Hospital-Acquired and Ventilator-Associated Pneumonia Early After Lung Transplantation: A Prospective Study on Incidence, Pathogen Origin, and Outcome
Bibliographic record
Abstract
BACKGROUND: Hospital-acquired (HAP) and ventilator-associated pneumonia (VAP) are important complications early (<30 days) after lung transplantation (LT). However, current incidence, associated factors, and outcomes are not well reported. METHODS: LT recipients transplanted at our institution (July 2019-January 2020 and October 2021-November 2022) were prospectively included. We assessed incidence and presentation of pneumonia and evaluated the impact of associated factors using regression models. We also evaluated molecular relatedness of respiratory pathogens collected peri-transplant and at pneumonia occurrence using pulsed-field gel electrophoresis (PFGE). RESULTS: In the first 30 days post-LT, 25/270 (9.3%) recipients were diagnosed with pneumonia (68% [17/25] VAP; 32% [8/25] HAP). Median time to pneumonia was 11 days (IQR, 7-13); 49% (132/270) of donor and 16% (44/270) of recipient respiratory peri-transplant cultures were positive. However, pathogens associated with pneumonia were not genetically related to either donor or recipient cultures at transplant, as determined by PFGE. Diagnosed pulmonary hypertension (HR, 4.42; 95% CI, 1.62-12.08) and immunosuppression use (HR, 2.87; 95% CI, 1.30-6.56) were pre-transplant factors associated with pneumonia. Pneumonia occurrence was associated with longer hospital stay (HR, 5.44; 95% CI, 2.22-13.37) and VAP with longer ICU stay (HR, 4.31; 95% CI, 1.73-10.75) within the first 30 days post-transplantation; 30- and 90-day mortality were similar. CONCLUSIONS: Prospectively assessed early pneumonia incidence occurred in ∼10% of LT. Populations at increased risk for pneumonia occurrence include LT with pre-transplant pulmonary hypertension and pre-transplant immunosuppression. Pneumonia was associated with increased healthcare use, highlighting the need for further improvements by preferentially targeting higher-risk patients.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".