Postoperative Nosocomial COVID-19 Infection in Cardiac Surgery: An Uncommon Event With High Mortality Rate
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has had a major impact on cardiac surgery patients. Significant reductions in access to surgical treatment have forced surgeons to prioritise patients and follow strict COVID-19 protocols to protect surgeons, staff, and patients. Adult cardiac surgery and the COVID-19 pandemic: aggressive infection mitigation strategies are necessary in the operating room and surgical recovery. Nosocomial infections among cardiac surgery patients have been reported and are associated with a high mortality rate. As a COVID-19 tertiary care centre and a tertiary cardiac centre, we tried to balance the need to operate on urgent cardiac cases while protecting patients and staff from COVID-19. METHODS: During the first wave of the pandemic, we performed 579 surgeries. We report findings from an outbreak of 4 nosocomial infections. RESULTS: All patients tested negative within 24 hours of surgery or admission. Three patients were positive after surgery, suggesting an overall nosocomial rate during the first wave of 0.5% (3/579). One patient admitted for evaluation tested positive during mass screening. Two of the 4 patients died after respiratory complications. No health care worker (HCW) or family member with direct contact with these patients tested positive for COVID-19. Nosocomial COVID-19 infection is uncommon when adhering to safety protocols. Although uncommon, the mortality rate is high (50%) in our series. CONCLUSIONS: As widespread vaccination of HCWs and high-risk individuals susceptible to COVID-19 is in progress, we suggest that cardiac surgery patients, when feasible, be vaccinated before surgery given this could prevent excess mortality, protect HCWs and reduce resource use.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".