Postoperative Infectious Pneumonia in Cardiothoracic Surgery: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background Postoperative infectious pneumonia (PIP) is a common and serious complication following cardiothoracic surgery, including coronary artery bypass grafting (CABG), valve interventions, and thoracic oncologic procedures. It is associated with increased morbidity, prolonged intensive care unit (ICU) stay, and healthcare burden. Methods We performed a systematic review and meta-analysis according to PRISMA 2020 guidelines. Studies published between January 2021 and December 2023 were identified from PubMed, Embase, and Scopus. Eligible studies reported the incidence and/or perioperative risk factors for PIP with odds ratios (ORs) and 95% confidence intervals (CIs). A random-effects model was used for pooled estimates. Study quality was assessed using the Newcastle-Ottawa Scale. The review was prospectively registered in PROSPERO 2025 CRD 420251057914. Available from https://www.crd.york.ac.uk/PROSPERO/view/CRD420251057914. Results Six high-quality cohort studies involving 4,392 patients were included. The pooled incidence of PIP was 14.8% (95% CI, 10.6%–19.2%). Incidence was highest after thoracic oncologic surgery (17.2%), followed by valve surgery (15.8%) and CABG (13.5%). Significant risk factors included prolonged mechanical ventilation >48 hours (OR: 3.46), age >70 years (OR: 2.71), chronic obstructive pulmonary disease (OR: 2.95), cardiopulmonary bypass time >120 minutes (OR: 2.63), and left ventricular ejection fraction <40% (OR: 2.38). Heterogeneity was moderate (I 2 = 46%) with no publication bias. Conclusions PIP remains a major postoperative concern. Identification of key risk factors enables targeted preventive strategies—early extubation, pulmonary optimization, and standardized care pathways—to reduce PIP incidence and improve outcomes.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".