The COMPASS-COVID-19-ICU Study: Identification of Factors to Predict the Risk of Intubation and Mortality in Patients with Severe COVID-19
Bibliographic record
Abstract
In some patients, SARS-CoV-2 infection induces cytokine storm, hypercoagulability and endothelial cell activation leading to worsening of COVID-19, intubation and death. Prompt identification of patients at risk of intubation is an urgent need. Objectives. To derive a prognostic score for the risk of intubation or death in patients with COVID-19 admitted in intensive care unit (ICU), by assessing biomarkers of hypercoagulability, endothelial cell activation and inflammation and a large panel of clinical analytes. Design, Setting and Participants. A prospective, observational study enrolled 118 patients with COVID-19 admitted in the ICU. On the first day of ICU admission, all patients were assessed for biomarkers (protein C, protein S, antithrombin, D-Dimer, fibrin monomers, FVIIa, FV, FXII, FXII, FVIII, FvW antigen, fibrinogen, procoagulant phospholipid dependent clotting time, TFPI, thrombomodulin, P-selectin, heparinase, microparticles exposing TF, IL-6, complement C3a, C5a, thrombin generation, PT, aPTT, hemogram, platelet count) and clinical predictors. Main Outcomes and Measures. The clinical outcomes were intubation and mortality during hospitalization in ICU. Results: The intubation and mortality rates were 70% and 18%, respectively. The COMPASS-COVID-19-ICU score composed of P-Selectin, D-Dimer, free TFPI, TF activity, IL-6 and FXII, age and duration of hospitalization predicted the risk of intubation or death with high sensitivity and specificity (0.90 and 0.92, respectively). Conclusions and Relevance. COVID-19 is related to severe endothelial cell activation and hypercoagulability orchestrated in the context of inflammation. The COMPASS-COVID-19-ICU risk assessment model is accurate for the evaluation of the risk of mechanical ventilation and death in patients with critical COVID-19. The COMPASS-COVID-19-ICU score is feasible in tertiary hospitals and could be placed in the diagnostic procedure of personalized medical management and prompt therapeutic intervention.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".