Hospital-Acquired Infections After Cardiac Surgery and Current Physician Practices: A Retrospective Cohort Study
Bibliographic record
Abstract
Background: The management of hospital-acquired infections (HAIs) with respect to physician practices remains largely unexplored despite increasing efforts to standardize care. In the present study, we report findings from a 2-month audit of all patients that have undergone cardiac surgery at a large referral center in Atlantic Canada. Methods: All patients who underwent cardiac surgical procedures during May and June 2013 at the Queen Elizabeth II Health Sciences Center in Halifax, Nova Scotia were identified. The prevalence of urinary tract infections (UTIs), pneumonia, leg harvest site infections, superficial sternal wound infections, deep sternal wound infections, and sepsis was examined to determine physician approaches in terms of verification rates (microbiology), time of diagnosis and duration of treatment. Continuous variables were compared using Student’s t -test and categorical variables were analyzed using Fischer’s exact test. Results: A total of 185 consecutive patients underwent cardiac surgical procedures, of which 39 (21%) developed at least one postoperative infection. The overall prevalence of infection types, from highest to lowest, was UTI (8%), pneumonia (7%), leg harvest site infection (5%), superficial surgical site infection (4%), and sepsis (2%). There were no deep sternal wound infections. The overall in-hospital mortality rate was 3.8% with a median length of stay (LOS) of 8 days. The overall infection verification rate was 50% (ranged from 100% in sepsis to 10% in leg harvest site infections). In all cases, a full course of antibiotics was administered despite negative microbiology cultures or limited evidence of an actual infection. Conclusions: HAIs are commonly treated without being verified and treatment is often not discontinued after negative cultures are received. Our findings highlight the fact that antibiotic treatment is not always supported by evidence, and the effect of this could contribute to increased selective pressure for antimicrobial resistant bacteria. J Clin Med Res. 2017;9(1):10-16 doi: https://doi.org/10.14740/jocmr2637w
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".