Effect of Pre-Operative Use of Medications on the Risk of Surgical Site Infections in Patients Undergoing Cardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Median sternotomy, the most common means of accessing the heart for cardiac procedures, is associated with higher risk of surgical site infections (SSIs). A limited number of studies reporting the impact of medication use prior to cardiac surgery on the subsequent risk of SSIs usually focused on antibacterial prophylaxis. The objective of the current study was to evaluate the effect of medications prescribed commonly to cardiac patients on the risk of incident SSIs. METHODS: The study analyzed data on consecutive cardiac surgery patients undergoing median sternotomy at a McGill University teaching hospital between April 1, 2011 and October 31, 2013. Exposure of interest was use of medications for heart disease and cardiovascular conditions in the seven days prior to surgery and those for comorbid conditions. The main outcome was SSIs occurring within 90 d after surgery. Univariate and multivariate logistic regression (adjusted odds ratio [AOR]) was used to evaluate the effect. RESULTS: The cohort included 1,077 cardiac surgery patients, 79 of whom experienced SSIs within 90 d of surgery. The rates for sternal site infections and harvest site infections were 5.8 (95% confidence interval [CI]: 4.4-7.3) and 2.5 (95% CI: 1.4-3.7) per 100 procedures, respectively. The risk of SSI was increased with the pre-operative use of immunosuppressors/steroids (AOR 3.47, 95% CI: 1.27-9.52) and α-blockers (AOR 3.74, 95% CI: 1.21-1.47). CONCLUSIONS: Our findings support the effect of immunosuppressors/steroids on the risk of SSIs and add evidence to the previously reported association between the use of anti-hypertensive medications and subsequent development of infection/sepsis.
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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 | 0.009 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".