Factors Associated with Mortality in Patients with COVID-19 Requiring Mechanical Ventilation: An International Cohort Study from 139 Intensive Care Unit Across 6 Continents
Bibliographic record
Abstract
Rationale: Patients with COVID-19 commonly develop severe hypoxemic respiratory failure and require invasive mechanical ventilation (MV). The disease burden and predictors of mortality in this population remain uncertain. Methods: Prospective observational cohort study from 139 intensive care units of the international COVID-19 Critical Care Consortium. Patients enrolled from January 14th through November 31st 2020 were included in the analysis. Patient's characteristics and clinical data were assessed. Multivariable Cox proportional hazards analysis was conducted to identify indipendent predictors of mortality within 28 days from commencement of MV. Results: 1578 patients on MV were included into the analysis. Mean±SD age was 59 years±13 and patients were predominantly males (66%). 542 Patients (34.4%) died within 28 days from commencement of MV. Nonsurvivors were slightly older (mean age±SD 62±13 vs. 59±13) and presented more frequently hypertension, chronic cardiac disease and diabetes. Median (IQR) PaO2/FiO2 upon commencement of MV was 96 (68-135) and 111 (81-173) in patients who did not survive vs. survivors, respectively (p=0.04). ECMO (13% vs 25%, p<0.01), inhaled nitric oxide (11% vs 15%, p=0.02) and recruitment manoeauvres (26% vs 31%, p<0.01) were used less frequently in patients who did not survive. Independent risk factors associated with 28-day mortality included age older than 70 years (hazard ratio [HR], 2.83;95% CI, 1.32-6.07), higher creatinine levels upon ICU admission (HR, 1.20;95% CI, 1.03-1.40), and lower pH within 24h from commencement of MV (HR, 0.12;95% CI, 0.02-0.62), while a shorter period (day) from early symptoms to hospitalisation reduced mortality risks (HR, 0.96;95% CI, 0.93-0.99). Conclusions: Our findings from a large international cohort of critically-ill COVID-19 patients on mechanical ventilation emphasises that elderly patients, not promptly admitted to the hospital, and who present higher creatinine levels and acidosis are at higher risk of mortality.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".