Validation of a commercially available SARS-CoV-2 serological Immunoassay
Bibliographic record
Abstract
Abstract Objectives To validate the diagnostic accuracy of a Euroimmun SARS-CoV-2 IgG and IgA immunoassay for COVID-19. Methods In this unmatched (1:1) case-control validation study, we used sera of 181 laboratory-confirmed SARS-CoV-2 cases and 176 controls collected before SARS-CoV-2 emergence. Diagnostic accuracy of the immunoassay was assessed against a whole spike protein-based recombinant immunofluorescence assay (rIFA) by receiver operating characteristic (ROC) analyses. Discrepant cases between ELISA and rIFA were further tested by pseudo-neutralization assay. Results COVID-19 patients were more likely to be male and older than controls, and 50.3% were hospitalized. ROC curve analyses indicated that IgG and IgA had high diagnostic accuracies with AUCs of 0.992 (95% Confidence Interval [95%CI]: 0.986-0.996) and 0.977 (95%CI: 0.963-0.990), respectively. IgG assays outperformed IgA assays (p=0.008). Taking an assessed 15% inter-assay imprecision into account, an optimized IgG ratio cut-off > 1.5 displayed a 100% specificity (95%CI: 98–100) and a 100% positive predictive value (95%CI: 97-100). A 0.5 cut-off displayed a 97% sensitivity (95%CI: 93–99) and a 97% negative predictive value (95%CI: 93–99). Substituting these thresholds for the manufacturer’s, improved assay performance, leaving 12% of IgG ratios indeterminate between 0.5-1.5. Conclusions The Euroimmun assay displays a nearly optimal diagnostic accuracy using IgG against SARS-CoV-2 in patient samples, with no obvious gains from IgA serology. The optimized cut-offs are fit for rule-in and rule-out purposes, allowing determination of whether individuals in our study population have been exposed to SARS-CoV-2 or not. IgG serology should however not be considered as a surrogate of protection at this stage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".