High correlation between detection of dengue IgG from dried blood spots and serum using an indirect IgG ELISA assay: A validation study in Fortaleza, Brazil
Bibliographic record
Abstract
BACKGROUND: Dengue virus (DENV) seroprevalence studies often rely on Enzyme-Linked Immunosorbent Assay (ELISA) testing of serum samples, but ELISA testing of dried blood spot (DBS) samples offer several advantages for field-based research in resource-limited settings. However, DBS' limited sample volume can be challenging for test sensitivity, requiring validation studies with standard methods (e.g., analysis of serum through ELISAs or Plaque Reduction Neutralization Tests (PRNTs)). In preparation for a large cluster randomized controlled trial, we conducted a pilot study in 2019 to validate the use of DBS compared to serum samples for DENV IgG testing. We aimed to identify the optimal DBS dilution for IgG detection and to estimate the correlation, magnitude of agreement, and sensitivity and specificity of IgG detection in DBS versus serum samples. METHODOLOGY/PRINCIPAL FINDINGS: We conducted this pilot validation study among 119 healthy participants in Fortaleza, Brazil to evaluate and optimize the detection of DENV IgG from DBS compared to serum. Each participant provided paired DBS and venous blood samples, which were evaluated for DENV IgG using the Panbio Dengue IgG indirect ELISA. DBS elution diluted 1:4 was optimal compared with serum results, with high correlation (r= 0.98) and near-perfect agreement (kappa = 0.95). At this dilution, DBS had a sensitivity of 100%, a specificity of 92.3%, a 97.9% positive predictive value, and a 100% negative predictive value compared with serum. CONCLUSIONS/SIGNIFICANCE: These results validate using DBS instead of serum for detection of prior dengue infection among similar populations in endemic regions, without sacrificing test sensitivity and specificity. The validity of using DBS for ELISA to detect prior dengue infection could have important implications for field-based research. A limitation to this study was that the potential for misclassification due to cross-reactivity (e.g., with Zika virus, Yellow Fever vaccine) was not assessed.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".