Pediatric cardiac surgical site infections: A single-center quality improvement initiative
Bibliographic record
Abstract
Objective Pediatric cardiac surgery site infections (SSI) represent significant morbidity. Our institution reported elevated SSI rates of 3.48 per 100 cases over a 5-year period above target rates of 2.5 per 100 cases. Therefore, as a quality improvement initiative, we implemented interventions with the goal of decreasing SSI rates by 30%. Methods Pediatric cardiovascular surgery patients (January 2021 to August 2023) who had SSI within 30 days of index operation were included (n = 1514) based on the National Healthcare Safety Network definition. Descriptive statistics were used to compare our preintervention cohort (pre-IV) (January 2021 to April 2022; n = 753) and postintervention cohort (post-IV) (May 2022 to August 2023; n = 761). Results In the post-IV cohort, we found a significant decrease in total SSI (1.97 SSIs per 100 cases [15 out of 761]) versus pre-IV (3.85 SSIs per 100 cases [29 out of 753]), demonstrating a 48% reduction ( P = .029). In our post-IV cohort, there was a significant reduction in superficial SSIs (pre-IV, 3.19 SSIs per 100 cases [24 out of 753] vs post-IV, 1.58 SSIs out of 100 cases [12 out of 761]; P = .04). Wounds presenting at 1 to 3 weeks were also reduced in our post-IV cohort (pre-IV, 2.66 SSIs per100 cases [20 out of 753] vs post-IV, 0.66 SSIs per 100 cases [5 out of 761]; P = .002). A significant reduction in SSIs in nonneonates was also noted (pre-IV, 2.79 SSIs per 100 cases [21 out of 753] vs post-IV, 0.92 SSIs per 100 cases [7 out of 761]; P = .007). Additionally, there was a significant reduction in SSIs associated with the Society of Thoracic Surgeons–European Association for Cardio-Thoracic Surgery Congenital Heart Surgery 1 mortality category ( P = .033) and the number of readmissions in the post-IV cohort ( P = .042). Conclusions A new surgical site dressing and multidisciplinary surveillance plan effectively reduced the overall burden of SSI rates at our institution. Future studies will address risk factors in specific subpopulations to further reduce SSIs at our institution.
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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 | 0.024 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| 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".