Comparison of NADAL COVID IgG/IgM rapid test and DiaSorin Liaison SARS-CoV-2 S1/S2 IgG assay across different blood sources and substrates
Bibliographic record
Abstract
The NADAL COVID-19 IgG/IgM (NADAL) assay, approved for rapid testing on serum or plasma, had not been approved nor tested as a point-of-care test on capillary blood. The study aim was to evaluate the performance of NADAL compared to that of the DiaSorin Liaison SARS-CoV-2 S1/S2 IgG (DiaSorin) assay (gold standard). We conducted two cross-sectional studies in participants aged ≥2 years, in whom different blood substrates (whole blood vs serum) and sources (venous vs capillary) were sampled. The co-primary endpoints were agreement in IgG detection between NADAL on venous serum and whole capillary blood vs DiaSorin on venous serum. Among 128 participants, 23.2% had a positive DiaSorin IgG assay. The NADAL IgG on venous serum exhibited near-perfect agreement (κ = 0.91) and high accuracy (0.94, 95% CI: 0.87-1.00), with a sensitivity of 0.88 and perfect specificity compared to DiaSorin; NADAL IgG on whole capillary blood exhibited moderate agreement (κ = 0.77) and accuracy (0.84, 95% CI: 0.73-0.95), with a sensitivity of 0.68 and perfect specificity. As for secondary outcomes, when the two assays were compared within the same source (venous or capillary) but with different substrates (NADAL on whole blood vs DiaSorin on serum), strong agreement and high accuracy were observed. Within-test DiaSorin IgG assay on capillary vs venous serum showed near-perfect agreement and high accuracy. Within-test NADAL IgG assays on whole capillary blood vs capillary or venous serum demonstrated high accuracy. In conclusion, NADAL on venous serum exhibited near-perfect agreement and high accuracy with DiaSorin on venous serum but lower between-test performance on different blood substrates. IMPORTANCE: Accurate serological assays are essential for assessing population immunity levels and identifying vulnerable subgroups with lower immunity on which to focus vaccination efforts. Although rapid tests may offer the possibility of easy point-of-care testing on whole capillary blood for these purposes, they may not be as robust as other assays when used on different blood substrates or sources than those in which they were approved. The observed variability in test performance across different substrates and blood sources highlights the importance of pretesting assays under the specific conditions in which they would be used to ensure optimal interpretation of the immune status in the community.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".