Predictors of invasive mechanical ventilation in hospitalized COVID-19 patients: a retrospective study from Jordan
Bibliographic record
Abstract
OBJECTIVES: To identify early indicators for invasive mechanical ventilation utilization among COVID-19 patients. METHODS: This retrospective study evaluated COVID-19 patients who were admitted to hospital from 20 September 2020, to 8 August 2021. Multivariable logistic regression and machine learning (ML) methods were employed to assess variable significance. RESULTS: Among 1,613 confirmed COVID-19 patients, 365 patients (22.6%) received invasive mechanical ventilation (IMV). Factors associated with IMV included older age >65 years (OR,1.46; 95%CI, 1.13-1.89), current smoking status (OR, 1.71; 95%CI, 1.22-2.41), critical disease at admission (OR, 1.97; 95%CI, 1.28-3.03), and chronic kidney disease (OR, 2.07; 95%CI, 1.37-3.13). Laboratory abnormalities that were associated with increased risk for IMV included high leukocyte count (OR, 2.19; 95%CI, 1.68-2.87), low albumin (OR, 1.76; 95%CI, 1.33-2.34) and high AST (OR, 1.71; 95%CI, 1.31-2.22). CONCLUSION: Our study suggests that there are several factors associated with the increased need for IMV among COVID-19 patients. These findings will help in early identification of patients at high risk for IMV and reallocation of hospital resources toward patients who need them the most to improve their outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".