Diagnostic performance of the fully automated Roche Elecsys SARS-CoV-2 antigen electrochemiluminescence immunoassay: a pooled analysis
Bibliographic record
Abstract
OBJECTIVES: Among the diagnostic tests that have recently become commercially available for diagnosing coronavirus disease 2019 (COVID-19), the fully-automated Roche Elecsys severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigen electrochemiluminescence immunoassay (ECLIA) is one of the most widespread for its adaptability within a system of laboratory automation, rapidity and high-throughput. This article is aimed to provide the results of the first pooled analysis of its accuracy for diagnosing SARS-CoV-2 infections. CONTENT: We carried out an electronic search in Scopus and Medline, without language or date restrictions (i.e., up to January 18, 2022), to identify articles where the diagnostic performance of Roche Elecsys SARS-CoV-2 antigen ECLIA was compared with that of reference molecular diagnostic techniques. SUMMARY: Overall, 11 studies were identified, 10 of which (n=6,095 swabs) provided necessary data for inclusion in a pooled analysis. The pooled diagnostic sensitivity, specificity and area under the curve (AUC) in nasopharyngeal samples were 0.68 (95%CI, 0.66-0.70), 0.99 (95%CI, 0.99-0.99) and 0.958 (95%CI, 0.936-0.980), respectively. The cumulative observed agreement with reference molecular assays was 89.5% and the kappa statistic was 0.735 (95%CI, 0.716-0.754). The pooled diagnostic sensitivity in samples with high viral load (i.e., cycle threshold values <28-30) was 0.95 (95%CI, 0.92-0.97). OUTLOOK: The results of this pooled analysis confirm that the fully-automated Roche Elecsys SARS-CoV-2 antigen ECLIA has high diagnostic specificity and optimal diagnostic sensitivity for identifying nasopharyngeal samples with higher viral load, thus making it a reliable technique for mass screening and for supporting strategies based on shorten isolation and/or quarantine.
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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 | 0.034 | 0.080 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.035 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".