The Duration of Mechanical Ventilation is the Main Cause of Bacterial/Fungal Superinfection in Critically Ill Patients with COVID-19 at Altitude
Bibliographic record
Abstract
Background. COVID-19 patients in intensive care units suffer from bacterial/fungal superinfections. However, the incidence and cause of such superinfections in high-altitude hospitals remain poorly investigated. Objectives. The aim of this study was to evaluate the frequency of bacterial/fungal superinfection in patients with COVID-19 hospitalized in the intensive care unit (ICU) of the Hospital Universitario San José de Bogotá, Colombia, located at an altitude of 2,651 meters above sea level (high altitude). The impact of corticosteroids on the development of infection was also evaluated. Methods. The cohort included 279 patients, of which 188 (67.4%) were male, 116 (42.3%) were treated with dexamethasone, and 48 (17.2%) were diagnosed with superinfection. A retrospective descriptive cohort study was performed to evaluate the association between bacterial/fungal superinfection frequency, corticosteroid treatment, mechanical ventilation, and mortality rate. Results. Our results showed that bacteremia was the most frequent diagnosis (n=20; 41.6%) of patients with superinfection, followed by pulmonary superinfection (n=17; 35.4%). The most frequently identified causative agents of superinfection were K. pneumoniae (n=23; 26.1%), C. albicans (n=10; 11.4%) and P. aeruginosa (n=8; 9.1%). Moreover, our results showed no association between corticosteroid treatment (or the use of empiric antibiotic treatment) and mortality. However, we found a significant association between bacterial/fungal superinfection and the number of days on mechanical ventilation. However, bacterial/fungal superinfection showed no impact on the mortality rate. Conclusions. We conclude that bacterial/fungal superinfection in ICU highland patients with SARS-CoV-2 treated at Hospital Universitario San José in Bogotá, Colombia, increases mainly in proportion to the time required for 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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".