Risk Factors for ICU Admission, Mechanical Ventilation and Mortality in Hospitalized Patients with COVID-19 in Hubei, China
Bibliographic record
Abstract
Purpose To examine the risk factors for Intensive Care Unit (ICU) admission, mechanical ventilation and mortality in hospitalized patients with COVID-19. Methods This was a retrospective cohort study including 432 patients with laboratory-confirmed COVID-19 who were admitted to three medical centers in Hubei province from January 1 st to April 10 th 2020. Primary outcomes included ICU admission, mechanical ventilation and death occurring while hospitalized or within 30 days. Results Of the 432 confirmed patients, 9.5% were admitted to the ICU, 27.3% required mechanical ventilation, and 33.1% died. Total leukocyte count was higher in survivors compared with those who died (8.9 vs 4.8 × 10 9 /l), but lymphocyte counts were lower (0.6 vs 1.0 × 10 9 /l). D-dimer was significantly higher in patients who died compared to survivors (6.0ug/l vs 1.0ug/l, p<0.0001. This was also seen when comparing mechanically versus non-mechanically-ventilated patients. Other significant differences were seen in AST, ALT, LDH, total bilirubin and creating kinase. The following were associated with increased odds of death: age > 65 years (adjusted hazard ratio (HR 2.09, 95% CI 1.02-4.05), severe disease at baseline (5.02, 2.05-12.29), current smoker (1.67, 1.37-2.02), temperature >39 ° C at baseline (2.68, 1.88-4.23), more than one comorbidity (2.12, 1.62-3.09), bilateral patchy shadowing on chest CT or X-ray (3.74, 1.78-9.62) and organ failure (6.47, 1.97-26.23). The following interventions were associated with higher CFR: glucocorticoids (1.60, 1.04-2.30), ICU admission (4.92, 1.37-17.64) and mechanical ventilation (2.35, 1.14-4.82). Conclusion Demographics, including age over 65 years, current smoker, diabetes, hypertension, and cerebrovascular disease, were associated with increased risk of mortality. Mortality was also associated with glucocorticoid use, mechanical ventilation and ICU admission. Take-Home Message COVID-19 patients with risk factors were more likely to be admitted into ICU and more likely to require mechanical ventilation.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".