Severe COVID-19 is characterised by inflammation and immature myeloid cells early in disease progression
Bibliographic record
Abstract
Summary SARS-CoV-2 infection causes a wide spectrum of disease severity. Immune changes associated with severe disease include pro-inflammatory cytokine production and expansion of immature myeloid populations. The relative importance of the immunological changes in driving progression to severe disease remain poorly understood. We aimed to identify and rank clinical and immunological features associated with progression to severe COVID-19. We sought to use tests available in an on-site diagnostic hospital laboratory to identify an immunological signature for severe disease development which could be detected prior to peak severity thereby allowing initiation of therapeutic interventions. We used univariate and multivariate analysis, including unbiased machine learning, to investigate the relationships between clinical and demographic characteristics, inflammatory markers, and leukocyte immunophenotypes with progression to severe disease in 108 patients and to rank these in importance. A combination of four features (elevated levels of interleukin-6 and C-reactive protein, coupled with reduced monocyte HLA-DR expression and reduced neutrophil CD10 expression), were strongly predictive of severe disease with an average prediction score of 0.925. Highlights Severe COVID-19 can be predicted by a combination of emergency myelopoiesis (CD10-neutrophils and HLA DR-monocytes) and inflammation (raised IL-6 and CRP) These changes can be identified from tests carried out prior to peak illness severity in a diagnostic laboratory This predictive model was derived from a cohort of patients with a wide range of ages, frailty and COVID-19 severity Graphical Abstract
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".