Comparison of real‐time and droplet digital PCR to detect and quantify SARS‐CoV‐2 RNA in plasma
Bibliographic record
Abstract
BACKGROUND: The presence of SARS-CoV-2 RNA in plasma has been linked to disease severity and mortality. We compared RT-qPCR to droplet digital PCR (ddPCR) to detect SARS-CoV-2 RNA in plasma from COVID-19 patients (mild, moderate, and critical disease). METHODS: The presence/concentration of SARS-CoV-2 RNA in plasma was compared in three groups of COVID-19 patients (30 outpatients, 30 ward patients and 30 ICU patients) using both RT-qPCR and ddPCR. Plasma was obtained in the first 24h following admission, and RNA was extracted using eMAG. ddPCR was performed using Bio-Rad SARS-CoV-2 detection kit, and RT-qPCR was performed using GeneFinder™ COVID-19 Plus RealAmp Kit. Statistical analysis was performed using Statistical Package for the Social Science. RESULTS: SARS-CoV-2 RNA was detected, using ddPCR and RT-qPCR, in 91% and 87% of ICU patients, 27% and 23% of ward patients and 3% and 3% of outpatients. The concordance of the results obtained by both methods was excellent (Cohen's kappa index = 0.953). RT-qPCR was able to detect 34/36 (94.4%) patients positive for viral RNA in plasma by ddPCR. Viral RNA load was higher in ICU patients compared with the other groups (P < .001), by both ddPCR and RT-qPCR. AUC analysis revealed Ct values (RT-qPCR) and viral RNA load values (ddPCR) can similarly differentiate between patients admitted to wards and to the ICU (AUC of 0.90 and 0.89, respectively). CONCLUSION: Both methods yielded similar prevalence of RNAemia between groups, with ICU patients showing the highest (>85%). RT-qPCR was as useful as ddPCR to detect and quantify SARS-CoV-2 RNAemia in plasma.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".