Epidemiology and microbiology of ventilator-associated pneumonia in COVID-19 patients: a multicenter retrospective study in 188 patients in an un-inundated French region
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic is responsible for many hospitalizations in intensive care units (ICU), with widespread use of invasive mechanical ventilation (IMV) which exposes patients to the risk of ventilator-associated pneumonia (VAP). The characteristics of VAP in COVID-19 patients remain unclear. METHODS: We retrospectively collected data on all patients hospitalized for COVID-19 during the first phase of the epidemic in one of the seven ICUs of the Pays-de-Loire region (North-West France) and who were on invasive mechanical ventilation for more than 48 h. We studied the characteristics of VAP in these patients. VAP was diagnosed based on official recommendations, and we included only cases of VAP that were confirmed by a quantitative microbiological culture. FINDINGS: We analyzed data from 188 patients. Of these patients, 48.9% had VAP and 19.7% experienced multiple episodes. Our study showed an incidence of 39.0 VAP per 1000 days of IMV (until the first VAP episode) and an incidence of 33.7 VAP per 1000 days of IMV (including all 141 episodes of VAP). Multi-microbial VAP accounted for 39.0% of all VAP, and 205 pathogens were identified. Enterobacteria accounted for 49.8% of all the isolated pathogens. Bacteremia was associated in 15 (10.6%) cases of VAP. Pneumonia was complicated by thoracic empyema in five cases (3.5%) and by pulmonary abscess in two cases (1.4%). Males were associated with a higher risk of VAP (sHR 2.24 CI95% [1.18; 4.26] p = 0.013). INTERPRETATION: Our study showed an unusually high incidence of VAP in patients admitted to the ICU for severe COVID-19, even though our services were not inundated during the first wave of the epidemic. We also noted a significant proportion of enterobacteria. VAP-associated complications (abscess, empyema) were not exceptional. REGISTRATION: As an observational study, this study has not been registered.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".