Performance of Immunoglobulin G Serology on Finger Prick Capillary Dried Blood Spot Samples to Measure SARS-CoV-2 Humoral Immunogenicity
Bibliographic record
Abstract
Abstract Importance Measuring humoral immunogenicity of Severe Acute Respiratory Syndrome Coronavirus 2 vaccines and finding population-level correlates of protection against coronavirus disease presents an immediate challenge to public health practitioners. Objective To study the diagnostic accuracy and predictive value of finger prick capillary dried blood spot samples tested using an anti-immunoglobulin G (IgG) serology assay to measure SARS-CoV-2 seropositivity and the humoral immunogenicity of COVID-19 vaccination. Design, Setting and Participants This cross-sectional study enrolled participants (n= 644) who had paired DBS and serum samples collected by finger prick and venipuncture, respectively, in British Columbia, Canada between January 12 th , 2020 and May 21 st , 2021. Samples were tested by a multiplex electrochemiluminescence assay for SARS-CoV-2 anti-Spike (S), -Nucleocapsid (N) and -receptor binding domain (RBD) IgG reactivity using a Meso Scale Discovery (MSD) platform. Additionally, unpaired DBS samples (n= 6,706) that were collected in the province during the same time period were included for analysis of SARS-CoV-2 anti-N IgG reactivity. Exposure Collection of a capillary dried blood spot by finger prick alone or paired with serum by venipuncture. Outcome Humoral immune response to SARS-CoV-2 measured by detection of anti-S, -N or - RBD IgG. Results In comparison to a paired-serum reference, dried blood spot samples possess a sensitivity of 80% (95% CI: 61%-91%) and specificity of 97% (95% CI: 95%-98%). Receiver operator characteristic curve analysis (ROC) found that participant DBS samples tested for anti-SARS-CoV-2 IgG by MSD V-PLEX COVID-19 Coronavirus Panel 2 assay accurately classify SARS-CoV-2 seroconversion at an 88% percent rate, AUC= 88% (95% CI: 81%-96%). Modelling found that a dried blood spot-based testing approach has a high positive predictive value (98% [95% CI: 98%-99%]) in a theoretical population with seventy-five percent COVID-19 vaccine coverage. At lower vaccine coverages of fifteen and forty-five percent, the test’s positive predictive value decreased, and the negative predictive value increased. Conclusion We demonstrate that dried blood spot collected samples, when tested using an electrochemiluminescence assay, provide a valid alternative to traditional venipuncture and should be considered to reliably detect SARS-CoV-2 seropositivity. Key Points Question What is the diagnostic accuracy and predictive value of immunoglobulin G serology on finger prick capillary dried blood spot samples to measure SARS-CoV-2 humoral immunogenicity? Findings In comparison to a paired-serum reference, dried blood spot samples tested for anti-SARS-CoV-2 IgG possess a sensitivity of 80% (95% CI: 61%-91%) and specificity of 97% (95% CI: 95%-98%). Dried blood spot testing has a positive predictive value of 98% (95% CI: 98%-99%) when modelled in a theoretical population with COVID-19 vaccine coverage of seventy-five percent. Meaning Dried blood spot samples have equal diagnostic accuracy to serum collected by venipuncture when tested by electrochemiluminescence assay and should be considered to reliably detect SARS-CoV-2 seropositivity.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".