Risk factors for COVID-19 and their association with mortality in Ecuadorian patients admitted to the ICU: A retrospective cohort multicentric study
Bibliographic record
Abstract
Several risk factors were associated with mortality in patients with coronavirus disease 2019 (COVID-19) infection in intensive care units (ICU). We assessed the effect of risk factors related to the characteristics and clinical history of the population, laboratory test results, drug management, and type of ventilation on the probability of survival/discharge from the ICU. A retrospective cohort multicentric study of adults with COVID-19 admitted to the ICU between March 2020 and December 2021. Data were collected from 6 hospitals in 5 cities in Ecuador. The primary outcome was ICU survival/discharge. Survival analysis was conducted using semi-parametric Cox proportional hazards models. Of those admitted to the ICU with COVID-19, (n = 991), mean age was 56.76 ± 13.14, and 65.9% were male. Regarding the primary outcome, 51.1% (n = 506) died and 48.9% (n = 485) survived. Of the group that died, their mean age was higher than the survivors (60.7 vs 52.60 years, respectively), and they had a higher prevalence of comorbidities such as arterial hypertension (37.2% vs 20.4%, respectively) and diabetes mellitus (26.9% vs 15.7%, respectively), with P < .001. In ventilatory management, 32.7% of patients used noninvasive ventilation and high-flow nasal cannula, and 67.3% required invasive ventilatory support. After adjusting for confounders, Cox regression analysis showed that patients were less likely to be discharged alive from the ICU if they met the following conditions: arterial hypertension (hazard ratio [HR] = 0.83 95% CI 0.723-0.964), diabetes mellitus (HR = 0.80 95% CI 0.696-0.938), older than 62 years (HR = 0.86 95% CI 0.790-0.956), obese (body mass index ≥ 30) (HR = 0.78 95% CI 0.697-0.887), 1 unit increase in SOFA score (HR = 0.94 95% CI 0.937-0.961), PaO2/FiO2 ratio <100 mm Hg (HR = 0.84 95% CI 0.786-0.914), and the use of invasive mechanical ventilation (HR = 0.68 95% CI 0.614-0.769). Risk factors associated with increased mortality were older age, obesity, arterial hypertension, and diabetes. Factors such as male gender, chronic obstructive pulmonary disease, acute kidney injury, and cancer reported in other investigations did not have the same effect on mortality in our study.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".