used to diagnose Hepatitis B: a Bayesian meta-analysis
Notice bibliographique
Résumé
Background More than 350 million people are infected with the Hepatitis B virus worldwide, with four million new cases every year. The prevalence of the Hepatitis B virus is highest in developing settings where laboratory equipment and diagnostic resources are limited. This creates a need for rapid point-of-care testing in order to screen blood donors and ensure timely diagnosis and treatment of infected individuals. Although studies have been conducted examining the accuracy of different tests, there has not been a synthesis of the available global evidence, or an assessment of the quality of evidence to date. Objective We conducted a systematic review of the global literature examining the sensitivity and specificity of rapid point-of-care tests used to diagnose Hepatitis B, and meta-analysed the data. Additionally, we conducted a critical appraisal of the quality of included studies. Methods Two reviewers conducted independent searches of five databases between the years of 1990 and 2010 for global evidence. Meta-analysis was performed grouping studies based on whether the index test identified HBsAg, both HBs and eAg, or antibody to HbsAg. We used Bayesian meta-analysis, accounting for the fact that all of the studies used imperfect reference standards (sensitivity and specificity assumed to range between 90% and 100%). The quality of all included full-text studies was assessed using the QUADAS and STARD checklists. Results A total of 17 studies were identified, of which 13 were from developing countries and which investigated 22 different index tests. When studies were pooled, the Determine HBsAg test showed a combined sensitivity of 98.76% and a specificity of 99.94%. Other HBsAg tests showed a lower combined sensitivity (96.77%) but comparable specificity (99.89%). The Amrad HBs+eAg test showed a combined sensitivity of 98.04% and a specificity of 99.04%, while the tests detecting antibody to HBsAg showed a combined sensitivity of 99.77% and a specificity of 96.08%. Studies were of poor-moderate quality with QUADAS scores ranging from 3 to 10/14 and STARD scores ranging from 7 to 14/25 see Abstract P3-S5.04 table 1. Abstract P3-S5.04 Table 1 Results from bayesian meta-analysis of diagnostic accuracy of hepatitis B point-of-care tests Program in R: Hierarchical model Assuming perfect reference standard (100%) Assuming imperfect reference standard (90%–100%) Subgroup Sensitivity (95% CI) Specificity (95% CI) Sensitivity (95% CI) Specificity (95% CI) HBsAg 94.76% (90.08 to 98.23%) 99.54% (99.03 to 99.953%) 96.77% (92.92 to 99.26%) 99.89% (99.55 to 100%) Determine–HBsAg 98.24% (94.74 to 99.98%) 99.96% (99.31 to 100%) 98.76% (94.67 to 99.99%) 99.94% (99.49 to 100%) Amrad–HBs+eAg 95.47% (88.88 to 99.44%) 99.81% (99.33 to 100%) 98.04% (93.39 to 99.83%) 99.95% (99.71 to 100%) Anti-HBs 93.15% (85.04 to 98.5%) 93.08% (81.9 to 99.99%) 99.77% (94.18 to 100%) 96.08% (86.38 to 100%) Conclusion The Amrad and Determine tests show the highest pooled accuracy. However, this could be explained by the fact that the other subgroups included studies examining different index tests with a wide range of accuracies. There is a need for more consistently designed studies, using ideal reference standards recommended by the CDC or Health Canada.
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 | 0,000 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 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 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».