Preoperative hospital length of stay as a modifiable risk factor for mediastinitis after cardiac surgery
Bibliographic record
Abstract
BACKGROUND: As high-risk cardiac patients frequently remain within hospital while waiting for surgery, the aim of the present study was to determine the role of preoperative length of hospital stay on mediastinitis, and also, to assess contemporary risk factors for this complication. METHODS: The source population consisted of 6653 consecutive patients undergoing coronary bypass surgery, valve surgery, or both between September 2000 and September 2009 at a single tertiary care hospital. A retrospective cohort analysis was used to assess the effect of 18 preoperative variables, including length of stay, on mediastinitis. RESULTS: Mediastinitis developed in 108 patients (1.6%) resulting in an in-hospital mortality rate of 13.9%. Independent predictors of mediastinitis included obesity (2.59, CI 1.58-4.23), COPD (2.44, CI 1.55-3.84), diabetes (2.16, CI 1.44-3.24), and impaired estimated glomerular filtration rate. Preoperative hospital stay was also found to be an independent risk factor leading to a 15% increased risk of mediastinitis per week of stay. The primary wound pathogen was coagulase negative staphylococcus (82%) followed by multi-flora isolates (49%), but was unrelated to hospital stay. CONCLUSIONS: In addition to the traditional risk factors, prolonged preoperative hospital stay is also a significant and potentially modifiable predictor for the development of mediastinitis following cardiac surgery. All efforts should be made to minimize the delay in operating on hospitalized patients awaiting heart surgery.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".