SARS-CoV-2 RNA viremia is associated with a sepsis-like host response and critical illness in COVID-19
Bibliographic record
Abstract
Abstract Background Severe COVID-19 is characterized by clinical and biological manifestations typically observed in sepsis. SARS-CoV-2 RNA is commonly detected in nasopharyngeal swabs, however viral RNA can be found also in peripheral blood and other tissues. Whether systemic spreading of the virus or viral components plays a role in the pathogenesis of the sepsis-like disease observed in severe COVID-19 is currently unknown. Methods We determined the association of plasma SARS-CoV-2 RNA with the biological responses and the clinical severity of patients with COVID-19. 250 patients with confirmed COVID-19 infection were recruited (50 outpatients, 100 hospitalised ward patients, and 100 critically ill). The association between plasma SARS-CoV-2 RNA and laboratory parameters was evaluated using multivariate GLM with a gamma distribution. The association between plasma SARS-CoV-2 RNA and severity was evaluated using multivariate ordinal logistic regression analysis and Generalized Linear Model (GLM) analysis with a binomial distribution. Results The presence of SARS-CoV-2-RNA viremia was independently associated with a number of features consistently identified in sepsis: 1) high levels of cytokines (including CXCL10, CCL-2, IL-10, IL-1ra, IL-15, and G-CSF); 2) higher levels of ferritin and LDH; 3) low lymphocyte and monocyte counts 4) and low platelet counts. In hospitalised patients, the presence of SARS-CoV-2-RNA viremia was independently associated with critical illness: (adjusted OR= 8.30 [CI95%=4.21 - 16.34], p < 0.001). CXCL10 was the most accurate identifier of SARS-CoV-2-RNA viremia in plasma (area under the curve (AUC), [CI95%], p) = 0.85 [0.80 - 0.89), <0.001]), suggesting its potential role as a surrogate biomarker of viremia. The cytokine IL-15 most accurately differentiated clinical ward patients from ICU patients (AUC: 0.82 [0.76 - 0.88], <0.001). Conclusions systemic dissemination of genomic material of SARS-CoV-2 is associated with a sepsis-like biological response and critical illness in patients with COVID-19. RNA viremia could represent an important link between SARS-CoV-2 infection, host response dysfunction and the transition from moderate illness to severe, sepsis-like COVID-19 disease.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".